Most companies deploy AI to cut costs. IKEA deployed AI to cut costs and accidentally discovered a billion-euro revenue stream hiding in the data their chatbot was collecting.
This is the story every CFO in every PE portfolio company should be reading right now. Not because of IKEA. Because of what it reveals about how most finance leaders think about AI — and how badly they’re getting it wrong.
The Setup
IKEA — or more precisely, Ingka Group, the largest IKEA retailer — built an AI chatbot called Billie. The brief was simple: handle level-one customer service enquiries. Reduce call volumes. Cut costs. The standard playbook.
Billie did its job. From 2021 to 2023, it resolved roughly 47% of customer enquiries it received — around 3.2 million interactions handled without a human, saving an estimated €13 million.
If you’re a CFO, that’s a clean win. Cost out, efficiency up, ROI positive. You’d put it in the board pack and move on.
IKEA didn’t move on.
The Signal Nobody Was Looking For
The interesting number wasn’t the 47% that Billie resolved. It was the other 53%.
When IKEA’s team analysed the enquiries Billie couldn’t handle, they found something unexpected. A huge proportion weren’t complaints or order issues. They were customers asking for help designing their homes.
People were calling IKEA — a furniture shop — and saying: I’ve got this room. What should I do with it?
This wasn’t in anyone’s business case. No strategy deck had “launch a design consultancy” on the roadmap. It was a signal buried in the noise of customer service data, and it would have stayed buried if someone hadn’t been paying attention.
The Pivot
Here’s where it gets good.
Instead of just improving Billie’s resolution rate — the obvious move, the one every consulting firm would have recommended — IKEA did something much smarter. They took 8,500 call centre workers and reskilled them as remote interior design consultants.
Read that again. Eight and a half thousand people. Not made redundant. Reskilled.
The AI handled the routine queries. The humans handled the high-value, creative, relationship-driven work that customers were already asking for. IKEA didn’t replace their workforce with AI. They promoted their workforce because of AI.
The result? Remote interior design sales hit €1.3 billion by the end of their 2022 financial year — 3.3% of Ingka Group’s total revenue. A brand new service line, created from a signal that existed in their customer service data all along. Their target is 10% of total sales in the coming years.
Why CFOs Get This Wrong
I’ve sat in enough board meetings to know how this story usually goes.
A CFO sees the AI chatbot business case. It says: deploy chatbot, save €13 million in customer service costs, payback in 18 months. They approve it. They monitor the cost savings. They report the efficiency gains. Job done.
That’s not wrong. But it’s incomplete.
The €13 million in cost savings is a rounding error compared to the €1.3 billion in new revenue. The chatbot wasn’t the product. The chatbot was a listening device.
Most AI business cases are framed as cost reduction exercises. Automate this process. Eliminate these headcount. Reduce that cycle time. And they work — the savings are real. But they’re also the least interesting thing AI can do.
The interesting thing is what AI reveals about your customers when you stop looking at it as a cost tool and start looking at it as an intelligence tool.
The PE Angle
If you’re a PE operating partner reading this, think about your portfolio.
Every portfolio company has customer service data. Most of it sits in a ticketing system that nobody reads except the support team. Some of it gets summarised in a monthly report that the board glances at between the P&L and the cash flow forecast.
What if that data contains the same signal IKEA found? What if there’s a billion-euro service line hiding in your Zendesk tickets?
The companies that will win the next decade aren’t the ones that use AI to do the same things cheaper. They’re the ones that use AI to discover things they didn’t know their customers wanted. That’s a fundamentally different value proposition — and it requires a fundamentally different kind of CFO.
The Kind of CFO That Catches This
The old-school CFO sees AI as a line item. A cost to manage, an efficiency to capture, an ROI to calculate.
The new-school CFO sees AI as an intelligence layer. Every automated interaction is a data point. Every pattern in that data is a potential business model. Every customer service complaint is a market signal.
IKEA didn’t need a McKinsey engagement to discover the design consultancy opportunity. They needed someone who looked at the chatbot’s failure cases and asked: why are these people calling us?
That’s not a technology question. It’s a business question. And it’s the kind of question that CFOs — with their bird’s-eye view of costs, revenues, and customer patterns — are uniquely positioned to ask.
The Uncomfortable Truth
Here’s what makes this story uncomfortable for a lot of finance professionals.
The €13 million saving was predictable. You could model it in advance, put it in a business case, and track it against plan. That’s the kind of AI outcome that finance teams are comfortable with.
The €1.3 billion revenue stream was unpredictable. It emerged from the data. Nobody forecast it. Nobody budgeted for it. It required curiosity, not spreadsheets.
If your AI strategy only captures the predictable value, you’re leaving the transformative value on the table. And in a competitive market, someone else will find it first.
What To Do About It
Three things, starting tomorrow:
1. Audit your AI for signals, not just savings. Every AI tool in your business is generating data about customer behaviour. Who’s reading it? What patterns are emerging? If the answer is “nobody” and “we don’t know,” you have a blind spot the size of IKEA’s design consultancy.
2. Look at the failures, not just the successes. IKEA’s breakthrough came from what Billie couldn’t do. The 53% failure rate wasn’t a problem to fix — it was a market to serve. What are your AI tools failing at? Those failures might be your biggest opportunities.
3. Stop framing AI as a cost play. If every AI business case in your portfolio starts with “reduce headcount” or “automate process,” you’re optimising for efficiency while your competitors are optimising for discovery. The cost savings are table stakes. The revenue signals are the game.
Mark Hendy is a PE-facing CFO and founder of Tanous Limited. He writes about the intersection of AI, finance, and business transformation at [markhendy.com](https://markhendy.com).
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