It wasn’t one dramatic moment that made me stop and take note of a move toward agentic artificial intelligence (AI). My attention was drawn more and more through increasing questions from clients.
For years, they’d been asking questions like, “Can this model summarize a contract? Can it classify these tickets? Can it draft an email?”
Then, about a year ago, the questions changed. Clients stopped asking whether a model could do a single task and started asking whether it could take action on its own. If you’re a chief information officer (CIO), that shift should change how you think about infrastructure, governance, security, and operating models.
How agentic AI differs from traditional enterprise AI
Traditional enterprise AI is advisory. It predicts and even recommends. It summarizes and surfaces insights. It can be powerful, but its power is limited by design. That’s because it generally hands the final decision to a human.
Agentic AI is different in one fundamental way. An agent doesn’t just tell you what to do; it can decide to do it.
The system can then chain those decisions through tools and workflows. It can open tickets and query systems of record. It can send approvals and update procurement details. It can trigger supply chain actions and escalate exceptions. It doesn’t pause for permission at every step unless you build it to.
That breaks a lot of our old accountability assumptions. Most enterprises are built around the idea that a human executes and is the one who takes responsibility. If an agent is the one moving a workflow forward, every leader in the chain needs to know who is accountable. What’s more, the answer to that question has to be figured out before deployment.
The challenge that comes with deploying agentic AI at scale
The reality of scaling AI across a large enterprise is always more challenging than the strategy deck makes it look. The deck makes assumptions such as clean data and a business where everyone agrees on the same priorities.
Real companies have data spread across a dozen legacy systems and workflows that only work because people know where the exceptions hide. A model that looked great in the demo can fall apart the moment it hits real-world inputs. And this is why Gartner predicts that 40% of agentic pilots will fail by the end of next year.
The strategy usually isn’t wrong. It’s just optimized for a company that doesn’t exist. This is why an engineering-first approach beats a strategy document. The latter won’t survive contact with legacy infrastructure, and by the time it doesn’t, the consultants who wrote it have moved on.
An engineering-first approach rethinks environments to prepare for agents that will reach broadly to get their job done. Every action an agent takes needs to be logged and attributable, and the governance layer you build has to prove the chain of custody for sensitive data at every hop.
How enterprises build the guardrails and governance that permit both autonomy and security
The best guardrails and governance always start with one simple principle: you should never grant autonomy upfront. Each system must earn that feature over time. You begin by giving agents a tightly scoped set of actions. You also enforce confidence thresholds that ensure that the system escalates a decision to you any time it isn’t sure. Always sandbox any autonomous workflow before it touches a live system.
When you put these guardrails in place, autonomy becomes a dial you turn up over time, based on track record and measured reliability.
The mistake I see most is treating autonomy like a switch that is either all the way on or off. The far safer and more scalable approach is gradual expansion with clear evidence at each step. It’s important to remember that these guardrails aren’t there to slow you down. They’re there to create the proof that lets you move faster later.
Making this happen requires that engineering-first approach I mentioned earlier. And that method needs to start long before anyone is debating which model to pick.
It begins at the data layer. Here, you must design security measures that enforce explainability and traceability. Think of these as engineering requirements that you test for just as thoroughly as you do for latency and uptime.
The difference between a governance design that’s bolted on and a design that’s built in is evident. That’s why bolted-on governance becomes like the dusty policy binder on the shelf, whereas built-in governance actually prevents incidents. When your controls are enforced by design, you see faster diagnosis when something fails. You get the traceability that executives can stand behind.
Why organizations need to rethink their AI strategies as agents begin executing complex business workflows
In most enterprises, the strategy revolves around choosing and tweaking the right model. But the model is rarely the hard part. The hard part is whether the underlying data can be trusted, whether there’s a governance layer that enables safe interaction with real business processes, and whether anyone actually owns what happens after launch.
This is why the data foundation is the part I insist on discussing first, even when it isn’t the most exciting part of the pitch. The right sequencing is to build governed pipelines, access controls, and observability first. Only then layer agent orchestration on top of that solid foundation.
Run your pilots and build traceability infrastructure in genuinely low-risk workflows. It’s always cheaper to build oversight into the architecture now than to retrofit it after something goes wrong.
Launch day has to be day one of operations. CIOs need clarity on who owns monitoring from the start. They need to know who responds when something breaks and how the system gets maintained and improved six months out.
Why CIOs need to prepare for more autonomous systems
Agentic systems are becoming the next standard operating model for large enterprises. The way is already clear. Enterprises already run on human teams that organize tasks across handoffs and cross-system workflows; agents just do that work faster and more consistently than people can.
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In places like customer operations and supply chain, we’re already moving past the early-adopter phase. Within the next two to three years, multi-agent systems will feel much more like the default way mid-sized to large enterprises run core workflows. Of course, that timeline depends entirely on whether the data and governance underneath are ready.
The advantage will go to whoever built the data trust and governance layer early enough that they can let agents operate with real autonomy. Everyone else will be stuck babysitting their agents. CIOs don’t get to wait for a mandate here. The accountability structure, the security model, the tracing, and the operational ownership all have to be designed before the first high-impact workflow goes live. Because once agents are executing rather than advising, you’re operating. And preparation is the difference between controlled autonomy and uncontrolled acceleration.
Editor’s Note: The opinions expressed here by the authors are their own, not those of impakter.com — Cover Photo Credit: Tara Winstead.
