AI in retail is no longer an emerging conversation — it is an operational reality. Walk into any major grocer today and AI is quietly running in the background: improving on-shelf availability, reducing waste and driving smarter markdown decisions that protect margin without alienating customers. Look at any retailer delivering true omnichannel experiences and you will find AI optimising fulfilment routing for online orders, deploying inventory more precisely — including complex fashion ranges where size, colour and return behaviour all factor into every decision — building loads more efficiently and systematically squeezing costs out of transportation networks. Step into the warehouse and AI is improving throughput, reducing dwell time and enabling teams to do more with the same resources.
Perhaps most significantly, AI is finally breaking down the silos that have long fragmented retail supply chains. End-to-end visibility and aligned operations — spanning retailers, their suppliers, those suppliers’ own supply bases and the carriers moving product between them — are no longer aspirational. They are becoming the operational standard for retailers serious about competing at pace.
These are not pilot programmes. They are live, daily capabilities reshaping how retail businesses operate and compete.
What this signals is a definitive shift from experimentation to front-line implementation, and with it, a fundamental change in how AI is being evaluated. Strategies previously characterised by pilot projects and proofs of concept have given way to a disciplined, value-first approach where pragmatism and bottom-line impact are top of mind. In 2026, the question is no longer whether to invest in AI, but how to ensure it delivers measurable business outcomes.
From generative to agentic
So what is driving this shift, and where is it taking the sector? AI adoption is maturing rapidly, and in many ways the overall argument has been won but potential must now translate into performance. CFOs and operational leaders are focused on clear ROI and financial accountability, with a much greater emphasis on specific outcomes: reduced waste, improved inventory accuracy, tighter forecast precision and lower working capital exposure.
From a technology and tools perspective, early AI implementations were not uniform and broadly fell into two camps. The first wave centred on AI and machine learning projects focused on prediction and optimisation: demand forecasting models, replenishment algorithms and inventory positioning tools that brought genuine precision to supply chain decisions. The second wave arrived with the rise of generative AI, which captured enormous attention and investment, centring on assistive tools that provided insight, answered questions and responded to prompts. Valuable as these tools are, they represent only one dimension of what AI can do for retail. The real prize is agentic AI, and it is a fundamentally different proposition.
By introducing greater autonomy across a wider range of processes, agentic AI is further extending the technology’s role beyond analysis to include guided decision-making and execution. AI agents are orchestrating activities across everything from connected planning and allocation to replenishment to transportation management, warehousing and day-to-day operations. This is accompanied by a move towards continuous, real-time optimisation rather than periodic, human-driven intervention, and by greater reliance on unified data models to support cross-functional decision-making.
In this context, enterprise value is determined by coordinated, end-to-end visibility rather than isolated point solutions, as has been the case over the previous few years. On top of this are the sector-specific business pressures, where issues such as tight margins and demand uncertainty are intensifying and exposing the limits of slow, fragmented decision-making.
Wherever you look, speed of response is increasingly determining competitive advantage, as is the ability to recover from disruption, which is now measured in hours rather than days or weeks. These issues also represent an opportunity to apply agentic AI, enabling processes to be continually optimised to limit business exposure to shocks and inefficiencies.
Human adoption and organisational alignment
It’s vital to understand, however, that technology maturity alone doesn’t guarantee that these advanced AI strategies will succeed. Agentic AI is not ‘plug and play’; it also depends on shifting the human focus towards re-skilling and structured human-AI collaboration. It also requires rethinking roles and responsibilities. For example, key stakeholders, from planners to front-line workers, must be equipped to use AI-enabled tools effectively. It’s no use integrating agentic AI into existing workflows if users lack confidence in using it or aren’t convinced of the value. For these people, the emphasis must be on practical application rather than theoretical capability, because without full organisational alignment, there is a very real risk that even the most advanced systems will underperform.
Adoption ultimately determines return on investment. AI tools must be actively used to address clearly defined retail challenges, not simply embedded for visibility. Ensuring that planners and front-line teams trust and rely on AI-driven recommendations is central to delivering measurable impact. The focus is therefore not just on deploying more advanced systems, but on driving practical value through consistent, day-to-day utilisation.
Be in no doubt, the competitive gap between retailers embedding AI into core workflows and those relying on fragmented tools is widening. Success in 2026 and beyond will be defined not by AI spend, but by sustained, measurable commitment across the business.
Andrea Morgan-Vandome
Andrea Morgan-Vandome is Chief Innovation Officer at Blue Yonder. With more than 25 years of leadership experience across enterprise software, supply chain and retail, Andrea has held executive roles at Nike, IBM Watson, Celect, Oracle and Retek.
