Four Steps To Make Agentic AI Actually Do Real Work

Handing over real work to agentic AI requires you to think about the workflow first and the technology second.

Despite an estimated 30-40 billion dollars of enterprise spending on generative AI, a 2025 MIT report shows that 95% of all AI pilots stall, as they deliver little or no impact on P&L. As organizations now rush into agentic AI, the obvious questions is how to get these systems out of the hype cycle and into real work.

To find out, I conducted 20 in-depth interviews with international and state-owned energy companies and technology service providers, and then tested a new approach that carefully considered the workflow AI agents have to become part of.

From the interviews it became clear, that agentic AI face a similar problem to brilliant new interns. When a new intern joins, the best learn the systems quickly, notice things others miss, and everyone says in week one, “We should get them into how we actually run this process.” Three months later, they are still orbiting at the edge: cleaning data, polishing slides, summarizing meetings. Not because anyone wants to waste them, but because nobody quite knows how to give them real responsibility without feeling exposed.

Most “agentic AI” is stuck in that same pattern. It summarizes, drafts and suggests. But very rarely does it own a clear slice of work from trigger to completion.

In three simple steps you can change this and get agentic AI do real work. I know because I have done it.

Step 1. Make AI that is already in the room visible

I never start with a blank sheet and “AI use cases”. I start at people’s desks.

I sit with a planner, an engineer, a buyer, a finance lead and ask them to walk me through a real decision: what they look at first, which systems they open, where a score or traffic light offers direction, what would feel harder if a particular tool disappeared tomorrow. As they talk, they almost always say some version of, “Most of the time I just go with what the AI tool suggests, unless it feels wrong.”

That’s the moment I capture. I translate their language into a simple one‑pager: “which job we do first”, “who we invite to interview”, “which invoice we stop”, “which anomaly we treat as noise”. It’s not a pretty architecture diagram, just a short list of concrete decisions where AI already shapes behavior. Often it’s the first time leaders see, in one place, where the system is already “in the room” when the organization decides.

That becomes our anchor. We are not talking about hypothetical agents, we are talking about real business decisions.

Step 2. Choose ONE decision to hand over to an agentic AI

Once that map is on the table, the tone changes. People stop talking in generalities and start arguing about the specifics. One person calls a step “just admin”, another insists, “If this goes wrong, it really hurts us.” My role here is to turn that instinctive sorting into a clear choice.

I guide the team towards decisions that are repetitive, tightly framed. The decisions I pick usually chew up time and attention but they are not high-impact. If something goes wrong, it is not a disaster. Following this line of reasoning, the type of work that comes to the fore are standard approvals, simple classifications, and routine parameters. Nobody is keen to do this type of work and that’s exactly why it’s a good candidate.

From that shortlist, we pick just one decision the group is actually prepared to let an agent take on. Then we put a very small, very sharp frame around it. I sit with the people who own the work and we write a single page in plain language that answers three things:

(1) What is the agent allowed to decide on its own?

(2) Under what limits and conditions?

(3) When should a human step in?

I keep pushing until it’s something a new joiner could read and say, “I understand what the system does, what we do, and when we intervene – and that feels sensible.”

In one sourcing flow, that one‑pager became the first artefact procurement, digital and risk all trusted enough to move. Following this process, a “nice prototype” that had been stuck for months finally went into a live trial, not because the model changed, but because the decision was defined, bounded and owned.

Step 3. Redraw the work so the agent isn’t just bolted on

If you stop at Step 2, you fall into the trap I see everywhere: the agent is live, dashboards look good, but people still run the old process “just in case”. The company pays for the agent and for all the human effort it was meant to save. To avoid that, I treat the next step as workflow design, not implementation detail.

I get the right people in a room and we sketch how this decision runs today: who touches it, in what order, with what inputs. As we draw, it usually becomes obvious how many hands a single decision passes through. Then we sketch the “tomorrow” version with the agent in the middle: which steps disappear completely, which move earlier or later, where humans will see only non‑standard or higher‑impact cases rather than every single one.

From there it is important to keep asking very practical questions: when the agent passes a case to a person, what exactly shows up on their screen? What are they expected to decide in that moment? Just as important, what are they no longer expected to redo because the agent has already done it? And on the responsibility side: what do they stop doing, what do they start doing instead.

In the high‑hazard workflow I’ve been following, that before/after sketch was the point where senior engineers changed their stance. They could see, step by step, where it was safe to stop double‑checking every output and where they still wanted a hard human gate. Safety remained non‑negotiable, but the “AI tax” - all the extra verification and rework finally came down because everyone could point to a picture on the wall and say, “This slice is the agent’s job now. This slice is still ours.”

Step 4. Identify decision owner

There is one more ingredient that my interviews made impossible to ignore: ownership.

In high‑hazard settings, people are clear that some decisions stay human, full stop. In commercial and enabling functions, some are open to letting agents decide standard cases, but only if someone clearly stands behind that choice. Across companies, I hear the same complaint: “too many people with a veto, nobody clearly in charge of how this decision is taken.”

That’s why at some point, one role in the business - not a platform or data team - has to say: “This is my decision. I’m prepared to let an agentic AI carry part of it under clear guardrails, and I will stand behind how we do that.”

Part of my work is to help identify that person for each decision, and to give them sufficient confidence that what they hand over is bounded and easy to understand. That’s what the structure through the map, the one‑page deal and the before/after workflow achieve. Equally important is to identify how decision makers can keep an eye on a few simple indicators like errors, overrides and near misses.

In the cases I’ve studied, real progress only starts once that ownership is explicit. Until then, agents stay stuck at the edges. They remain inters.

Using the 4 steps

There are people who build agentic AI, and those who are supposed to live with them in day-to-day operations. Getting agentic AI to do actual work means the latter to be genuinely on board. Unfortunately for those on the operational frontline, the value of agents is often fuzzy, while the cost of babysitting systems keeps rising.

To avoid turning an expensive system into just a slightly smarter dashboard, use the four steps. They force you to integrate agentic AI into the workflow, not just onto the screen.

About the author: Ayten Agalarova (Hajiyeva) is the country manager of BP in Georgia. She has 25 years of cross-functional leadership experience spanning operations, digital transformation, commercial, finance, and supply chain. Her work and research focuses on human–agentic AI collaboration, with particular emphasis on how organizations redesign workflows, decision rights, and operating models as agentic AI moves from experimentation into real business processes, especially in high-reliability environments.