For most of the last decade, artificial intelligence has played a supporting role in business. It helped organisations analyse performance, forecast demand, and make smarter recommendations faster than before. Useful, certainly. But for all its progress, one line stayed pretty much intact: AI told you what to do. People still actually did it.
That’s starting to change.
A new breed of systems — what people are increasingly calling agentic AI — is pushing artificial intelligence from a tool for insight into something that actually takes action. Not advising on what should happen. Doing it.
That’s not an incremental shift. It’s a structural one.
From decision support to execution
What makes agentic AI different isn’t that it’s smarter. It’s that it’s more operational.
These systems are built around objectives, not prompts. Give one a goal and it breaks it into steps, interacts with software, and adjusts based on what happens — without someone managing every move. Where traditional AI might spit out a recommendation, agentic systems increasingly carry out the workflow itself.
For the first time, AI is starting to sit inside the execution layer of a business, not just alongside it.
Why now?
This hasn’t happened because of one big breakthrough. It’s several things converging at once.
Today’s foundation models can reason across multi-step tasks rather than producing one-off outputs. Enterprise systems have become far more accessible through APIs, so AI can actually interact with the operational guts of a business. And improvements in memory and context mean these systems can maintain continuity over time, rather than starting from scratch with every interaction.
Put those things together and you get AI that can move beyond isolated tasks and into ongoing workflows. That’s the meaningful difference: it’s no longer just generating intelligence. It’s starting to participate in execution.
What’s already happening in practice
“Agentic AI” might sound like fresh jargon, but its effects are already showing up across industries.
Klarna’s AI assistant now handles two-thirds of incoming customer service queries, resolving them faster than human agents and operating at a scale that would otherwise require hundreds of additional staff. It hasn’t just improved efficiency — it’s materially changed the economics of their customer support operation.
At Amazon, AI has long been embedded in logistics, but the shift now is from optimisation toward coordination — influencing inventory placement, routing decisions, and increasingly real-time supply and purchasing calls.
In the Shopify ecosystem, merchants are using AI to generate storefronts, refine product listings, and dynamically adjust merchandising. Chunks of the commercial lifecycle are starting to run with minimal human involvement.
Closer to home, Gymshark is a good illustration of what this looks like for a mid-market business. They’ve used AI-driven systems to handle customer experience at scale and sharpen forecasting and inventory management — allowing a fast-growing brand to operate with a level of responsiveness that used to be the exclusive territory of much larger companies.
The common thread across all of these: AI isn’t just analysing or recommending anymore. It’s involved in getting things done.
A bigger shift than most businesses realise
Most of the conversation around AI right now is still focused on productivity — doing existing tasks faster or cheaper. That’s not wrong, but it misses the more important story.
The structural shift is this: AI is beginning to sit between businesses and the way work actually gets performed.
And that has a knock-on effect that’s easy to underestimate. As AI systems become intermediaries for decision-making and execution, they become intermediaries for commercial relationships too. Increasingly, customers won’t interact with organisations the way they do today. AI systems will evaluate options, compare offerings, and complete transactions on their behalf.
In that world, visibility isn’t just about marketing or brand strength anymore. It becomes a question of whether a business is machine-legible — whether its products, pricing, and processes can be understood and acted upon by external AI systems.
Organisations that aren’t set up for that risk becoming, effectively, invisible.
How adoption is actually playing out
Despite all of this, most organisations aren’t attempting a wholesale overhaul. The path to adoption is turning out to be more pragmatic than transformative, at least in the near term.
The typical starting point is somewhere bounded and measurable — customer service, marketing operations, internal reporting, select operational workflows. Places with clear inputs and outputs and well-defined ways to measure whether something’s working.
From there, capabilities tend to expand into adjacent processes. It’s evolutionary, not disruptive — but the direction is consistent.
What this means for leadership
For senior leaders, the more honest framing here isn’t about technology adoption. It’s about operating model design.
The real question isn’t whether AI can be introduced into the organisation — it obviously can. The harder question is where it can safely take on responsibility for execution. That requires clarity on process ownership, data quality, system connectivity, and governance frameworks that most organisations are still figuring out.
It also requires a genuine shift in how leaders think about AI — not as a productivity layer bolted on top of how things already work, but as a potential participant in operational delivery itself.
The organisations that handle this well probably won’t be the most aggressive adopters. They’ll be the most deliberate ones — clear on use cases, tight on boundaries, focused on outcomes they can actually measure.
Where this leaves us
The last wave of digital transformation helped businesses make better decisions.
The next wave will increasingly help them execute those decisions.
Agentic AI represents a move from augmenting intelligence to delegating operations. It’s early days, but it’s already reshaping how work is distributed — across teams, systems, and now software agents that act independently.
So the strategic question isn’t really whether this becomes widespread. It will. The question is how quickly your organisation is ready to restructure around it.
Because as execution becomes automated, competitive advantage will depend less on having access to good information — and more on the ability to actually act on it.
