Profound Logic Software

Join us in Atlanta, October 26–29, for NAViGATE at IBM TechXchange!

Real workshops. Roadmap previews. Futurization in action. That’s what we're bringing to , now co-located with IBM TechXchange, so you get full access to both conferences.

Join thousands of technologists building real-world solutions. Don't wait! Register your team now: https://hubs.la/Q04r8pT30.

One line from this engagement stuck with us. 👇

A global logistics leader, one of the largest freight transportation networks in the world, running millions of transactions daily on IBM i, reached an inflection point. Their systems worked. Their business logic was solid. And they were still losing ground because the platform couldn't keep up with the pace of change the business needed.

Their leadership's read on the situation? "Modernization would buy time. Futurization would build advantage." 🎤 ⬇️

That's not a technology decision. That's a business strategy decision. And it's the right frame for any organization sitting on decades of refined IBM i business logic that still runs the operation, but can't move as fast as the market is moving.

The engagement delivered zero operational disruption. Low-code capabilities the development team adopted immediately. A foundation now in place for AI and IoT integration. And an organization that went from "we can't fall behind" to "we're positioned ahead."

Same IBM i. Different trajectory.
Read the full case study: https://hubs.la/Q04rnk_r0

For CTOs and IT managers considering transformation on IBM i, you should know that the platform work and the people work require completely different playbooks.

Most teams have the platform work covered. What stalls projects is the second one.
Five moves do most of the heavy lifting on the people side.

Answer "what does this mean for me" before the first change lands. The leading cause of resistance isn't disagreement. It's the absence of a clear, specific answer to that question for each affected group.

Involve users rather than informing them. Pull respected end users into early phases as testers and advisors. People defend what they helped build, and their endorsement travels further than any executive memo.

Match the messenger to the message. Strategic "why we're doing this" lands best from senior leaders. "Here's how your job changes" lands best from direct managers.

Getting this wrong creates a credibility gap that rumors fill.

Sequence for confidence, not just technical convenience. Start where an early visible win builds trust, then expand. You don't have to do everything at once, and you probably shouldn't.

Let the architecture carry the reassurance. Coexistence means you can promise continuity and mean it. That's a very different position than asking people to trust a system they've never seen will be ready years from now.

The fastest path to value from futurization is never just faster code. It's faster acceptance.
Read the full report: https://hubs.la/Q04rnqFR0

A successful pilot proves one thing, the approach can work on one program without breaking what the business depends on. It doesn't prove the approach holds up when the dependencies are tangled, the documentation is thin, and there are 900 more programs that haven't moved yet.

That gap is where most IBM i futurization initiatives quietly stall and the math is worth being honest about.

73% of what runs on IBM i is homegrown, custom-built code shaped by thousands of small decisions that were never documented anywhere except in the code itself. And 21% of IBM i shops run that environment with just 3-5 developers, a number that has held steady for a decade. Large portfolios, lean teams, and a business that depends on all of it running correctly every day.

At that scale, program-by-program transformation isn't a labor problem. It's a structural one. A four-person team cannot reasonably map a thousand-program dependency web by hand in any timeframe a business can tolerate. The approach that works on a pilot needs to be fundamentally different from the approach that
works on a portfolio.

Portfolio-wide futurization maps the whole environment first, which programs share subroutines, where business rules appear in multiple places with subtle variations, where the highest-risk technical debt actually sits. It prioritizes by business impact, not alphabetical order.

It verifies continuously rather than requiring a single high-stakes review at the end of a multi-year project.

The pilot was the right first step. The question is what comes after it. Read the full breakdown: https://hubs.la/Q04rnr210.

Every IBM i staffing firm has an AI story now.....and most of them mean the same thing, developers who use an AI assistant to write code faster. That's a faster human. It's not a different model for getting work done.

Our staff augmentation runs on an agentic orchestration platform. Our professionals operate autonomous agents that execute full build-test-fix cycles inside your environment. Not suggestions. Not drafts. Compiled, tested, validated code ready for developer review.

The distinction matters because the bottleneck in most IBM i development isn't how fast someone can write code. It's how much of the ex*****on cycle requires a human at every step. Agentic orchestration changes that. The developer shifts from executing to directing.

Output scales. Quality holds.

When every firm says they use AI, the question worth asking is what the AI actually does and what it leaves on the developer's plate.

Talk to us about what that looks like in practice: https://hubs.la/Q04rnj680

Most IBM i shops evaluating AI tools aren't asking which model to use. They're asking whether the tool understands RPG. The model underneath gets treated as an implementation detail.

That's worth a second look.

No single AI model is best at everything. Some handle long dependency chains better. Some are faster on well-defined, repetitive tasks. Some have deeper exposure to enterprise code patterns. RPG and COBOL sit in an unusual spot: not obscure, but underrepresented compared to Python or JavaScript in most training datasets. Model performance on IBM i work is genuinely uneven.

There's a simpler question worth asking any AI platform you evaluate. Can you see which model handled a given task, compare two models against the same input, and change your answer later without re-architecting your AI strategy?

If the answer is no, that's not an implementation detail. That's a strategic bet you're making by default.

Wondering if there's a better way? 🚨SPOILER ALERT: There is.
Check it out: https://hubs.la/Q04qxBsx0

A food retail company with over 50 years in business, hundreds of store locations, farming, processing, distribution, and retail all under one roof, was running store operations and maintenance workflows on IBM i green screens.

Field technicians couldn't report part purchases and usage in real time.
District managers couldn't see store-level performance without going through IT.
Equipment sat down longer than it needed to because the information to fix it wasn't accessible where the work happened.

By harnessing Profound AppDev they converted the green-screen applications to browser-based interfaces without touching the underlying RPG or COBOL. They gave District and Area Managers role-based dashboards with live visibility into performance across every location. Maintenance teams started logging part usage from the field in real time.

Faster equipment repairs. Smarter decisions from live data. Lower management overhead. And a foundation now in place for agentic AI to extend the platform further.

None of it required replacing the IBM i systems that had run the business for fifty years. And we can do the same for you.

Read the full case study: https://hubs.la/Q04qxwDq0

How One Food Retailer Futurized Store Ops Without Ripping Out Legacy Systems Real-time dashboards for area managers. Faster equipment repairs for maintenance teams. See how Profound AppDev helped a 50+ year food retail and dairy company futurize IBM i operations while keeping RPG and COBOL intact.

Multi-month IBM i development projects don't have to take months anymore.

Agentic coding environments handle code generation, testing, and validation autonomously. That compression is real: work that used to sit in a queue for a quarter can move to production in weeks. Not because the code is lower quality. Because the ex*****on loop no longer depends on a developer being present for every step of it.

CoderFlow brings that capability to every engagement model. Whether we own the project end to end, embed developers on your team, or set up the agentic environment that lets your existing developers move up to 10x faster, the tooling comes with us.

The features sitting in your backlog are not stuck because of effort. They're stuck because the capacity model for getting them done hasn't changed.

See how AI-accelerated development works: https://hubs.la/Q04pNdCv0

Technical debt doesn't stay the same. It compounds.

Every workaround added to a fragile codebase makes the next change harder. Every deferred fix increases the risk that the next release breaks something unexpected. Every patch layered on top of a system that was never designed for it consumes more maintenance budget than the one before.

IBM i organizations are feeling this in a specific way right now. IT budgets increasingly consumed by maintenance, patches, and firefighting leave less and less for the investments the business actually needs. Meanwhile, the developers who know where all the bodies are buried are approaching retirement, which makes existing debt exponentially harder to address.

The cost of unresolved technical debt isn't staying the same. It's going up. 😳

Our futurization process starts by mapping the environment to identify high-impact debt areas, those that carry the most operational and financial risk, before a single line of code is touched. You cannot build a future on an unstable foundation, and you cannot fix the right things without knowing which ones are actually costing you the most.

Ready to smash your technical debt? 👉 https://hubs.la/Q04qxvVr0

Ask an AI coding tool to fix a bug, and it hands you a suggestion. You still compile it. You still test it. You still debug whatever the model missed. You still validate it against how the system actually runs. The tool accelerated the writing. The engineering cycle that determines whether the code actually works is still yours.

In a clean greenfield environment, that gap is manageable. In an IBM i environment running RPG, COBOL, multi-library structures, and decades of interdependent business logic, the distance between "generated" and "verified" is often the majority of the work.

Verified, ready-to-commit code has been compiled against the real build environment. Tests have passed. Failures were caught, fixed, and retested autonomously until the output met defined acceptance criteria. The developer reviews a completed, bounded change with enough context to understand what ran and why.

A suggestion generated in the cloud and pasted into an IDE is not that. It is plausible. Those two things are not the same. Read how: https://hubs.la/Q04p96P_0