Between December 2025 and January 2026, McKinsey circulated an in‑depth questionnaire to 27 C‑suite executives across the European consumer industry—covering retail, consumer packaged goods, apparel and fashion, and related consumer services—to learn about AI strategies, investment priorities, organizational readiness in talent, data and technology, and progress toward scaling AI. Most respondents are CIOs or CTOs, nearly a quarter are chief data or chief data and analytics officers, and 16 of the 27 work at enterprises employing more than 20,000 people.
The responses show a notable paradox: 23 of 27 executives report increased AI activity over the past year and none report cutting back, yet only six say AI initiatives have delivered at least a 1 percent EBIT impact, and more than half answer that it is still too early to assess financial effects.
That tension—rapidly growing ambition but limited measurable financial outcomes—may define the sector’s near‑term AI journey. Companies are experimenting across the value chain, but many have not yet developed the capabilities required to turn experimentation into enterprise‑scale impact.
Ambition, budgets and where AI is being used
Ambition is high: more than half of respondents describe their three‑year AI ambitions as either “significant” or “transformative.” Investment intentions reflect that: 22 of 27 plan to increase AI spending over the next year. Most expect budget rises of 10–30 percent; five anticipate increases of more than 30 percent. Current spend is already material: over half allocate at least 5 percent of their digital budgets to AI initiatives, and more than a third allocate between 10 and 30 percent.
AI activity spans many domains. Marketing and growth are the most common focus (19 respondents), followed closely by software development and technology operations (18 and 17 respondents respectively). Demand forecasting, customer experience, pricing and promotions, supply‑chain operations and product development also frequently appear in organizations’ AI portfolios.
Strategy alignment and the pilot trap
Strategic alignment often lags behind activity. Ten of 27 leaders describe their AI strategy as “developing”—parts of the organization have strategies but no integrated enterprise approach. An equal number call their strategy “emerging,” meaning early discussions or initial plans are under way. Only a small minority report a fully established strategy with clear value targets and a prioritized roadmap.
Many organizations have built sizable pipelines of AI initiatives but struggle to scale them. Eight respondents manage portfolios of ten to 20 projects; six report more than 50. On average, only about 10 percent of initiatives have reached scaled deployment. Many projects remain in proof‑of‑concept or development stages, and more than a third have not even started—creating a broad but shallow pipeline that has not yet converted into enterprise‑scale impact.
Capability gaps: talent, literacy, data and technology
Execution capability appears to be the main constraint. Talent is the most significant bottleneck: only nine respondents say they have the right people in place to develop and scale AI effectively. The challenge is often assembling the right mix of data scientists, machine‑learning engineers, product managers and domain experts rather than simply replacing workers.
AI literacy across the wider workforce has also not kept pace. Nearly two‑thirds describe organizational AI literacy as developing—structured training exists but adoption is uneven. Only two respondents report widespread day‑to‑day AI use across their organizations.
Data infrastructure and technology platforms remain major limitations. Three‑quarters rate data infrastructure below the desired maturity level; technology infrastructure shows a similar pattern, with only one respondent saying their systems are robust enough to support enterprise‑scale AI deployment.
One positive sign is that nearly half report strong alignment between business and technology teams, although a quarter still report significant misalignment. Given AI’s cross‑functional nature, this collaboration will be crucial to translating investments into outcomes.
Financial outcomes, risks and regulation
Financial results mirror the deployment pattern: more than half of respondents say it is too early to determine whether AI has had a measurable impact on EBIT. Among the nine who report impact, only two cite substantial financial gains (5–10 percent EBIT improvement). Where value has appeared, it most often comes from operational efficiency—cost reduction was the most commonly cited benefit—followed by improvements in customer satisfaction and innovation.
Respondents are actively managing risks around cybersecurity, data privacy, regulatory compliance and output accuracy. Workforce displacement, reputational risks and fairness concerns also appear on leadership agendas. So far, relatively few report significant negative incidents from AI deployments, likely reflecting early‑stage adoption and emerging governance frameworks.
Regulation will further shape AI’s adoption in Europe. The EU AI Act, phased in through 2026, introduces new rules for transparency, risk classification and governance. For customer‑facing consumer companies, those rules will affect personalization, pricing and workforce management. Leaders that approach compliance as a trust‑building opportunity rather than a mere cost may be better positioned as the regulatory environment matures.
Breaking the paradox: practical steps
McKinsey’s Rewired playbook lays out a pathway many leaders are following:
- Focus investment on the highest‑value use cases rather than spreading resources evenly. Leading organizations define a clear strategy, pick a small number of big bets to scale, and reserve a smaller share of resources for exploration (often following an 80/20 mindset).
- Make data and technology sufficient, not perfect. Rather than waiting to close every gap, prioritize what’s needed to support priority use cases, deploy quickly, learn and iterate—the speed of execution enables movement from pilot to scale.
- Embed AI into how work gets done. Successful organizations place AI talent inside business teams, invest in upskilling the broader workforce, and align incentives and ownership with business outcomes so AI becomes part of the operating model rather than a parallel effort.
- Scale in stages: prove value in targeted domains, scale what works, and invest over time in adoption, change management and leadership alignment—activities that often require more effort than the technology work alone.
Conclusion
AI has moved from the periphery to a strategic priority across most large European consumer organizations. Budgets are growing and applications span more parts of the value chain than ever before. Yet activity and investment alone are not producing widespread financial results. The next leaders will likely be those who concentrate on measurable outcomes and build the foundational capabilities—talent, data, technology and governance—needed to scale the initiatives that matter most.
Acknowledgements: the authors thank Astrid Oleander, Joe Boden and Kari Ringi for their contributions. The article was edited by Larry Kanter in the New York office.
This article contributes to McKinsey’s ongoing AI research and aims to help business leaders understand the forces transforming the consumer industry, identify strategic impact areas and prepare for the next wave of growth.
