Industries — Retail

From forecasts to decisions that execute.

Demand, pricing and supply signals are already there. The step is letting AI agents act on them, within the rules merchandising and supply teams set, with a record of what they did.

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Reference case Global fashion retailer

AI portfolio rebuilt around value: one intake, upfront value estimation, and the discipline to stop

  • Every initiative valued upfront, before build, against the P&L line it was meant to move
  • One cross-domain intake replacing per-function wish lists
  • Initiatives that did not pay back stopped, and the capacity moved to the ones that did
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Reference case Global apparel brand

AI-driven in-season operating model: from siloed trading decisions to connected decision making

  • Significant EBIT uplift through reduced markdowns, fewer stockouts and improved close-out decisions
  • A handful of cross-functional AI squads built and scaled, from data science to production engineering
  • In-season decisions connected: forecast, inventory, markdown and personalisation read from the same signal
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73%

of retailers can't join their customer, product, and inventory data into a single decision-ready view.

The Friction

The data is there. The decisions aren't.

Retail has spent a decade buying point solutions — CDPs, ESPs, demand-forecasting tools, loyalty platforms, BI suites. Each one promised to be the source of truth. None of them are.

The result: merchants make markdown decisions from gut, marketing optimises a channel without seeing margin, and the board hears another quarter of "we're investing in data and AI" without a concrete answer to what it's returning.

The Strategic Vision

The opportunity sits in connecting the silos.

The unlock isn't a single heroic use case — it's AI woven across the chain, fed by signals from beyond your own four walls, and tuned continuously rather than once a season.

External signals — feed every stage
Competitor assortmentsSocial signalsSearch trendsWeather & events
  1. 1

    Demand sensing

    • Beyond own sales data.
    • External signals, continuous.
    • Feeds every function.
  2. 2

    Design & development

    • Generative variation at scale.
    • Designer as curator.
    • Cost of options → zero.
  3. 3

    Assortment & buying

    • Continuous, not pre-season.
    • Small buys, topped up.
    • Planner as system designer.
  4. 4

    Production & supply

    • Suppliers as a platform.
    • Small-batch economics.
    • Portfolio as continuous test.
  5. 5

    Marketing & content

    • Content as manufacturing.
    • Hero stays human.
    • Tail at near-zero cost.
  6. 6

    In-season optimisation

    • Season as one system.
    • Levers sequenced by cost.
    • Where latent margin lives.
AI capabilities — connect across the chain
Real-time decisioning

Pricing, allocation, and content tuned continuously — not on a quarterly calendar. The signal from every stage feeds every other.

Generative content & design

Design variations and the marketing long tail produced at near-zero marginal cost. Hero work stays human; the rest scales.

Demand & supply forecasting

One forecast joined across sensing, assortment, and production — not three different versions arguing in a meeting.

Data & knowledge landscape — the foundation

A unified semantic layer — where retail silos break down.

Customer, product, inventory, and supplier mean the same thing across every system that touches them. The unlock isn't another integration project — it's a shared semantics layer that lets AI work across the chain instead of inside each silo.

CustomerProductInventorySupplier

Tailored Use Cases

Where retail leaders see the fastest return.

Use cases we see deliver measurable margin or efficiency within two to three quarters.

01

Demand forecasting & allocation

SKU-store-week forecasts that beat the planner's gut and the vendor template — feeding allocation, replenishment, and open-to-buy.

02

Dynamic pricing & markdown optimisation

Price and markdown decisions that respect competitive position, inventory health, and brand guardrails — at scale.

03

Personalised customer lifecycle

Trigger-based journeys built on a real customer graph — not a list export from yesterday's CDP.

04

Assortment & space optimisation

Cluster-level assortment and planogram decisions informed by sell-through, basket affinity, and local demand signals.

Our Framework

Assess, define, embed.

Three phases, one architecture. Each phase produces something the next stands on, and each ends with a decision the organisation can take without us.

Assess · Weeks 1 to 6

Name what is structurally missing

The gaps between the assistants in place and the work that could be handed over.

Define · Weeks 7 to 18

Foundational architecture and decision register

Foundational architecture, decision register, platform requirement and selection where needed.

Embed · From week 19

Governance, components, builder teams

Governance, components and builder teams until each element has an owner inside.

FAQ

Common questions from retail leaders

We already have a data warehouse and a BI team. Why do we need this?

A warehouse is a foundation, not a strategy. A readiness assessment tells you whether that foundation supports the AI decisions your board is asking for — and where the gaps are between today's BI cadence and a decisioning operating model.

How is this different from what a Big Four consultancy offers?

We don't sell implementation hours or software. The assessment is a fixed-scope, fixed-fee diagnostic delivered by partners — not a junior team building toward an upsell.

Can you work with our existing vendors and platforms?

Yes. We're vendor-independent. We assess your current estate as-is and recommend the smallest viable change to get you to your target posture.

How long until we see results?

The assessment delivers a usable roadmap in 4–6 weeks. The first pilot typically goes live within two quarters. Measurable business impact tracks the pilot's success metrics.

Further Reading

Selected insights for retail leaders

How Shein industrialised the fashion value chain, and what every retailer can learn
AI

How Shein industrialised the fashion value chain, and what every retailer can learn

Years ago Inditex revolutionised the fashion operating model. Store managers fed back daily on what was selling and what customers were asking for. Production sat close to home in

· 10 min
Agentic Commerce: A Practical Guide for Retailers
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Agentic Commerce: A Practical Guide for Retailers

Agentic commerce is gaining traction as AI platforms position themselves to handle end-to-end shopping experiences. OpenAI's recent commerce protocol launch makes the intent clear:

· 3 min
How to build an AI use case portfolio: a practical framework
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How to build an AI use case portfolio: a practical framework

Part 2 of 4 — AI Transformation: From Ambition to Impact One of the questions I get most often is some version of: "What is the AI use case that…

· 5 min

Book a Data & AI Readiness Assessment.

30 minutes. Tailored to retail. No pitch deck, no obligation.

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