Ingka Group has made meaningful progress in defining what AI should achieve - demand forecasting, personalisation, store operations and co-worker support are named priority domains. However, the translation from group-level ambition to unit-level operating intent varies significantly across markets and formats. Leaders in different geographies describe their AI priorities in different terms, and it is not always clear how local AI initiatives connect to the group value framework. The deliberate boundary-setting question - where AI should not play a role - remains underdeveloped.

Which three specific business outcomes should your AI investments be measurably improving by the end of 2026 - and who is accountable for each?

Ingka Digital and the Group AI function provide a structural home for AI development, and there are governance frameworks for responsible AI use. However, in practice, AI capability decisions at the operational level - how store managers adopt AI tools, how HR integrates AI into people processes, how co-worker capability is built - remain informally steered. Investment prioritisation between AI initiatives is not always visible across the organisation. Leadership modelling of AI curiosity is present at senior levels but inconsistent in the middle management layer that determines everyday adoption.

Who has real accountability for AI capability development at the market level - and does that person have the mandate, resources and visibility to act on it?

Ingka has invested substantially in AI tools and platforms - from demand forecasting and route optimisation to co-worker support systems. But the redesign of work to match these tools has lagged behind. In most cases, AI is being bolted onto existing workflows rather than used to rethink how work is structured. The organisation has not yet developed a systematic approach to mapping the human capabilities its people need to work effectively alongside AI, and learning and development investments are not reliably connected to the AI adoption journey. The question of where human judgement must remain central - in customer interactions, in people decisions, in value interpretation - has not been resolved across the organisation.

For the three roles most affected by AI in Ingka's operations, what does the redesigned version of that work look like - and who is responsible for designing it?

Ingka Group has invested significantly in its data architecture - from customer data platforms to store-level operational data. This creates a genuine foundation for AI-enabled work. The data governance practices at group level are mature relative to peers. However, accessibility and trust at the market and store level remain uneven: in some markets, store managers and co-workers report not being able to find or trust the data they need to make AI-informed decisions. Institutional knowledge - the practical know-how that resides in experienced co-workers - is not systematically captured, creating fragility when people move on.

How does a store manager in a mid-size market access the data and knowledge they need to make an AI-informed decision today - and what breaks in that process?

Ingka's cultural foundation - the emphasis on togetherness, learning and doing things differently - creates genuine psychological safety for experimentation in many parts of the organisation. Co-workers are generally willing to engage with new ways of working when they understand the purpose. However, the way AI is being communicated and introduced varies widely: in some markets it feels collaborative and co-designed, in others it is perceived as a top-down mandate. The learning culture that Ingka values is not yet consistently applied to AI adoption. The feedback loops that would allow the organisation to learn from both successful and unsuccessful AI experiments are not systematically in place.

How does Ingka distinguish between co-workers who are genuinely adopting AI capability and those who are performing adoption - and how does the organisation learn from both?

At Ingka Group's scale - 476 stores, 31 countries, multiple formats - measuring the business value created by AI investments is structurally difficult. The organisation tracks AI activity (adoption rates, tool usage, deployment progress) but has limited visibility of AI outcomes (what specific business value those activities are creating). The difference between an AI tool being used and an AI tool creating sustainable business value is not consistently measured. AI outcomes are reviewed at the digital/technology level but are not reliably visible at the leadership level that makes investment decisions. As a result, the feedback loop between AI investment and strategic learning is slow and incomplete.

If Ingka's AI investments from 2023 to 2025 had to justify themselves in terms of measurable business value today, what evidence exists - and who in the organisation can access and interpret it?

Establish a cross-market AI Capability Architecture function with real mandate - not just a central AI team, but a network of people responsible for building the human and work conditions that make AI valuable at Ingka's scale.

Prioritise work redesign over tool deployment. For the three to five roles most materially changed by AI, design what that work looks like - with the people who do it, not just for them.

Close the value realisation gap before expanding AI investment. Define what business value looks like for Ingka's top AI priorities, agree who is accountable for measuring it, and build a leadership-level visibility mechanism.

Build market-level AI leadership capability. The group-to-market translation gap is the most operationally critical constraint. Middle leaders in each market need the capability, mandate and data to make AI capability decisions locally.

Create a co-worker learning system for the AI era - not just training courses, but reflective practice cycles, experiment-and-learn structures, and honest feedback mechanisms that tell the organisation what is actually working.

Deana Nannskog

Capability Architect - Founder, Kin Innovation Agency

nannskog.com

AI Capability Pulse - Ingka Group - June 2025

This is a simulated case study for demonstration purposes.