“CEOs are realizing that AI is not simply another layer of automation. It is a catalyst for rebuilding the enterprise itself,” said David Furlonger, VP Analyst at Gartner.
Retailers could go from manually cross-referencing three spreadsheets to an agentic system that flags stocking issues before they affect sales. AI could personalize offers based on what a shopper is looking at in store, or monitor supply chain activity and take action when an order hasn't arrived. AI can already deliver tangible results in individual parts of the business.
As you consider where AI fits into your own business, the bigger challenge is deciding which results justify further investment, how you will measure the return, and what it takes to turn a successful use case into lasting business value.
The challenge of scaling AI across the enterprise
Retailers are putting AI to work across a growing range of business activities. According to the National Retail Federation, popular use cases include IT coding and app development (75%), office productivity tools (73%), and cybersecurity and fraud prevention (66%). Retailers also report some of their strongest AI returns in IT application development and customer personalization.
Those results can make a strong case for investing further. But success in one area doesn't automatically translate into results elsewhere. McKinsey found that nearly two-thirds of organizations have not yet begun scaling AI across the enterprise. Costs, accuracy, employee skills, and the work involved in putting AI into everyday operations can all make it harder to extend a successful initiative.
As you assess an AI investment, you need to look beyond the initial result. Can it deliver enough value to justify taking it further? Can you measure that value? And can you apply what works elsewhere in the business?
Why retail AI pilots don't always deliver value
An AI pilot can produce a promising result without creating a business case that holds up as the initiative expands. According to a global IBM study, only 25% of AI initiatives delivered expected ROI, and only 16% scaled enterprise-wide.
Consider a mid-size fashion retailer implementing an AI replenishment agent. During testing, it predicts stockouts, flags slow movers, and suggests reorder quantities. Sounds fine, right? But once it's live, the problems start to emerge.
Sales and inventory information sits in separate systems. Stock movements don't update at the same time, and product information doesn't always match between systems. Within weeks, the recommendations start diverging from what's happening on the shop floor. Planners stop trusting them and return to spreadsheets.
The pilot may have shown that the idea works, but the challenge is making that result reliable enough to use more widely. As you expand your initiative, each location may need different adjustments or workarounds. What worked in one store may not work the same way in the next, making it harder to turn a successful pilot into something your entire business can rely on.
What it takes to scale AI in retail
A strong AI business case depends on more than proving that the technology works. You also need confidence that the results can be trusted, measured, and repeated without taking on unnecessary risk.
Three questions can help determine whether an AI investment is ready to grow:
1. Can people trust the result?
An AI recommendation is only useful if employees believe it reflects what is actually happening in the business.
If sales, inventory, product, or customer information is inaccurate or inconsistent, AI can produce recommendations that don't match reality. Employees may quickly stop acting on them, putting the expected return at risk.
Reliable business information gives teams greater confidence in AI and makes it easier to measure whether an initiative is delivering the expected results.
2. Can the approach work elsewhere?
An AI initiative may work well in one store. The next step is to see whether the same approach can deliver value in other locations and markets.
Suppose an AI tool helps one store reduce stockouts or saves employees time. Can you apply it elsewhere without having to redesign the process each time?
The easier it is to extend a successful use case, the more value you can get from the original investment.
3. Can you limit the cost of mistakes?
AI can support decisions, recommend actions, and increasingly take action itself. That creates an important business question: where should AI act independently, and where should a person make the final call?
Clear rules and human review can help catch problems before they become costly. A recommendation that needs correcting in one store is manageable.
The same mistake repeated across hundreds of locations can have a very different financial impact. Clear oversight can help businesses capture the value of automation while keeping the potential cost of mistakes in check.
Why your technology foundation matters for retail AI ROI
You can have a strong AI use case and still face practical barriers when you try to put it into everyday operations. Sales, inventory, customer, and supply chain information may sit in different systems, making it harder to give AI the information it needs and for employees to act on its recommendations.
A unified retail platform such as LS Central can help reduce some of that complexity. Because LS Central extends Microsoft Dynamics 365 Business Central, you can also take advantage of the wider Microsoft ecosystem, including Microsoft Fabric, Power BI, Copilot, and AI agents, as you explore new ways to use AI.
By bringing core retail operations onto one platform, you can:
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Make more informed decisions with a clearer, real-time view of your business
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Put AI to work where it can deliver the most value for your business
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Give AI reliable business information to produce more useful insights and recommendations
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Build on your technology investment over time as your priorities evolve
A more consistent retail foundation gives you a stronger basis for deciding where AI can deliver value across your business.
Turning your retail AI investments into real business value
You don't need dozens of AI pilots to get value from AI. You need to know which initiatives are delivering measurable results, understand what makes them work, and build on those that show clear business value.
AI investment comes down to a few fundamentals: What business outcome will this improve? How much will it cost to achieve? How will you measure the result? And can you apply what works more broadly across the business?
Answering those questions early gives leadership a clearer basis for deciding where further investment makes sense and what needs to be in place to support it.
Don't let another AI pilot stall before it pays off. Talk to LS Retail experts to discover how LS Central can help AI scale across your business.
