Greggs: Making a pandemic-era app team predictable, and trusted again
The challenge
Greggs launched a mobile app so customers could order and earn rewards, but the rushed setup of technical ways of working created friction with Marketing and other departments. They needed Scrum Master support for two squads plus broader agile coaching across the business.
What we did
We refined Story Points for capacity-based tracking, coached Product on data-driven prioritisation, and ran prioritisation workshops and alignment sessions that taught non-technical teams how agile actually works. We used data to prove the payback of addressing tech debt.
// run against The Tenhaw Way, published in full and free to adopt without engaging us
The outcome
Delivery became predictable, confidence in the technical department recovered, and prioritisation conversations became realistic and focused. The data showing tech debt accelerated timelines strengthened collaboration across departments.
Limits, and what is withheld
Cross-functional trust and a shared, data-backed language for value are prerequisites for agentic change. Agents amplify whatever operating culture they land in, so the culture has to be sound first. What does not transfer is the technology. This was coaching and prioritisation work with no AI in it.
Why a buyer usually lands on this one
Written for the person arriving mid-programme with a question.
The department nobody outside it believes
The recurring pattern here is not technical. A team ships under pressure, ways of working get invented on the way, and within a year the rest of the business treats every estimate from that department as fiction. Prioritisation then becomes a negotiation about credibility rather than about value.
AI makes this worse before it makes it better. A head of AI inheriting a department in that position will find the blocker to their programme is not the model, it is that no commitment the department makes is believed, so nothing can be sequenced.
Tech debt, argued with numbers
The claim that paying down tech debt accelerates delivery is usually made as an engineering opinion and lost as a budget conversation. We made it with throughput data instead, which is how it survived contact with Marketing.
The same argument is coming for every organisation trying to scale AI agents onto an estate whose interfaces and data are undocumented. Agents are unusually sensitive to exactly what tech debt describes: inconsistent schemas, undocumented side effects, and workflows that only exist in one person's head.
What this engagement does not claim
The same caveats the case studies hub carries, narrowed to this engagement so nothing here is a surprise to your analyst.
- 01
Not an AI engagement.
Scrum Master support for two squads and agile coaching across the business. No models, no agents.
- 02
The recovery of trust is qualitative.
Delivery predictability was measured. The recovery of confidence in the technical department is reported as it was described to us at the time, not as a metric.
Other engagements
Sector first, because that is the next question. All twelve are on the hub, grouped into the two we would call AI work and the ten we would not.
Landing the Discovery+ launch on a CEO-set deadline
6, development teams on the launch, one of them the visual rebrand team James ran
Turning three merged teams into one delivery unit through workflow design
3, merged teams aligned
Coordinating five agile teams through a £1bn e-commerce re-platform
£1bn, re-platforming programme
See how we did it
A real engagement walked through by the person who led it, then the same method applied to yours.
- The ways of working, published in full and free to adopt without hiring us.
- The target operating model James co-led at HSBC: designed and piloted for 500 teams, with global rollout due in 2026 and not yet rolled out.
- The AI build inside a live London specialty insurer: a working proof of concept, month by month, with the client anonymised to a market.
Everything the call covers about our work is already published on this site. What it adds is the person who did that work, and your own situation put through the same method.
The 30-minute discovery call starts with your problem. This one starts with our work.
Pick a time on cal.comWant the same thing, in your organisation?
A 30-minute call with James Rooney. We will tell you which parts of this we have done before and which we would be doing for the first time, and you will leave with a rough scope either way.
most start with a fixed-price AI Readiness Audit · £30k–£90k · 6–8 weeks
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