Summary

• HyperFinity says 91% of retailers feel pressure to adopt AI, while only 46% have a defined strategy backed by value cases.
• Retailers expect AI to move from analysis into operational decision-making across customer service, inventory, replenishment, pricing, and promotions.
• The gap is skills, governance, value measurement, and operating-model change, not access to AI tools.

HyperFinity says UK retail decision-makers are under mounting pressure to adopt AI, but many still lack the strategy, skills, and governance needed to turn that pressure into measurable commercial value.

The Leeds-based retail actionable intelligence company surveyed 200 retail decision-makers and found that 91% feel moderate or significant pressure to adopt AI to remain competitive. Only 46% said they have a well-defined AI strategy supported by clear value cases, while 42% have identified potential use cases but remain uncertain about the commercial value they will deliver.

The findings capture a familiar shift in enterprise AI adoption. Retailers have largely moved past the question of whether AI belongs on the agenda, but many are still working out how it should change trading, merchandising, loyalty, inventory, pricing, customer operations, and decision-making cadence.

More than half of respondents described AI as one of the most important initiatives in their business, while competitor activity is influencing roadmaps for a further 39%. Competitive pressure can force organisations to modernise data and decision-making, although it can also lead to hurried programmes where tools are deployed before teams understand which decisions they want to improve.

Retail is especially exposed to that risk because AI can appear useful almost everywhere. Customer service, product recommendations, demand forecasting, marketing, media spend, promotions, replenishment, pricing, fraud, and workforce planning all offer plausible use cases. Without a disciplined strategy, pilots can spread across departments without changing the operating model.

HyperFinity’s research suggests agentic AI is now entering those discussions. Only 27% of respondents said their teams are fully prepared for agentic AI deployment, while a quarter described their workforce as only somewhat ready and 5% said they were not ready at all. That skills gap matters because agentic systems are not simply reporting tools. They can recommend, sequence, or automate parts of decision-making.

The study found that 83% of retailers believe AI will either lead decisions or automate them across retail operations within the next year. Half expect AI to lead operational decisions while humans provide oversight and strategic direction, while 33% believe AI will automate most trading, customer, and operational decisions with minimal human involvement.

Role-by-role differences show where confidence breaks down. Ecommerce directors were the most cautious, with only 9% expecting AI to automate most decisions, compared with 42% of chief data officers and 35% of chief customer officers. Teams closest to digital trading execution appear less ready to hand over decisions than colleagues looking across data or customer strategy.

The strongest retail AI use cases may not be the most dramatic. Customer service and inventory replenishment are likely early candidates for agentic AI because they contain repeatable tasks and clear operational signals. Pricing, loyalty, promotions, and customer engagement are more complex because they require commercial judgement, brand context, margin trade-offs, and a view of long-term customer behaviour.

Automation can reduce manual effort, but optimisation changes commercial outcomes. A retailer that automates poor decisions faster will not become more competitive. The gain comes when AI improves the quality, timing, and evidence base of decisions that affect margin, stock availability, customer retention, and promotional effectiveness.

The next phase of retail AI will be less about buying tools and more about redesigning decision systems. Retailers need clean data, accountable owners, test-and-learn methods, governance over automated decisions, and measurement frameworks that separate genuine commercial lift from activity. The pressure to adopt AI is widespread. The returns will go to retailers that can slow the hype down enough to build better decisions into daily operations.