Earlier this year, IKEA's Chief Digital Officer Johanna Roos said one thing in a McKinsey interview that stood out: "The risk of trying to do everything and ending up doing nothing." That's a surprising line coming from the person overseeing digital transformation at a company operating more than 400 stores across 60 countries. At that scale, you'd expect a dedicated AI team and a budget for outside consultants to spare. Yet the question she raised first wasn't about capability — it was about sequencing. What do you do first?
Agentic AI is the technology direction global companies are racing to experiment with right now. It carries out multi-step tasks on its own, connects to outside systems, and makes judgment calls as it executes. A chatbot can answer "What's your return policy on this item?" Agentic AI takes that same question, pulls up the customer's purchase history, checks the shipping schedule, and completes the return — no handoff required. IKEA is on a journey to apply this across its customer experience. But the first wall the company hit wasn't a technical one.
Even 400 Stores Couldn't Dodge the Priority Problem
The most consequential part of IKEA's CDO's McKinsey interview wasn't about agentic AI as a technology — it was her answer to how the company decided what to roll out first. Once you adopt agentic AI, a flood of seemingly possible use cases shows up all at once: automated customer service, inventory forecasting, delivery management, personalized product recommendations, employee training support. Even at IKEA's scale, pursuing all of it simultaneously wasn't an option. The CDO put her resources into establishing criteria for what to choose first.
That choice isn't just about technical feasibility — organizational culture, data readiness, and customer trust all factor in together. Agentic AI accesses data, calls external systems, and sometimes makes judgment calls involving customer information. A single misstep in that process hits brand trust directly. IKEA has said that customer experience quality and maintaining trust come first. Which AI features get switched on is decided underneath that standard.
Once agentic AI is live, it creates a web of agents connected to other agents. One agent checks inventory, another coordinates delivery schedules, and a third sends a message to the customer — a chain reaction. For that chain to function properly, data quality and system stability need to be solid at every connection point first. That technical reality is the backdrop for the CDO's warning about the risk of trying to do everything and ending up doing nothing.
The Gap in Scale Doesn't Cancel Out the Lesson
There's a fair objection here: you can't put IKEA's infrastructure on the same footing as the reality facing a solo entrepreneur or a one-person PM operation in Korea. IKEA has a data engineering team, a CDO, and a budget for outside consultants. Telling someone running a business alone to "build an agentic AI strategy" can sound hollow. Dig deeper and IKEA is a company that has been accumulating data for decades. When its agentic AI checks inventory, it's built on standardized product codes and a global logistics database. For someone working solo, building that foundation in the first place can be a far longer, harder task on its own. A simple comparison — "if IKEA can do it, so can we" — creates an illusion that makes the barrier to execution look lower than it actually is.
But the priority confusion IKEA ran into isn't proportional to scale. The smaller the organization, the less room there is for error, so a wrong call on sequencing turns fatal faster, not slower. If you're a solo PM who tried using Claude or GPT to automate customer service first, only to find months later that it hasn't actually cut your working hours, the CDO's warning won't feel unfamiliar. The more AI tools flood the market, the more that not-knowing-where-to-start feeling shows up — and it shows up regardless of organizational size.
What a Solo Operator Should Check First Before Using Agentic AI
So where can you actually start?
The real value of agentic AI lies in delegating repetitive, multi-step work. For someone working alone, the biggest time sinks usually cluster in three areas: gathering information, drafting content, and organizing internal communication. Building an agentic AI workflow around just one of these first is the practical version of the "what do you choose first" question the IKEA CDO raised.
If you have a weekly task of compiling industry news and sending it to clients, that's a good candidate for handing the entire flow to agentic AI. Have the AI gather information by keyword, summarize it, and produce a formatted draft — then you just review and send. Expanding into another work area before this one is fully stable puts you in the exact situation the CDO warned against. Running several things at once while one of them isn't working properly makes it nearly impossible to tell where the failure is coming from.
As AI tools get more capable, the skill an individual needs more of isn't technical fluency with the tools themselves. It's the ability to design which tasks get delegated to AI and which judgment calls stay with you. The fact that a CDO leading an organization with hundreds of AI specialists personally set the priority criteria points in this direction. The more complex the chains of tasks AI carries out, the more the quality of the output comes down to the judgment of the person who designed that chain in the first place. I'd argue this is the most practical skill an individual needs in the age of agentic AI.
IKEA's agentic AI journey looks like a flashy transformation story from a global company, but the question at its core applies just as directly to a small operation. What do you do first, and what do you put off? Get that judgment wrong, and even good tools pile up without direction. The more work you can hand off to AI, the more the work left for a human narrows down to one thing: deciding what gets handed off first.
