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Retail AI

The why behind slowed AI operationalization for grocery retailers

07/29/2026

In this new age of AI, the question retail leaders are being asked from every direction is: “What are you doing with AI?” Budgets are being carved out, vendors are being evaluated, and pilots are being launched with real optimism behind them. But when it comes time to explain what those investments have actually produced, the answers quickly become vague.

Organizations have greater access to AI than ever before. However, even with the most capable AI tools to date, weaving this technology into workflows remains difficult. The underlying challenge lies in AI operationalization. Embedding AI into how a business actually runs, day-to-day, is where most organizations get stuck, because the people making the decisions often lack a clear view of how the business operates on the ground. Most organizations find themselves caught in the gap between “we have a pilot” and “this revolutionizes the way we operate.” Retailers need to understand why that gap exists and what it takes to close it.

Why most retail AI investments slow

Retailers have become very good at launching AI pilots. Companies such as Walmart, Target, Sephora, Amazon, and Home Depot lead the pack in moving AI from pilot to practice. They leverage AI to power everything from supplier negotiations to personalized shopping experiences. Even so, 64% of retail executives are running AI pilots, and only 26% have scaled them across the enterprise. What separates those who scale from those stuck in pilot mode is how well the company understands its own operations before ever touching the technology. Successful implementations must build upon operational excellence, rather than simply patching up problems.

In the age of technology, the standard operating procedure is to leverage technology to fix business problems, as opposed to first analyzing and addressing the business process and then seeing how technology can elevate operations. Anytime an organization intends to scale, the process is the same regardless of what businesses are scaling and how they are trying to do so. Business leaders must ask the same questions: What is the end-to-end flow? How is it operating currently? What can we replicate or repeat with minimal effort? When businesses start asking these questions, input and ownership should be sourced from the right places instead of being siloed to a single team.

More often than not, when organizations implement a new technology, IT teams own the work. While these teams have the technical competencies necessary to ensure proper deployment, sole ownership can result in success metrics being technical rather than business-oriented. By nature, this defeats the intended purpose of implementing AI. The technology gets built in isolation from the process it needs to improve, and by the time anyone notices, momentum dissipates. Beyond the sunk cost of a stalled or failed pilot lies the risk of a larger blow to operational adoption. In turn, this creates organizational skepticism that makes the next initiative harder to fund, harder to staff, and harder to get company-wide buy-in. The need for shared ownership, through cross-functional work and change management, is critical.

When a pilot fails, organizations need to avoid pinning blame solely on the technology, its use case, or technical implementation. The real question should be: Where is the business process breaking or carrying unnecessary complexity? A business problem requires a greater investment from business leaders. Grocery retailers need a process that is sustainable, scalable, and as efficient as possible so that technology has the foundation to take the organization to limits it could not reach before.

What “AI operationalization” actually means

Before organizations can close the gap between pilot and practice, they need a clear understanding of their target. AI operationalization is AI that is embedded into a repeatable business process, measured against business outcomes, and maintained without extraordinary effort. A system with a single point of failure is not operationalized. If it depends on one person, one workaround, or a quarterly scramble to retrain the model, it is still a pilot. And an expensive one.

In grocery retail, this distinction shows up in ways that are hard to ignore. A demand forecasting tool that predicts weekend produce volume is not truly operationalized if the department manager still places the order manually because they do not trust the output. A labor scheduling model is not operationalized if the store director overrides it every week because the parameters no longer reflect how the store actually runs. Three factors need to work in unison for the value to be realized:

1. Process integration requires AI to be woven into how the process actually flows, not how it should flow. Organizations that skip this step introduce a parallel system that competes with the existing process.
2. People and change management means adoption is achieved through training, role clarity, and buy-in. What determines whether teams actually use the technology is whether they understand the value it will bring.
3. Defining the win means success has to be defined, scoped, and documented before deployment, not after. In grocery, that means deciding before go-live whether to target reducing shrink, improving in-stock rates, or lowering labor costs.

The technology can be working exactly as designed and still delivering little to no operational value since not every part of a grocery operation offers the same return on AI investment. Organizations that try to operationalize everywhere at once often succeed nowhere and fall further behind. The highest-performing grocery retailers are deliberate about where they start.

In customer-facing operations, the return shows up when AI is applied to loyalty personalization, promotional recommendations, and self-checkout optimization. These are areas where years of untapped customer data can translate directly into bigger baskets and more frequent visits. In back-office and fulfillment, AI closes the lag between data and decision that drives out-of-stock rates and excess markdown volume, allowing teams to act faster and reduce the operational cost to get there. Compliance and loss prevention applications can drive immediate return to the bottom line. Reaching those outcomes does not happen by chance. It requires a clear process and deliberate sequencing, which is where most organizations discover the real work begins.

The four stages every grocery retailer has to get right

Moving from pilot to fully operationalized AI is not a one-time project with a single go-live date. It is a staged, ongoing process. Grocers that treat it as a one-time deployment almost always find themselves back at square one within 18 months. Regardless of what transition the organization faces, whether it is a leadership transition, a system overhaul, or even a store remodel, change can reset the institutional knowledge that was holding the organization together. For an organization to successfully operationalize AI, it needs to get these four stages right. These stages are not a technology implementation methodology, but rather a business transformation framework. This distinction sets successful grocers apart.

• Stage 1: Diagnose before you build. Map what occurs on the floors and in the back office before anything gets scoped. In grocery, this diagnostic work often surfaces a significant gap between how leadership believes the operation runs and how it actually runs at the store level. Organizations need to lean on their frontline staff to most accurately map these processes, such as pulling the last 90 days of manual overrides from a current system to see where AI can contribute most.
• Stage 2: Design for the business, not the technology. Once the process is mapped, the AI use case can be defined. It must be defined early with the business owner, not just the technology team. This stage is where baseline metrics are established, and the change management plan gets developed in parallel to the technical plan. Before leaving this stage, every organization should be able to answer these questions:
  • What does success look like in 90 days?
  • Who will be held accountable for those outcomes?
  • What does the frontline team need to understand before this goes live?
• Stage 3: Deploy in phases, not all at once. Phased rollouts are how organizations build trust with their customers and frontline employees. Starting with a single process, department, or store does not put an organization behind. It gives retailers time to refine processes through a feedback loop before the stakes become too high for mistakes. Trust from employees in the technology is built through consistent performance at a small scale, building confidence and buy-in with each rollout.
• Stage 4: Plan for sustainability before go-live, not after. A system that depends on one person to monitor it, one team to maintain it, or one champion to keep it alive is not sustainable. Ownership has to be assigned by name, retraining has to be scheduled on a calendar, and performance monitoring has to live inside existing operating rhythms. A human has to remain in the process at every stage to ensure performance and make a decision when the situation falls outside what the model was built to handle.

Even with these steps as helpful suggestions to start the AI implementation process, a grocery retailer might not be ready for AI to be introduced. If any of the symptoms below sound familiar, a reevaluation is overdue:

• AI is owned by the technology team with no named business sponsor.
• Success metrics are technical, not business-oriented.
• There is no training plan for frontline teams.
• The process has not been mapped, and the system has not been updated to reflect how the business actually operates today.

The grocers winning with AI are not who you think

AI is already reshaping grocery retail. The organizations that will capture that value are not the ones with the most sophisticated technology or the largest budgets. They are the ones that did the work most organizations skip: investing in understanding their operations at the store level before ever touching the technology, aligning their people, and building the infrastructure to sustain change over time.

Sendero works alongside grocery and retail organizations to close that gap. We assess where organizations are in the operationalization journey, identify the process gaps holding them back, and build the roadmap from experimentation to execution. Whether that means diagnosing a stalled pilot, designing a change management plan, or building the sustainability infrastructure to protect the investment long term, the path from pilot to results that show up in the business starts with doing the work most organizations skip. Partner with our consultants today to get started.

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