Trade is tough.
Margins are tight.
Cost conversations still dominate boardrooms.
In that environment, AI can feel important, but perhaps not urgent.
Back in 2023, we explored how AI was unlocking powerful opportunities for customer-centric transformation, from personalisation and fraud detection to pricing optimisation and deeper consumer insight. All of that still holds true.
What’s changed is that Retail AI strategy has shifted from pilots and proofs of concept to structural transformation. The question facing leadership teams today is:
Are we embedding AI deeply enough to change our cost base and competitive position, or are we still experimenting while others move ahead?
What Is the Real ROI of AI in Retail
Retail leaders are currently laser-focused on labour inflation, shrinkage, logistics efficiency, colleague safety and working capital discipline.
Interestingly, those same pressure points are exactly where AI can deliver measurable value.
With leaders under growing pressure to prove ROI, AI has moved from being a “cool experiment” to something that needs to earn its place in the P&L.
According to KPMG’s 2026 AI in Retail report, 64% of consumer and retail CEOs now rank AI as a top investment priority.
This momentum has been building for some time. In 2024, PwC found that around 70% of CEOs believed GenAI would reshape how value is created in their industry.
Further research shows that global investment in AI is increasing, up 33% over the past 12 months. But at the same time, scrutiny has risen, with nearly two-thirds of leaders feeling pressure to demonstrate returns.
AI In a Cost-Constrained Market
McKinsey estimates that AI could potentially deliver a productivity lift of $400–660 billion in annual value to the retail and consumer packaged goods market, impacting everything from customer service to supply chain management.
What does this mean in practice?
In grocery → AI clusters deliveries geographically and adjusts time windows dynamically to reduce “empty miles.”
In fashion → It predicts sell-through rates, triggering smarter pricing decisions before margin erosion sets in.
The real challenge is leadership appetite.
Waiting for better trading conditions might lower short-term risk, it could also significantly widen the capability gap.
The early winners are already embedding AI directly into distribution, inventory management, and trading decisions. While the industry is moving fast, only a select few have truly reshaped their operating models.
Retail AI Adoption: UK vs US
There does seem to be a difference in tone across the Atlantic.
US retailers, often with greater scale and capital flexibility, appear more willing to invest ahead of certainty. In these organisations, dedicated AI leadership roles are becoming more common.
UK retailers, under sharper margin pressure, are understandably more cautious. Their focus tends to be more on immediate efficiency gains rather than broad-scale transformation.
That caution is valid. However, in my conversations with UK boards, the hesitation is because of timing. And yet, history suggests that downturns, particularly from the post-2008 period, are often when structural advantages are built.
Who Should Own AI in the Retail C-Suite?
This is where things become less straightforward.
In some organisations, AI sits under the CIO.
In others, a dedicated Chief Data & AI Officer is emerging.
In a smaller number, AI is CEO-sponsored and embedded directly into commercial strategy.
And the results of AI structures vary significantly too:
Where AI remains positioned purely as a technology initiative, progress tends to be incremental.
However, where it’s tied directly to margin, pricing and supply chain decisions, it tends to accelerate transformation.
To determine the right fit, CEOs and Boards need to ask:
- Where will AI actually move the dial in our business?
- Who owns pricing?
- Who is responsible for cash and working capital?
- Who controls customer data?
- Who is accountable for margin?
Leadership design should follow value creation. The title itself matters less than clarity of mandate. Someone must own the roadmap, and be accountable for commercial outcomes, not just technical deployment.
What AI Capabilities Must Retail CEOs and Boards Build Now?
To close the AI execution gap, Boards need to rethink structure, capability and governance, especially as AI is increasingly shapes how products are discovered and evaluated (known as agentic commerce).
According to IBM:
- 41% of consumers use AI assistants to research products
- 33% use them to review comparison
- 31% use them to search for deals
Those who build the right foundations today, and compete effectively for digital recommendation, are more likely to emerge with a lasting structural advantage.
That starts with five core capabilities:
- Non-executive directors who can challenge AI strategy
- AI KPIs linked directly to margin and P&L, not just “pilot success.”
- Clear governance around automated pricing and data-driven decisions
- Breaking down silos between IT and commercial teams
- Moving the business from “What happened?” to “What happens next?”
How Redgrave Can Help
Redgrave specialises in aligning leadership talent with the future of retail. Whether you are a big-box retailer, or a pure-play D2C brand, we help you find the leaders who understand these modern pressures.
To discuss how to structure and attract a leadership team capable of scaling AI, contact Paul Williams, Head of Redgrave’s Retail Practice. With extensive experience in Board and senior-level appointments, Paul is well-positioned to help you find the talent that fits your specific culture and strategic goals.
