AI-native delivery

AI-native delivery: when AI coding tools haven't sped delivery up

Use AI across requirements, building, testing and review. Tenhaw helps your team establish an issue-to-merge pipeline and review gates on real work.

Learn on real work. Six weeks of workshops and pairing, £24,000 fixed. Engineering programme scope.

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30 minutes with James Rooney to discuss your team, workflow and first scope.

See the evidence and approach
The job to be done

What we can help you deliver

For product, engineering and QA leaders who already have AI coding tools and want to change how useful, checked work reaches users.

Give the build the whole requirement

Write the intended outcome and acceptance evidence with enough context to expose gaps before coding. Your experts resolve the decisions the source material cannot settle.

Keep review close to the build

Make security, tests and human acceptance part of the issue-to-merge pipeline. Your repository’s policies and review gates continue to apply.

Measure what reaches users

Follow cycle time, accepted changes, rework and the people’s ability to run the method. Use individual comparisons with their limits, rather than a general productivity multiplier.

Use a 30-minute call to discuss the outcome your team needs.

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Working together

Build the capability around real work

  1. 01

    Practise on a real workflow

    Choose work the team owns and bring product, engineering and QA into the same requirement and acceptance discussion.

  2. 02

    Establish the working method

    The six-week engineering programme combines two workshop days with four weeks of weekly pairing. It leaves an issue-to-merge pipeline and review gates in your repository.

  3. 03

    Use the published how-to library

    The Tenhaw Way sets out the method and its checks free of charge. Use the guides to extend the practice as your team becomes familiar with it.

Ask how this approach would fit your data, systems and team on a 30-minute call.

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Evidence and approach

Read the evidence behind the approach

One reported outcome-ticket comparison at PPL

On one epic, outcome-based tickets produced 54 front-end changes against 1 for a prescriptive story in a similar cycle time. This is one reported comparison, not a general productivity benchmark; the associated greenfield build remained a working proof of concept and did not go into production.

Read the PPL delivery-method comparison

Book 30 minutes to ask James about the work and how it relates to your situation.

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Scope and price

Choose a starting engagement

Agree the workflow, deliverables and team before work starts. These are the existing engagements this use case can sit within; prices exclude VAT.

Learn

Fixed price · from £8,500

One day or six weeks

Learn to use AI on work your team already owns.

See scope, team and terms

Agentic Proof of Concept

Fixed price · £20k–£55k

2–4 weeks

Build and test one AI workflow in your own tenancy.

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90-day outcome programme

Fixed price · £120k or £180k

90 days

One workflow in production and in use in 90 days.

See scope, team and terms

Use a 30-minute call to decide which scope is a sensible starting point.

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Read further

Explore the method and related use cases

book a call

Talk through the work you want to change.

A 30-minute discovery call with James Rooney. Bring a workflow, product or team challenge and we will discuss a sensible first scope.

Book a 30-minute callRead the AI case studies first

most start with a fixed-price AI Readiness Audit · £44,000 · 4 weeks · working prototypes

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Frequently asked questions

Why haven’t AI coding tools made us faster?

Tools alone leave the surrounding work unchanged. Requirements, handoffs, testing and review can still constrain delivery. Tenhaw’s working method connects these activities so the team can test whether the change improves accepted work.

What is an outcome ticket?

It describes the result and acceptance evidence, giving the builder context to choose an implementation. At PPL, one reported comparison on the same epic produced 54 front-end changes against 1 for a prescriptive story in a similar cycle time; it is not a general productivity benchmark.

What changes in code review for AI-written code?

Review remains a human responsibility under your repository’s controls. Check the implementation against requirements and acceptance examples, with security review inside the build. At PPL, security was reviewed roughly every fifth prompt during the proof of concept.

How do you measure the effect?

Measure accepted work, cycle time, review effort and rework against the starting position. Keep different measures distinct: PPL’s reported 70% confidence was one engineer’s self-reported confidence after the paired two-week working proof of concept, not a productivity measure.

Do we need new tools?

The work starts with the tools and infrastructure approved for your engagement. The method changes how requirements, building, testing and review fit together; the tool choice follows the task and your policies.

How long does it take?

The Learn engineering working-method programme runs for six weeks at £24,000 fixed. A separate proof of concept or 90-day programme is available where the requirement is to prove a workflow or take it into production and use.

Is the method published?

Yes. The Tenhaw Way and its how-to library are published free. They describe the delivery method, the practical steps and the checks a team can use.