In a recent Enterprise Talk podcast, OutSystems CEO Woodson Martin highlighted three areas where agentic AI is transforming how enterprises design, deliver, and operate software, revealing a shift that goes far deeper than simple speed or cost.
1. Defining what to build, not just how to build it
Ask any CIO what slows projects down, and you’ll hear a familiar refrain: it’s not the coding – it’s the requirements. Business stakeholders often bring half-formed ideas, vague requests, and conflicting priorities. Weeks are lost in workshops and rewrites before a single line of code delivers value.
According to Martin, this is a key area where agentic AI is becoming a quiet ally for technology leaders. Business users can now describe a need conversationally and instantly see a working prototype. AI agents, like OutSystems’ Mentor, can turn that prompt into an initial app structure, data model, and workflow. Product and IT teams then iterate on something tangible rather than debating abstractions.
The impact is profound: the “what are we even building?” phase compresses from weeks to days, closing the gap between business intent and IT delivery. AI isn’t just accelerating software development, it’s transforming how ideas become solutions.
2. Managing the entire software lifecycle
While many AI tools today focus on helping developers build new applications from a simple prompt, Martin says the real challenge for enterprises lies beyond creation. Managing complex systems through their entire lifecycle, spanning iterative development, multi-team coordination, security, and long-term reliability, matters just as much as speed of delivery. AI is now stepping into this broader role, transforming not only how software is built but how it’s maintained, protected, and optimised.
Agentic AI brings intelligence to every stage of the lifecycle: monitoring application behaviour, anticipating performance issues, and strengthening system resilience. Imagine being able to predict what will happen when traffic spikes on Black Friday, or when a denial-of-service attack threatens your infrastructure. Through continuous observability and proactive analysis, AI agents can flag risks before they disrupt operations, helping IT teams stay ahead of failures rather than react to them.
For large enterprises, this shift means more than operational efficiency. It’s about ensuring reliability, scalability, and security at every step.
3. Turning document-heavy work into autonomous flows
Some of the most powerful early wins for agentic AI aren’t flashy chatbots, they’re deeply practical solutions to data-heavy, document-driven processes. Mortgage origination, insurance underwriting, legal case intake, compliance reviews, and claims processing all fit this description.
One lender Martin referenced previously handled hundreds of mortgage applications per week with manual underwriter reviews. After adopting agentic AI built on OutSystems, AI agents began reading and classifying supporting documents, such as bank statements, payslips, and policies. While specialised agents scored eligibility and summarised results, only incomplete or ambiguous cases were escalated to human experts.
The outcome was dramatic: throughput jumped from hundreds to tens of thousands of applications per week, with over 90% processed end-to-end without human touch. Human staff, in turn, focused on customer conversations and complex decision-making, work that truly requires judgment and empathy.
Rethinking what software and agentic AI can do
Martin asserts that Agentic AI isn’t just another layer of automation or a faster way to code, it’s a shift in how enterprises think about software altogether. Software now collaborates. Agentic AI is redefining what enterprise software can do, and who does the work.
