IKEA’s Approach to Turning AI into Measurable Value

Estimated reading time: 5 minutes · Last updated: 26 September 2026

IKEA is steering its AI work toward measurable business outcomes across customer experience, operations and employee productivity, Parag Parekh, IKEA’s chief digital officer, told ISMG at the HumanX Conference on September 25, 2026. Parekh said the company ties each AI use case to commercial metrics — sales growth, cost reduction or employee hours saved — and treats that combined dollar impact as the single ROI yardstick. He outlined a split between generative models for design and planning, traditional models for fulfillment, and agentic AI for back-office automation. These priorities and examples were first reported by BankInfoSecurity.

I just combine the total dollar impact in terms of growth, or impact in terms of cost. That's the one metric we follow to say what's the impact of AI and how good the ROI is.

Parag Parekh, chief digital officer, IKEA

A single ROI metric: how IKEA links AI to business outcomes

IKEA treats return on investment as a financial linkage between technology and business impact rather than as a technology scorecard. Parag Parekh says the company adds together the dollar effect from sales growth, the savings from lower costs and the value of employee hours freed; that combined figure is the metric that decides whether an AI pilot scales. This approach forces teams to show a revenue or cost line item change tied to a deployment, or to quantify hours saved where the benefit is productivity.

That discipline shapes vendor selection, pilot scope and rollout tempo. Teams building recommendation engines or design tools must present before-and-after comparisons that map to basket size, conversion rates or planning cycle time. Back-office pilots using agentic AI are measured on staff-hours reclaimed and error reduction. The practical consequence is that projects without a clear dollar pathway are deprioritised, which shortens the list of candidate pilots and accelerates the ones with measurable payback.

Key metric in practice

By insisting on a single combined metric, IKEA avoids the common trap of treating model accuracy as a proxy for value. Parekh’s example reframes technical KPIs into finance-language outcomes: lift in sales, avoided spend and hours saved. That makes the case for scaling an AI use case part finance review, part product approval.

Build versus buy and driving adoption across a large workforce

Parekh described a pragmatic stance on whether to develop AI capabilities internally or to adopt external solutions: choose the path that delivers measurable value fastest. For commodity capabilities where time-to-value is short, IKEA favours buy; for differentiating features tied to core customer experience, it leans to build. The company evaluates total cost of ownership and the expected contribution to the combined ROI metric before committing to either route.

Adoption across thousands of employees and multiple countries depends on both measurable usage and demonstrable business value. Parekh emphasised that showing managers concrete improvements in daily workflows and team output is essential to achieving scale. That means instrumenting pilots to capture usage metrics, mapping features to job tasks, and reporting the resulting changes in hours spent or transaction throughput. Without those numbers, rollouts stall because local leaders cannot justify changing established processes.

Where IKEA draws boundaries: responsible AI and governance

IKEA pairs commercial tests with guardrails on privacy, fairness and company values. Parekh said certain choices belong off limits to automation where human judgement or candidate privacy should prevail, and that responsible AI principles guide those decisions. That governance covers data handling for customer-facing features, the use of AI in hiring and candidate assessment, and the conditions under which agentic systems may act autonomously on behalf of the company.

Embedding those boundaries requires cross-functional checks: legal and privacy teams sign off on data use, HR signs off on any assessment tools, and ethics or compliance functions advise on fairness. The result is an approval path that sits alongside the ROI gate: a use case must clear both business-impact thresholds and responsible-AI checks before IKEA will expand it.

Why IKEA’s approach could scale — and where it might stall

The bottom line

IKEA’s AI programme links technical experiments directly to business outcomes through a single combined ROI metric, and it balances speed with governance by choosing build or buy based on time-to-value. That setup channels investment to uses that demonstrably lift sales, cut costs or reclaim employee hours while keeping data privacy and fairness front of mind. For organisations deciding how to scale enterprise AI, IKEA’s model offers a practical template: demand a dollar pathway to scale, pair it with cross-functional sign-off, and accept that some long-term or strategic bets will need an alternative case to clear the ROI gate.

What to watch

• Watch for IKEA’s next public update or report on AI pilots; no date has been set.
• Watch for follow-up presentations from Parag Parekh at industry events after September 26, 2026; no date has been set.