Alessio Alionço is the founder and CEO of Pipefy, a global leader in AI-driven business process automation solutions.
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An IT leader’s top mandate used to be: “Keep the systems running.” Success was measured in uptime.
AI is changing what organizations need most from their IT leaders, and many of these leaders haven’t yet grasped what this means for their roles.
In the old model, IT managed infrastructure and applications. In the AI era, however, IT must think bigger. Your organization needs leadership that can define and manage the sequences, rules, exceptions, handoffs and the governance layer that makes all of these AI tools work together reliably.
As the leader of an organization that develops AI automation solutions, I see this disconnect often among IT leaders who treat AI orchestration as a relatively simple infrastructure issue rather than a strategy.
Traditional IT leader thinking asks: How do we connect these systems? How do we route these workflows? What do we do when one of the pipelines has a leak? This is infrastructure thinking.
AI orchestration, on the other hand, is strategic. Orchestration means you decide which processes get automated, in what sequence, with what human oversight and with what rules when something goes wrong.
These are business decisions disguised as technical ones. Rather than focusing only on infrastructure, IT leaders must now understand how to use orchestration to achieve faster cycle times, better compliance and a more consistent customer experience.
At its core, AI orchestration is an architectural decision about where control lives. Without that orchestration, your AI program is generally an (often very expensive) collection of experiments.
Deloitte found that a lack of orchestration can significantly limit agentic AI's potential business value. In my experience, the reason is that individual AI tools deliver local value: One agent summarizes tickets, one model scores leads and another bot processes invoices. Each one of them may work individually. However, if none of the individual tools work together to compound the benefits, the C-suite and board will likely wonder why the gains that AI promised haven’t materialized.
IT leaders must examine every AI agent, every automation and every workflow and answer the same question: Who governs the sequence, exceptions, handoffs and accountability? If that question isn’t answered deliberately, the vendor with the most surface area in your stack answers it for you. By the time you notice, the technical debt of unorchestrated AI has grown into something that takes you years to untangle.
Your chance to make those decisions intentionally is right now. You can’t afford to wait until you have workflows figured out or until bots are completing projects independently. Because if there’s one thing IT leaders know about a system—even a great system—it's that, eventually, it is bound to break down.
For leaders who recognize the gap but don’t yet have an orchestration strategy in place, you’ll have to hold yourself back from acting on your first instinct, which would be to launch a full platform evaluation.
Instead, your first move should be to take your single highest-value AI deployment and map every handoff it touches—system to system, AI to human, exception to resolution. Put the full sequence on paper. Identify every step where something gets passed, decided or escalated. Find every point where something can go wrong and where there’s no defined path forward.
The resulting map will show you exactly where your orchestration layer is missing. It will also produce a concrete, defensible business case for building one. It's difficult to act on theoretical gaps, but this strategy can surface real failures, real delays and real compliance risks that are likely already happening in production.
There’s one final question most IT leaders are still avoiding, and it’s the most important one: Where does accountability live when an AI-driven process makes a wrong decision?
Most organizations have deployed AI without clarifying this key point of responsibility. Only 33% of organizations have "defined escalation pathways when AI systems misbehave," according to an American Arbitration Association survey.
In my experience, no one is forcing the issue—not the CEO, not the board, not the vendors selling the tools. It’s uncomfortable because the honest answer is usually “We don’t know yet.”
But that ambiguity can surface in a compliance failure, a customer incident or a regulatory inquiry. By then, you may be in full-on crisis management mode, making it too late for a strategic conversation.
My advice to IT leaders has been to initiate that conversation now, before the first incident inevitably occurs. Uncomfortable though it may be, at some point, the ball will drop. The faster you know whose job it is to pick it up, the more you can limit the damage.
Most IT leaders I speak with already know something is off. They can feel the gap between what their AI investments promised and what they're actually delivering. The most common culprit is that no one defined the governing logic above the tools rather than a bad tool choice.
Your next move here is clear: Define where control lives, who owns the exceptions and what happens when something goes wrong. In other words, at this stage of AI implementation, deliberate leadership choices can be more impactful than technical or application-level questions.
It’s clear that the role of IT leaders in organizations is changing. There’s a lot of work to be done to set the stage for the smooth operation of your AI program now and into the future. Instead of viewing it as another hurdle to get over, however, I invite you to think of it as an opportunity for IT to step into a more holistic organizational leadership role in your organization.
Architecture thinking (versus infrastructure thinking) requires IT to look at the landscape of your operation as a whole. This broader view can result in AI living up to its potential.
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