What does Adaptive Edge do?

Adaptive Edge is a UK strategy and innovation consultancy. We help organisations decide what to change, then make the change real: strategy built with the people who must carry it, AI adoption inside real work, and behaviour change that sticks.

How is Adaptive Edge different from a traditional strategy consultancy?

We do not hand over a deck and leave. Strategy is made in working sessions with your own teams, so the people who must deliver it own it from day one. We prove an approach in one team before asking the whole organisation to change.

Who does Adaptive Edge work with?

Global organisations across healthcare, industrials, media, retail technology and financial services, including ITAB Group, Freudenberg, Sky and Sandvik Coromant. Engagements run across Europe and beyond, remote and on site.

How does Adaptive Edge approach AI adoption?

We put AI to work inside real workflows rather than running abstract training. Teams learn on their own live work, we measure what actually changes, and we scale only what proves useful.

What does a typical engagement look like?

Most engagements combine focused research, co-created working sessions, and hands-on tools built for your context. They range from a single workshop to a multi-phase transformation programme.

How do we start a conversation?

Use the contact form on the site. We usually begin with a short call to understand the decision you are facing, and we will tell you plainly if we are not the right fit.

AI Transformation Is Not a Technology Project

By Nathan Waterhouse · 2026-08-21

In 2021, Ingka Group, the largest IKEA franchisee, deployed an AI assistant called Billie to handle customer service enquiries. Two years later Billie was handling nearly half of them, around 3.2 million conversations, worth roughly 13 million euros in operational savings, as reported by PYMNTS. The savings are not the story.

The story is what happened to the people. Ingka retrained around 8,500 call centre workers as remote interior design consultants, with a reskilling programme covering digital sales, room planning and relationship management. The remote customer channel those consultants staff was generating 1.3 billion euros by the end of fiscal 2022, about 3.3 per cent of total sales, with a target of 10 per cent by 2028. In fiscal 2025 their teams helped more than 73,000 customers plan kitchens and rooms remotely, and the wider business is partway through a 30,000-person AI literacy programme. A cost centre became a sales channel.

The nature of demand

Steve Baker of LTI Associates, who I have worked alongside for years, sent me the case after a client of his raised it, and his first reaction gave me the most useful lens on it I have heard. It took him straight back to his Lean days in call centres twenty years ago, where the first diagnostic question was always the same: what is the nature of demand in this system? What is the number one reason people call? Almost nobody could answer it. The measures were all inputs, call volumes and handle times, and finding out what the calls were actually about meant asking agents to codify every conversation or paying someone to listen to hundreds of recordings.

AI removes that constraint. Every call centre in the world already records its calls "for training purposes", and almost none of them read the recordings as research. Ingka is the rare company that acted on both sides of the question at once: an assistant came to absorb a large share of contacts, while the same workforce and the same customer conversations became the foundation of a design-help business. The public record shows the two moves running side by side rather than one being discovered inside the other; it also shows what holding both questions can yield, an efficiency and a business. Organisations that ask their data only "where can we save money" will reliably find the deflection and miss the need, because the need appears only when you ask what customers are struggling to get done.

The reversal most organisations run

The first question I hear in client rooms about AI is almost always some version of "which tasks can it take". The second question, what the people do instead, tends to arrive much later, after deployment, and it arrives framed as a problem. Deploy the technology, bank the savings, then work out what to do with the capacity you have freed. Ingka ran the sequence the other way round. The destination for the freed capacity was designed first; the AI then freed it.

The gap between those two sequences shows up in the numbers. PYMNTS Intelligence found that half of CFOs expect AI to create new roles requiring new skills, while only 12 per cent feel very prepared to manage the workforce transition. Read those two figures together. Most finance leaders can see the change coming, and almost none of them believe their organisation is ready to shape it. The deployment side of AI gets the budget, the steering committee and the vendor beauty parade. The transition side, deciding what people become, gets a slide near the end of the deck.

Technical work and adaptive work

Ronald Heifetz's distinction between technical and adaptive challenges explains why the sequence matters so much. I keep returning to it, most recently in Why Design Thinking Needs an Upgrade for the AI Era, because it separates the two kinds of work an AI deployment contains. Choosing a model, integrating it, rolling it out: technical work, solvable with expertise you can hire. Turning 8,500 service agents into a revenue-generating design channel: adaptive work. New skills, yes, and also new decision rights, new measures of success, and a shift in identity. People whose job was absorbing complaints became people whose job is selling kitchens. No vendor can deliver that, and no central programme office can do it to people from a distance.

The last step of the leap deserves its own look, because it was the bravest one. Ingka could have released 8,500 call agents and hired interior designers instead. They chose to retrain, and that choice makes sense in the light of how IKEA has always hired: for cultural fit and behaviour ahead of technical expertise. Teaching kitchen planning to people who already carry the culture is a smaller bet than finding thousands of designers who fit. Organisations that treat people as interchangeable containers of skills default the other way, swapping people rather than growing them, and will read this story as an oddity rather than an option.

Organisations that treat AI transformation as a technology project do the technical work well and leave the adaptive work to chance. The result is familiar to anyone who has watched a transformation programme from the inside: the tool arrives, the savings case is declared met, and the organisation is smaller but no more capable than before. The capacity the technology freed was spent rather than invested.

What standing capability looks like

When leaders ask me what investing that capacity actually involves, I describe four things a team should still have after the consultants leave.

An operating rhythm. Experiment loops that run on a fixed cadence, weekly or fortnightly rather than quarterly: pick a live assumption, test it, read the result, decide, go again. The rhythm is the capability; a one-off sprint is just an event.

Instruments the team keeps. The tools used in the engagement become the client's tools. A global medical technology company we worked with through three phases of strategy work ended up not with a report but with a self-serve toolkit; their teams now run their own strategy activation sessions without us in the room. That is the test worth applying to any capability claim: what runs when the advisers are gone?

People who own it. During an engagement we facilitate. Before the end, someone inside facilitates and we coach. The handover is not a document; it is a person who can run the loop and knows they are allowed to.

A visible measure. Something the organisation can watch move: alignment across teams tracked over time, or how long decisions take to reach the person who can make them. Without a measure, capability is a feeling. With one, it is a management fact that survives a change of sponsor.

The demand behind the Ingka story was read once, decisively. The standing version of that is a rhythm of its own: a monthly reading of what customers are asking for, what is shifting, what the tone of the conversations is telling you, with time in the diary to decide what it means. Steve framed the challenge underneath that as three questions. Will you make the time to look? When the message is inconvenient, can you allow yourself to hear it? If you hear it, are you prepared to change the organisation in response? Most transformation programmes fail on the second or third, long after the dashboard is built.

Ingka's own answer is visible in what they chose to count. The 13 million euros Billie saved is a footnote in their reporting next to the 1.3 billion euros the retrained teams sell and the 73,000 customers those teams served last year. They measure the new capability rather than the absence of the old cost.

The question that comes before deployment

None of this argues against the technology. Billie is a good AI assistant, and choosing it well was necessary. Necessary is not the same as sufficient, and the organisations that get this right are distinguished by a question they can answer before the deployment starts rather than after it finishes: what does the freed capacity become?

If the only answer is "savings", the deployment will still work, and you will get a smaller organisation with the same habits it had before. If the answer names a destination, new work the freed people will do and the capability they will need to do it, then the same deployment buys you something compounding. Helping organisations answer that question, and build the standing capability to keep answering it as the technology keeps moving, is the work we do at Adaptive Edge.