Discovery: Landing the Discovery+ launch on a CEO-set deadline
The challenge
Discovery+ had a hard launch date announced by the CEO and six teams that needed to redesign and merge content. The visual rebrand team James was asked to run was badly overloaded relative to the time available.
What we did
We moved delivery onto a data-driven footing, recalibrating Story Points to reflect actual capacity after completion rather than optimistic estimates. Across three sprints we presented Head of Delivery with Happy, Normal, and Sad path forecasts, made the probability of missing the date undeniable, and issued daily recommendations to lift speed and quality.
// run against The Tenhaw Way, published in full and free to adopt without engaging us
The outcome
Discovery+ and Eurosport got the clarity to manage delivery under real constraints and hit the launch. By the end of the engagement both teams could run those forecasting and tracking practices autonomously.
Nobody was told the date was impossible. They were shown what would have to be true for it to be possible.
Limits, and what is withheld
Probabilistic forecasting and confidence intervals are exactly what leadership needs to govern an agentic transformation: not a single guessed date, but a range you can plan and intervene against. What does not transfer is any agentic content. There were no agents on this engagement, and the forecasting method is the only thing that carries over.
Why a buyer usually lands on this one
Written for the person arriving mid-programme with a question.
The board has announced the date. Now what?
This is the most common shape of the AI programmes we are called into. A date exists, it was announced by someone senior, and the delivery evidence for it does not. The instinct is to re-plan. What actually works is to stop presenting a single date and start presenting a distribution.
Here that meant three forecasts every sprint, happy, normal and sad, built from measured capacity rather than optimistic estimates, put in front of the Head of Delivery until the probability of missing was undeniable.
Why this matters more for an agentic programme, not less
An AI programme director is asked for a date on work carrying more uncertainty than a rebrand: model behaviour shifts underneath you, integration surfaces turn out to be unowned, and the evaluation criteria are often still being argued about in week three. A single date on that is fiction, and everyone in the room knows it.
A range with a stated confidence, updated weekly from real throughput, is defensible to a board and, more usefully, actionable. It tells you which week to add a person, cut a scope item or move the date, while moving it is still cheap.
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.
Delivery, forecasting and coordination. No models, no agents, no AI in the deliverable.
- 02
We ran one workstream, not the launch.
James was asked to run the visual rebrand team and to make delivery risk visible across the programme. The launch was landed by six teams and the organisation around them.
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.
Making a pandemic-era app team predictable, and trusted again
2, squads: Mobile App and Integration
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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