The Monday morning of Week 5 started with an argument.

Maya walked into the office at 7:30 AM to find Jake sitting at his desk, arms crossed, staring at his monitor with an expression that could curdle milk.

“Morning,” Maya said cautiously.

“We need to talk,” Jake said.

Maya set down her coffee. “Okay. What’s up?”

“I got your email last night,” Jake said. “The one about Week 5 plans. Database modernization using Oracle Cloud Exadata.”

“Right,” Maya said. “That’s the plan for this week—”

“You want to move our databases to a managed service,” Jake interrupted. “You want to give Oracle control of our databases.”

“I want us to use Oracle Exadata on Google Cloud,” Maya corrected. “Which is Oracle’s engineered system running on GCP infrastructure. It’s still Oracle Database, still fully featured, still something you have administrative access to.”

“But Oracle manages the infrastructure,” Jake said. “The storage, the patching, the high availability configuration. We lose control.”

“We lose operational burden,” Maya countered. “There’s a difference.”

Subscribe now

“Maya, I’ve been a DBA for twenty-six years,” Jake said, his voice tight. “I know how to manage Oracle databases. I know how to tune, optimize, and keep them running. You’re asking me to hand that over to a service that I can’t see inside of, can’t customize the way I want, can’t—”

“Can’t spend forty hours a quarter patching?” Maya suggested gently. “Can’t spend weekends troubleshooting storage performance? Can’t wake up at 3 AM when a backup script fails?”

Jake was quiet.

Maya sat down next to him. “Walk me through what you’re really worried about.”

Jake took a breath. “I’m worried that if we move to managed Exadata, my job becomes irrelevant. If Oracle handles the infrastructure, the backups, the patching, the high availability—what’s left for me to do? I become a glorified report writer. And then in a year, someone asks why we’re paying a DBA salary for work that doesn’t require a DBA.”

“Is that what you think I’m trying to do?” Maya asked. “Eliminate your position?”

“I don’t know,” Jake said honestly. “Two months ago, we were running PeopleSoft the traditional way. Now we’re doing infrastructure-as-code, observability platforms, and cloud migrations. Every week, something I used to do manually gets automated. At some point, you automate me out of a job.”

Maya was quiet for a moment, choosing her words carefully.

“Jake, do you remember last week? When we built the observability dashboards?”

“Yeah.”

“Who configured Oracle to export those metrics?” Maya asked. “Who knew which wait events matter for PeopleSoft performance? Who explained why we should monitor the DB file sequential read versus the DB file scattered read?”

“I did,” Jake said.

“Right. Because you understand Oracle performance at a level none of us do. Now let me ask you something else: how much time do you spend per month on tasks that require that deep expertise versus tasks that are just… keeping the lights on?”

Jake thought about it. “Maybe twenty percent on actual performance tuning and optimization. The rest is backups, patches, monitoring disk space, restarting failed jobs, dealing with tablespace extensions—”

“Operational toil,” Maya finished. “Important work, but not work that requires twenty-six years of expertise. What if we could flip that ratio? What if eighty percent of your time was spent on performance optimization, data architecture, query tuning, and helping developers write better SQL? What if the tool got automated?”

“That’s what managed Exadata does?” Jake asked skeptically.

“That’s what it enables,” Maya said. “Let me show you something.”

She pulled up a document she’d been working on over the weekend: a detailed comparison of their current Oracle environment versus Oracle Exadata on GCP.

Current State: Oracle Database on Self-Managed RAC

Jake’s Time Allocation (Monthly):

• Backup management and monitoring: 12 hours

• Patch management and testing: 16 hours (quarterly spike: 40 hours)

• Storage management: 8 hours

• High availability configuration and testing: 6 hours

• Performance monitoring: 10 hours

• Performance tuning and optimization: 8 hours

• Incident response and troubleshooting: 12 hours

• Capacity planning: 4 hours

• Documentation and runbooks: 4 hours

Total: 80 hours/month on average

Breakdown:

• 70% operational toil (backups, patches, storage, HA, monitoring, incidents)

• 30% strategic work (optimization, tuning, capacity planning)

Maya scrolled down to the next section.

Future State: Oracle Exadata Database Service on Google Cloud

Jake’s Time Allocation (Monthly):

• Backup management: 0 hours (automated by Oracle)

• Patch management: 2 hours (reviewing and approving automated patches)

• Storage management: 0 hours (automated scaling)

• High availability configuration: 0 hours (built-in, automated)

• Performance monitoring: 4 hours (dashboard review, alert investigation)

• Performance tuning and optimization: 30 hours

• Query optimization and developer support: 20 hours

• Data architecture and design: 15 hours

• Capacity planning: 3 hours (simplified with automated scaling)

• Strategic database projects: 6 hours

Total: 80 hours/month

Breakdown:

• 8% operational toil (patch approval, monitoring, capacity planning)

• 92% strategic work (optimization, tuning, architecture, developer support)

Jake studied the comparison. “You’re saying I’d spend the same amount of time, just on different things.”

“Not just different things,” Maya said. “More valuable things. Things that actually leverage your expertise. Right now, you’re spending twelve hours a month babysitting backups. Oracle’s Exadata service handles that automatically with built-in snapshot capabilities and point-in-time recovery. Those twelve hours could be spent optimizing SQL queries that are costing us database performance.”

“But what about when something goes wrong with the backups?” Jake asked.

“Then Oracle’s support team troubleshoots it,” Maya said. “Because it’s their infrastructure, their responsibility. You’re not on call at 3 AM because a backup script failed. They are.”

“And if I need to tune something at the storage layer?” Jake pressed.

“You can’t,” Maya admitted. “Because Oracle’s engineers optimize Exadata’s storage layer. But here’s the thing—when was the last time you actually needed to tune storage parameters for performance?”

Jake thought about it. “Honestly? Not in years. We set it up correctly initially, and it’s been stable since then. Most performance issues are bad SQL or missing indexes, not storage configuration.”

“Exactly,” Maya said. “So we’re protecting your ability to tune something you rarely need to tune, at the cost of you spending twelve hours a month managing backups you shouldn’t have to think about.”

She pulled up another document. “Let me show you the cost analysis. Because this isn’t just about your time—it’s about total cost of ownership.”

Current State: Self-Managed Oracle RAC on Premise

Infrastructure Costs:

• Database servers (2 nodes): $180K capital (5-year depreciation: $36K/year)

• Storage (SAN): $240K capital (5-year depreciation: $48K/year)

• Network equipment: $40K capital (5-year depreciation: $8K/year)

• Data center space, power, cooling: $24K/year

• Hardware maintenance contracts: $32K/year

Software Costs:

• Oracle Database Enterprise Edition licenses: $94K (already owned)

• Oracle RAC licenses: $47K (already owned)

• Oracle support contracts (22% annually): $31K/year

Personnel Costs:

• Jake’s time on database infrastructure: 80 hours/month × $85/hour = $81,600/year

• Infrastructure team support: $24K/year

Total Annual Cost: $284,600/year

Maya scrolled to the comparison.

Future State: Oracle Exadata Database Service @ Google Cloud

Infrastructure Costs:

• Exadata X9M Quarter Rack: $168,000/year (consumption-based pricing)

• Includes: compute, storage, networking, all infrastructure

• Includes: automated backups, patching, and high availability

• Includes: Oracle’s 24/7 infrastructure support

Software Costs:

• Oracle Database Enterprise Edition: included in Exadata service

• Oracle RAC: included in Exadata service.

• Oracle support: included in Exadata service

Personnel Costs:

• Jake’s time on database infrastructure: 6 hours/month × $85/hour = $6,120/year

• Jake’s time on strategic work: 74 hours/month (no additional cost, reallocated time)

• Infrastructure team support: $0 (no longer needed)

Total Annual Cost: $174,120/year

Annual Savings: $110,480

Jake stared at the numbers. “We’d save $110,000 per year by moving to Exadata?”

“And that’s conservative,” Maya said. “I didn’t account for the cost of our time during the quarterly patch cycles. I didn’t include the cost of storage upgrades every three years. I didn’t factor in the opportunity cost of incident response time. The real savings are probably closer to $140,000 annually.”

“But we already own the hardware,” Jake protested. “That’s sunk cost.”

“True,” Maya said. “But the hardware is six years old. In two years, we’ll need to replace it. That’s another $460,000 capital expense that we can avoid entirely by moving to Exadata as a service. Plus, we can decommission the data center space and stop paying for power and cooling.”

“What about the Exadata cost?” Jake asked. “That’s $168,000 per year. That’s not cheap.”

“It’s not,” Maya agreed. “But it’s less than what we’re spending now, and it includes things we currently pay for separately—software licenses, support contracts, infrastructure maintenance. Plus, it scales. Right now, if we need more database capacity, we have to buy another SAN array and another server. Six-month lead time, huge capital expense. With Exadata on GCP, we can scale up in hours by adjusting our consumption tier.”

Jake was carefully reading the cost breakdown. “You said Jake’s time on database infrastructure drops to six hours per month. What are those six hours?”

“Reviewing automated patch schedules and approving them, checking capacity trends, and investigating any performance alerts that come through observability,” Maya said. “The stuff that actually requires your judgment. Everything else—the backups, the storage management, the HA configuration, the routine monitoring—that’s handled by Oracle’s automation.”

“And the other seventy-four hours I’m supposedly spending on strategic work,” Jake said. “What does that actually look like?”

Maya pulled up a new document. “I’m glad you asked. I’ve been thinking about this a lot. Here’s what a modern DBA role looks like when you’re not buried in operational toil.”

The Strategic DBA: Redefining Value

Performance Optimization (30 hours/month):

• Proactive SQL tuning based on observed query patterns

• Identifying and fixing performance anti-patterns

• Working with application developers on database-efficient code

• Analyzing and optimizing batch job performance

• Eliminating performance bottlenecks before users notice them

Data Architecture (15 hours/month):

• Designing efficient database schemas for new functionality

• Planning and executing data model improvements

• Evaluating and implementing new Oracle features

• Capacity planning based on business growth projections

• Data archiving and retention strategy

Developer Enablement (20 hours/month):

• Reviewing SQL queries in code reviews

• Teaching developers database best practices.

• Creating reusable query patterns and templates

• Building tools that help developers write better SQL

• Pair programming on complex database interactions

Platform Evolution (9 hours/month):

• Evaluating new database technologies and features

• Planning database modernization initiatives

• Contributing to platform architecture decisions

• Researching industry best practices

• Strategic projects (machine learning, analytics, etc.)

Jake read through the list, his expression slowly changing from defensive to thoughtful.

“This is what you think I should be doing instead of managing backups,” he said.

“This is what you’re uniquely qualified to do,” Maya said. “Nobody else on the team can do this work at the level you can. Priya’s a good developer, but she doesn’t understand Oracle internals as well as you do. Tom knows infrastructure, but he can’t tune SQL. Sarah’s brilliant at automation, but she doesn’t have your depth of database expertise.”

“But they can all click a button to run a backup,” Maya continued. “And frankly, Oracle’s automated backup system can do it better than any of us because it’s designed specifically for that purpose. So why are we paying you $85 an hour to babysit backups when you could be preventing the kind of performance issues that slow down the entire university?”

Jake was quiet, processing.

“Let me ask you something,” Maya said. “In the last month, how many times have you wanted to work on a database performance issue but couldn’t because you were dealing with operational overhead?”

Jake exhaled. “Last week. We had a batch job running slowly—probably a missing index or a bad query plan. But I was in the middle of patch testing, and by the time I got to it, the job had finished. It’ll probably be slow again next month, but I haven’t had time to investigate properly.”

“That’s exactly what I’m talking about,” Maya said. “You have expertise that could have prevented that slow batch job. But you were spending your time on patch testing that could be automated. That’s a misallocation of your talent.”

“Okay,” Jake said slowly. “I see the argument. But I still have concerns. What about control? Right now, if I need to change an Oracle parameter, I can. With Exadata as a service, do I lose that?”

“Some parameters you can still change,” Maya said. “Database-level configuration, optimizer settings, memory allocation within your allocated resources—all of that’s still under your control. What you can’t change is infrastructure-level stuff like storage configuration or networking. But again, when was the last time you needed to change those?”

“Fair point,” Jake admitted.

“And here’s the thing,” Maya continued. “The infrastructure parameters Oracle sets for Exadata are based on thousands of customer deployments and years of engineering. They’re probably better than what we configured six years ago when we set up our RAC cluster.”

Tom had wandered over during the conversation and was listening from the doorway.

“Can I jump in here?” Tom asked.

“Please,” Maya said.

“Jake, I had the same reaction you’re having when Maya talked about infrastructure as code,” Tom said. “I felt like we were throwing away fifteen years of experience. But what I learned is that automation doesn’t replace expertise—it multiplies it. My knowledge of how to configure an application server is now in Chef cookbooks that anyone can run. That doesn’t make me less valuable. It makes my knowledge more valuable because it’s reusable.”

“And you’re not worried about job security?” Jake asked.

“I was,” Tom admitted. “But then I realized—organizations don’t pay us to click buttons. They pay us to solve problems and make good decisions. If all we’re doing is clicking buttons, then yeah, we should worry. But if we’re solving problems? That’s always going to be valuable.”

Sarah had joined them now, too, along with Marcus and Priya.

“What’s going on?” Sarah asked.

“Jake’s concerned about moving to managed Exadata,” Maya explained. “He’s worried about losing control and becoming irrelevant.”

“Oh, I can speak to that,” Sarah said. “Before I came here, I worked at a company that moved from self-managed databases to Amazon RDS. The DBAs freaked out initially. Same concerns—losing control, becoming unnecessary.”

“What happened?” Jake asked.

“Their jobs got way more interesting,” Sarah said. “They stopped spending time on backups and patches and started building internal database tooling. They created query analysis frameworks that automatically identified slow queries and suggested optimizations. They built a data pipeline automation. They became strategic partners to the development teams instead of people you ticket when the database is full.”

“And nobody got laid off?” Jake asked.

“Nobody,” Sarah confirmed. “They just redirected their energy to higher-value work. One guy became the data architecture lead. Another one built an entire internal analytics platform. The third one began providing database performance consulting to all development teams. They went from firefighters to architects.”

Maya pulled up one more document on the screen. “Jake, I want to show you something else. This is what happens to organizations that resist managed services.”

The Traditional DBA Career Path: Self-Managed Databases

Year 1-5: Learning database administration, operational basics
Year 6-15: Deep expertise in backup/recovery, performance tuning, HA configuration
Year 16-25: Senior DBA, mentoring others, and making architecture decisions
Year 26+: ???

Common challenges:

• Skills become increasingly niche as the industry moves to managed services.

• Operational toil increases with system complexity.

• Difficult to stay current with modern data platforms

• Career mobility decreases (fewer companies want self-managed DBAs)

• Burnout from on-call and operational burden

The Modern DBA Career Path: Managed Services + Strategic Work

Year 1-5: Learning database fundamentals
Year 6-15: Performance optimization, query tuning, data modeling
Year 16-25: Data architecture, platform strategy, cross-platform expertise
Year 26+: Database architect, data platform lead, strategic advisor

Advantages:

• Skills remain relevant (performance optimization always matters)

• Learning time freed up for new technologies (cloud platforms, analytics, ML)

• Career mobility increases (strategic skills transfer across companies)

• Better work-life balance (less on-call operational burden)

• Higher compensation (strategic roles pay more than operational roles)

Jake studied both paths. “You’re saying that by resisting managed services, I’m actually limiting my career growth.”

“I’m saying the industry is moving to managed services whether we like it or not,” Maya said gently. “AWS, GCP, Azure—they’re all betting big on managed databases. Oracle is investing heavily in Exadata as a service. In five years, most enterprise databases will be managed services. DBAs who only know how to manage infrastructure will struggle. DBAs who can optimize performance and design data architectures will thrive.”

“And you think I can make that transition?” Jake asked.

“I know you can,” Maya said. “Because you’re already doing it. Last week, you spotted a performance issue in our monitoring data before it became critical. That’s the kind of proactive work that managed services enable. You weren’t busy patching, so you had time to actually analyze performance trends.”

Priya spoke up. “Jake, can I ask you something? What part of your DBA work do you actually enjoy?”

Jake thought about it. “The puzzle-solving. When someone says, ‘this query is slow,’ and I get to figure out why. Looking at execution plans, finding the missing index or the bad join, fixing it, and seeing the performance improve. That’s satisfying.”

“That’s the work Maya’s saying you’d get to do more of,” Priya said.

“Yeah,” Jake said quietly. “I guess it is.”

Marcus added, “And honestly, Jake, we need you to do more of that. I’ve got integration queries that I know could be faster, but I don’t know enough about Oracle to optimize them. If you had more time to work with me on that, our integrations would be way more efficient.”

“Same with me,” Priya said. “I write SQL in my customizations, but I know I’m probably doing dumb things. I could use a DBA who has time to review my queries and teach me better patterns.”

Jake looked at his team—people who wanted to learn from him, who valued his expertise, who needed him to do more strategic work than he currently had time for.

“Okay,” Jake said finally. “I’m still nervous about this. But I see the argument. Show me what this Exadata migration actually looks like.”

Maya pulled up the migration plan she’d drafted. “We’re not ripping and replacing. We’re doing a methodical migration with extensive testing.”

Exadata Migration Plan: 6 Weeks

Week 5 (This Week): Planning and Preparation

• Jake evaluates Exadata capabilities vs. the current RAC setup.

• Identify any features we use that work differently on Exadata.

• Build a test plan for functionality validation.

• Set up Exadata test environment on GCP.

Week 6: Development Environment Migration

• Migrate the dev database to Exadata.

• Run full test suite

• Validate performance, functionality, and integrations.

• Team learns Exadata management interfaces.

Week 7: QA Environment Migration

• Migrate the QA database using the refined process.

• Extended testing with real workloads

• Performance benchmarking

• Backup/recovery testing

Week 8: Performance Validation

• Compare performance metrics: Exadata vs. RAC.

• SQL query performance analysis

• Batch job timing validation

• User acceptance testing

Week 9-10: Production Migration Planning

• Final migration runbook

• Rollback procedures

• Communication plan

• Schedule maintenance window

Production Migration: Week 11

• Execute during scheduled maintenance.

• Monitor closely post-migration

• Jake’s full attention is on performance validation.

“Six weeks from planning to production,” Maya said. “And you’re involved in every step. You’re the one validating that Exadata meets our performance requirements. You’re the one deciding if we’re ready to migrate production. This isn’t me taking control away from you—it’s you using your expertise to evaluate a better platform.”

Jake nodded slowly. “Okay. I can work with this. But I have conditions.”

“Name them,” Maya said.

“One: if we migrate to Exadata and performance is worse than our current RAC setup, we roll back. I’m not going to sacrifice database performance to save money.”

“Agreed,” Maya said. “Performance is non-negotiable.”

“Two: I’m the one who decides when we’re ready to migrate production. If I say we need more testing, we take more time.”

“Absolutely,” Maya said. “You’re the database expert. I trust your judgment.”

“Three: I want time allocated for learning. If I’m going to become this strategic DBA you’re describing, I need to actually learn new skills. Performance tuning, data architecture, and modern analytics platforms. I can’t just figure it out on my own while doing my regular job.”

“I can support that,” Maya said. “How about four hours per week for focused learning? Online courses, conferences, certifications—whatever you need.”

“Four hours per week is good,” Jake said. “And one more thing: I want regular check-ins with you about my career development. If we’re redefining what a DBA does, I want to make sure I’m actually growing into that role and not just spinning my wheels.”

“Monthly one-on-ones focused on career development,” Maya offered. “On top of our regular status meetings. We’ll talk about what you’re learning, what projects you want to work on, and where you want to grow.”

“Okay,” Jake said, extending his hand. “I’m in. Let’s evaluate Exadata.”

Maya shook his hand. “Thank you for being open to this. I know it’s uncomfortable.”

“It’s terrifying,” Jake corrected with a slight smile. “But Tom’s right—I don’t want to be clicking buttons for the next ten years. If managed services free me up to do more interesting work, I should at least give it a fair evaluation.”

As the impromptu meeting dispersed, Sarah lingered behind with Maya.

“That was well handled,” Sarah said quietly. “You could have pulled rank and just mandated the migration. But you made the case and let him come to the decision himself.”

“People don’t resist change,” Maya said. “They resist being changed. If I’d just mandated Exadata, Jake would have complied but resented it. By walking him through the reasoning and giving him control over the migration, he’s bought in. Now he’ll make this succeed because he chose it.”

“Very Machiavellian,” Sarah said, grinning.

“Very practical,” Maya corrected. “We have eight weeks left to prove PeopleSoft modernization works. I need my team engaged and motivated, not compliant and resentful. Jake’s expertise is critical to this working. I need him all-in.”

“Do you think Exadata will actually be better than our current setup?” Sarah asked.

“Honestly? I think it’ll be roughly equivalent performance-wise with significantly lower operational burden,” Maya said. “But the real win isn’t performance—it’s freeing Jake to do higher-value work. If we can show Harrison that we’re not just cutting infrastructure costs, but also making our team more strategic and valuable, that’s a compelling story.”

“Strategic team modernization,” Sarah said. “Not just technology modernization.”

“Exactly,” Maya said. “The platform doesn’t matter if the people operating it aren’t growing.”

Wednesday Afternoon: The Exadata Evaluation

By Wednesday afternoon, Jake had spent two days deep in Oracle Exadata documentation, architecture diagrams, and performance whitepapers. The team gathered for his evaluation presentation.

“Alright,” Jake said, pulling up his slides. “Maya asked me to evaluate whether Oracle Exadata Database Service on Google Cloud is a viable replacement for our current RAC environment. Here’s what I found.”

He clicked to his first slide: a comparison chart.

Oracle RAC (Current) vs. Exadata (Proposed)

Architecture:

• RAC: Two database nodes, shared SAN storage, application-level high availability

• Exadata: Integrated system with compute nodes and intelligent storage servers

Performance Features:

• RAC: Standard Oracle performance (dependent on our tuning)

• Exadata: Smart Scan, Hybrid Columnar Compression, Storage Indexes, Flash Cache

High Availability:

• RAC: Node failover (we manage), manual storage failover

• Exadata: Automatic failover, storage redundancy built in, automated recovery

Backup/Recovery:

• RAC: RMAN scripts we maintain, recovery time depends on backup size

• Exadata: Automated snapshots, incremental forever backups, fast recovery

Patching:

• RAC: Manual patch application, extensive testing, quarterly 40-hour effort

• Exadata: Rolling patches with zero downtime, Oracle-tested combinations

“First thing I looked at was whether Exadata could actually run PeopleSoft,” Jake said. “Answer: yes. Oracle specifically supports PeopleSoft on Exadata. In fact, several large universities are already running it—Ohio State, University of Michigan, Penn State. So we’re not pioneers. We’re followers, which is good for risk management.”

He clicked to the next slide.

Performance Analysis

“Second question: will it be faster, slower, or the same as our current setup? This is what I spent most of my time on.”

Jake pulled up a detailed analysis. “Exadata has several performance features we don’t have with standard RAC. Smart Scan pushes query processing down to the storage layer, dramatically speeding up full-table scans. Hybrid Columnar Compression can reduce storage requirements by 10x for historical data. Flash Cache speeds up frequently accessed data.”

“But here’s the thing,” Jake continued. “Those features mainly help with analytics workloads and data warehouse queries. PeopleSoft is primarily an OLTP workload—lots of small transactions, index lookups, not many full table scans. So we won’t see massive performance gains from Smart Scan.”

“So it won’t be faster?” Tom asked.

“I didn’t say that,” Jake clarified. “The flash cache and faster storage will help our random read performance, which PeopleSoft does a lot of. And the automated performance tuning that Exadata does in the background is better than what I do manually. Overall, I’d expect 10-20% better performance for typical PeopleSoft operations, with potentially much better performance for reporting and analytics.”

“What about batch jobs?” Priya asked.

“That’s where it gets interesting,” Jake said. “Our batch jobs do a lot of full table processing. Smart Scan could significantly speed those up—potentially 30-50% faster. But I won’t know for sure until we test with real workloads.”

He clicked to the next slide: Operational Considerations.

“Third question: what do we give up by moving to a managed service?”

What We Lose:

• Direct access to storage layer configuration

• Ability to customize RAC parameters that we never customize

• Some diagnostic capabilities (replaced by Exadata-specific tools)

• The satisfaction of manually managing infrastructure (not actually a loss)

What We Gain:

• Automated patching with zero downtime

• Better performance out of the box

• Oracle’s 24/7 infrastructure support

• Automated scaling (add capacity in hours, not months)

• Advanced performance features (Smart Scan, compression, flash cache)

• Simplified disaster recovery (automated snapshots, fast recovery)

“Honestly?” Jake said. “Most of what we ‘lose’ is stuff I rarely touch. We set up our RAC cluster six years ago and haven’t changed the fundamental configuration since. I’ve tweaked database parameters, sure, but I can still do that on Exadata. The infrastructure-level stuff we lose access to? I won’t miss it.”

“What about the diagnostic tools?” Marcus asked. “You use those for troubleshooting, right?”

“I use Oracle’s diagnostic tools—AWR reports, SQL tuning advisor, execution plans,” Jake said. “All of those still work on Exadata. What I lose is access to low-level storage diagnostics. But Exadata has its own diagnostic tools that are actually better for that platform. It’s not a loss—it’s a different toolset.”

Jake pulled up his next slide: Risk Assessment.

Migration Risks:

High Risk:

• None identified

Medium Risk:

• Performance regression for specific queries (mitigation: extensive testing)

• Team learning curve on Exadata management (mitigation: training and Oracle support)

• Migration execution issues (mitigation: detailed runbook, tested rollback)

Low Risk:

• Compatibility issues (PeopleSoft is certified on Exadata)

• Functional gaps (Exadata is a superset of RAC capabilities)

“I spent a lot of time looking for showstopper risks,” Jake said. “Things that would make this migration a bad idea. I didn’t find any. The biggest risk is that we screw up the migration itself, not that Exadata can’t handle our workload.”

Sarah raised her hand. “What about vendor lock-in? Once we’re on Exadata, are we stuck with Oracle forever?”

“We’re already stuck with Oracle,” Jake said bluntly. PeopleSoft runs on Oracle Database. Whether it’s RAC in our data center or Exadata on GCP, we’re committed to Oracle. The Exadata service doesn’t increase our lock-in—it just changes who operates the infrastructure.”

“Fair point,” Sarah said.

Jake pulled up his final slide: Recommendation.

Recommendation: Proceed with Exadata Migration

Rationale:

• Meets all functional requirements

• Expected 10-20% performance improvement for OLTP, 30-50% for batch

• Reduces operational burden by ~90%

• Lowers total cost by $110K annually

• Enables strategic DBA work vs. operational toil

• Low migration risk with a proven rollback plan

Conditions:

• Complete testing in dev and QA before production migration

• Performance validation with real workloads

• Team training on Exadata management

• My approval is required before production migration.

“I’m recommending we do this,” Jake said. “Not because Maya told me to, but because it’s legitimately a better platform for less money with less operational overhead. The only way I’d recommend against it is if we found performance issues in testing. But I don’t expect to find them.”

The room was quiet.

“Questions?” Jake asked.

Tom spoke up. “Jake, are you actually comfortable with this? You were pretty opposed on Monday.”

“I was,” Jake admitted. “Because I was thinking about what I’d lose. But after two days of really looking at Exadata—reading the architecture docs, reviewing performance benchmarks, and thinking through the operational model—I’m convinced this is the right move. Not just for cost savings. For operational excellence.”

“What changed your mind?” Priya asked.

“Two things,” Jake said. “First, I realized I was defending my ability to do work I don’t actually want to do. Managing backups isn’t fulfilling. Patching databases isn’t fun. I was defending that work because I thought it made me valuable. But it doesn’t. What makes me valuable is my ability to optimize database performance and to design effective data architectures. Exadata frees me up to do more of that.”

“And second?” Maya prompted.

“Second, I realized that resisting managed services is like… you know how some sysadmins refused to learn virtualization ten years ago because they thought it was ‘not real infrastructure’? And then virtualization became standard, and those sysadmins became obsolete? I don’t want to be that person. The industry is moving to managed services. I can either complain about it and become irrelevant, or I can adapt and stay valuable.”

“That’s a pretty mature perspective,” Marcus said.

“I’m twenty-six years into my career,” Jake said. “I’d like to have another twenty years. That means evolving with the industry, not fighting it.”

Maya stood up. “Alright. Jake recommends proceeding with the Exadata migration. I agree. Unless anyone has serious objections?”

Silence.

“Good,” Maya said. “Here’s the plan for the rest of this week. Jake, you’re setting up the Exadata test environment on GCP. Sarah, you’re helping him with the GCP integration. Tom, start documenting our application server connection strings so we can update them during migration. Marcus, review our integrations that hit the database directly—we need to make sure they’ll work on Exadata. Priya, work with Jake on the testing plan. Lisa, start building the migration runbook.”

“What about you?” Jake asked.

“I’m updating our cost model for Harrison’s weekly report,” Maya said. “And drafting the business case for Exadata that we’ll use in the final Week 12 presentation. This is a big deal—$110,000 in annual savings is significant. I want to make sure we communicate the value clearly.”

As the team dispersed to start their work, Jake pulled Maya aside.

“Thank you,” he said quietly.

“For what?” Maya asked.

“For not just ramming this through,” Jake said. “You could have said, ‘We’re migrating to Exadata, deal with it.’ Instead, you made the case, showed me the data, and let me come to my own conclusion. That matters.”

“You’re the database expert,” Maya said simply. “If you thought Exadata was a bad idea, I would have listened. I’m not trying to push technology for technology’s sake. I’m trying to build a better operation. And that only works if the people doing the work believe in it.”

“I believe in it now,” Jake said. “And I’m actually excited about this. For the first time in years, I’m thinking about database work as something more than just keeping the lights on.”

“That’s what I was hoping for,” Maya said. “Now go set up that test environment. I want to see Exadata running by the end of the week.”

Friday Afternoon: First Test

By Friday afternoon, Jake had the Exadata test environment running on Google Cloud. The team gathered to watch the first database restore from their production backup to the new platform.

“This is it,” Jake said, initiating the restore process. “Production database backup from last night, restoring to Exadata. On our old system, this would take about six hours. On Exadata with their snapshot technology…”

They watched the progress indicator.

Forty-two minutes later, the restore was completed.

“Forty-two minutes,” Jake said, staring at the screen. “For a 1.2 terabyte database. That’s… that’s eight times faster than our current restore process.”

“That’s your disaster recovery time,” Maya said. “Forty-two minutes from disaster to running database.”

“Run a query,” Tom suggested. “Let’s see if it actually works.”

Jake opened SQL Developer and connected to the Exadata instance. He ran a complex query that he knew typically took about eight seconds on their production system.

It returned in 4.3 seconds.

“That’s almost twice as fast,” Jake said, running it again to make sure. 4.2 seconds. “Same query, half the time.”

“Is that the Smart Scan feature?” Priya asked.

“Probably a combination of things,” Jake said, pulling up the execution plan. “Flash cache, faster storage, and yeah, some Smart Scan optimization. This query does a full table scan on a large table, which is exactly what Smart Scan helps with.”

He ran another query—a typical PeopleSoft transaction query with lots of index lookups.

Production time: 0.3 seconds
Exadata time: 0.2 seconds

“About 30% faster on OLTP queries too,” Jake noted. “The flash cache is helping with hot data.”

Sarah was watching the Exadata performance monitoring console. “Look at the storage I/O. Those storage servers are doing offload processing—they’re filtering data before sending it to the database layer. That’s really cool architecture.”

“It is,” Jake admitted. “Oracle actually did something smart here. Instead of just throwing faster hardware at the problem, they redesigned the architecture to push processing closer to the data.”

Marcus had a question. “What about our batch jobs? Can we test one?”

“Let’s try the nightly student enrollment batch,” Jake said. “In production, that typically runs for two hours. Let me kick it off here with yesterday’s data.”

He started the batch job, and they watched the progress.

One hour and fourteen minutes later, it was completed.

“That’s 38% faster than production,” Jake said, checking the logs. “Same data volume, same processing logic, just running on better infrastructure.”

“So everything’s faster,” Tom summarized. “Restore time, query time, batch time. All better than what we have.”

“So far,” Jake cautioned. “This is one day of testing with a handful of queries. We need weeks of testing with real workloads before I’m comfortable migrating production.”

“But you’re not seeing any red flags?” Maya asked.

“None,” Jake said. “Which honestly surprises me. I expected to find something that didn’t work well. Some queries were slower, and some features were incompatible. But so far? It’s just better.”

“Maybe Oracle actually knows what they’re doing with this product,” Sarah suggested.

“Don’t get crazy,” Jake said with a smile. “But yeah, they did good work here. This is legitimately impressive infrastructure.”

Maya pulled up the week’s summary on the screen.

Week 5: Database Modernization

Decision Made:

• Migrate from self-managed Oracle RAC to Exadata Database Service on GCP.

• Jake’s full evaluation and recommendation

• Team consensus and buy-in

Test Environment:

• Exadata Quarter Rack deployed on GCP

• Production database restored (42 minutes vs. 6 hours)

• Initial performance testing (10-40% faster across workloads)

• No compatibility issues identified

Business Value:

• Annual cost savings: $110,480

• Operational time savings: 74 hours/month (Jake’s time)

• Performance improvement: 10-40% depending on workload type

• Risk reduction: Automated backups, faster DR, Oracle infrastructure support

Next Steps:

• Week 6: Dev database migration and testing

• Week 7: QA database migration and extended testing

• Week 8-10: Performance validation and production migration planning

“This is what I’m reporting to Harrison on Monday,” Maya said. “One week from ‘we need to evaluate this’ to ‘we have a working test environment and initial positive results.’ That’s execution.”

“Can I add something to the report?” Jake asked.

“Of course.”

“Include that the DBA team fully supports this migration,” Jake said. “I want Harrison to know this isn’t being forced on us. We evaluated it, we tested it, and we believe it’s the right move.”

“That’s valuable,” Maya said, making a note. “I’ll quote you directly in the status report.”

As the team started to pack up for the weekend, Tom pulled Jake aside.

“Hey, I wanted to say something. Your presentation on Wednesday was really good. You could have just rubber-stamped Maya’s plan, but you did real analysis. That matters.”

“Thanks,” Jake said. “I figured if I’m going to recommend a major migration, I should actually understand what we’re migrating to.”

“I think you surprised Maya a little,” Tom said. “In a good way. She was probably expecting more resistance.”

“I surprised myself,” Jake admitted. “Monday morning, I was ready to dig in and fight this. But the more I looked at it objectively, the more I realized the fight was about ego, not engineering. I didn’t want to admit that there might be a better way to run databases than how I’ve been doing it for twenty years.”

“That’s hard to admit,” Tom said.

“Yeah,” Jake agreed. “But you know what’s harder? Spending the next twenty years doing work I don’t enjoy because I was too proud to change. I’d rather spend my time optimizing SQL than babysitting backups. If Exadata lets me do that, I’m all for it.”

They walked out together, leaving the conference room with its whiteboard full of Exadata architecture diagrams and performance benchmarks.

Maya stayed behind for a moment to update her timeline.

Five weeks down. Seven weeks to go.

Week 1: Honest assessment and baseline
Week 2-3: Infrastructure as Code
Week 4: Observability
Week 5: Database modernization decision

They were more than on track. They were ahead.

And more importantly, her team was evolving. Jake had gone from defensive to analytical to advocating for change. That transformation—from “we lose control” to “this is legitimately better”—was exactly what Maya needed to see.

Because the twelve-week challenge wasn’t just about proving PeopleSoft could be modernized.

It was about proving that traditional IT teams could modernize themselves.

One mindset shift at a time.

Technical Takeaway: The Managed Database Services Decision Framework

Jake’s journey from resistance to advocacy illustrates the critical decision framework for PeopleSoft DBAs evaluating managed database services:

The Core Question

Not: “Can we manage databases ourselves?”
But: “Should we manage database infrastructure ourselves?”

These are fundamentally different questions with different answers.

The Traditional DBA Value Proposition

For decades, DBAs justified their value through operational tasks:

• Managing backups and recovery

• Applying patches and updates

• Configuring high availability

• Monitoring disk space and storage

• Troubleshooting infrastructure issues

• Performing routine maintenance

This work is necessary. It’s also increasingly commoditized.

The Managed Services Shift

Managed database services (Oracle Exadata, AWS RDS, Azure SQL Database, Google Cloud SQL), and automated operational tasks:

• Backups happen automatically with point-in-time recovery.

• Patches apply with zero downtime on managed schedules.

• High availability is built in and automated.

• Storage scales automatically

• The provider handles infrastructure monitoring.

• Routine maintenance is automated.

This shift terrifies traditional DBAs because it appears to eliminate their value.

The Reality: Strategic vs. Operational Work

DBAs create value in two categories:

Operational Work (automatable):

• Backup management

• Patch application

• Storage management

• Infrastructure monitoring

• Routine maintenance

• Disaster recovery execution

Strategic Work (not automatable):

• SQL query optimization

• Execution plan analysis

• Index strategy and design

• Data modeling and architecture

• Performance troubleshooting

• Capacity planning based on business trends

• Developer education and support

• Database platform evolution

Managed services eliminate operational work. They amplify strategic work.

The Time Allocation Analysis

Most PeopleSoft DBAs spend:

• 60-80% of time on operational tasks

• 20-40% of time on strategic work

With managed services:

• 5-10% of time on operational oversight

• 90-95% of time on strategic work

Same total hours. Radically different value creation.

The Cost Model

Self-Managed Database Total Cost:

• Infrastructure (servers, storage, networking)

• Software licenses and support

• Data center (space, power, cooling)

• DBA operational time

• Infrastructure team support

• Opportunity cost of DBA time on toil

Managed Database Service Total Cost:

• Service subscription (includes infrastructure, software, support)

• DBA strategic time

• Training and skill development

For most organizations, Managed services cost 30-50% less than self-managed while delivering better performance and reliability.

The Performance Reality

Managed database services often perform better than self-managed:

Why?

• Specialized hardware (Exadata, custom AWS instances)

• Optimized configurations based on millions of workloads

• Advanced features (Smart Scan, storage offload, flash cache)

• Regular performance tuning by provider engineers

• Latest patches and optimizations are applied automatically.

When self-managed might be better:

• Highly specialized configurations requiring deep customization

• Regulatory requirements preventing cloud deployment

• Existing infrastructure with capacity to spare

• Extremely cost-sensitive scenarios (rare)

For typical PeopleSoft workloads, Managed services match or exceed self-managed performance.

The Control Argument

What you actually lose:

• Infrastructure-level configuration (storage, networking)

• Ability to apply custom patches outside provider schedules

• Direct hardware access for diagnostics

What you retain:

• Database-level configuration and tuning

• SQL optimization and query control

• Schema design and modifications

• User and security management

• Application-level performance tuning

The key insight: Most of what DBAs think they need control over, they rarely actually change.

The Career Development Perspective

Traditional DBA skills are decreasing in value:

• Manual backup/recovery procedures

• Infrastructure hardware troubleshooting

• Physical storage management

• On-premise high availability configuration

Modern DBA skills are increasing in value:

• SQL performance optimization (always relevant)

• Cloud database architecture

• Multi-database platform expertise

• Data modeling and design patterns

• Performance analysis and tuning

• Developer enablement and education

• Database automation and tooling

Managed services force DBAs to develop higher-value skills.

The Decision Framework

Evaluate managed services when:

• Operational overhead exceeds 50% of DBA time.

• Infrastructure is aging and needs replacement.

• Disaster recovery is difficult or untested.

• Team lacks deep infrastructure expertise.

• The organization is moving to the cloud.

• Want to reduce operational risk.

Proceed with managed services if:

• Performance testing shows equivalent or better results.

• Cost analysis shows savings (usually 30-50%)

• Team is willing to adapt skillsets.

• Provider supports your database platform and version.

• Regulatory requirements are met.

Stay with self-managed if:

• Truly unique configuration requirements exist

• In-house expertise dramatically exceeds provider capability (rare)

• Regulatory constraints prevent cloud deployment

• Cost analysis definitively favors self-managed (very rare)

The Implementation Approach

Phase 1: Evaluation (1-2 weeks)

• Deep dive on provider capabilities

• Compatibility verification

• Feature gap analysis

• Cost modeling

Phase 2: Proof of Concept (2-3 weeks)

• Deploy test environment

• Restore production data

• Performance benchmarking

• Functionality validation

Phase 3: Migration Planning (2-3 weeks)

• Detailed migration runbook

• Rollback procedures

• Team training

• Risk mitigation planning

Phase 4: Staged Migration (4-6 weeks)

• Migrate the dev environment

• Extended testing in dev

• Migrate QA environment

• Performance validation in QA

• Production migration with careful monitoring

Phase 5: Optimization (ongoing)

• Leverage advanced provider features

• Tune for specific workloads

• Continuous performance improvement

The Team Transformation

Successful managed services adoption requires:

Leadership:

• Clear communication about role evolution

• Career development support

• Training budget allocation

• Patience during the learning curve

DBAs:

• Willingness to learn new tools and approaches

• Focus on strategic skill development

• Embrace of automation (not resistance)

• Trust in provider capabilities

Organization:

• Recognition that value shifts from operational to strategic

• Investment in DBA skill development

• Support for new ways of working

The Bottom Line

The decision between managed and self-managed databases isn’t about technology capabilities.

It’s about organizational priorities:

Choose self-managed if: You want control over infrastructure and are willing to pay (in time and money) for that control.

Choose managed services if: You want to redirect DBA expertise toward strategic work that directly impacts business outcomes.

For most PeopleSoft organizations, managed services are the better choice.

Not because DBAs are unnecessary.

But because DBAs are too valuable to spend their time on operational toil that can be automated.

Jake learned this lesson in Week 5.

The question is: will your DBAs learn it before or after the industry makes the decision for them?

Subscribe now

A strong roadmap reflects how the organization wants to operate in the future. It connects business priorities to technology capabilities in a measurable, adaptable way. Without this alignment, even well-funded initiatives struggle to deliver meaningful outcomes. ERP modernization should not be treated as a one-time project. It should be structured as a continuous evolution guided by clear decision points.

Step 1: Establish Strategic Objectives

Every effective roadmap begins with a clear articulation of strategic objectives. These objectives should reflect both business priorities and operational realities. CIOs need to define what modernization is meant to achieve, whether that is increased agility, cost optimization, improved data visibility, or enhanced user experience. Without this clarity, it becomes difficult to evaluate tradeoffs or measure success.

Strategic objectives also help align stakeholders. Business leaders, finance teams, and IT organizations often have different expectations for ERP outcomes. A shared set of objectives ensures that decisions are evaluated against consistent criteria. This alignment reduces friction later in the roadmap. It also strengthens executive sponsorship and accountability.

Step 2: Assess the Current State Honestly

An honest assessment of the current ERP environment is essential. This assessment should go beyond infrastructure and include processes, integrations, data quality, and team capabilities. Many organizations underestimate the strength of what they already have. Others overlook areas where technical debt or inefficiency has accumulated.

A comprehensive assessment should include:

• Core system stability and performance metrics

• Integration landscape and data flow complexity

• Customizations and their business value

This level of visibility allows CIOs to separate perception from reality. It also highlights areas where modernization can deliver immediate value. Without this baseline, roadmap decisions are based on incomplete information.

Step 3: Define the Target Operating Model

The target operating model describes how the ERP environment will function in the future. It includes decisions about cloud strategy, integration patterns, governance, and team structure. This model is not about specific tools. It is about defining how work gets done and how systems interact.

A well-defined operating model provides a blueprint for modernization. It clarifies roles and responsibilities across IT and business teams. It also establishes standards for deployment, monitoring, and change management. These standards reduce variability and improve predictability.

CIOs should ensure that the operating model supports both stability and innovation. The goal is not to optimize for one at the expense of the other. A balanced model enables continuous progress without introducing unnecessary risk.

Step 4: Prioritize Modernization Initiatives

Once objectives and operating models are defined, the next step is prioritization. Not all modernization efforts deliver equal value. CIOs must identify initiatives that provide the highest impact with manageable risk. This requires evaluating both technical complexity and business benefit.

Effective prioritization often focuses on areas such as:

• Infrastructure modernization to improve scalability and cost efficiency

• Integration improvements to accelerate data flow and reduce friction

• Automation initiatives that reduce manual effort and increase reliability

These initiatives create momentum without requiring large-scale disruption. They also build confidence across stakeholders. Over time, this approach allows organizations to modernize incrementally while maintaining operational continuity.

Step 5: Establish Governance and Metrics

Governance is the mechanism that keeps the roadmap on track. It ensures that decisions align with strategic objectives and that progress is measurable. Without governance, even well-designed roadmaps can drift. CIOs need to define how decisions are made, how priorities are adjusted, and how success is evaluated.

Metrics play a critical role in this process. They provide visibility into whether modernization efforts are delivering the intended outcomes. Metrics should go beyond uptime and include delivery speed, cost efficiency, and business impact. Clear measurement reinforces accountability and supports continuous improvement.

Strong governance also creates transparency. Stakeholders can see how decisions are made and how progress is tracked. This visibility builds trust and reduces resistance to change.

Step 6: Execute in Phases, Not Events

ERP modernization should be implemented in phases rather than as a single transformation event. Phased execution reduces risk by limiting the scope of change at any given time. It also allows organizations to learn and adapt as they move forward. Each phase builds on the previous one, creating a cumulative effect.

Phased approaches also improve resilience. If a particular initiative encounters challenges, it does not derail the entire roadmap. CIOs can adjust priorities and timelines without compromising overall strategy. This flexibility is essential in complex environments.

Over time, phased execution creates a pattern of continuous improvement. The organization becomes more comfortable with change and better able to manage it. This capability is one of the most valuable outcomes of a well-designed roadmap.

Turning Strategy into Sustained Progress

An effective ERP modernization roadmap is not a static document. It is a living framework that evolves with the organization. CIOs who approach modernization as a continuous process are better positioned to adapt to change and capture new opportunities. They avoid the pitfalls of both stagnation and overreach.

The ultimate goal is not modernization for its own sake. It is the ability to deliver consistent, measurable value to the business. A well-structured roadmap provides the clarity and discipline needed to achieve that goal.

☕ This Week’s Focus

Edition 30 marks a major milestone—30 editions of The Modern ERP Digest.

What started as a way to consolidate PeopleSoft, ERP, and cloud insights has grown into a consistent signal for what’s actually happening in the market. And if there’s one theme that stands out this week, it’s this:

ERP is being redefined in real time.

Subscribe now

We’re seeing a collision of forces between AI agents, multicloud architectures, workforce shifts, and evolving expectations of what ERP systems should actually do. The conversation is no longer about systems of record. It’s about systems of action.

At the same time, the PeopleSoft ecosystem continues to evolve in practical, meaningful ways, especially in areas like containerization and AI integration.

Let’s get into it.

☁️ Oracle, Cloud & AI Momentum

🔗 Oracle Multicloud: What’s New
https://blogs.oracle.com/cloud-infrastructure/oracle-multicloud-whats-new-blog
Oracle continues expanding its multicloud strategy, reinforcing a reality many enterprises already accept: no single cloud will meet every need. The focus is shifting toward interoperability and platform flexibility.

🔗 Oracle’s AI Pivot and “Agentic Applications” Strategy
https://markets.financialcontent.com/stocks/article/finterra-2026-4-9-the-ai-landlord-inside-oracles-pivot-to-fusion-agentic-applications
Oracle is leaning heavily into agent-based applications—systems that can act, decide, and automate end-to-end processes. This represents a shift from traditional ERP workflows to more autonomous, AI-driven operations.

👥 Workforce & Organizational Signals

🔗 Oracle Layoff Stories and Employee Perspective (Business Insider)
https://www.businessinsider.com/oracle-employee-laid-off-after-almost-10-years-learned-lessons-2026-4
Personal stories from layoffs highlight the human side of transformation. Behind every strategic shift are real teams navigating uncertainty and change.

🔗 Severance Benchmarking Under the Microscope (HR Executive)
https://hrexecutive.com/oracles-layoff-package-puts-severance-benchmarking-under-the-microscope/
Oracle’s severance approach is drawing attention as organizations rethink how they manage workforce transitions during large-scale transformation efforts.

📊 ERP Strategy & Industry Direction

🔗 Why Your ERP Is Now a Liability—Not an Asset (Manufacturing.net)
https://www.manufacturing.net/artificial-intelligence/article/22964443/why-your-erp-is-now-a-liability-not-an-asset
A provocative take: ERP systems that fail to evolve can become constraints rather than enablers. The key issue isn’t ERP itself, it’s whether it can adapt to modern demands.

🔗 The Next Evolution of ERP: Intelligence Built Around People (Diginomica)
https://diginomica.com/next-evolution-erp-intelligence-built-around-people
ERP is shifting toward more human-centered, intelligence-driven design. AI isn’t just optimizing processes—it’s reshaping how users interact with enterprise systems.

👩‍💻 PeopleSoft Community & Practice

🔗 PeopleSoft Images on Containers (Oracle Blog)
https://blogs.oracle.com/peoplesoft/peoplesoft-images-on-containers
Oracle continues advancing containerization for PeopleSoft, enabling more flexible deployments and aligning with modern platform engineering practices. This is a key step toward scalable, cloud-native PeopleSoft environments.

🔗 PeopleSoft AI Agent – Copilot Studio (YouTube)

This demo showcases how AI agents can integrate with PeopleSoft workflows using tools like Copilot Studio. It’s a glimpse into how conversational and automated interfaces may become part of the PeopleSoft experience.

💬 Aaron’s Take

Thirty editions in, one thing has become very clear:

ERP isn’t standing still—and neither can we.

The narrative that ERP is “dead” keeps resurfacing, but that’s not what’s actually happening. ERP is evolving—sometimes quietly, sometimes aggressively—into something far more dynamic.

AI agents, multicloud strategies, and composable architectures are all pushing ERP beyond its traditional boundaries. But at the same time, platforms like PeopleSoft are adapting in practical ways, through containers, AI integrations, and lifecycle tools.

The real risk isn’t that ERP becomes obsolete.
The real risk is that organizations fail to evolve how they use it.

The teams that win in this next phase won’t chase every trend. They’ll focus on:

• Integrating intelligently
• Automating deliberately
• Modernizing incrementally
• Leading with architecture, not hype

That’s where real progress happens.

🌐 Join the Community

If you want to stay current on ERP strategy, PeopleSoft innovation, and cloud platform trends, subscribe at www.peoplesoftcloud.com.

Each week you’ll get:

✅ Curated ERP and PeopleSoft insights
✅ Context around AI and cloud developments
✅ Practical, real-world guidance

Thanks for being part of the first 30 editions.
On to the next 30.

Subscribe now

However, stability can create a false sense of security. Over time, systems that are not actively modernized drift away from the business’s needs. Integration becomes more difficult, reporting becomes slower, and innovation moves elsewhere. These shifts rarely trigger immediate alarms, but they accumulate into meaningful constraints. What appears safe in the moment can become limiting over time.

The Hidden Risks of Stagnation

The risks of not modernizing are rarely captured in traditional risk registers. They do not show up as outages or critical incidents. Instead, they emerge gradually through reduced agility, increased manual effort, and declining alignment with business priorities. These risks are harder to quantify, which makes them easier to ignore.

Several patterns tend to emerge in stagnant ERP environments:

• Increasing reliance on manual workarounds to bridge system gaps

• Slower delivery timelines for new capabilities and integrations

• Growing disconnect between IT capabilities and business expectations

These patterns create friction that compounds over time. Teams spend more effort maintaining workarounds than delivering new value. Business units begin seeking alternatives outside IT governance. The organization becomes less coordinated and more reactive.

Read more

☕ This Week’s Focus

Edition 29 highlights a growing tension in the ERP and cloud space: acceleration versus alignment.

Oracle is pushing aggressively into AI, with infrastructure investments, partnerships, and now large-scale agent-based automation. At the same time, workforce changes and market reactions remind us that transformation always comes with trade-offs.

Subscribe now

Across the ERP landscape, the conversation is shifting toward ecosystems and composability. Organizations are no longer asking “Which ERP should we choose?” but rather “How should our ERP fit into a broader architecture?”

Meanwhile, the PeopleSoft community continues to move forward with clarity by leveraging OCI, expanding Cloud Manager capabilities, and aligning with the 2026 roadmap.

⚙️ Oracle, AI & Market Signals

🔗 Oracle Layoff Plan Raises Questions (Yahoo Finance)
https://finance.yahoo.com/news/oracle-layoff-plan-raises-questions-011119909.html
Reports of layoffs are raising questions about how Oracle is balancing cost management with its aggressive investments in AI and the cloud. It’s a reminder that large-scale transformation often includes difficult operational decisions.

🔗 Oracle + NVIDIA at GTC 2026: Key Announcements (OCI Blog)
https://blogs.oracle.com/cloud-infrastructure/oracle-nvidia-gtc-2026-key-announcements
Oracle and NVIDIA announced deeper collaboration around AI infrastructure, including expanded GPU capabilities and performance optimizations. The focus remains clear: enable enterprise-scale AI workloads with high-performance compute.

🔗 Oracle Unleashes 1,000 AI Agents (CloudWars)
https://cloudwars.com/ai/oracle-unleashes-1000-ai-agents-to-automate-entire-industries/
Oracle is introducing a large portfolio of AI agents designed to automate complex business processes across industries. This marks a significant step toward agentic ERP, where systems don’t just process data; they act on it.

📊 ERP Strategy & Architecture

🔗 Why ERP Ecosystems Are a Strategic Architecture Decision (Forbes)
https://www.forbes.com/councils/forbestechcouncil/2026/03/20/why-erp-ecosystems-are-a-strategic-architecture-decision/
This piece makes a strong case that ERP is no longer a standalone system, it’s part of a broader ecosystem. Integration strategy, data flow, and platform alignment now matter as much as core functionality.

🔗 Composable ERP: Architectural Reality or Executive Imperative? (ERP Today)
https://erp.today/composable-erp-architectural-reality-or-executive-imperative/
Composable ERP continues gaining attention as organizations look to assemble capabilities rather than replace entire systems. The challenge is balancing flexibility with governance and long-term maintainability.

👩‍💻 PeopleSoft Community & Practice

🔗 Enabling AI for PeopleSoft with OCI (PeopleSoftCareer)
https://blog.peoplesoftcareer.com/enabling-ai-for-peoplesoft-with-oci/
A practical guide to integrating AI capabilities into PeopleSoft using Oracle Cloud Infrastructure. It outlines how AI services can enhance existing workflows without disrupting the core system.

🔗 Cloud Manager Image 21 (Oracle PeopleSoft Blog)
https://blogs.oracle.com/peoplesoft/cloud-manager-image-21
Oracle highlights updates to Cloud Manager Image 21, including improvements to provisioning, lifecycle management, and automation. Cloud Manager continues evolving into a key platform tool for PeopleSoft operations.

🔗 PeopleSoft 2026 Product Roadmap (PeopleSoftCareer)
https://blog.peoplesoftcareer.com/peoplesoft-2026-product-roadmap/
An overview of the 2026 roadmap, including continued investment in UX, AI capabilities, and platform enhancements. The roadmap reinforces Oracle’s long-term commitment to PeopleSoft.

💬 Aaron’s Take

Edition 29 reinforces a shift that’s been building for a while: ERP is no longer a destination; it’s a component of a larger system.

Oracle’s AI push, NVIDIA partnerships, and agent-based automation are all part of a broader strategy to move beyond traditional ERP boundaries. But those moves come with real operational considerations, including cost, workforce alignment, and execution risk.

At the same time, the ERP conversation itself is evolving. Ecosystems and composability are replacing monolithic thinking. The question isn’t whether ERP changes, it’s how well it integrates with everything around it.

PeopleSoft continues to fit into this new model in a very practical way. Instead of being replaced, it’s being extended and connected to AI services, managed through Cloud Manager, and aligned with a clear roadmap.

The organizations that succeed won’t be the ones chasing the biggest changes. They’ll be the ones who thoughtfully architect, deliberately integrate, and consistently evolve.

🌐 Join the Community

If you want to stay current on ERP strategy, PeopleSoft innovation, and cloud platform trends, subscribe at PeopleSoftCloud.com.

Each week you’ll receive:

✅ Curated ERP and PeopleSoft insights
✅ Context around AI and cloud developments
✅ Practical guidance for enterprise technology teams

See you next week for Edition 30.

☕ This Week’s Focus

Edition 28 highlights a theme that continues to define ERP strategy in 2026: evolution over disruption.

Subscribe now

Across the industry, the most successful organizations aren’t abandoning their ERP foundations. Instead, they’re layering AI, automation, and modern architecture patterns on top of stable platforms. Oracle’s financial performance and market momentum reinforce the scale of its cloud ambitions, while analysts and practitioners alike encourage enterprises to modernize intelligently rather than rush into costly replacements.

Meanwhile, the PeopleSoft ecosystem continues to deliver practical updates, from tax reporting changes to developer automation techniques, demonstrating that modernization often happens through incremental capabilities rather than sweeping change.

Let’s look at the signals shaping the landscape this week.

⚙️ Oracle, Cloud & Market Signals

🔗 Well-Architected Design for Resiliency with Oracle Database@AWS
https://aws.amazon.com/blogs/database/well-architected-design-for-resiliency-with-oracle-databaseaws/
AWS outlines architecture guidance for running Oracle Database workloads with resiliency and high availability. The takeaway is clear: hybrid and multicloud patterns are becoming normal for enterprise workloads, especially when reliability and geographic redundancy are priorities.

🔗 Oracle Stock Surges on AI and Cloud Momentum (Axios)
https://www.axios.com/2026/03/10/oracle-stock-ai-cloud-computing
Axios reports renewed investor confidence in Oracle as AI and cloud demand continue driving revenue expectations. Markets are increasingly viewing Oracle not just as a database company, but as a major infrastructure provider for enterprise AI workloads.

🔗 Oracle Announces Fiscal Year 2026 Third Quarter Financial Results
https://investor.oracle.com/investor-news/news-details/2026/Oracle-Announces-Fiscal-Year-2026-Third-Quarter-Financial-Results/default.aspx
Oracle’s quarterly results reinforce the same trend: cloud infrastructure and AI-related demand remain key growth drivers. The company continues investing heavily in capacity and infrastructure as enterprise workloads scale.

📊 ERP Strategy & Industry Direction

🔗 Don’t Rip and Replace PeopleSoft—Pair It with Emerging Tech Instead (CIO.com)
https://www.cio.com/article/4132315/dont-rip-and-replace-peoplesoft-pair-it-with-emerging-tech-instead.html
CIO.com reinforces a strategy many experienced ERP leaders already follow: extend PeopleSoft with modern tools rather than replacing it prematurely. Integrations, automation layers, and AI services allow organizations to modernize while protecting the stability of their core systems.

🔗 AI in ERP: How Intelligence Is Reshaping Enterprise Platforms (NetSuite)
https://www.netsuite.com/portal/resource/articles/erp/ai-erp.shtml
This overview highlights how AI is becoming embedded across ERP workflows—forecasting, automation, anomaly detection, and decision support. The shift isn’t toward replacing ERP systems, but toward making them more intelligent.

🔗 Gartner: Embedded AI in Cloud ERP Will Accelerate Financial Close by 30%
https://www.gartner.com/en/newsroom/press-releases/2026-02-24-gartner-predicts-embedded-ai-in-cloud-erp-applications-will-drive-a-30-percent-faster-financial-close-by-2028
Gartner predicts that embedded AI capabilities will significantly accelerate financial close processes by 2028. Automation of reconciliations, anomaly detection, and workflow orchestration is expected to reshape finance operations.

👩‍💻 PeopleSoft Community & Practice

🔗 IRS FIRE to IRIS: What PeopleSoft Customers Need to Know for 2026 Filings
https://blogs.oracle.com/peoplesoft/irs-fire-to-iris-what-peoplesoft-customers-need-to-know-for-2026-filings
Oracle outlines the transition from the IRS FIRE system to IRIS for tax reporting. Organizations using PeopleSoft must understand the changes and prepare their reporting processes accordingly to ensure compliance.

🔗 PeopleTools Sessions of Interest at Blueprint 4D 2026
https://blogs.oracle.com/peoplesoft/peopletools-sessions-of-interest-at-blueprint-4d-2026-2
Oracle highlights key PeopleTools sessions at Blueprint 4D, including topics on platform capabilities, automation, and modernization strategies. These sessions provide a good roadmap for teams planning upgrades or exploring new features.

🔗 PeopleSoft 2026 Product Roadmap Overview
https://blog.peoplesoftcareer.com/peoplesoft-2026-product-roadmap/
This overview summarizes major roadmap directions for PeopleSoft in 2026, including continued UX improvements, AI integrations, and platform modernization initiatives.

🔗 Automating Document Merges in PeopleSoft with Java and PeopleCode
https://newpeoplesoft.wordpress.com/2026/03/11/automating-document-merges-in-peoplesoft-pdf-jpg-and-png-consolidation-with-java-and-peoplecode/
A practical technical guide showing how to automate document merging processes inside PeopleSoft. It’s a great example of how PeopleCode and Java integration can streamline operational workflows.

💬 Aaron’s Take

Edition 28 reinforces something that’s becoming increasingly obvious across the ERP landscape: the future of ERP isn’t replacement—it’s augmentation.

Organizations that rush into rip-and-replace transformations often underestimate the complexity and cost involved. Meanwhile, teams that thoughtfully integrate modern technologies, including AI services, automation layers, and observability tools, can dramatically improve their ERP environments without destabilizing them.

Oracle’s market momentum reflects the infrastructure side of this story. Demand for cloud capacity and AI platforms is growing rapidly, and Oracle is positioning itself as a key provider of that infrastructure.

But the more interesting story may be happening inside existing ERP platforms like PeopleSoft. Incremental improvements, better integrations, and smarter automation are quietly transforming how these systems deliver value.

In other words, modernization doesn’t always look dramatic. Sometimes it looks like disciplined progress.

🌐 Join the Community

If you want to stay current on ERP strategy, PeopleSoft innovation, and cloud platform trends, subscribe at PeopleSoftCloud.com.

Each week you’ll receive:

✅ Curated ERP and PeopleSoft insights
✅ Context around cloud and AI trends
✅ Practical guidance for enterprise technology teams

See you next week for Edition 29.

Subscribe now

PeopleSoft remains one of the most deeply embedded enterprise platforms in higher education, the public sector, and complex commercial environments. The investment is not limited to licensing or infrastructure. It includes decades of configuration, alignment of business logic, reporting maturity, and institutional knowledge. Replacing that foundation means replicating it elsewhere, often at a higher cost and with greater risk. The executive question should not be whether the platform is new, but whether it continues to deliver measurable value.

Total Cost of Ownership Versus Total Cost of Change

Most ERP replacement conversations focus on subscription pricing comparisons. Vendors highlight lower licensing costs or bundled features to create the impression of savings. However, the total cost of ownership rarely tells the full story without considering the total cost of change. Implementation, retraining, integration rebuilds, and stabilization periods often dwarf projected licensing differences.

The total cost of change includes both visible and hidden components. These typically include:

• Multi-year implementation services and integration rebuilds

• Productivity loss during transition and retraining cycles

• Temporary parallel system costs and stabilization overhead

Organizations frequently underestimate the duration of these transition costs. Even successful implementations can take years for operational efficiency to match the pre-migration baseline. From an executive standpoint, staying on PeopleSoft may represent the lower-risk, higher-return decision when these full lifecycle costs are modeled honestly.

The ROI of Existing Customization and Process Alignment

PeopleSoft environments often reflect years of business process refinement. Customizations, bolt-ons, and integrations are rarely random. They represent accumulated decisions that align technology with organizational reality. Replacing the platform means re-evaluating or recreating that alignment from scratch.

While customization is sometimes portrayed as technical debt, it is often institutional intelligence encoded in software. That intelligence carries real value. Eliminating it without a compelling return introduces operational friction and compliance risk. The ROI of staying on PeopleSoft includes preserving the alignment that has been built up.

When CIOs quantify the cost of rebuilding custom integrations and reporting structures in a new system, the financial case becomes clearer. Even modest environments can represent thousands of hours of development and configuration work. Preserving that investment is not conservatism. It is disciplined capital management.

Stability as a Financial Asset

Stability is frequently undervalued because it lacks a marketing narrative. When a system runs consistently, it does not generate headlines. However, stability has measurable economic value. Predictable operations reduce incident costs, improve audit performance, and protect institutional reputation.

Financial leaders understand the value of reduced volatility. A stable ERP reduces the likelihood of large, unplanned expenditures tied to system failures or rushed remediation. It also reduces executive distraction during periods of organizational change. These benefits compound quietly over time.

Staying on PeopleSoft allows organizations to redirect capital from reactive system replacement toward proactive modernization. Cloud migration, automation, and analytics enhancements often produce higher marginal returns than wholesale reimplementation. Stability, when leveraged intentionally, becomes a funding source for innovation rather than an obstacle.

Leveraging Modernization Without Replacement

Remaining on PeopleSoft does not imply stagnation. Modernization can occur at the infrastructure, integration, and operational layers without replacing the core system. Cloud deployment, observability tooling, and automation pipelines can materially improve cost efficiency and delivery speed. These improvements generate measurable operational gains without destabilizing business processes.

CIOs can often capture modernization value through targeted initiatives such as:

• Infrastructure optimization in public cloud environments

• Standardized deployment pipelines and automation frameworks

• Data integration enhancements that improve reporting velocity

Each of these investments strengthens the platform while preserving institutional alignment. This approach maximizes return on existing investment instead of resetting the balance sheet. It also reduces the execution risk associated with large-scale transformation programs.

The Strategic Advantage of Optionality

Perhaps the most underappreciated benefit of staying on PeopleSoft is the optionality it offers. By avoiding a forced replacement, CIOs preserve strategic flexibility. They retain the ability to modernize incrementally, test new platforms selectively, and respond to regulatory or market shifts without being locked into a single trajectory.

Optionality has financial value because it reduces irreversible commitments. Large ERP replacements often represent multi-year bets with limited exit paths. Staying on PeopleSoft allows organizations to evaluate alternatives from a position of strength rather than from a position of urgency. That leverage improves negotiation power and long-term resilience.

From an executive standpoint, the decision to remain on PeopleSoft should not be framed as defensive. It can be a proactive capital-allocation decision grounded in returns, risk mitigation, and strategic flexibility.

A Disciplined Approach to ERP Leadership

The executive case for staying on PeopleSoft rests on disciplined analysis rather than nostalgia. It requires modeling full lifecycle costs, valuing institutional alignment, and recognizing stability as an asset. When these factors are considered honestly, the narrative shifts. Staying on PeopleSoft often emerges as a rational and financially responsible strategy.

This does not mean that replacement is never appropriate. It means that replacement should be justified by a clear, measurable advantage rather than external pressure or perception. CIOs who make this distinction lead with clarity and credibility. In the next article, we will explore how to communicate this case effectively to boards and executive peers.

The Hidden Costs of ERP Migration

The Costs That Rarely Make the Slide Deck

ERP migration proposals often present a clean financial narrative. Licensing projections, implementation timelines, and high-level efficiency gains dominate the executive summary. What receives far less attention are the secondary and tertiary costs that surface only after contracts are signed. These costs are not theoretical. They are structural consequences of large-scale system replacement.

Hidden migration costs rarely appear as line items labeled “risk” or “disruption.” Instead, they emerge through extended stabilization periods, increased reliance on consulting, and declining internal confidence. By the time these effects are visible, reversing course is no longer practical. For CIOs, understanding these hidden dimensions is critical before committing to replacement.

Productivity and Institutional Knowledge Loss

One of the most underestimated costs of ERP migration is the decline in productivity. During transition periods, teams operate in parallel modes of learning and delivery. Business users must adapt to new workflows, new interfaces, and new reporting structures. Even when training is thorough, confidence drops before it rises.

Institutional knowledge is another invisible asset that erodes during migration. Long-tenured administrators and analysts often carry contextual understanding that cannot be easily documented. When those individuals disengage or exit during transformation cycles, organizations lose operational memory. That loss translates into slower troubleshooting, longer cycle times, and increased dependency on external advisors.

The financial impact of knowledge erosion is rarely modeled. Yet it directly affects delivery speed and decision quality. Over time, this erosion compounds into measurable performance drag.

Integration Rebuild and Data Realignment

ERP systems rarely operate in isolation. They anchor dozens or even hundreds of integrations across finance, HR, analytics, and operational systems. Migration requires either rebuilding or rearchitecting these connections. Each rebuild introduces new testing cycles, new failure points, and new governance requirements.

Data realignment also presents hidden complexity. Historical data structures often do not map cleanly into new platforms. Transformation logic must be rewritten and validated, frequently under tight deadlines. Even minor discrepancies can create audit concerns and reporting inconsistencies.

These integrations and data adjustments extend well beyond initial go-live. Organizations often spend years refining connections that previously operated reliably. The cost of rebuilding trust in data is rarely included in financial models.

Escalating Vendor and Consulting Dependency

Migration initiatives typically require significant external expertise. Implementation partners, system integrators, and vendor specialists become central to delivery. While this support accelerates initial deployment, it can create a long-term dependency if internal capability is not intentionally rebuilt. Consulting costs often exceed original projections.

Vendor dependency also shifts leverage dynamics. Contract structures, licensing adjustments, and roadmap alignment may become more constrained after migration. Organizations that once exercised autonomy over their ERP environment may find themselves navigating predefined boundaries. That shift can affect innovation timelines and negotiation strength.

The strategic cost of reduced leverage is difficult to quantify, but it is very real. Over time, limited flexibility can restrict competitive advantage.

Organizational Fatigue and Cultural Impact

Large ERP migrations demand sustained attention from leadership and operational teams. Multi-year programs often compete with other strategic initiatives for funding and executive focus. This prolonged intensity creates organizational fatigue. When fatigue sets in, risk tolerance declines and innovation slows.

Cultural impact is equally significant. Teams that experience repeated disruptions may become hesitant to embrace future transformation efforts. Confidence in IT leadership can weaken if promised outcomes are delayed. These cultural effects influence long-term performance more than short-term budget overruns.

Unlike infrastructure costs, cultural impact does not appear in financial statements. However, it shapes the organization’s ability to execute future strategy. Ignoring this dimension creates blind spots in executive planning.

A Framework for Evaluating Hidden Costs

CIOs can mitigate hidden migration costs by expanding evaluation frameworks beyond licensing comparisons. A disciplined assessment should include:

• Modeled productivity decline during transition periods.

• Institutional knowledge retention strategies and succession planning.

• Full integration rebuilds inventories and data validation timelines.

• Multi-year consulting and vendor dependency projections.

Including these dimensions produces a more honest financial model. It also strengthens credibility with boards and executive peers. When leaders clearly articulate hidden costs, decision quality improves.

Migration may still be justified in certain scenarios. However, it should proceed with full awareness of both visible and invisible implications. Hidden costs do not eliminate modernization as an option. They simply require it to be evaluated with greater rigor.

Read more

☕ This Week’s Focus

Edition 27 centers on a theme that keeps resurfacing in 2026: ERP modernization is less about replacement and more about refinement.

Oracle continues to land major public-sector wins and expand its cloud footprint in federal modernization initiatives. At the same time, real-world ERP implementation stories remind us that success depends on thoughtful customization—not over-customization, not blind standardization.

Inside the PeopleSoft ecosystem, Cloud Manager continues to mature, PeopleTools 8.62 capabilities are expanding, and natural language experiences are moving from concept to delivered functionality.

Let’s break it down.

⚙️ Oracle, Public Sector & Cloud Expansion

🔗 Oracle Lands Air Force Win (Yahoo Finance)
https://finance.yahoo.com/news/oracle-lands-air-force-win-152003927.html
Oracle secured a significant contract win with the U.S. Air Force, reinforcing its position in federal cloud and infrastructure services. Public-sector validation remains one of Oracle’s strongest credibility anchors in 2026.

🔗 OCI to Support CMS Modernization Initiative (Oracle Press Release)
https://www.oracle.com/news/announcement/oracle-cloud-infrastructure-to-support-centers-for-medicare-and-medicaids-modernization-initiative-2026-02-11/
Oracle Cloud Infrastructure will support the Centers for Medicare & Medicaid Services’ modernization efforts. This is another large-scale healthcare transformation backed by OCI, signaling continued trust in Oracle’s ability to deliver secure, scalable environments.

Together, these stories reinforce an important point: while AI narratives dominate headlines, it’s infrastructure reliability and compliance capabilities that win major contracts.

📊 ERP Strategy: Why Smart Customization Matters

🔗 What Midwest Groundcovers Learned from Implementing a New ERP System (Greenhouse Grower)
https://www.greenhousegrower.com/technology/what-midwest-groundcovers-learned-from-implementing-a-new-erp-system/

This implementation story highlights a lesson seasoned ERP teams already understand: the danger isn’t customization. Rather, it’s unintentional customization.

Midwest Groundcovers discovered that blindly replicating legacy processes inside a new ERP system can stall transformation. At the same time, refusing all customization in the name of “best practice” can create operational friction.

The sweet spot is disciplined customization. Where processes are evaluated, simplified where possible, and extended only when business value justifies it.

For PeopleSoft teams, this lesson resonates. Event Mapping, Page & Field Configurator, and delivered frameworks exist precisely to enable thoughtful extension without destabilizing the core. Smart customization preserves upgradeability. Reactive customization destroys it.

👩‍💻 PeopleSoft Community & Practice

🔗 Image Highlights: Cloud Manager Image 21 (YouTube)

This video walks through key updates in Cloud Manager Image 21, including refinements in environment lifecycle automation, provisioning enhancements, and improved orchestration capabilities. For teams managing PeopleSoft in OCI, Cloud Manager continues evolving into a true platform operations tool—not just a deployment assistant.

🔗 Mastering Notification Composer & Landing Page Notifications in PeopleTools 8.62 (NewPeopleSoft Blog)
https://newpeoplesoft.wordpress.com/2026/02/11/mastering-notification-composer-and-landing-page-notifications-in-peopletools-8-62/
A detailed guide to using Notification Composer and landing page alerts effectively. These features are powerful when governed well, and distracting when overused. The key is intentional communication design.

🔗 Is GenerateComponentPortalURL Still Relevant? (JSMPros)
https://blog.jsmpros.com/2026/02/is-generatecomponentportalurl-still.html
A thoughtful technical exploration of whether older PeopleCode patterns remain appropriate in modern Tools releases. The answer isn’t binary. Some patterns evolve; others remain valuable when used properly. Modernization requires understanding, not reflexively replacing, core capabilities.

🔗 Natural Language Assistant in PeopleSoft (Oracle Blog)
https://blogs.oracle.com/peoplesoft/natural-language-assistant-in-peoplesoft
Oracle introduces Natural Language Assistant capabilities within PeopleSoft, enabling users to interact with the system more conversationally. This marks a tangible step toward embedded AI experiences inside PeopleSoft—not as an external bolt-on, but as part of the user workflow.

💬 Aaron’s Take

Edition 27 reinforces something I’ve seen repeatedly across ERP programs: durability beats drama.

Oracle’s public-sector wins demonstrate that reliability, compliance, and scale still matter more than hype. At the implementation level, stories like Midwest Groundcovers highlight that success comes from disciplined decisions, not extremes.

And inside the PeopleSoft world, the pattern continues: Cloud Manager gets stronger, Tools capabilities deepen, and AI becomes more practical.

The organizations that win in 2026 won’t be the ones chasing the newest acronym. They’ll be the ones who:

• Standardize where possible
• Customize where justified
• Automate where repeatable
• Govern where necessary

That’s not flashy. It’s effective.

🌐 Join the Community

If you’re finding value in these weekly breakdowns, subscribe at PeopleSoftCloud.com for:

✅ ERP and PeopleSoft modernization insight
✅ Cloud infrastructure and automation strategy
✅ Practical AI context for enterprise systems

See you in Edition 28.

The reality is this:

PeopleSoft didn’t fail us.
Our operating model failed PeopleSoft.

When organizations struggle with PeopleSoft, the root cause is rarely the software itself. It’s almost always the way we deploy, govern, and own it. If you moved this same legacy mindset and operating model to any other ERP, it would look slow, brittle, and outdated, too.

Subscribe now

Legacy Thinking Creates “Legacy Systems”

For years, many PeopleSoft environments have been run with habits formed in a very different era:

* Manual builds that only one or two people understand.
* Environment drift that makes every change risky.
* Release processes held together by spreadsheets and tribal knowledge.
* Monitoring that relies on users reporting problems after the fact.

None of that is a PeopleSoft limitation. That’s an operating model problem.

If you applied those same practices to a modern SaaS-adjacent platform, it would degrade just as quickly. Complexity compounds when discipline is missing.

Modernization Is an Operating Shift, Not a Rip-and-Replace

True PeopleSoft modernization starts with a mindset shift: treating PeopleSoft as a platform, not just an application.

Platform thinking changes the questions leaders ask. Instead of “How fast can we get off PeopleSoft?” the question becomes “How do we operate this platform in a modern, resilient way?”

That leads directly to fundamentals:

* Automation instead of hand-built environments
* Repeatable infrastructure instead of one-off fixes
* Version control instead of shared folders
* Observability instead of reactive troubleshooting

When these elements are in place, PeopleSoft behaves very differently. Deployments become predictable. Failures become visible early. Teams spend less time firefighting and more time improving.

Governance and Ownership Matter More Than Tools

Another quiet failure point is ownership. In many organizations, PeopleSoft sits in an awkward gap between infrastructure, applications, and the business. No single group owns the full lifecycle, so decisions get fragmented and slow.

Modern platforms require clear ownership models. Someone must be accountable for how the system is built, operated, secured, and improved over time. Without that clarity, even the best tooling can’t compensate. This is why many “modernization” efforts stall. They focus on technology choices while leaving governance untouched.

The Real Question Leaders Should Be Asking

The most important question isn’t whether PeopleSoft is still viable.

It’s this: Are we operating it in a way that would succeed on any platform?

If the answer is no, moving to the cloud or to SaaS won’t fix the underlying issue. It will just shift the pain and usually increase the cost.

PeopleSoft didn’t fail us.
Our operating model failed PeopleSoft.

And the good news is that operating models can be redesigned, without starting over. If you fix the operating model, PeopleSoft is a differentiator, not an anchor.

Today, that framing creates friction. When PeopleSoft is viewed only as an application, investment decisions focus on maintenance rather than capability. Teams optimize for minimizing disruption instead of enabling outcomes. Over time, the system becomes isolated from broader enterprise strategy, even though it remains central to business operations.

An application mindset also limits leadership imagination. It encourages CIOs to think defensively rather than strategically. As enterprise architectures evolve, this narrow framing increasingly fails to reflect how PeopleSoft is actually used.

What It Means to Treat PeopleSoft as a Platform

A single interface or deployment model does not define a platform. It is defined by its ability to enable other systems, teams, and capabilities. When PeopleSoft is treated as a platform, it becomes an anchor point for data, process, and integration rather than a standalone workload. This shift changes how leaders evaluate value.

Platform thinking recognizes that PeopleSoft already powers critical enterprise workflows. It also acknowledges that those workflows generate data and events that other systems depend on. Instead of insulating PeopleSoft from change, a platform mindset exposes it in controlled and intentional ways. APIs, integration frameworks, and automation become first-class design considerations.

Most importantly, platform thinking reframes PeopleSoft as an enabler of innovation rather than an obstacle. That reframing unlocks new conversations between IT and the business. The system stops being something to work around and starts being something to build on.

Read more

The CIO arrived at 8 AM sharp, along with CFO Patricia Winters and VP of Business Operations James Chen. Maya had the conference room set up with her laptop connected to the projector, the infrastructure repository open in one window, a terminal in another.

“Good morning,” Harrison said, settling into his chair. “I understand you have a demonstration.”

“We do,” Maya said. “Three weeks ago, you gave us twelve weeks to prove PeopleSoft modernization could compete with SaaS migration. I want to show you what we’ve accomplished in the first three weeks.”

She pulled up her summary slide:

Weeks 1-3: Foundation Complete

• Week 1: Honest assessment, baseline metrics, quick wins

• Week 2-3: Infrastructure as Code implementation

• Result: 99.7% faster environment provisioning, $178K annual savings (dev only)

“Show me the demo,” Harrison said—no preamble, straight to business.

Maya opened her terminal. “I’m going to provision a complete PeopleSoft environment from scratch. When I run this command, Terraform and Chef will build a database, application servers, web servers, networking, storage, and configuration—everything needed for a functional PeopleSoft instance.”

She typed the command and hit enter.

“How long will this take?” Winters asked.

“About seventy-five minutes,” Maya said. “Our baseline three weeks ago was eighteen days.”

Winters blinked. “Eighteen days to seventy-five minutes?”

“Correct. And I’m not doing anything during these seventy-five minutes except watching automated processes run. Previously, this would have required multiple engineers working across multiple teams for days.”

They watched as infrastructure was provisioned in real-time. Harrison asked sharp questions—What happens if it fails? How do you validate correctness? Can you rebuild production this way? Maya answered each question confidently, occasionally pulling in Jake or Tom for technical details on database configuration or application-tier setup.

Seventy-three minutes later, the PeopleSoft sign-in screen appeared.

Harrison was quiet for a moment. “That’s… impressive. Walk me through the business value.”

Maya pulled up her cost analysis. “This dev environment costs us $110 per month to run versus $14,000 in our data center. Admin time to maintain it drops from forty hours per quarter to near zero. Total savings for this one environment: $178,000 annually. We have four environments. Extrapolating—”

“Seven hundred thousand per year,” Winters finished, doing the math. “That’s significant.”

“And that’s before we account for improved velocity,” Maya added. “When our developers can spin up test environments in an hour instead of waiting weeks, they move faster. When we can rebuild from disaster in ninety minutes instead of hoping our untested backup scripts work, we reduce risk. When our infrastructure knowledge lives in version-controlled code instead of tribal memory, we eliminate key person dependencies.”

Harrison leaned back. “What’s your plan for the next nine weeks?”

“Week 4, which starts today: CI/CD pipelines for automated testing and deployment of customizations. Week 5: Database modernization using managed services. Weeks 6-7: Observability and monitoring infrastructure. Week 8-9: Security automation and integration modernization. Weeks 10-12: Cost validation, scaling tests, and final presentation.”

“You’re on track?” Harrison asked.

“We’re ahead of track,” Maya said honestly. “I expected the infrastructure as code work to take three weeks with lots of struggles. We completed it in two weeks, delivering high-quality results. The team is learning faster than I anticipated.”

Harrison stood. “Good. Keep going. I want weekly updates. And Maya—this is good work. Genuinely impressive. But you’re still proving a concept. I need to see this working in production before I can take it to the board as an alternative to the SaaS plan.”

“Understood,” Maya said. “By Week 12, we’ll have production evidence.”

After the executives left, Maya’s team emerged from their desks where they’d been nervously monitoring the demo.

“How’d it go?” Tom asked.

“Harrison called it genuinely impressive,” Maya said. “Which from him is practically a standing ovation.”

“So we’re good?” Jake asked.

“We’re good for now,” Maya said. “But he’s right—we’re still proving a concept. We need production validation. Which means everything we build in the next nine weeks needs to be production-ready, not just demos.”

She pulled up the Week 4 plan on the screen. “Speaking of which: CI/CD pipelines. This week, we’re going to automate the testing and deployment of PeopleSoft customizations. No more manual exports and imports. No more emailing project files around. No more deployments that take six hours. We’re building a pipeline.”

“Before we dive into that,” Sarah said carefully, “we need to talk about something that happened over the weekend.”

Maya’s stomach dropped. “What happened?”

“Production incident. Saturday at 2:47 AM. Integration Broker went down. Took forty-three minutes to detect. A batch job had failed, and someone checked their email. Took another ninety minutes for Marcus to troubleshoot and fix. Total outage: two hours, thirteen minutes.”

“What caused it?” Maya asked.

“Web server ran out of memory and crashed,” Marcus said. “Took the IB domain with it. We don’t have memory monitoring, so I didn’t know what was wrong until I SSH’d into each server, checked logs, found the out-of-memory errors, and restarted services.”

“Two hours to diagnose a memory issue,” Maya said flatly.

“In my defense, the logs are spread across seventeen servers with no aggregation,” Marcus said. “I had to check each one manually. And our monitoring only watches whether processes are running, not whether they’re healthy.”

Maya walked to the whiteboard and wrote in large letters: “WEEK 4 REVISED PLAN: OBSERVABILITY FIRST.”

“Here’s the thing,” Maya said, turning to face her team. “We can build the prettiest CI/CD pipeline in the world, but if we can’t see what’s happening in production, we’re still operating blind. Saturday’s incident proved that. We need observability before we need CI/CD.”

“What’s the difference between monitoring and observability?” Priya asked.

“Great question,” Maya said. “Monitoring tells you something is broken. Observability tells you why it’s broken and helps you understand system behavior. Right now, we have basic monitoring—we know when a process dies. But we don’t have observability. We can’t answer questions like ‘Why is the system slow?’ or ‘What changed before this error started?’ or ‘Which integration is causing database contention?’”

She drew three columns on the whiteboard: Logs, Metrics, Traces.

“These are the three pillars of observability,” Maya explained. “Logs tell you what happened—detailed records of events, errors, transactions. Metrics tell you how the system is performing, including CPU, memory, response times, throughput. Traces tell you the path a request takes through your system—from web server to app server to database and back.”

“We have logs,” Tom pointed out.

“We have logs scattered across seventeen servers in different formats with no way to search them efficiently,” Maya corrected. “Marcus spent ninety minutes manually grepping through log files on Saturday. That’s not observability. That’s archaeology.”

“So what does good observability look like?” Lisa asked.

Maya pulled up her laptop and opened a screenshot she’d saved from her previous role—a Grafana dashboard with colorful charts showing system metrics, error rates, and response times, all in real-time.

“This is what we’re building toward,” she said. “A single pane of glass where we can see everything happening in our PeopleSoft environment. Application server health. Database performance. Integration throughput. User experience metrics. Error rates. Everything.”

“And when something breaks?” Jake asked.

“We see it immediately,” Maya said. “The dashboard shows the anomaly. We can drill into logs filtered by timeframe and component. We can see what changed right before the problem started. We can correlate events across different systems. Instead of spending ninety minutes figuring out what’s wrong, we spend five minutes confirming what we already suspect and fifteen minutes fixing it.”

“That sounds expensive,” Tom said. “Enterprise monitoring tools cost a fortune.”

“It would be,” Maya agreed, “if we were buying commercial tools. But we’re going to build it using open source: OpenSearch for log aggregation and analysis, Prometheus for metrics collection, Grafana for visualization, and Tempo for distributed tracing. Total cost: mostly our time to implement, plus about $200/month in infrastructure to run it.”

Sarah was nodding enthusiastically. “This is actually going to make our lives so much better. I’ve worked with observability stacks before. Once you have good observability, you can’t imagine working without it.”

“Okay,” Maya said. “Here’s the Week 4 plan, revised. Monday through Wednesday: we implement centralized logging with OpenSearch. Every log from every PeopleSoft component flows into a single location where we can search it. Thursday through Friday: we implement metrics collection with Prometheus and build our first Grafana dashboards. Next week, we’ll add distributed tracing and alerting.”

“That’s a lot for one week,” Marcus said.

“It is,” Maya admitted. “But we’re not building perfection. We’re building ‘better than Saturday night.’ If by Friday we have centralized logs and basic metrics, we’ve massively improved our ability to troubleshoot. The rest can evolve.”

“Who’s doing what?” Priya asked.

“Marcus, you’re leading the OpenSearch implementation since you already researched it in Week 1. Sarah, you’re helping Marcus with the technical architecture. Jake, you’re instrumenting the database to export metrics—Oracle has built-in monitoring views we can scrape. Tom, you’re configuring log shipping from all the application servers. Priya and Lisa, you’re documenting what we learn and building runbooks for using the observability tools.”

“And you?” Tom asked.

“I’m building the Grafana dashboards and figuring out what we need to measure to prove our system is healthy,” Maya said. “Plus, I’m talking to other teams who might want to consume our metrics. If we’re building an observability platform, we should make it useful beyond just PeopleSoft.”

She capped the marker. “Questions?”

“Yeah,” Jake said. “What do we do about Saturday’s incident? Do we need to file some kind of post-mortem or incident report?”

“We do,” Maya said. “And we’re going to use it as a teaching moment. Let me show you what a blameless post-mortem looks like.”

Read more

👋 Welcome Back

After a short hiatus, The Modern ERP Digest is back with Edition 26.

Sometimes a pause is useful. It gives space to see which stories actually matter after the initial headlines fade. Coming back into 2026, one thing is clear: the ERP conversation has sharpened. AI hype is colliding with legal scrutiny, capital markets are watching Oracle closely, and enterprises are making more deliberate, modular decisions about how ERP fits into their future.

Subscribe now

This edition reflects that shift. It blends market pressure, AI ambition, and very real PeopleSoft progress—exactly where ERP leadership lives today.

☕ This Week’s Focus

Edition 26 centers on credibility and consequence.

Oracle’s AI narrative is being tested by lawsuits and investor scrutiny, even as enterprises continue adopting Oracle technologies at scale. At the same time, PeopleSoft continues to move forward with meaningful HCM enhancements, and the broader ERP market is openly embracing best-of-breed and agentic AI models.

This isn’t a step backward. It’s a sign of maturity.

⚙️ Oracle, Markets & AI Pressure

🔗 Investors Sue Oracle, Alleging Executives Sold $18.7B Amid AI Hype (InvestmentNews)
https://www.investmentnews.com/regulation-legal-compliance/investors-sue-oracle-allege-executives-dumped-187b-amid-ai-hype/265164
Investors have filed lawsuits alleging that Oracle executives sold significant stock during peak AI enthusiasm. Regardless of outcome, the story underscores how closely Oracle’s AI messaging is being examined—and how accountability follows ambition.

🔗 Oracle Plans $50B Capital Raise to Support Cloud Infrastructure Demand (ERP Today)
https://erp.today/oracle-plans-50b-capital-raise-to-support-cloud-infrastructure-demand/
Oracle’s reported plan to raise $50B for cloud infrastructure highlights the scale of demand it’s betting on. This move reinforces Oracle’s belief that AI and cloud workloads will continue to grow, but also raises expectations around execution and returns.

🔗 U.S. Enterprises Increasingly Adopting Oracle Solutions (Yahoo Finance)
https://finance.yahoo.com/news/u-enterprises-increasing-adopting-oracle-180845415.html
Despite market noise, adoption data tells a steadier story. U.S. enterprises continue expanding their use of Oracle platforms, suggesting that while sentiment swings, operational decisions remain grounded in capability and trust.

📊 ERP, AI & Industry Direction

🔗 Reimagining ERP for the Agentic AI Era (MIT Technology Review – Report Overview)
https://www.technologyreview.com/2026/01/20/1129965/reimagining-erp-for-the-agentic-ai-era/
This downloadable report explores how ERP systems are evolving from systems of record into systems of action. Agentic AI, orchestration layers, and autonomous workflows are central themes. The key takeaway: ERP isn’t being replaced—it’s being repositioned.

🔗 ERP in 2026: More AI, More Best-of-Breed Add-Ons (CIO.com)
https://www.cio.com/article/4121113/erp-in-2026-more-ai-more-best-of-breed-add-ons.html
CIO.com outlines a clear trend for 2026: core ERP platforms paired with specialized tools. Rather than monolithic replacements, organizations are layering AI, analytics, and niche capabilities on top of stable ERP foundations.

🔗 ERP Survey Shows Customers Re-Evaluating Vendor Value (The Register)
https://www.theregister.com/2026/01/19/erp_survey_rimini_street/
Survey results highlighted by The Register show growing scrutiny of ERP vendor costs and roadmaps. Customers are demanding clearer value, longer viability, and less forced migration—signals that favor platforms teams truly understand.

👩‍💻 PeopleSoft Community & Practice

🔗 What’s New in PeopleSoft HCM Update Image 54 (Oracle Blog)
https://blogs.oracle.com/peoplesoft/whats-new-in-peoplesofts-hcm-update-image-54-key-enhancements-and-features
Oracle details the latest HCM Image 54 enhancements, including usability improvements, feature refinements, and continued investment in employee and manager self-service. It’s another reminder that PeopleSoft HCM remains an actively evolving platform.

🔗 Cornell’s Duffield Gift Highlights Long-Term PeopleSoft Impact (WSJ)
https://www.wsj.com/us-news/education/cornell-duffield-gift-engineering-donation-peoplesoft-fc1894a3
The Wall Street Journal recounts how Cornell’s engineering program benefited from alum success tied in part to PeopleSoft’s early growth. It’s a rare mainstream acknowledgment of PeopleSoft’s lasting influence in higher education and enterprise technology.

💬 Aaron’s Take

Edition 26 reinforces a pattern that’s becoming impossible to ignore: ERP is no longer about promises—it’s about proof.

Oracle is betting big on AI and cloud, and the markets are watching closely. Lawsuits, capital raises, and adoption metrics all tell different parts of the same story. Ambition attracts scrutiny, and scrutiny demands results.

At the same time, PeopleSoft teams are quietly moving forward—adopting new HCM features, modernizing selectively, and operating within architectures they actually understand. Across the industry, organizations are choosing composition over replacement and intention over impulse.

That’s not hesitation. That’s maturity.

If 2025 was about sorting hype from signal, 2026 is shaping up to be about earning trust—technically, financially, and operationally.

I’m glad to be back. Let’s keep going.

🌐 Join the Community

If you’re reading this after the break, now’s a great time to subscribe at PeopleSoftCloud.com for:

✅ Weekly ERP, PeopleSoft, and cloud insights
✅ Practical context around AI and platform decisions
✅ Signals that matter for real enterprise teams

Thanks for sticking around—and welcome back to The Modern ERP Digest.

Every major cloud provider can technically run PeopleSoft. That fact alone does not make them equivalent. Each platform encodes assumptions about control, customization, pricing, and governance. CIOs need to understand those assumptions before committing the organization to a multi-year operating model.

The most crucial cloud question is not whether PeopleSoft will run. The real question is what kind of enterprise you are building once it does.

Cost Is More Than Infrastructure Pricing

Cloud cost conversations often start with compute and storage pricing. While those numbers matter, they rarely determine long-term spend. The most significant cost drivers tend to be operational complexity, licensing interactions, and the degree of automation an organization can realistically sustain. These factors vary significantly by cloud platform.

Some environments encourage standardization and infrastructure-as-code, which lowers marginal costs over time. Others rely more heavily on managed services that trade simplicity for long-term expense. CIOs must look beyond first-year projections and understand how costs behave at scale. A platform that looks inexpensive early can become restrictive and expensive as requirements grow.

Cloud cost discipline depends on visibility, governance, and internal capability. The more control the organization retains, the more options it has to optimize. That control is not evenly distributed across providers.

Read more

What has changed is how leaders think about that reality. Instead of forcing everything into a single platform, CIOs increasingly focus on orchestration, integration, and clear ownership. Hybrid ERP enables organizations to continue innovating while preserving stability in core systems. This balance matters because it helps ensure that the pace of change is manageable across all systems. Treating every system as equally disposable creates unnecessary risk.

Stability and Innovation Are Partners

A common misconception in ERP strategy is that stability and innovation sit on opposite ends of a spectrum. In this framing, stable systems are slow, and innovative systems are fragile. This thinking leads organizations to push core platforms aside in pursuit of speed. Over time, that approach undermines both reliability and confidence.

In practice, stability enables innovation when intentionally used. A reliable system of record provides clean data, predictable processes, and trusted outcomes. Innovation builds more effectively on that foundation than on constantly shifting platforms. When leaders recognize this relationship, strategy shifts from replacement to enablement.

Hybrid ERP works because it assigns different roles to different systems. Core ERP remains focused on transactional integrity and governance. Surrounding platforms handle experimentation, user experience, and rapid iteration. Each system plays to its strengths instead of competing for relevance.

Read more

Sarah and Priya had demonstrated a working Python script that automated the PeopleTools patch download process. This was a task that typically consumed half a day of manual clicking through Oracle Support, checking versions, and organizing files into the correct directory structure. Their script took twelve minutes.

Jake had completed his disaster recovery test. The actual RTO? Fourteen hours and twenty-two minutes, which was better than his pessimistic estimate, but still unacceptable by modern standards. He’d documented every step, identified three configuration errors in the backup scripts, and updated the runbooks. “At least now we know,” he’d said.

Marcus had delivered a compelling one-page proposal for OpenSearch-based log aggregation, including a cost estimate ($3,200/year for hosted OpenSearch vs. $0 for self-hosted on existing infrastructure) and a clear value proposition: “Instead of SSHing into seventeen servers and grepping logs for forty minutes, we could search centralized logs in under two minutes.”

Tom had documented the development environment’s configuration, covering everything from Oracle initialization parameters to Tuxedo domain settings to web server tuning. “It was even more complicated than I thought,” he’d admitted. “No wonder new people take six months to understand our environment.”

Lisa had thoroughly tested Sarah’s patch script, found two edge cases that broke it, and worked with Sarah to fix them. “It actually works,” she’d reported, sounding surprised. “We could use this for real.”

Maya had sent her first weekly status report to Robert Harrison with actual metrics: baseline deployment time (6.5 hours average), baseline MTTR (2.3 hours), baseline environment provisioning time (18 days), baseline DR RTO (14.4 hours). All of these were terrible numbers. But now they were documented, measured, and ready for improvement.

Harrison’s response had been brief: “Good start. Show me better numbers next week.”

Now it was Monday morning of Week 2, and Maya had gathered the team for what she knew would be the hardest conversation of the twelve-week challenge.

“Week 2,” she announced, “is when we stop scripting and start engineering.”

Tom looked up from his coffee. “What’s the difference?”

Maya pulled up a slide on the conference room screen. “Scripts are how you automate a task. Infrastructure as Code is how you automate an entire system. Scripts are what got us through Week 1. IaC is what gets us to Level 3.”

She clicked to the next slide: a comparison table.

Scripts vs. Infrastructure as Code:

Scripts

• Procedural (do this, then this)

• Imperative commands

• Hard to maintain

• Environment-specific

• No version control standard

• Manual sequencing

• Hope it works

Infrastructure as Code

• Declarative (make it look like this)

• Desired state

• Self-documenting

• Portable

• Git-native

• Dependency management

• Testable and repeatable

“Here’s our goal for Weeks 2 and 3,” Maya said. “We’re going to take Tom’s 847 lines of documentation and turn it into infrastructure code that can provision a complete PeopleSoft environment, including database, application tier, web tier, and everything else, from scratch. On Google Cloud Platform.”

The room went very quiet.

Jake broke the silence. “Using what tools?”

“Terraform for infrastructure provisioning,” Maya said. “Chef for configuration management. Git for version control. Industry standard stack used by thousands of companies.”

“I don’t know Terraform,” Jake said flatly.

“None of us do,” Maya replied. “We’re going to learn.”

“Maya,” Tom said carefully, “I’ve been doing PeopleSoft infrastructure for fifteen years. I’ve written hundreds of shell scripts. Bash, PowerShell, and even some Perl back in the day. I know how to automate. Why do I need to learn a new tool?”

Here it was. This was the resistance Maya had been expecting.

She sat down across from Tom. “Tell me about your shell scripts. The ones that provision environments.”

“What about them?”

“How many do you have?”

Tom thought. “Maybe… twenty? Thirty? For different tasks.”

“And when you need to provision a new environment, you run them in sequence?”

“Right. First, the OS configuration script, then the Oracle install script, then the PeopleSoft app tier script—”

“And if one fails partway through?”

“I fix whatever broke and rerun it. Sometimes I have to clean up partial installations first.”

“And if someone else needs to run your scripts?”

Tom shifted uncomfortably. “They’d need to know the sequence. Some scripts use hard-coded paths that need to be updated across environments. And there are dependencies between scripts that aren’t obvious unless you know what you’re doing.”

“So your scripts work great,” Maya said, “if you’re the one running them, in the right order, with the right environment-specific changes, and nothing fails. How long would it take to teach someone else—say, Sarah—to provision an environment using your scripts?”

“I don’t know. A few weeks? She’d need to shadow me through a few builds.”

“And if you got hit by a bus tomorrow?”

“Please stop using that example,” Tom said.

“The point stands,” Maya replied. “Your scripts are better than nothing. They’ve served us well. But they’re not infrastructure-as-code. They’re Tom Patterson as code. And we can’t build a modern operation on tribal knowledge, no matter how well-scripted.”

Jake jumped in. “I’ve got the same thing with my Oracle automation. My scripts work because I wrote them and I know all the gotchas. But Maya’s right; they’re not transferable. When we hire someone new, it takes six months before I trust them to run a database build without me watching.”

“Exactly,” Maya said. “Infrastructure as Code solves this. With Terraform, we declare what infrastructure we want and how we want it. The Terraform code says we want these VMs with these specs, this database with these parameters, these network routes, these firewall rules. Terraform figures out how to make it happen. The code is self-documenting. It’s in Git, so we have version control and change history. It’s testable. And most importantly, it’s repeatable. Run it once, run it a thousand times, you get the same result.”

“But I don’t know how to write Terraform,” Tom repeated.

“You didn’t know how to write shell scripts fifteen years ago either,” Maya said. “You learned.”

“I was thirty then. I’m forty-five now. Learning gets harder.”

Sarah spoke up. “I’m twenty-six, and I’m learning this stuff alongside you. If you want, I can help. Terraform syntax is different from bash, but the concepts aren’t that foreign. You’re still describing what needs to happen. You’re just doing it in a different language.”

Tom looked at Sarah, then at Maya. “This is really happening? We’re throwing out fifteen years of scripts and starting over?”

“Not throwing out,” Maya corrected. “Evolving. Your scripts taught us what to automate. Now we’re making that automation maintainable, scalable, and cloud-native. By Week 3, we’ll have infrastructure code that can spin up a complete PeopleSoft environment in under two hours instead of eighteen days.”

“Two hours?” Tom’s skepticism was evident. “For a full PeopleSoft stack?”

“Two hours,” Maya confirmed. “That’s the power of IaC on cloud infrastructure. No waiting for the infrastructure team. No manual installations. No configuration drift. Just code, version control, and automation.”

Jake leaned back in his chair. “Okay, I’ll bite. Walk us through what this actually looks like. Not the code itself; just conceptually, what are we building?”

Maya smiled. She’d prepared for this.

The Architecture Conversation

Maya pulled up a diagram she’d sketched over the weekend. “Let’s talk about how we’re going to structure this. The key to good infrastructure-as-code is modularity. You start by breaking the whole system into logical, reusable pieces.”

She pointed to the diagram showing five boxes: Network, Compute, Database, Storage, and Configuration.

“We’re going to build five modules,” Maya explained. “Each one is responsible for one piece of the puzzle. The Network module handles VPCs, subnets, firewall rules, and load balancers. The Database module provisions our Oracle Cloud SQL instance with all the right parameters. Compute handles the VMs. This’ll build application servers, web servers, and process schedulers. Storage manages our file shares and backup buckets. And Configuration is where Chef comes in, actually, to set up the software.”

“Why break it up that way?” Marcus asked.

“Reusability,” Maya said. “Let’s say we get the Network module working for dev. That exact same module can be used for QA, stage, and prod. All we need to do is pass in different parameters. Same code, different inputs, different environments. That’s how we guarantee environmental parity.”

“And it’s maintainable,” Sarah added. “If we need to change how networking works, we change the Network module. We don’t have to hunt through seventeen different shell scripts trying to find where firewall rules are configured.”

“Okay,” Tom said slowly. “I can see the value in organization. But what’s the actual workflow? How does this replace what we do today?”

Maya walked to the whiteboard. “Today, when you need to build an environment, what’s the process?”

Tom recited from memory. Submit a ticket to the infrastructure team for VMs. Wait a week. Get VMs. Spend two days installing and configuring the OS. Call Jake to install Oracle, which takes him two days. Wait for Jake to finish. Install the application tier, which takes me three days if nothing breaks. Configure domains, set up web servers, which takes another two days. Testing connectivity and fixing issues takes another day. So, best case, seven to eight days of actual work spread across three weeks of calendar time.”

“And every environment is slightly different because they were built at different times by different people using slightly different procedures,” Maya said.

“Right,” Tom admitted.

“Now here’s the new workflow,” Maya said. “Someone, it really doesn’t matter who it is, goes to the infrastructure repository in Git. They navigate to the environment they want to build. They run three commands: terraform init, terraform plan, and terraform apply. Then they go get coffee for ninety minutes. When they come back, the environment is running.”

“That’s it?” Lisa asked.

“That’s it,” Maya said. “Terraform reads the infrastructure definition, figures out what needs to be created and in what order, provisions everything in GCP, then hands off to Chef to configure the software. Everything we do manually today gets encoded into the infrastructure definition. Tom’s OS configuration knowledge goes into Chef recipes. Jake’s Oracle setup expertise goes into database configuration files. My domain setup procedures become Chef cookbooks.”

“Wait,” Jake said. “You keep mentioning Chef. Why Chef specifically? I’ve heard of Puppet, Ansible, all these configuration management tools. What makes Chef the right choice?”

“Good question,” Maya said. “Chef has a few things going for it for our use case. It’s mature and battle-tested; it’s been around since 2009. It has strong enterprise support. The Ruby-based DSL is actually pretty readable once you get past the initial learning curve. And critically, it has good patterns for managing the kind of complex, stateful configuration that PeopleSoft environments need.”

“Plus,” Sarah interjected, “Chef has this concept of cookbooks and recipes that maps really well to how we think about PeopleSoft configuration. You have a cookbook for Oracle setup, a cookbook for PeopleTools installation, a cookbook for domain configuration. Each cookbook contains recipes for specific tasks. It’s very modular.”

“Isn’t the learning curve steep?” Priya asked.

“All of these tools have a learning curve,” Maya acknowledged. “But Chef’s actually not bad. The DSL reads almost like plain English; you’re declaring resources and their desired state. ‘This file should exist with these contents and these permissions.’ ‘This service should be running.’ ‘This package should be installed.’ It’s declarative, like Terraform, which means you describe what you want, not how to get there.”

Tom was taking notes. “So Terraform provisions the infrastructure, including the VMs, the database, and the network. And Chef configures the software on that infrastructure?”

“Exactly,” Maya said. “They’re complementary tools. Terraform is great at infrastructure provisioning, but not great at managing complex application configuration. Chef is great at configuration management, but it is not designed for infrastructure provisioning. Together, they cover the full stack.”

“How do they work together?” Marcus asked. “Like, mechanically?”

“Terraform provisions a VM and can run Chef automatically as part of the provisioning process,” Maya explained. “So Terraform creates your application server VM, and as part of the creation process, it installs the Chef client and runs the appropriate cookbooks. By the time Terraform finishes, you have a fully configured application server ready to join the domain.”

Jake was nodding. “Okay, I’m starting to see how this fits together. But here’s my concern: I know how to configure an Oracle database. I know every parameter, every tablespace setting, every tuning option. How do I translate that knowledge into Chef recipes?”

“That’s actually the beauty of it,” Maya said. “You don’t have to learn Chef deeply to contribute your Oracle knowledge. Sarah’s going to become our Chef expert, and she’ll learn the syntax and the patterns. Your job is to tell her what a properly configured PeopleSoft database looks like. She will need to know things like what parameters need to be set, which tablespaces need to exist, and which users need to be created with what privileges. She translates your expertise into Chef recipes. You review them to make sure they’re doing the right thing.”

“So I don’t have to become a Chef programmer?” Jake sounded relieved.

“You should learn enough to read and understand Chef recipes,” Maya said. “But you don’t need to be an expert. We’re playing to everyone’s strengths. Jake knows Oracle. Tom knows application server configuration. Priya knows the PeopleSoft domain setup. Sarah knows coding patterns and modern tooling. We combine that knowledge.”

“What about testing?” Lisa asked. “With my manual builds, I can see each step happening. How do I test infrastructure that builds automatically?”

“That’s one of the biggest advantages of IaC,” Maya said. “We build automated validation into the process. After Terraform provisions the database, we run tests—can we connect? Is the tablespace configuration correct? Are the backup jobs scheduled? After Chef configures an application server, we test—did the PeopleTools installation complete? Is the domain configured correctly? Are the processes running?”

“And if something fails?” Tom asked.

“Then the build fails, and we know exactly where it failed and why,” Maya said. “No more ‘I think it’s configured correctly.’ We have automated validation that proves it’s configured correctly. And because everything’s in code, we can test changes before we apply them to production. Want to change an Oracle parameter? Change it in the code, apply it to dev, and run your validation tests. If it works, promote the same code change to QA, then to prod. The code that builds dev is the same code that builds prod, just with different size parameters.”

“That’s environmental parity,” Sarah said. “It’s a big deal. Right now, our dev environment is kind of like prod, but not exactly. Different Oracle patches, slightly different configurations, different disk layouts. So sometimes things work in dev and break in prod. With IaC, dev and prod are identical except for scale. Same configurations, same versions, same everything. That massively reduces ‘works on my machine’ problems.”

Tom was quiet for a moment. “Okay. I’m starting to see the value. But I need you to be honest with me about something, Maya. How hard is this going to be to learn? Because I’m looking at two weeks to learn Terraform and Chef well enough to build production-grade infrastructure, and that seems… ambitious.”

Maya sat down next to him. “It’s going to be hard,” she said honestly. “The first few days, you’re going to feel lost. Terraform has its own language—HCL—and its own way of thinking about infrastructure. Chef has Ruby DSL and its own concepts. You’re going to write code that doesn’t work. You’re going to get error messages you don’t understand. You’re going to want to just write a bash script because it’s faster and you know how.”

“That’s not a great sales pitch,” Tom said.

“But here’s the thing,” Maya continued. “You’re not learning to become a Terraform expert or a Chef expert. You’re learning enough to encode your infrastructure knowledge in a maintainable way. By the end of Week 2, you won’t be an expert. But you’ll be able to read infrastructure code and understand what it’s doing. By the end of Week 3, you’ll be able to write basic modules. That’s sufficient. Expertise comes with time.”

“And we’re all learning together,” Priya added. “I don’t know this stuff either. None of us does except Sarah. So when you get stuck, you’re not the only one struggling.”

Jake raised his hand. “One more conceptual question before we dive in. Why are we doing this specifically on GCP? Why not AWS or Azure?”

“Honestly? Partly because I have more experience with GCP,” Maya admitted. “But also, GCP has really strong Oracle support now with the new Oracle Exadata at GCP offering. This provides Oracle databases with the ability to do automated backups, patching, and high availability, which is already built in. That’s huge for us. AWS has Oracle, too, and Azure has managed Oracle, so we’re not locked in. But for learning, we needed to pick one and go deep.”

“Here’s the thing though,” Sarah said. “Once we have IaC working on GCP, porting it to AWS or Azure isn’t that hard. The concepts translate. Terraform supports all the major cloud providers. Chef works the same everywhere. So we’re learning patterns that are portable.”

“The specific cloud provider matters less than you think,” Maya said. “What matters is the operational model—infrastructure as code, version-controlled configurations, automated validation, repeatable builds. That model works on any cloud. We’re just using GCP as the learning platform.”

Marcus leaned forward. “So what’s the actual plan? How are we spending the next two weeks?”

Maya pulled up the schedule she’d built. “Today and tomorrow: intensive training. We’re all going through the HashiCorp Terraform tutorials and the Chef learning resources. Not trying to become experts—just getting familiar with the concepts and syntax. Wednesday through Friday this week: hands-on experimentation. Everyone picks one piece—Jake, you’re exploring Terraform’s database provisioning. Tom, you’re looking at compute resources. Marcus, you’re diving into networking. Priya, you’re prototyping Chef cookbooks for PeopleSoft configuration.”

“What about Week 3?” Lisa asked.

“Week 3 is building for real,” Maya said. “Monday through Thursday, we’re writing the actual infrastructure code for our dev environment. Each person owns their module. We do code reviews—yes, we’re reviewing infrastructure code just as we do application code. Friday, we do the demo. We run the infrastructure build from scratch in front of each other and prove it works.”

“And if it doesn’t work?” Tom asked.

“Then we debug and fix it,” Maya said simply. “But I don’t think it will work, I know it will. Because we’re going to test as we build. Every module gets validated independently before we integrate it.”

She stood up and walked to the whiteboard. “Here’s what success looks like at the end of Week 3: We can provision a complete PeopleSoft environment from code in under two hours. We have version-controlled infrastructure that anyone on the team can deploy. We have automated validation that proves the environment is configured correctly. And we have evidence—real, demonstrable evidence—that we’re operating at a higher level than we were three weeks ago.”

“That’s a lot for two weeks,” Tom said.

“It is,” Maya agreed. “But look at what we did in Week 1. Sarah and Priya automated patch downloads. Jake validated our DR process. Marcus designed our logging strategy. Tom documented 847 lines of configuration. You all did hard things last week. This week is just a different kind of hard.”

“What if we fail?” Jake asked quietly.

“Then we learn why we failed and adjust,” Maya said. “But here’s what I know about this team: you’re all capable of way more than you think you are. Three weeks ago, how many of you would have said you could write a Python script to automate patch downloads?”

Sarah raised her hand. No one else did.

“And now Priya’s comfortable enough with Python to pair with Sarah on that script,” Maya said. “That’s growth. That’s what these twelve weeks are about—discovering that you can learn things you thought were beyond you.”

She grabbed the marker. “Okay. Let’s talk about what each person is focusing on. Jake, you’re building the database module for Terraform and the Oracle configuration cookbook for Chef. Your expertise is Oracle, and now you’re encoding that expertise in infrastructure code.”

Jake nodded slowly. “I can do that. Tell Terraform what database we need, tell Chef how to configure it.”

“Exactly. Tom, you’re on the compute module and the application tier Chef cookbooks. Everything you documented last week about how app servers should be configured—that becomes Chef recipes.”

“So I’m writing down what I normally do manually, just in Chef’s language instead of a runbook,” Tom said.

“Right. Marcus, you’re handling networking, including the VPCs, subnets, firewall rules, and load balancers. All the network configuration that’s currently scattered across different systems and tickets? That all becomes Terraform code.”

“And Priya,” Maya continued, “you’re the Chef expert for PeopleSoft-specific configuration. Domain setup, process scheduler configuration, and web server setup. You know PeopleSoft configuration better than anyone. Now you’re teaching Chef how to do it.”

“What about me?” Lisa asked.

“You’re QA and validation,” Maya said. “As each module gets built, you’re testing it. You’re writing automated tests to verify that the infrastructure is configured correctly. And you’re documenting the gotchas. We need documentation about what broke, how we fixed it, and what we learned.”

“Sarah, you’re our technical lead on this,” Maya said. “You’re learning Terraform and Chef at an expert level, and you’re helping everyone else. You’re also setting up the Git repository structure, the code review process, and the CI/CD pipeline for testing infrastructure code.”

“Wait, we’re doing code reviews on infrastructure?” Tom sounded surprised.

“Absolutely,” Maya said. “Infrastructure is code now. We review code. Jake proposes a change to the database module? It goes through a pull request. I review it, or Sarah reviews it, or you review it. We ask questions: ‘Why this parameter?’ ‘What happens if this fails?’ ‘Is this tested?’ Same discipline we’d use for application code.”

“That’s… actually going to catch mistakes,” Tom admitted. “How many times have I made a manual configuration change without a second set of eyes on it?”

“Exactly,” Maya said. “Code review isn’t about distrust. It’s about quality and knowledge sharing. When Jake submits a database module change, I learn about Oracle configuration by reviewing his code. When Tom submits an app tier change, Jake learns about application server setup. We all get smarter.”

She capped the marker. “Questions before we dive into training?”

“Yeah,” Jake said. “What happens when we hit Week 4 and we’re supposed to be building CI/CD pipelines, but we’re still struggling with IaC?”

“Then we adjust,” Maya said honestly. “The twelve-week timeline is aggressive. We’re going to hit roadblocks. But we can’t let perfect be the enemy of good. If at the end of Week 3 we can build an environment in four hours instead of two, that’s still a massive win. If our Chef cookbooks don’t cover every edge case, but they cover 80% of the configuration, that’s progress.”

“We’re not trying to be perfect,” Sarah said. “We’re trying to be better. And we’re trying to prove to Harrison that modernizing PeopleSoft is possible and valuable.”

“Exactly,” Maya said. “The goal isn’t perfection. The goal is demonstrable improvement plus a pathway to keep improving. If we can show Harrison that we’ve cut environment provisioning from 18 days to 4 hours, reduced configuration errors, and eliminated tribal knowledge dependencies, we’ve made the case.”

Tom stood up. “Alright. I’m in. But I reserve the right to complain when my Terraform code doesn’t compile or whatever the right term is.”

“Complaining is encouraged,” Maya said. “Giving up is not. Everyone clear on the plan?”

Nods around the room.

“Good. Training resources are linked in Slack. Sarah’s got office hours 2-4 PM daily if you get stuck. And remember—we’re all learning together. I’ll be going through the same Terraform tutorials you are. There are no stupid questions.”

As the team dispersed to start their training, Jake lingered behind.

“Maya,” he said quietly. “I need you to know something. I’m scared.”

Maya turned. “Scared of what?”

“That I can’t do this,” Jake said. “I’ve been an Oracle DBA for twenty-six years. I know Oracle inside and out. I know every parameter, every wait event, and every performance tuning trick. But Terraform? Chef? Infrastructure as code? I feel like I’m starting over. And I’m not twenty-five anymore. What if I can’t keep up?”

Maya sat down next to him. “Jake, do you remember Week 1? Your disaster recovery test?”

“Yeah.”

“You found three errors in backup scripts that had been running for seven years. Errors no one else would have caught because they don’t understand Oracle the way you do. That expertise doesn’t disappear just because you’re learning new tools.”

“But—”

“Infrastructure as code doesn’t replace your Oracle knowledge,” Maya continued. “It amplifies it. Right now, your expertise lives in your head and in scripts only you understand. After Week 3, your expertise will be codified, version-controlled, and teachable. You’re not starting over. You’re multiplying your impact.”

“What if I slow everyone down?”

“Then we slow down,” Maya said simply. “But I don’t think you will. You know what I think? I think you’re going to struggle for a few days, get frustrated, maybe write some Terraform code that doesn’t work. But then something’s going to click. And by Week 3, you’re going to be teaching the rest of us the right way to architect database infrastructure in code. Because you understand Oracle databases at a level none of us do.”

Jake was quiet for a moment. “You really believe that?”

“I know it,” Maya said. “I wouldn’t have put you on the database module if I didn’t think you could do it. You’re not just learning Terraform, Jake. You’re becoming the person who can bridge traditional Oracle DBA expertise with modern cloud operations. That’s a rare and valuable skill. In five years, every company will want DBAs who can do both. You’re going to be ahead of the curve.”

“Okay,” Jake said, standing up. “I’m going to go start this training. But I’m going to complain a lot.”

“That’s the spirit,” Maya said.

After Jake left, Maya sat alone in the conference room, looking at her twelve-week timeline on the whiteboard. Week 1: Assessment—complete, with green checkmarks next to each deliverable. Week 2-3: Infrastructure as Code—in progress.

Her phone buzzed. An email from Robert Harrison: “Heard you’re moving the team to new tools. Make sure this doesn’t disrupt production operations. Can’t afford incidents during the transformation.”

Maya typed back: “Understood. All IaC development is happening in isolated dev environments. Production untouched until Week 8+ and fully tested.”

She didn’t mention that “not disrupting production” would be the hardest part. They still had to maintain current operations while learning an entirely new operational model. It was like rebuilding an airplane mid-flight.

But that’s what the next ten weeks were going to be: running production on the old model while building the new model in parallel, then executing a clean cutover when the new model was proven.

No pressure.

Maya opened her laptop and navigated to the HashiCorp learning portal. If she was going to ask her team to learn this, she needed to be able to help them when they got stuck.

Two weeks. By the end of Week 3, they’d have an infrastructure that used to take eighteen days, now provisioned in hours.

And her team would have learned that they were capable of more than they thought.

One Terraform module at a time.

Read more

The reality, however, is far more complex. Large ERP modernization efforts frequently exceed budgets, stretch timelines, and generate disruption well beyond initial projections. Even when implementations technically succeed, organizations often struggle to realize the expected benefits. Teams spend years stabilizing, retraining, and reworking processes that once ran smoothly. This gap between promise and outcome marks the beginning of the modernization paradox.

Why ERP Modernization Efforts Fail More Often Than Expected

Most ERP modernization failures are not the result of poor leadership or a lack of effort. Instead, they stem from the fact that ERP systems are deeply embedded in daily organizational operations. Over time, these systems accumulate countless decisions, exceptions, and adaptations—many of which are undocumented and invisible in process diagrams. When leaders misjudge the extent of this embedded complexity, they also misjudge the scale of risk involved. As a result, that risk escalates rapidly once implementation is underway.

Another contributing factor is decision pressure. CIOs face constant signals that modernization is urgent and delay is dangerous. Vendor messaging, peer announcements, and executive expectations can compress evaluation timelines and discourage dissent. In those environments, skepticism is often interpreted as resistance instead of prudence. The resulting decisions may look decisive, but they are frequently under-informed.

Failure rarely presents itself as a single dramatic event. Instead, it appears gradually through prolonged stabilization periods, missed delivery goals, and growing frustration across IT and business teams. Over time, leaders realize that the organization is no more agile than before. In some cases, it is less adaptable due to new constraints that were not fully visible at the start.

Subscribe now

Read more

The word “legacy” has become a weapon in enterprise software sales. Consultants wield it to create urgency. Vendors use it to manufacture obsolescence. And somewhere in the middle, CIOs are being pressured to abandon functional, mission-critical systems based on fear rather than fact.

But here’s what nobody wants to tell you: the distinction between “legacy” and “modern” has almost nothing to do with when the software was written.

It has everything to do with how it’s architected, deployed, and operated today.

Subscribe now

What “Legacy” Actually Means

Let’s be precise about terminology. A system is legacy when it exhibits these characteristics:

It can’t integrate. Modern systems expose APIs and connect seamlessly with other platforms. Legacy systems are data islands that require expensive middleware or manual processes to exchange information.

It can’t scale. Modern systems leverage cloud infrastructure to grow with demand. Legacy systems hit capacity walls that require months of procurement and provisioning to address.

It can’t evolve. Modern systems support rapid iteration, automated testing, and frequent releases. Legacy systems require change freezes, maintenance windows measured in hours, and risk tolerance that slows innovation to a crawl.

It can’t be maintained. Modern systems have active communities, current documentation, and available talent. Legacy systems depend on tribal knowledge and vendors who charge premium rates for basic support.

Notice what’s missing from this list? The year the codebase was created.

Read more

🎉 Edition 25 – A Milestone Worth Celebrating

This week marks a small but meaningful milestone: Edition 25 of The Modern ERP Digest.

I started this Digest last year with a simple goal: cut through the noise. There was (and still is) a flood of information across PeopleSoft, ERP, cloud platforms, AI, and infrastructure. Valuable insights were scattered across blogs, press releases, analyst reports, and community posts, but were rarely brought together in one place.

The Digest exists to do exactly that: consolidate what matters, add practitioner context, and help ERP leaders make sense of a rapidly shifting landscape. Reaching 25 editions tells me there’s real value in that approach, and I appreciate everyone who’s been reading, sharing, and challenging ideas along the way.

Now, on to this week’s signals.

☕ This Week’s Focus

Edition 25 reflects a familiar but intensifying theme for 2026: AI ambition colliding with operational reality.

Oracle continues to place big bets on AI-driven platforms as markets scrutinize leadership changes and long-term returns. At the same time, ERP vendors are reshaping the competitive field, and PeopleSoft practitioners are doubling down on performance, governance, and day-to-day operational excellence.

This is what modern ERP leadership looks like right now. It includes balancing strategy, execution, and risk.

⚙️ Oracle, AI & Platform Strategy

🔗 Retailers Mitigate Risk with Oracle’s AI-Driven Supply Chain Collaboration
https://www.prnewswire.com/news-releases/retailers-help-mitigate-risk-with-oracles-ai-driven-supply-chain-collaboration-302657847.html
Oracle highlights how retailers are using AI-driven collaboration tools to improve visibility and resilience across supply chains. The key takeaway isn’t the AI buzz, it’s the focus on risk mitigation and coordination, two areas where ERP still delivers real value.

🔗 New Adapters and Connectivity Enhancements in Oracle Integration 26.01
https://blogs.oracle.com/integration/new-adapters-and-connectivity-enhancements-in-oracle-integration-26-01
Oracle Integration 26.01 introduces expanded adapters and connectivity improvements. For hybrid ERP environments, these enhancements continue the push toward cleaner, more maintainable integration patterns rather than brittle point-to-point solutions.

🔗 Oracle Announces Board Departures (CNBC)
https://www.cnbc.com/2026/01/09/oracle-announces-board-departures-george-conrades-and-naomi-seligman-.html
Oracle announced upcoming board departures, a reminder that leadership evolution often accompanies strategic inflection points. Governance matters, especially when organizations are making multi-decade bets on AI infrastructure.

🔗 Can Oracle Stock Rally in 2026? (Forbes)
https://www.forbes.com/sites/greatspeculations/2026/01/05/can-oracle-stock-rally-possible-and-here-is-how/
Forbes explores scenarios where Oracle could see renewed stock momentum—driven by cloud revenue growth, AI services, and disciplined execution. It’s a useful lens for separating fundamentals from speculation.

🔗 Inside Oracle’s $50B AI Bet (PredictStreet)
https://markets.financialcontent.com/stocks/article/predictstreet-2026-1-7-the-ai-goliath-inside-oracles-50-billion-bet-on-the-future-of-computing
A deeper look at the scale of Oracle’s AI investments and what success would need to look like to justify them. The article reinforces a familiar truth: big bets only pay off when adoption follows.

📊 ERP Market & Industry Shifts

🔗 Ellucian Acquires Anthology’s SIS and ERP Business (Campus Technology)
https://campustechnology.com/articles/2026/01/05/ellucian-officially-acquires-anthologys-sis-and-erp-business.aspx
Ellucian’s acquisition reshapes the higher-ed ERP market yet again. Consolidation continues, and institutions are being forced to reassess vendor strategy, roadmap confidence, and long-term platform viability.

🔗 Five ERP Strategic Implications for Operations Leaders in 2026 (ERP Today)
https://erp.today/five-erp-strategic-implications-for-operations-leaders-in-2026/
ERP Today outlines five trends shaping operational leadership this year, including automation and AI governance, as well as platform consolidation. The throughline is clear: operations leaders are now platform leaders.

👩‍💻 PeopleSoft Community & Practice

🔗 Upcoming Webinar: Troubleshooting PeopleSoft Performance
https://gideontaylor.com/event/troubleshooting-peoplesoft-performance-five-costly-mistakes-and-the-mechanic-mindset-that-helps-you-fix-them/
This upcoming webinar focuses on common PeopleSoft performance mistakes and a practical “mechanic mindset” for diagnosing issues. A solid opportunity for admins and DBAs looking to sharpen real-world troubleshooting skills.

🔗 PeopleSoft Change Assistant – A Practical Walkthrough
https://curiousdba.netlify.app/post/peoplesoftchangeassistant/
A hands-on look at PeopleSoft Change Assistant, including how it works, where it helps, and how to avoid common pitfalls. This kind of grounded tooling knowledge continues to separate smooth upgrades from painful ones.

🔗 Managing Ad Hoc PSQueries in PeopleSoft
https://blog.psftdba.com/2026/01/managing-ad-hoc-psqueries-in-peoplesoft.html
A pragmatic guide to controlling and organizing ad hoc PSQueries. Governance here isn’t about restriction. But rather it’s about protecting performance and maintaining trust in shared reporting environments.

💬 Aaron’s Take

Reaching 25 editions reinforces something I’ve seen repeatedly in ERP work: consistency matters more than noise.

Oracle is making bold AI investments. ERP vendors are consolidating. Markets are optimistic one week and skeptical the next. None of that changes the day-to-day reality for ERP teams who are responsible for keeping systems fast, secure, and useful.

The strongest organizations right now aren’t chasing every headline. They’re improving integration discipline, understanding performance bottlenecks, governing reporting responsibly, and making thoughtful platform decisions.

That’s the work this Digest has always tried to highlight—and it’s the work that will define successful ERP teams in 2026.

Thanks for being part of the first 25 editions. Onward.

🌐 Join the Community

If you’re new here or joining us as we move deeper into 2026, you can subscribe at PeopleSoftCloud.com to receive:

✅ Weekly ERP, PeopleSoft, and cloud insights
✅ Practitioner-focused context
✅ Platform and operations strategy
✅ Signals worth paying attention to

Here’s to the next 25.

One of the most common performance mistakes in PeopleSoft environments is assuming that increasing Process Scheduler concurrency increases throughput.

Subscribe now

It doesn’t. It usually does the opposite.

Concurrency controls how many processes can run at once, not how many should. When teams raise concurrency to “speed things up,” they often create hidden contention across CPU, memory, database sessions, and I/O. The result looks like higher utilization but feels like slower jobs, longer queues, and unpredictable runtimes.

The problem is that Process Scheduler doesn’t understand workload priority. A critical financial close job and a low-value reporting extract compete the same way unless you explicitly separate them. When concurrency is too high, expensive processes overlap, amplify resource pressure, and slow each other down.

Another overlooked issue is peak stacking. Many environments schedule dozens of jobs to start simultaneously. Even with adequate hardware, this creates artificial load spikes that appear to be capacity problems but are actually scheduling issues.

A better approach is controlled parallelism:

• Lower global concurrency

• Stagger start times

• Separate schedulers by workload type

• Align concurrency with actual database and OS limits

When teams do this, they often see faster completion times without adding resources. The system becomes more predictable, easier to tune, and less fragile during peak windows.

If your Process Scheduler feels “busy but slow,” this is one of the first places to look.

Small change. Real impact.

The emergency team meeting on Friday afternoon had gone about as well as Maya expected, which is to say, poorly.

She’d opened with the truth: the CIO wanted to migrate to SaaS, the decision was effectively made, and she’d bought them twelve weeks to prove there was a better path. Twelve weeks to modernize or start updating resumes.

Subscribe now

Jake Morrison, her senior DBA with seventeen years at Riverside, had gone pale. “They want to replace Oracle with what? Workday’s solution? That’s not even a real database.”

Tom Patterson, her application server admin, had laughed. “Twelve weeks? Maya, we can’t even complete a PeopleTools upgrade in twelve weeks. You want us to rebuild our entire operational model?”

Sarah Chen, the junior developer who’d joined the team eight months ago, had been the only one who looked intrigued rather than terrified. “What exactly do we need to prove?”

“That we can operate PeopleSoft like a modern cloud-native platform,” Maya had said. “Infrastructure-as-code. Automated deployments. Real observability. The works.”

“We don’t know how to do any of that,” Tom had said flatly.

“Then we learn,” Maya had replied. “Starting Monday.”

The meeting had ended with grudging acceptance, or at least, no one had quit on the spot. Maya considered that a win.

Now it was Monday morning, 7:00 AM, and Maya stood in Conference Room B with a fresh pot of coffee and a whiteboard that read: “Week 1: Assessment - Know Thyself.”

Her team filtered in: Jake with his Oracle certification mug, Tom with a notebook he’d been using since 2009, Sarah with her laptop covered in Python stickers that suddenly seemed prescient, and the rest of the team, including Lisa (Portal admin), Marcus (Integration Broker specialist), and Priya (Application Designer developer).

“Good morning,” Maya said. “Welcome to Week 1 of the rest of your careers.”

“Too early for inspirational speeches,” Jake muttered into his coffee.

“Fair enough. Here’s the non-inspirational version: we’re going to spend today conducting the most honest assessment of our capabilities that this team has ever done. No ego. No defensiveness. No, ‘but we’ve always done it this way.’ We’re going to document every manual process, every piece of tribal knowledge, every technical debt bomb sitting in our infrastructure. And then we’re going to prioritize what to fix first.”

“This sounds terrible,” Tom said.

“It is,” Maya agreed. “But you know what’s more terrible? Discovering in Week 11 that we missed something critical because we weren’t honest with ourselves in Week 1.”

She divided the whiteboard into five columns: Infrastructure, Deployment, Operations, Skills, and Quick Wins.

“Let’s start with infrastructure,” Maya said. “Tom, walk us through how we currently provision a new PeopleSoft environment.”

Tom flipped open his ancient notebook. “Okay, so first I submit a ticket to the infrastructure team for VMs.”

“Stop,” Maya interrupted. “How long does that take?”

“Depends. If they’re busy, maybe a week?”

“Keep going.”

“Once we get the VMs, I install the OS manually. Then I need Jake to install Oracle.”

“Which takes me two days,” Jake interjected. “Database install, configuration, applying patches, creating tablespaces, setting up backup jobs.”

“All manual?” Maya asked.

“Well, I have a script for some of it,” Jake said defensively. “But yeah, mostly manual. Each environment has its own quirks.”

“What quirks?”

Jake shifted uncomfortably. “You know. Different storage layouts, different memory configurations, different networking setups. The dev database is configured differently from QA, which is different from production.”

“Why?”

“Because… they were built at different times by different people?”

Maya wrote on the whiteboard under Infrastructure: “Environment provisioning: 2-3 weeks, completely manual, no standardization, configuration drift.”

“That seems harsh,” Tom said.

“Is it inaccurate?”

Tom said nothing.

“Okay,” Maya continued. “After Jake installs Oracle, then what?”

“Then I install the application server, process scheduler, web server, and PIA,” Tom said. “That’s another three days if nothing goes wrong.”

“How often does something go wrong?”

“Every single time,” Tom admitted. “Wrong library path, missing dependencies, PeopleTools patches that need to be applied in a specific order that isn’t documented anywhere except in my head.”

Maya added: “App tier installation: 3+ days, failure-prone, zero documentation, tribal knowledge-dependent.”

“When you put it like that, we sound incompetent,” Lisa said quietly.

“You’re not incompetent,” Maya said. “You’re operating in a model that was designed twenty years ago. But that model doesn’t scale, doesn’t automate, and doesn’t survive if Tom gets hit by a bus.”

“Thanks for that image,” Tom said.

“Let’s talk about deployments,” Maya continued, moving to the second column. “Priya, how do we currently move customizations from development to production?”

Priya, who’d been silent until now, spoke carefully. “I make the changes in Application Designer in dev. Test them. Then I create a project. Export the project to a file. Email the file to myself. Copy it to the QA server. Import it. Test again. Then repeat for production.”

“How long does that take?”

“For a small change? Maybe two hours. For a larger project with dependencies? I’ve had deployments take an entire day.”

“What happens if something breaks in production?”

“I… manually roll it back?”

“Do you have automated tests?”

Priya looked at Maya like she’d suggested summoning demons. “Automated tests? For PeopleCode?”

Maya wrote: “Deployment process: 100% manual, no CI/CD, no automated testing, high risk, slow rollback.”

“Operations,” Maya said, moving to the third column. “Marcus, when was the last time we had a production incident?”

Marcus, who managed their Integration Broker and application messaging, checked his phone. “Thursday. IB went down for forty minutes.”

“How did you know it was down?”

“A user called and said their integration wasn’t working.”

“So our monitoring is… end users?”

“We have Tuxedo monitoring,” Marcus said defensively. “It just doesn’t alert properly. And the logs are spread across seventeen different servers. So when something breaks, I SSH into each server, grep through logs, and try to correlate timestamps.”

“How long does incident resolution typically take?”

“If I can find the problem quickly? An hour. But if it’s a weird issue? I’ve spent eight hours troubleshooting before.”

Maya added: “Observability: User-reported incidents, distributed logs with no aggregation, no tracing, slow mean-time-to-resolution.”

The whiteboard was starting to look like an indictment.

“Backups,” Maya said. “Jake, how do we back up the database?”

“RMAN scripts that run nightly,” Jake said. “They’ve been running for… six years? Seven?”

“Have you ever tested a restore?”

Silence.

“Jake.”

“I tested it when I first set it up,” Jake said. “In 2018.”

“So we’re trusting seven-year-old backup scripts that haven’t been validated in seven years to protect our entire university’s enterprise data?”

“When you say it like that...”

“How long would it take to restore from backup if we lost production right now?”

Jake did some mental math. “Database restore from RMAN? Maybe six hours. Then we’d need to rebuild the application tier, reconfigure everything… twelve hours? Eighteen if we hit problems?”

Maya wrote: “Disaster Recovery: 12-18 hour RTO, untested backup procedures, manual recovery process.”

The room had gone very quiet.

“Skills assessment,” Maya said, moving to the fourth column. This was going to hurt. “Show of hands: who has used Git for version control?”

Sarah’s hand went up. No one else’s.

“Infrastructure as code? Terraform, Chef, anything?”

No hands.

“CI/CD pipelines? Jenkins, GitLab CI, GitHub Actions?”

Sarah raised her hand tentatively. “I used GitHub Actions in a personal project once.”

“Container orchestration? Kubernetes, Docker?”

No hands.

“Cloud platforms? GCP, AWS, Azure console?”

No hands.

“Anyone here written Python?”

Sarah’s hand. That was it.

Maya wrote: “Skills: Traditional admin skillset, no DevOps experience, no cloud experience, no automation frameworks, no modern tooling.”

She capped the marker and turned to face her team. Six faces stared back at her with varying expressions of defensive discomfort.

“Okay,” Maya said. “Here’s what I see. We have a team of smart, dedicated people running a critical enterprise platform using operational practices from 2005. We have no automation, no standardization, no observability, no disaster recovery confidence, and no modern technical skills. If we tried to document our processes, half of them exist only in Tom’s and Jake’s heads. If we lost a key team member, we’d be in crisis. And we’re spending most of our time on manual toil instead of improvement.”

“That’s a pretty bleak assessment,” Jake said.

“It’s an honest assessment,” Maya replied. “And here’s the thing, this isn’t unique to us. I’d bet 70% of PeopleSoft shops operate exactly like this. It works, sort of, until it doesn’t. But it’s expensive, risky, and slow. And it’s why consultants can walk in here and say ‘your PeopleSoft operation costs $4.2 million a year’ and make it stick.”

“So what do we do?” Lisa asked.

Maya moved to the fifth column: Quick Wins.

“We prove we can change,” Maya said. “Today, we pick one thing, one small thing, that we can automate or improve in the next week. Something that will give us a victory, build confidence, and demonstrate that this team can evolve.”

“Like what?” Tom asked.

“You tell me,” Maya said. “What’s the most annoying manual process you deal with every day? The thing that makes you think ‘there has to be a better way to do this’?”

Sarah spoke up first. “The quarterly critical patch DPK application process. Every time Oracle releases a patch bundle, we spend three days downloading files, reading the readme, manually applying the patch, checking the logs, and fixing errors.”

“And we have to do it for every environment and every server,” Priya added. “So a single critical patch bundle becomes two weeks of work across dev, QA, and production.”

“Can we automate that?” Maya asked.

“I don’t know,” Sarah said. “I’ve never tried.”

“Then that’s your Week 1 project,” Maya said. “You and Priya. Write a script in Python, Bash, or whatever works best that automates the critical patch download and application process. Document it. Test it in dev. If it works, we’ve just saved ourselves two weeks every quarter.”

Sarah and Priya exchanged glances. Sarah nodded. “Okay. We can try.”

“Jake,” Maya said. “You mentioned your RMAN backups haven’t been tested in seven years. This week, your job is to perform a complete restore test in a non-production environment. Document the actual restore time, identify any problems, and update the runbooks. Make disaster recovery a known quantity instead of a hope.”

Jake grimaced. “That’s going to be tedious.”

“Yes,” Maya agreed. “But when we’re presenting to the CIO in Week 12, and he asks about business continuity, I want to tell him we have a tested, documented DR process with a validated RTO. Can you do that?”

“Yeah,” Jake said reluctantly. “I can do that.”

“Marcus,” Maya said. “Observability. I want you to spend this week researching log aggregation solutions. We need to get all our PeopleSoft logs, including application server, web server, process scheduler, and integration broker, flowing into a single place where we can search them. Start with open-source options. Elasticsearch, OpenSearch, whatever. See what versions are compatible with our PeopleTools versions, and then document what it would take to implement.”

“That sounds complicated,” Marcus said.

“It is,” Maya said. “Which is why you’re starting with research, not implementation. By Friday, I want a one-page proposal that says ‘here’s how we could aggregate our logs, here’s what it would cost, here’s what value we’d get.’ Can you do that?”

Marcus considered. “Yeah. I can do that.”

“Tom,” Maya said. “You’re going to start documenting. Pick one environment, let’s say dev, and document every single configuration setting, every directory path, every environment variable, every tuning parameter. Everything that lives in your head and your notebook needs to live in a wiki or a Git repo by the end of the week.”

“All of it?” Tom looked pained.

“The dev environment,” Maya clarified. “Think of it as the template for the infrastructure-as-code we’re going to build later. But we can’t automate what we can’t describe.”

“This is going to be incredibly boring,” Tom said.

“Welcome to Week 1,” Maya replied.

She turned to Lisa. “Portal admin is cleaner than most of this, but I want you working with Sarah and Priya on the patching automation. Your job is to test it, break it, and make sure it actually works. Be the QA.”

“I can do that,” Lisa said.

Maya stepped back from the whiteboard. “Here’s what success looks like at the end of Week 1: Sarah and Priya have a working patch automation script. Jake has a tested DR runbook with real numbers. Marcus has a log aggregation proposal. Tom has documentation for one complete environment. Lisa has validated that the patch script actually works.”

“That’s… a lot for one week,” Tom said.

“It’s a fraction of what we need to accomplish in twelve weeks,” Maya said. “But it’s a start. And more importantly, it’s how we prove to ourselves that we can do this. Right now, you’re all thinking this is impossible. By Friday, you’ll have evidence that it’s possible.”

“And if we fail?” Jake asked.

“Then we learn why we failed and try again,” Maya said. “But here’s the thing, we’re not trying to be perfect. We’re trying to be better. Any improvement is a win.”

She grabbed the eraser and cleared a space on the whiteboard. “Let me show you where we’re trying to get to.”

The Modernization Maturity Model

Read more