Greggs: Making a pandemic-era app team predictable, and trusted again

Scrum Master support for two squads, Mobile App and Integration, plus agile coaching across the wider business, over six months. Greggs had 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. We refined Story Points so tracking followed real capacity. Product was coached to prioritise from data, and the workshops and alignment sessions we ran taught non-technical teams how agile actually works. By the end delivery was predictable and prioritisation conversations were realistic, and confidence in the technical department had recovered.

More than it looks. This was Scrum Master support for two squads and agile coaching across the wider business, and the preconditions it built are the ones agentic change depends on. Agents amplify whatever operating culture they land in, so cross-functional trust and a shared, data-backed language for value have to be sound before automation arrives. Six months bought Greggs predictable delivery. It also bought prioritisation conversations that were realistic and focused, and a technical department the rest of the business trusted again.

Some of the work on this site predates the company, and we are straightforward about which. Greggs sits in the group of engagements delivered under the Tenhaw banner, alongside Anglo American, Yondr, Colart, Tecknuovo and Globelynx. Several in that group predate incorporation and were delivered by James Rooney personally on contract, and we will walk you through which is which on a call. The distinction matters more for the paperwork than for the work. Tenhaw is founder-led, James leads every engagement personally, and the six months of Scrum Master support and coaching described in this study happened as written, whichever banner it sat under.

By making delivery predictable enough that commitments mean something again. At Greggs the problem was never technical. An app team had been stood up at speed during the pandemic, ways of working were invented on the way, and the friction with Marketing and other departments had eroded confidence in the whole technical department. We rebuilt the numbers first, refining Story Points so tracking reflected real capacity, then ran alignment sessions and prioritisation workshops that put those numbers in front of the other departments. Trust followed the data. Once delivery became predictable, prioritisation conversations turned realistic and focused, and confidence in the technical department recovered.

Use the delivery data. The claim that paying down tech debt speeds up delivery is usually made as a technical argument and lost as a budget conversation, because nobody outside engineering can weigh an engineer's opinion. At Greggs we used delivery data to prove the payback of addressing tech debt, and the numbers are what let the argument survive contact with Marketing and the other departments. The data showing tech debt work accelerated timelines strengthened collaboration across the departments. The wider lesson is that a shared, data-backed language for value turns the hardest prioritisation arguments into something every department can judge on the same terms.

Because ways of working invented under pressure harden into the operating model. Greggs stood its app team up at speed so customers could order and earn rewards, and the rushed setup created friction with Marketing and other departments. The pattern recurs, and it is not a technical one. A team ships under pressure, estimates stop being believed, then prioritisation turns into a negotiation about credibility rather than value. At Greggs it took a six-month engagement of capacity-based tracking and coaching that reached beyond engineering to make delivery predictable and the department trusted again.

Yes, and at Greggs coaching beyond engineering was a stated part of the brief, not a side effect. Alongside Scrum Master support for the Mobile App and Integration squads, we ran prioritisation workshops and alignment sessions that showed non-technical teams how agile actually works. The point was never to make Marketing run stand-ups; it was to give every department a shared, realistic picture of capacity, so conversations about what gets built next start from data rather than assertion. That organisation-wide coaching is a large part of why predictable delivery turned into recovered trust across departments.

Because a department whose commitments are not believed cannot sequence anything, including an AI programme. A head of AI inheriting a distrusted technical department will find the blocker is not the model. It is that no estimate the department makes is taken seriously, so nothing can be planned against. The Greggs engagement is what fixing that looks like. Six months of Scrum Master support and coaching made delivery predictable and made the department's numbers believable again. An AI programme gets planned against those numbers like everything else, so they have to be believable before the technology arrives.

It would be sold as Programme and Delivery Management at £18k–£35k per month, depending on scope. The Greggs work, Scrum Master support across two squads plus agile coaching beyond engineering, sits squarely in that service. What you are buying is senior delivery leadership that makes output predictable and gives the business numbers it can plan against. Tenhaw engagements are retainer-shaped with an exit date agreed at kickoff and 30 days' notice either side, and a month that delivers no measurable value is reported as a failed month. A 30-minute call is the quickest way to size your version of it.

Give every side the same numbers and coach them to argue from them. At Greggs, friction between the technical department and Marketing meant prioritisation ran on assertion. Refining Story Points for capacity-based tracking rebuilt the numbers, Product was coached to prioritise from data, and the prioritisation workshops and alignment sessions put that evidence in front of every department at once. That is the shift the study records. Prioritisation conversations became realistic and focused, and even the hardest sell, spending delivery time on tech debt, was won with delivery data rather than opinion. Shared numbers that everyone believes leave politics very little to work with.

Not all of it, and the real decision is which parts. Pay down the retail tech debt that sits directly in the path of the work you intend to automate, and keep the rest costed and visible. Tech debt and an AI modernisation programme draw on the same delivery capacity, so it gets settled as a budget question and not an engineering one. At Greggs we used delivery data to show that tackling tech debt accelerated timelines, which gave Marketing and the other departments a shared basis for agreeing the order of work. That same data lets you sequence modernisation around the debt that actually blocks it. Clearing the lot first is rarely the cheapest route.

Usually it is the way they work. Greggs shows the difference clearly. The app team was stood up at speed so customers could order and earn rewards, and it was the rushed setup of technical ways of working, not the engineering, that created friction with Marketing and other departments. The tell is where the disbelief sits. One team missing its own dates is a team problem. Several departments no longer believing any estimate is an operating model problem, and more pressure on the team will not shift it. What made delivery predictable again was six months of Scrum Master support and capacity-based tracking, with coaching that reached beyond engineering.

Both, and at Greggs that was rather the point. Product was coached to prioritise from data, but the friction sat between the technical department and Marketing, so a workshop with only Product in it would have moved the argument rather than settled it. We ran prioritisation workshops and alignment sessions together, the sessions teaching non-technical teams how agile actually works so everyone in the room could read the capacity numbers. Once the people asking for work and the people doing it were looking at the same figures, prioritisation became a choice between options instead of a test of anyone's credibility.

It often does at first. What you do with that first honest picture decides the engagement. Capacity-based tracking replaces optimistic estimates with what the team has actually demonstrated, so the early numbers tend to be smaller than what the business believed it had been promised. At Greggs the refined Story Points went straight into prioritisation workshops with Marketing and the other departments. They were the basis for deciding what came next, not a scorecard for what had gone before, and confidence in the technical department recovered. Used as a stick, the same data destroys the trust it was gathered to rebuild.