Retail
whether AI creates value, but how quickly retailers can move beyond pilots and into operational advantage.
Trends and predictions for 2026 Based on our research, four shifts are likely to define how AI adoption evolves over the next 12 months. While these trends apply across the retail sector, they are particularly relevant for tech retailers, who face faster product cycles, more volatile demand, and greater competitive pressure. More than a quarter of retailers believe AI-driven supply chain
and logistics optimisation would have the most significant impact on their organisation. Yet in 2025, only around one in five had implemented it. That gap is set to close rapidly. Nearly seven in ten retailers plan to introduce AI-enabled supply
chain capabilities within the next 12 months, signalling a decisive shift from intent to execution. Adoption will not be uniform. Fashion retailers currently lead, with around a quarter already deploying these capabilities, compared with just 14 per cent of heritage retailers. General retailers show the strongest near-term momentum, with roughly two-thirds planning change within six months. Building and trade suppliers remain more cautious, with just over a third expecting to move at the same pace. For tech retailers, the implications are significant. Fast product
refresh cycles, global supply chains, and demand volatility mean logistics inefficiencies translate quickly into margin erosion and customer dissatisfaction. In 2026, many will be forced to move beyond isolated AI deployments and tackle more complex use cases spanning pricing, inventory, and logistics, even though these are harder to implement and govern.
Data integration becomes the real priority As retailers expand AI into pricing, forecasting, and supply chain operations, data integration moves from a technical concern to a strategic constraint. AI cannot deliver consistent outcomes if customer, inventory, pricing, and supplier data remain fragmented. In 2026, many retailers will prioritise integrating existing systems
over deploying additional AI tools. The limits of disconnected data are becoming harder to ignore, particularly where AI decisions rely on near real-time insight. This shift will be most pronounced in hybrid and multi-cloud
environments. Our data shows that fragmented architectures reduce confidence in the ability to integrate and scale AI, slowing progress on advanced use cases. For these organisations, simplifying data flows and reducing platform complexity will directly determine how far AI can be embedded into core operations. Retailers that treat integration as part of the AI programme itself
will operationalise advanced use cases sooner and with greater certainty. Those who continue to treat it as a prerequisite or parallel initiative will find ambition consistently outpaces delivery.
AI decision-making skills become a bigger constraint As AI moves closer to operational decision-making, the skills challenge shifts. The primary barrier will no longer be access to tools, but the ability to interpret outputs, trust recommendations, and act on them at speed.
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“Nearly seven in ten retailers plan to introduce AI-enabled supply chain capabilities within the next 12 months,
signalling a decisive shift from intent to execution.” Without deliberate governance, retailers risk either over-reliance
on AI outputs or reluctance to act on them at all. Both are problematic. This includes uncertainty around data sensitivity, personal information usage, and which datasets should be restricted for specific applications or roles. In 2026, retailers that invest in commercial and operational AI
literacy alongside technical capability will be better positioned to embed AI into pricing, forecasting, and supply chain decisions. Where this capability is absent, AI adoption will stall. Tools may exist, but leaders will hesitate to rely on them for high-impact operational choices, limiting AI to experimentation rather than execution.
Organisational alignment becomes the true differentiator Our research highlights clear differences in AI prioritisation across IT leadership roles. CTOs tend to focus on customer service automation, IT directors prioritise forecasting and analytics, and infrastructure teams emphasise integration readiness and platform simplification. Awareness and intent are high, particularly among teams closest to delivery. Yet competing priorities and siloed decision-making continue to
dilute impact. The challenge for 2026 is not enthusiasm, but coordination. Retailers that align IT leadership around shared commercial
outcomes will extract the greatest enterprise-level value from AI. Rather than running parallel initiatives, they will focus on a single objective: using AI to improve margin, availability, and operational performance at scale. Where this alignment is absent, AI progress will remain fragmented, even as investment continues. Looking ahead, success will come from tightly scoped use cases,
clean data, and clearly measurable outcomes. In 2025, many broad AI transformation efforts under-delivered. In 2026, boards should back smaller, well-defined initiatives that can be scaled once value is proven. AI delivers its greatest impact when it improves how a business
actually operates, not when it simply produces impressive demonstrations.
January/February 2026 | 7
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