Martin sees a broad spectrum of outcomes across enterprises: some genuinely moving 30% or more of their AI initiatives into production, others still trapped in endless proofs of concept.
The biggest difference isn’t the model they picked. It’s how clearly they define the problem.
Too many organisations start with “We should do something with AI” and then launch pilots with fuzzy goals. They play with agents, prompts, and prototypes but struggle to answer a basic question: Which metric are we trying to move?
The organisations that are breaking out of pilot mode do something more old-fashioned – and more disciplined . Before they build:
In other words, AI doesn’t replace product thinking or basic governance . It makes both more important. Without clear outcomes and feedback loops, you just get faster at generating… more experiments.
One of the most visible ways AI is accelerating development is “vibe coding”: describing what you want in natural language and watching an AI agent generate an application.
Martin calls it an “intoxicating” experience – in a good way . Instead of weeks of specs and wireframes, business stakeholders can sit with an AI, explain what they’re trying to do, and see a working prototype emerge. For IT leaders, this can radically compress the slowest part of the lifecycle: figuring out what to build.
But there’s a catch. If vibe coding is the first date – fast, exciting, full of possibility – enterprise software is the marriage: long-term, demanding, and unforgiving of shortcuts. Under the surface of that instant app, the code may be messy, inconsistent and undocumented. Security assumptions may be unclear. Dependencies may be brittle. Governance is usually nonexistent.
Martin cites a friend who built a three-tier web app over the weekend using AI and cloud services to share neighbourhood security camera footage. The app worked. The problem? He’d created “a mountain of tech debt” and knew that as soon as neighbours asked for changes, it would become unmanageable.
For CIOs and CTOs, that story is playing out inside the enterprise today: everyone can generate software; not everyone is generating something you want near your core systems.
The implication is not “don’t vibe code.” It’s: treat vibe coding as a powerful front-end to a disciplined software development platform and process . Use it to explore, prototype and converge on requirements – but don’t confuse “it runs” with “it’s production-ready.”
Underneath the excitement sits a tension every enterprise leader feels: large language models are inherently non-deterministic. Ask the same question twice, and you may get different answers. That’s wonderful for creativity – and dangerous when you’re in a regulated industry or dealing with money, safety, or compliance.
Martin’s view is that the way forward isn’t to try to make AI behave like traditional software, but to pair non-deterministic reasoning with deterministic execution.
He describes a pattern many enterprises are adopting:
The broader lesson: AI accelerates development not by replacing traditional architecture, but by sitting alongside it. Platforms that bake in observability, lifecycle management, security, and repeatable code generation give you a chassis sturdy enough to carry the ambiguity that AI introduces.
The enduring role of humans
As AI adoption grows, many IT leaders are asking the same question: Is AI designed to replace humans or work alongside them?
Martin is unequivocal. He uses AI every day – to research customers, understand industries, and prepare for meetings. But the part of his job he values most – building relationships, understanding context, earning trust – isn’t up for automation. The same will be true for many roles in IT and the business.
For software teams, AI will reshape developers’ work, not eliminate it . Some skills will become less central (handwriting boilerplate, re-implementing common patterns), while others will become vital: system design, security, data governance, agent orchestration, and the ability to connect technology to business outcomes.
Platforms like OutSystems matter not because they make AI possible, but because they make AI manageable. They give organisations:
AI accelerates enterprise software development in all the ways the hype promises: faster prototypes, shorter cycles, more automation. But the organisations that will truly benefit aren’t the ones with the flashiest demos. They’re the ones that pair AI with disciplined platforms, clear goals and human orchestration and oversight.