Digital Retail Advisory
AI in retail is not a technology question — it is a commercial question. Advisory that helps retailers identify where AI creates genuine value, build the capability to capture it, and avoid the costly distractions in between.
The Problem
Most retailers are running AI pilots in isolated pockets — a chatbot here, a recommendation engine there — without a coherent view of where AI creates the most commercial value or how these capabilities connect into a broader strategy. The experiments rarely compound.
AI platform vendors sell capability, not context. The use cases they promote are generic; the ROI projections are optimistic; and the implementation assumptions rarely account for the data quality, team capability, and operating model changes that retail AI actually requires.
AI investments in retail are easy to justify in broad terms and hard to justify at a project level. Without a clear line from the AI capability to the commercial metric it is supposed to move — revenue, margin, cost, conversion — prioritisation is arbitrary and accountability is absent.
What This Covers
A structured review of where AI creates genuine commercial value across the retail operation — prioritised by impact, feasibility, and the organisation's current capability to execute. Cuts through vendor hype to identify the opportunities worth pursuing now.
Advisory on applying AI to customer experience — product recommendations, personalised content and offers, dynamic pricing, and the data and technology architecture that makes personalisation commercially viable at scale rather than a feature demo.
AI-driven demand forecasting that reduces stockouts, improves markdown management, and connects inventory decisions to commercial outcomes. Covers model selection, data requirements, and integration with existing planning and trading workflows.
Applying AI to the commercial decisions at the core of retail: ranging, pricing, promotional planning, and product discovery. Advisory on where automation creates value, where human judgement remains essential, and how to design the human-AI workflow.
AI-driven search and product discovery — semantic search, visual search, and the ML models that improve findability and conversion. Framed around the commercial impact on conversion rate, basket size, and the customer experience metrics that drive retention.
A sequenced, commercially grounded AI roadmap for the retail business — connecting AI capability investments to the revenue and efficiency outcomes the business is trying to achieve, phased to build on quick wins and develop the data foundations that compound over time.
Why It Works
Most AI advisory lacks retail depth. Most retail advisory lacks AI literacy. This practice brings both together — understanding the commercial mechanics of how retail businesses make money, and the practical realities of how AI capabilities are built, deployed, and maintained.
AI has a gravitational pull toward the technically interesting rather than the commercially valuable. The advisory is anchored on the question that matters: which AI investments will move the metrics the business cares about, at what cost, and on what timeline.
No partnerships with AI platforms or implementation vendors. The advice reflects the commercial interests of the business — including when a particular AI investment is not worth making yet, or when a vendor's capability does not match the maturity it is being sold as.
Who This Is For
Businesses that have run AI pilots and seen early results, and are now trying to work out how to scale AI capability across the business in a way that compounds commercially rather than staying at the proof-of-concept stage.
Product, digital, and technology teams that need a prioritised view of where to invest in AI capability — grounded in commercial impact and sequenced against the data foundations and organisational readiness the business actually has.
Boards and executive teams preparing to make significant AI investments and wanting an independent perspective on where the commercial value is, what the realistic build timeline looks like, and how to evaluate the proposals coming from vendors and implementation partners.
Get in Touch
Share where you are with AI in your retail business — what you’ve tried, what you’re trying to work out, or where the investment case isn’t yet clear. Patrick will respond with a direct view on how the practice can help.
Prefer to email directly? patrick@rechsteiner.io