AI’s impact on retail can feel like a high-speed train, racing past each proverbial station. Just a year ago, retailers were looking to determine where generative AI had its place. Now, organizations have moved well past initial exploration, many having completed pilots, tested use cases, and shown early signs of value. It’s clear that AI is not a moment passing by and will continue to play a meaningful role in how retailers operate and compete.
Growth and innovation are very exciting and bring forth a new set of challenges. Demand for AI Agents grows as pilots show value. Teams across the business quickly begin to see opportunities to automate work, improve decisions, and move faster. The result is often a long list of agent ideas, each with a valid use case and an eager internal sponsor. Governance is critical to ensure focusing your resources on AI agents aligned with company strategic priorities that drive measurable value and safely scaling into production.
The maturity curve has shifted
As to be expected, retailers are moving beyond the earliest stage of the AI adoption maturity curve. The center of gravity has shifted to how organizations can be using AI to reinvent functions and connect improvements across the enterprise. AI needs to be a business-lead transformation that is supported by technology.
Experimentation is valuable and necessary in the pilot phase for learning a new paradigm of working and revealing gaps in core technology foundation needed for success. Agents can deliver real value across the company whether its supply chain using an agent for managing disruptions or customer service teams using AI to support an issue resolution times. The risk of fragmentation grows as the volume of requests grows. The next phase includes AI Agent collaboration across the organization, e.g. The Supply Chain Disruption Agent can collaborate with the Customer Service Agent to address resolving a late order shipment.
Fragmented operating silos can quickly snowball into dozens of disconnected agents, redundancies, and a lack of clear ownership. Some agents may be valuable, others may be interesting, but ultimately low priority. It can be easy to increase unknown risks if agents have access to sensitive data, customer information, or critical business decisions without the right controls. What’s needed in these instances is discipline; a clear organizational model for governance and scale.
Shifting pilot mode to prioritized scale
What will separate retailers in the era of AI agents is not quantity, but rather those agents that drive measurable value supporting company strategy. Understating which agents to build, why they matter, and how they connect to strategic priorities is critical to investing in agents. Stop thinking of agents as the strategy, but rather, as a way to execute against a strategy.
Start with identifying what business priorities are most important to advance and can be assisted with the use of agents. Could be a goal of improving inventory productivity, could be a creating more a personalized approach, maybe it’s reducing waste – regardless, it needs to start with clear identification of what those goals are.  The business priorities will map to business processes to focus on improvement and reinvention.
In understanding where agents can drive the most value, retailers are simultaneously creating a practical way to manage demand. This helps avoid the trap of building AI because a team has an interesting idea, instead focusing on those that support a measurable business outcome. That model should assess business impact, define requirements, assign controls, and monitor performance over time.
Every agent should have a purpose, an owner, a risk profile, a value case and a way to measure whether it is working. As agent requests increase, this will ensure retailers have a repeatable model to register, evaluate, prioritize, and measure.
Value created by connected workflows
The next wave of value will come from agents that operate across business functions, not solely within them. Take for example a demand forecasting agent that identifies a patio furniture line underperforming in northern markets as summer demand begins to slow. The insight is useful in isolation, but significantly more valuable comes when it can trigger coordinated action. A merchandising agent might recommend a targeted Labor Day promotion, and a supply chain agent might suggest shifting excess inventory to warmer-weather regions where outdoor demand remains stronger.
There is enormous potential to fundamentally change the operating model by helping different parts of the business respond to the same signal in a coordinated way. Retail has always been interconnected, and AI agents should reflect that reality rather than reinforce functional silos.
The iceberg beneath AI readiness
The visible parts of AI tend to get the most attention: large language models, agent platforms, and new product releases. While important, they’re only the surface. Data readiness sits beneath, just as vital. Agents need access to accurate, timely, and trusted information. If product data, customer data, inventory data, or pricing data is fragmented across systems, agent performance will suffer.
Governance is another foundational requirement. As agents begin to do more and in turn, have more access, there need to be clear guidelines for access. Enterprise knowledge and business processes must come with security, permissioning, and clear data classification. This will only become more important as agents move from assisting individual employees to influencing operational decisions.
Amid all this rapid technological change, the human side can’t be lost. Employees will increasingly need the confidence, skills, and trust to use and manage AI effectively. Human judgement is still (and will continue to be) an essential component, so there must be understanding as to how AI fits into workflows. One thing is abundantly clear, responsible AI must be treated as foundational.
AI governance and scale
One of the most important shifts retailers can make now is moving from an AI vision designed to orient pilots to an AI governance model designed to support scale. When done well, governance models should help the organization move faster by creating clear decision rights and reducing confusion.
Practically, that means creating a consistent process to register each app or agent, evaluate and prioritize requests, assess business impact, define requirements, assign the right controls, and monitor performance once deployed. Retailers have already proven that AI pilots can work. The next test is whether they can turn those pilots into a sustainable operating capability. The retailers that get this right will be well positioned to sustainably build their operating capabilities through AI.
Rob Want is a business technology leader with a lifelong connection to retail and consumer goods starting in the family business and evolving into a career focused on transforming operations through applications, analytics, and automation. His hands-on experience across accounting, merchandising, supply chain, sales, and IT gave him a front-row seat to the day-to-day challenges teams face. That foundation helps him connect strategy with execution and bridge the gap between business needs and technology solutions. For over a decade, Rob led delivery and customer service teams, implementing and supporting business applications and infrastructure for omni-channel retailers and wholesalers. He then spent eight years leading global solution architects, aligning industry trends with business applications for the Retail & Consumer Goods industry. Today, his focus has expanded across the full spectrum of digital transformation—spanning Data & AI, Modern Workplace, Business Applications, Security, and Strategic Advisory.
