Scale · AI enablement and adoption
Enthusiasts race ahead while others dabble or opt out. The earlier tool training did not stick, the licences go unused, and leaders cannot tell whether AI is improving delivery or just adding cost.
We move beyond awareness and tool training to build practical, lasting capability. Working with your teams on real challenges, we develop the skills, confidence and ways of working that turn AI potential into measurable value. We did it to ourselves in 2024, and have done it for large engineering organisations since.
THE PROBLEM
Almost every engineering leader has run an AI enablement programme. Almost none can show the board what it changed. The reasons are consistent.
Ten percent of your AI users are flying. Most dabble. Some have opted out. The average is unremarkable and the licence bill is not.
A vendor workshop, a lunch-and-learn, a Confluence page. Three months later nobody can remember what was covered, and everyone prompts differently.
Without standards for prompting, context, security and review, everyone invents their own. The code drifts and the seniors spend their time fixing it.
Leaders cannot say whether throughput, quality or satisfaction has moved. So the next investment decision is made on who shouts loudest.
THE PROCESS
Enablement programmes run from eight to sixteen weeks for an engineering organisation, with the measurement in place from week two, so the result is a number rather than a feeling.
Map
A developer baseline: tool usage per person, licence utilisation, repository complexity, test coverage, review cycle time and confidence, using our EPIQ framework. We find the enthusiasts, the dabblers and the opt-outs, and understand why.
You hold: An honest adoption baseline, per team, with the metrics you will judge the programme by.
Design
A shared method: standardised developer environments, repository-level context files, system prompts and IDE configuration tailored to your codebase and security standards. A practitioner-as-trainer model is designed around your squads, with internal champions identified.
You hold: A method every engineer will work from, and a champions network ready to carry it.
Build
Hands-on upskilling inside the squads: our practitioners pair-programme with your engineers on real backlog items, model prompt engineering and spec-driven development habits, and run certification pathways for Copilot, Claude and OpenAI. Enaible embeds the method into the daily workflow, and dashboards track PR cycle time, velocity uplift, test coverage and defect rates from the baseline.
You hold: Engineers working from the shared method on real tasks, with adoption and confidence measured per person.
Run
Internal champions take over the pairing, the standards and the dashboards. The metrics are reported to leadership against the baseline, and the next tranche of teams is enabled by your own people.
You hold: Adoption that compounds, led from inside, with a measured return on the licences.
THE OUTCOME
What good looks like:
PARTNERS AND ECOSYSTEM
More than 400 GitHub Copilot certifications covering over 90% of our engineers, 100+ Anthropic-certified practitioners and 750+ OpenAI certifications.
We are tool-neutral by design: the enablement is built around the tools you have already bought — Copilot, Claude Code, Codex, Cursor — rather than the ones we would like to sell you. Certification pathways run with each vendor directly.
400+ Copilot certifications.
Covering more than 90% of our engineers, so the people pairing with your squads hold the certification they are coaching towards.
Preferred Services Partner.
100+ certified practitioners. Claude Code is one of the tools the method is built around, not one we are selling into it.
Select Partner.
Certification pathways run with the vendor directly, so your engineers finish with a credential that means something outside your organisation.
Alongside them we enable on Cursor, Microsoft and AWS tooling — whichever your engineers already have in the IDE.
BUILT IN-HOUSE, PROVEN ON CLIENT WORK
Puts AI in the work
A workflow layer that puts AI into the processes people already use, so adoption sticks beyond the first month.
For this work: The difference between training and adoption is whether the method lives in the daily workflow. Enaible puts it there, so the habits built in the Build step are still there three months later.
LIVE IN BUSINESSES LIKE YOURS
Skyscanner
GitHub Copilot and AI-assisted testing embedded across mobile and data squads: 75% applied agentic workflows immediately, and automated test coverage went from 18–22% to 80–88%.
Read the case study
OAG
AI-assisted development embedded alongside the team: prototype hypothesis generation from 6 days to 1, and performance-testing effort down 75%.
Read the case study
CreateFuture in a short space of time has already delivered a multiple return on our investment.
James Conway
Director of Technology, tombola
THE FIRST STEP
Two weeks gives you per-team usage, licence utilisation and confidence data, and a clear view of who is flying, who is dabbling and who has opted out. Several clients have found the baseline alone justified cutting the licence bill before anything else.
