AI is no longer a future-facing experiment in retail and is increasingly embedded in how retailers and brands plan, decide, and execute. Yet as organisations rush to adopt new capabilities, a critical gap is emerging. Some are turning AI into a real operating advantage, while others remain stuck in cycles of pilots and proofs of concept.
The opening panel at Retail Innovation Forum London 2026 surfaced a clear reality in that innovation readiness is not about access to AI tools. It is about whether the foundations, behaviors, and partnerships are in place to turn AI into everyday decision-making power. Across retailers, brands, technology providers, and investors, the conversation converged on a shared conclusion. Readiness, not technology, is now the real differentiator.
Below are six themes that emerged.
AI Readiness is built on data discipline, not speed
“Data for the sake of data isn’t enough. The real value comes from connecting data into insights and decisions.”
Successful AI adoption starts long before any model is deployed. Organisations that are making progress today are those that have invested in data that is clean, connected, and fit for decision-making. Speed of adoption matters far less than confidence in the inputs shaping outcomes.
AI acts as a multiplier. Where data is trusted and well governed, it accelerates insight and execution. Where it is fragmented or poorly understood, it exposes gaps quickly. Product inconsistencies, incomplete customer views, and undocumented operational assumptions surface the moment AI is asked to operate in real workflows.
What this means: AI readiness is ultimately about decision-grade data. The organisations that move from experimentation to scale are those that can rely on their data to support real decisions, not just analysis.Having data is not the same as being ready to use it
“There can be confusion between volume and AI readiness. We’ve got lots of data, but is it in a state we can actually use?”
An organization may assume it is AI-ready because it has accumulated large volumes of data. In practice, volume often hides complexity rather than capability.
Critical knowledge is scattered across manuals, PDFs, internal documentation, emails, and frontline experience. Taxonomies vary by function with context lost between systems. Translating this reality into AI-ready inputs requires deliberate effort, shared ownership, and ongoing maintenance.
The challenge becomes even more acute for real-time use cases. When AI is expected to operate at the moment of interaction or execution, ambiguity and latency are no longer acceptable.
What this means: Readiness is less about how much data exists and more about whether information is structured, contextualised, and accessible at the point of decision.Use case discipline is the antidote to pilot fatigue
“Ninety‑five percent of GenAI pilots fail, but we keep going nonetheless.”
Across the industry, there is growing fatigue with AI pilots that never reach production. In most cases, the failure is not technical. It stems from unclear ownership and weak alignment to real business decisions.
AI creates value when it improves a specific decision that someone owns and uses regularly. When initiatives are framed as experiments rather than decision infrastructure, adoption stalls. When AI is embedded into existing workflows, momentum builds and scale follows.
What this means: Readiness is demonstrated through disciplined prioritisation. The signal of maturity is not how many pilots are launched, but how many AI capabilities become part of daily operations.Culture and operating models determine adoption
“AI is no longer future‑facing. Organisations are now thinking about how they move beyond experimentation into embedding.”
Even with strong data and clear use cases, AI struggles to take hold without cultural readiness. AI changes how expertise is expressed, how questions are asked, and how decisions are justified.
Organisations are navigating new tensions around accountability, trust in outputs, and evolving roles. Teams must learn not only how to use AI, but how to work alongside it. Leadership plays a critical role in setting expectations, encouraging learning, and making it safe to adapt ways of working.
What this means: AI readiness is an organisational change journey. Training, communication, and clarity around decision ownership matter as much as model performance.Trust and governance are now core readiness requirements
“Customers are prepared to accept AI, but they need to be able to trust the retailer.”
As AI increasingly influences customer-facing decisions, trust has moved from a compliance concern to a source of competitive advantage. Customers expect relevance, transparency, and respect for privacy. Internally, teams need clarity on accountability, explainability, and appropriate use.
Governance frameworks that are overly restrictive slow progress. Those that are too permissive risk eroding trust. The most effective approaches treat governance as an enabler of speed, scale, and confidence rather than a barrier to innovation.
What this means: Responsible AI is not optional. It is a prerequisite for sustained value creation and long-term differentiation.Innovation Readiness Is Increasingly an Ecosystem Capability
“In a world of agentic AI and beyond, community is the new oil.”
One of the clearest signals from the discussion was that few organisations can build AI readiness alone. The pace and complexity of change are pushing innovation beyond the boundaries of individual enterprises.
Retailers are partnering to accelerate data modernisation. Startups are pressure-testing solutions against real operational constraints. Investors are prioritising teams that understand deployment and adoption, not just technical novelty. Increasingly, the fastest learning and the lowest risk come from ecosystem collaboration.
This is why open innovation models matter. Ecosystems create shared signal on what works, reduce duplication, and shorten the path from experimentation to scale.
What this means: Readiness is no longer just an internal capability. It is shaped by the partners, platforms, and communities an organisation chooses to engage.
In Summary: Readiness is the real differentiator
The opening panel at Retail Innovation Forum London 2026 reinforced a simple truth. AI readiness is not a moment or a milestone. It is an operating capability.
Organisations that invest in data foundations, align AI to real decisions, prepare their people, and engage the ecosystem will move from pilots to impact. Those that do not risk being trapped in perpetual experimentation while the industry moves on.
We would like to extend our sincere thanks to the leaders who contributed their time and perspectives to the Innovation Readiness panel at Retail Innovation Forum London 2026. Their collective experience across retail, consulting, and technology helped ground the conversation in practical realities and shared learning.
Panel participants:
Tom Kemp, Head of Group Innovation, Tesco
Sanjay Nand, Head of Applied Innovation, Capgemini
Cem Kent, Co‑Founder and CEO, Harmonya
Oliver Crowley, Head of Data and Analytics, Ecrebo
Anthony Lye, Chairman and CEO, Quid
Moderator:
Sandra Stanley, Chief Data Science Officer, dunnhumby
Thank you to all involved for contributing to a thoughtful, experience‑led discussion on what it truly takes to move from AI experimentation to enterprise‑wide impact.
Continue the Conversation at Retail Innovation Forum Americas 2026
For those interested in exploring more insights like these and continuing the conversation on innovation readiness, ecosystem collaboration, and the future of retail, we will be hosting the Retail Innovation Forum Americas 2026 on September 10, 2026 in Bentonville, Arkansas. The Forum will bring together retailers, brands, startups, and investors to share practical perspectives on how emerging technologies are reshaping decision‑making, operations, and customer experience across the retail value chain. Learn more about the event and how to participate at Retail Innovation Forum Americas 2026.
Innovation insights powered by the dunnhumby Retail Innovation Network.
The Network brings together retailers, brands, startups, and investors to turn emerging technology into practical, scalable impact. To stay connected and explore future Retail Innovation Forums, visit dunnhumby ventures.
Innovation insights powered by the dunnhumby Retail Innovation Network
Events like the Retail Innovation Forum showcase the strength of the dunnhumby Retail Innovation Network, the world’s fastest-growing open innovation community for retail technology. With members spanning retailers, brands, startups, and investors, the Network is driving meaningful progress across the industry. We extend our sincere thanks to all participants, speakers, and attendees who made this event a success.
To receive insightful thought leadership and engagement, explore joining the Retail Innovation Network, the fastest growing open innovation networks in retail, for free at dunnhumby.com/innovation and find out more about the next Retail Innovation Forum.
