How Agentic AI Accelerates Enterprise AI Transformation

Most companies now have AI inside their everyday tools, and far fewer have changed how work gets done. In McKinsey's 2026 survey, 80% of respondents said AI had improved their individual productivity, 37% reported any EBIT impact and only 6% qualified as AI high performers, a share unchanged from 2025.

Agentic AI is often presented as the way out of that gap, and it can be, provided the work around the agents is redesigned first. This article covers what changes, what slows the effort down and where to start.

What is AI transformation, and where does agentic AI fit?

AI transformation is the organisation-wide change in how work gets done, with AI built into roles, workflows and operating models. McKinsey describes it as redesigning all three around what AI now makes possible. Agentic AI fits wherever work runs across several steps, because an agent can carry it toward a goal.

A chat assistant helps one person with one task. An agent given a goal plans the steps, calls other tools and keeps going until the job is done, as our guide to agentic AI explains. Because it works on the whole sequence, an agent can change how a process runs, which an assistant used by one person rarely does.

Why do most AI transformations stall after the pilot?

The gains stay with individuals. People work faster, yet the business does not see it in its results, which is what the McKinsey figures above show. Writing about its own programme in September 2026, Microsoft said it first treated AI like a technology rollout and learned that access and usage do not equal transformation, since a tool licensed to over 200,000 people does not change how the work gets done.

The usual symptom is a long list of ideas and no process that has changed. McKinsey adds that many transformation efforts address only one part of the system, whether workforce, organisational design or change management, when all of them need to change together.

How does agentic AI accelerate AI transformation?

Agentic AI accelerates AI transformation when it is given a redesigned workflow to run. It shortens the path from pilot to process, because an agent can carry work from start to finish, and what is built for one team can be reused by the next.

Part of the speed comes from where agents work. Nearly three-quarters of AI high performers fundamentally redesigned workflows because of AI, up from 55% a year earlier, against roughly one in four of the other organisations. An agent that owns a workflow end to end makes that redesign practical, and Microsoft reports that cycle time fell by up to 75% in selected workflows, according to its own internal analysis of a supply-chain programme that deployed more than 111 agents.

Part comes from reuse. McKinsey describes an agentic mission factory that takes one business outcome, redesigns the work as a human and agent workflow, builds the agents and controls, launches it and keeps the reusable assets for the next mission. When several teams share one agent, each later project starts with something already built.

And part comes from timing. An agent acts inside real systems, so questions of access, ownership, cost and human review have to be settled at the first deployment, while they are still small.

The same pattern shows up in scaling: high performers are more than three times as likely as others to be scaling agents in most business functions. ISAGRI started from usage that was individual and uneven across its teams, with no shared method. Once it had one, it measured 5 to 10 code contributions per developer per week at Geofolia, up from 1 to 5, and cut the time from idea to development-ready specification by 30% to 50%.

What has to change before you give a workflow to agents?

Microsoft's rule, which it calls lean before agents, is to redesign a workflow before an agent takes it over. An agent running a clumsy process will run it faster, clumsiness included. Four decisions come first:

• Pick an outcome the business already tracks, such as the time to resolve a ticket or to close the books.
• Map the workflow as it runs today and remove the steps that exist only because of old tools or handoffs.
• Decide what the agent can access and do, how its actions are monitored and where a person reviews or approves. Microsoft treats these as part of the redesign itself.
• Keep the first scope small enough to go live within a quarter, as we recommend in our guide to choosing use cases.

The access and review decisions are the core of agentic AI governance, and they cost less to make before launch.

What slows down an agentic AI transformation?

Once agents multiply, two problems tend to appear. The first is sprawl. Gartner expects the average Fortune 500 company to run more than 150,000 AI agents by 2028, up from fewer than 15 in 2025, and only 13% of organisations believe they have the right governance in place. Our piece on agent sprawl covers what to consolidate.

The second is cost. In Futurum's September 2026 survey, 46.9% of enterprises reported running over budget on AI. Agents bill for every step they take, so the total is hard to forecast, as we explain in controlling agentic AI costs.

Who should lead an agentic AI transformation?

In practice, three people: a business owner accountable for the outcome, a technical partner who builds and secures the agents, and someone whose job is the work in between. That last role is the AI operator, and in most companies it is best filled by someone already inside who knows how the business runs.

Microsoft found that employees with context, capability and room to act become the engine of the change. Training therefore belongs inside the programme, and it is what our AI Academy programme is built for.

How do you measure whether agentic AI is speeding up the transformation?

Licence counts and agent counts say little. The useful measures sit at the level of the workflow and the team:

• The cycle time of the redesigned workflow, before and after. At Trench Group, the monthly reporting commentary went from 8 hours to 1 hour, one of three applications that reached production out of twelve built in three days.
• Adoption by team, measured as the share of the work the agent now carries.
• The objectives the team was already accountable for. Time saved on a task nobody cared about does not count, and spend can be tied to the team that owns it.

For an outside benchmark on the whole programme, McKinsey's figures are the ones to beat: 37% of respondents report any EBIT impact and 6% qualify as high performers.

Frequently asked questions about agentic AI transformation

What is the difference between AI transformation and digital transformation?
Digital transformation moved work onto connected digital systems. AI transformation changes who or what does the work inside those systems and how decisions get made. The two overlap, and most AI transformations rely on digital foundations already being in place.

Do you need agentic AI to transform with AI?
No. Assistants and copilots cover individual tasks and drafting, and many companies start there. Agents earn their place where work runs across several steps and systems.

Where should an agentic AI transformation start?
With one outcome, one workflow and one team, chosen so the first agent is live within a quarter. Write down the workflows you decide not to automate yet, so the list stays usable.

How long does an agentic AI transformation take?
There is no reliable benchmark. Microsoft's supply-chain programme ran from September 2025 to August 2026 with a cross-functional team of more than 150 people and deployed more than 111 agents. A first workflow in a single team can be live much sooner.

Choosing which workflow to redesign first, and what the agents around it may access, is where our AI First Organization engagement starts. Book 30 minutes and we will look at your first three.