AI insights are the catalyst of real change in the future, an essential component of meeting the quickening pace of retail. Many businesses have already undertaken the first steps of AI readiness. They’ve audited their data and its stores, they’ve aligned internal resources and attitudes, and they’ve made a commitment to a smarter retail future. But make no mistake, the next step on the journey is not to “do AI”. It is to build the foundational systems that make that future possible.
While many retailers are aware of AI’s potential, the reality is that few are ready to support it at scale. Foundational systems aren’t flashy – day-to-day, they may even go completely unnoticed – but the process of rethinking data architecture, consolidating siloed tools, and designing modular and scalable systems creates an essential layer onto which every other AI tool can build and grow. It’s a layer many overlook – but the one that makes or breaks future AI success.
Data done right
Even a retailer that has never considered the possibilities offered by AI understands that data is essential. In the boardroom, on the shop floor, when negotiating with suppliers or planning marketing activities, decisions are made by the numbers. But data on its own doesn’t create intelligence – structure does. And that’s far from a given.
Many retailers still operate with disparate tech stacks, relying on manual workarounds to connect data across pricing, promotions, supply chain and category planning. This disconnection means time is wasted extracting and cleaning data. It leads to different teams working from different numbers. And, crucially, it provides no realistic platform for automation. In short, the old approach is brittle, slow, and no longer viable.
The core of foundational tools
To get value from AI, retailers must create the conditions under which AI can actually work. That is the role of foundational systems. And while each retailer’s implementation will look different, all foundational systems share certain key characteristics:
Centralised data layer: AI needs to know where to look. All critical retail data – assortment, pricing, promotions etc. – should be unified in one place in real time. This does not have to stop at the retailer’s internal data; supplier and wider market data could also be incorporated into this central repository.
AI-ready structure: Just having the data at hand is not enough. It must be structured and stored in such a way that machine learning models can use it to create advanced insights. That means implementing systems which ensure data is clean, properly labelled, and consistent.
Modular architecture: Change is inevitable, and AI applications require the flexibility to grow and evolve. The shift to AI is not a monolithic one, so foundational systems must be in place to support the implementation of new automations and applications as and when they are required.
Real-time connectivity: It’s not only about clean data. Foundational platforms link to other business systems – point of sale, customer management, warehousing and so on – ensuring that all decisions reflect what’s happening on the ground.
Building foundational confidence
Building foundational systems isn’t about starting from scratch, nor is it about digitising what’s already being done. It’s an opportunity to rethink the way decisions are being made, understanding what’s broken and unlocking new value through back-end systems, which do the heavy lifting. It shifts retailers from a reactive, firefighting mode to a proactive one. What foundational infrastructure really delivers is readiness – these tools don’t make the decisions, but they make better decisions possible.
Consider promotions as an example. With a foundation in place, a retailer doesn’t see (too late) which promotions have underperformed. They can use unified data to simulate outcomes before launch. This means category managers can see real-time product performance metrics, marketing teams can ensure campaigns and forecasts match up, supply chain teams can adjust orders before demand occurs, rather than in response to shortages.
Changing course – carefully
There’s an understandable temptation, given the potential of AI, to dive straight in. But off-the-shelf tools or machine learning add-ons do not address the systemic fragmentation of retail functions – such tools won’t deliver on the full potential of AI, and may serve to further fracture the brittle systems which prop up the classic retail environment.
Similarly, retailers may be so enticed by AI that they attempt a full-scale digital transformation effort, ripping out legacy systems wholesale in favour of modern tools. While it’s easy to appreciate the enthusiasm, this is a costly, risky and often unnecessary route to AI retailing. Carefully, cleverly implementing modular foundational infrastructure allows for business as usual, with AI benefits drip-fed as and when they are ready to go. Start small, prove the value, then scale.
Ready for the next step
Foundational systems pave the way for much more advanced projects. They’re core to being able to pilot AI projects, and they form an essential base for safely implementing the automated AI decisioning, dynamic pricing, and predictive replenishment functions that retailers naturally think of when the subject of AI is broached. And while they might seem like infrastructure work, the reality is that these systems are really about retail performance.
With strong foundations, retailers can drive towards higher margins, faster execution, and greater agility in an always-moving market. AI-ready data stores do not replace decision makers, but instead elevate them, allowing them to shift their focus to strategic leadership. They connect intention and execution and open the door to the next phase of retail AI. Build a stable platform now, and the future of retail will build itself.
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