AI Readiness Assessment for a Heritage Apparel Retailer Piloting Design and Loyalty AI
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Overview
What we built
A heritage denim and apparel retailer had let nine AI pilots spring up across design and loyalty with no shared foundation and no way to compare them. We assessed all nine and handed leadership one scored roadmap to fund instead.
In plain terms: this heritage denim and apparel retailer runs 48 stores alongside a growing direct-to-consumer channel, and AI had arrived one team at a time. The design studio was running generative design experiments, marketing was trialling loyalty scoring, and altogether nine pilots existed, each on its own vendor stack with its own data. Leadership had no shared data foundation underneath any of it, no ownership model to say who was accountable, and no consistent way to judge which of the nine pilots actually deserved further investment.
We ran a six-week AI readiness assessment spanning data infrastructure, team capability, vendor contracts and governance across design, merchandising and loyalty. Every one of the nine pilots was scored on a value-versus-feasibility matrix, and we mapped where tooling duplicated itself between teams. The result was a sequenced 18-month roadmap that made a unified customer data layer the first build and set clear stop decisions for the rest. Leadership approved the roadmap within 3 weeks of the final readout, the first initiative was live within 4 months of sign-off, and consolidating overlapping vendor spend cut related costs by 27%, roughly $150K annually.
The Problem
Scattered pilots, no direction
The retailer's AI activity had grown one pilot at a time rather than as a plan. Across the business nine disconnected pilots were running: generative design experiments in the studio exploring new prints and cuts, and loyalty scoring trials in marketing testing which customers to target next. Each pilot belonged to a different team, and each team had chosen a different vendor stack to run it on, with 48 stores and a growing direct-to-consumer channel generating data none of the pilots could see in full.
Nobody owned the question of which pilots deserved to continue. There was no shared data foundation underneath the nine efforts, so a loyalty model and a design tool that could have shared customer or product data instead ran on separate, disconnected pipelines. There was no ownership model either, so when a pilot stalled or a vendor contract came up for renewal, no single person was accountable for deciding what happened next.
With nine pilots each reporting its own version of progress, leadership had no consistent way to compare them, and by default every pilot kept running, and every vendor kept being paid, whether or not the pilot was actually working.
Nine ungoverned pilots
Generative design experiments in the studio and loyalty scoring trials in marketing ran independently, each on its own vendor stack with no shared method for comparing results.
No shared data foundation
None of the nine pilots could draw on a common data layer, so design and loyalty efforts each rebuilt the same customer and product data from scratch.
No ownership model
No single person was accountable when a pilot stalled or a vendor contract needed renewing, so struggling pilots kept running by default rather than by decision.
Duplicated vendor spend
Different teams on different vendor stacks meant overlapping tools were being paid for across design, merchandising and loyalty without anyone comparing the bills.
What it was costing them
Every pilot left unreviewed kept its vendor contract running and its team's time committed, whether or not it was working. Overlapping tooling across the nine pilots meant the retailer was often paying more than one vendor for a similar capability, and with no shared data foundation, insights from one pilot, such as loyalty scoring, could not inform another, like the design studio's experiments, even where the underlying customer data overlapped.
The Solution
Scored roadmap for AI investment
We treated the assessment as evidence gathering first. Over six weeks we examined data infrastructure, team capability, vendor contracts and governance across design, merchandising and loyalty, building the first complete picture of what the retailer already had running and what it was already paying for across all nine pilots.
With that picture in hand, we scored every active pilot on a value-versus-feasibility matrix, using one consistent set of criteria instead of nine separate arguments. We also mapped where tooling duplicated itself across teams, surfacing vendor contracts that were quietly paying for the same capability twice.
The assessment closed with a sequenced 18-month roadmap rather than a scorecard alone. It made a unified customer data layer the first build, since design and loyalty pilots alike depended on it, and it set clear stop decisions for pilots that had not earned their place, so the roadmap could move forward without carrying every existing pilot along.
Key decisions
Score every pilot, not favourites
All nine pilots were assessed against the same value-versus-feasibility matrix, so continuation decisions rested on evidence rather than on which team pitched loudest.
Map duplicated tooling first
Vendor contracts across design, merchandising and loyalty were compared before the roadmap was written, surfacing overlapping spend the retailer could recover immediately.
Unified data layer comes first
The 18-month roadmap sequenced a unified customer data layer as its first build, since every later initiative, from design to loyalty, would depend on it.
Set stop decisions, not just starts
Pilots that had not earned their place were given explicit stop decisions in the roadmap, ending the default of letting every pilot run indefinitely.
Six weeks, one readout
The assessment was scoped to six weeks with a single final readout, so leadership could approve a roadmap quickly rather than waiting on an open-ended review.
Measurable Impact
What changed after launch
The assessment turned nine ungoverned pilots into a single funded plan. Of the nine pilots audited, 4 were consolidated into funded initiatives, and leadership approved the 18-month roadmap within 3 weeks of the final readout, evidence that a scored, sequenced plan can move faster than pilot-by-pilot debate ever did.
The first roadmap initiative, the unified customer data layer, was live within 4 months of sign-off, and consolidating the overlapping vendor and tooling spend uncovered during the assessment cut related costs by 27%, roughly $150K annually. The retailer also kept the value-versus-feasibility method going forward, so new AI ideas now enter one evaluation process instead of arriving as another disconnected pilot.
Pilot governance
Nine disconnected pilots, no shared evaluation method
4 funded initiatives on one scored 18-month roadmap
Data foundation
No shared data layer across design and loyalty
Unified customer data layer live within 4 months
Vendor spend
Duplicated tooling across nine pilots and vendor stacks
Overlapping spend cut 27%, roughly $150K annually
Decision speed
Pilots ran indefinitely with no stop decisions
Roadmap approved within 3 weeks of final readout
Headline results
9 disconnected AI pilots audited and consolidated into 4 funded initiatives
Overlapping vendor and tooling spend cut by 27%, roughly $150K annually
18-month AI roadmap approved by the leadership team within 3 weeks of the final readout
First roadmap initiative, a unified customer data layer, live within 4 months of sign-off
Tech & Tools Used
What powered the build
Every tool below earned its place in this engagement. Here is the part each one played.
Python (pandas)
Profiled data quality across the nine pilots during the readiness assessment, quantifying gaps and duplication before any scoring began.
Google BigQuery
Held consolidated data pulled from the design and loyalty pilots, giving the assessment one place to compare what each pilot actually had.
dbt
Modelled the shared metrics behind the value-versus-feasibility scoring, so every pilot was measured against the same governed definitions.
Airbyte
Replicated data out of the separate vendor stacks into the shared warehouse, so the nine pilots could be compared without custom pipeline work.
Looker Studio
Powered the scoring dashboards and roadmap readouts leadership used to approve the 18-month plan.
scikit-learn
Used to sanity-check the feasibility of the loyalty scoring pilot's approach as part of the readiness assessment.
Great Expectations
Ran automated data-quality checks across the pilots' datasets, feeding the evidence behind each pilot's readiness score.
Miro
Hosted the value-versus-feasibility scoring sessions where pilots were ranked against one another using shared criteria.
Notion
Holds the 18-month roadmap, the stop decisions and the ownership assignments, so any team can trace what was agreed and why.
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