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The consequential AI work that actually moves the needle for enterprises
Our take
In 2026, enterprise technology leaders are shifting toward a pragmatic approach to AI, prioritizing practical governance, orchestration, and integration within existing systems. In a recent OutSystems webinar, software executives emphasized the importance of moving from AI agent prototypes to scalable, agentic systems that deliver measurable ROI. As organizations embrace new AI technologies, they aim to accelerate productivity and enhance delivery. The discussion highlighted the critical role of enterprise platforms and the emerging significance of generalist developers and enterprise architects in navigating this transformative landscape.
Presented by OutSystems
After two years of flashy AI demos, rushed agent prototypes, and breathless predictions, enterprise technology leaders are striking a more pragmatic tone in 2026. In a recent webinar hosted by OutSystems, a panel of software executives and enterprise practitioners made the case that the most consequential AI work happening now is focused on the practical matters of governance, orchestration, and iteration, along with integrating agents into the systems they've spent decades building.
Enterprise leaders are increasingly focused on fundamentals. The priority is using new AI technologies
to accelerate productivity, improve delivery, and produce measurable business results.
Three elements shape this work:
The move from AI agent prototypes to agentic systems that deliver measurable ROI in production
The growing role of enterprise platforms in governing, orchestrating, and scaling AI agents safely
The rise of the generalist developer and enterprise architect as the most valuable technical profiles in an era of AI-generated code
Against this backdrop, the panel discussed governance frameworks, the economics of enterprise AI, and the limits of large language models without orchestration. The conversation ultimately turned to how leading organizations are building multi-agent systems grounded in existing enterprise data and workflows.
Agents in the real world
Enabling agents to work in production across the enterprise is best accomplished with a unified platform that handles development, iteration, and deployment. And that'swhere capabilities like the Agent Workbench in the OutSystems platform matter, said Rajkiran Vajreshwari, senior manager of app development at Thermo Fisher Scientific. It provides the infrastructure to learn, iterate, and govern agents at scale.
His team at Thermo Fisher has moved away from single-task AI assistants in customer service to building a coordinated team of specialized agents using the workbench. When a support case arrives, a triage assistant classifies the request and dynamically routes it to the right specialist agent, whether that’s an intent and priority agent, a product context agent, a troubleshooting agent, or a compliance agent.
"We don’t have to think about what will work and how. It’s all pre-built," he explained. "Each agent has a narrow role and clear guardrails. They stay accurate and auditable.”
Governing the risks of shadow AI
A new category of risk emerges when AI makes it possible for anyone in a company to generate production-level code without IT oversight. Basically, this is ungoverned shadow AI. These homegrown products are prone to hallucinations, data leakage, policy violations, model drift, and agents taking actions that were never formally approved.
To get ahead of the risk, leading organizations need to do three things, said Luis Blando, CPTO of OutSystems.
"Give users guardrails. They’re going to use AI whether you like it or not. Companies that seem to be getting ahead are using AI to govern AI across their full portfolio,” he explained. “That is the difference between shadow AI chaos and enterprise-grade scale.”
Eric Kavanagh, CEO of The Bloor Group, noted that governance requires a layered set of disciplines that includes securing data, monitoring models for drift, and making deliberate choices about where AI connects to existing business processes.
“Companies don’t have to be manually creating these controls," he added. "A lot of those guardrails and levers are baked in to platforms like OutSystems.”
Why the real orchestration challenge is models vs. platforms
Much of the early excitement around enterprise AI focused on selecting the right large language model. Now the harder challenge, and far more durable source of value, is orchestration. This includes routing tasks, coordinating workflows, governing execution, and integrating AI into existing enterprise systems.
Scott Finkle, VP of development at McConkey Auction Group, noted that LLMs, however impressive, are pieces of complex workflows, not final solutions. Organizations should be ready to hot-swap between Gemini, ChatGPT, Claude, and whatever emerges next without having to rebuild the agentic system around it.
A platform with orchestration capabilities makes that possible. It manages the lifecycle, provides visibility, and ensures processes execute reliably, even as AI handles the reasoning layer on top.
“The AI and the models change, the workflows can change, but the orchestration remains the same," Finkle said. "That’s how we’re going to extract value out of AI.”
The economics of enterprise AI investing
Security, compliance, governance, and platform-level AI capabilities will all command greater investment in 2026, particularly as AI moves into core workflows like finance and supply chain. Enterprises should favor incremental wins rather than expect big, immediate gains.
“We’re focusing on base hits," Finkle said. "The way it counts is by getting something into production and having it make an impact. Big investments in pilot projects that don’t make it into production don’t save any money. It’s not going to happen overnight, but over time I think we’ll see tremendous savings.”
There's still a split in how enterprises are approaching AI transformation. Some start from scratch and reimagine every process. Others, especially those with billions of dollars in existing infrastructure depreciating in-house, want AI to integrate with their systems. They want agentic systems to reuse data, APIs, and proven processes while speeding up delivery. The agent platform approach serves both camps, but particularly the latter. Organizations can deploy agents where they add clear value while preserving the integrity of established, deterministic workflows.
The rise of the enterprise architect and the generalist developer
As AI accelerates code generation, bottlenecks in software delivery are dissolving. In its place is a premium on systems thinking. This is the ability to understand the broader enterprise architecture, decompose complex business problems, and reason about how AI integrates with existing infrastructure. Kavanagh pointed to enterprise architects specifically as the professionals best positioned to capitalize on this moment.
“We’re entering a very interesting age of the generalist," he explained. "The better you know your enterprise architecture and your business architecture and how those things align, the better off you’re going to be. ”
“The result is faster delivery with fewer interruptions and fewer bugs," Kavanaugh said. "You can focus on the non-repetitive tasks. It’s a benefit to the developer, to the business, and to the whole IT organization.”
Catch the entire webinar here.
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