Does your team already use AI? Can you prove it improves delivery?
We train your engineering team, already using Copilot, Cursor, or Claude Code, to work better with AI, measuring before and after with real delivery and adoption metrics.
What is AI Engineering Training?
AI Engineering Training is a methodology for AI adoption inside engineering teams, delivered by 23people as a 2 to 16 week guided accompaniment, training the team in how to work better with AI. Each accompaniment measures 4 metric families (delivery, adoption, AI usage, competency) at T0 before and T1 after, compared against a real baseline.
AI Adoption Measured, Not Promised
We train your engineering team to work better with the AI tools it already uses, with a shared methodology and before-and-after measurement at team level.
- Shared methodology, not individual tool use
- T0 → T1 measurement of delivery, adoption, and competence
- Guided accompaniment, not self-paced courses
Who this is for
AI Engineering Training is designed for in-house engineering teams that have already adopted AI tools, but lack a shared methodology and a system to measure impact.
Teams already using AI, but with inconsistent adoption
Your engineers use Copilot, Cursor, Claude Code, or other AI agents, but each person does it their own way. Code quality and speed vary across squads.
Engineering leaders who need to show ROI
You've been asked to demonstrate the return on your AI investment, and today you don't have metrics to back it up. The program measures the delivery, adoption, and competence delta for your team.
Teams seeing the hidden cost of AI in their way of working
Quality incidents have been attributed to AI-assisted work, or your security team is overloaded with review volume. The methodology integrates review and governance practices.
Not for
- Teams that haven't adopted AI tools yet and need a generic introduction.
- Individual training or self-paced courses. We work with full teams with guided accompaniment.
- Teams looking for a specific AI tool more than a methodology.
How it works: from diagnosis to accompaniment
Two stages, focused on your team and its way of working.
1 to 2-week assessment
We capture a baseline (T0) of delivery, AI adoption, and competence metrics for the team. We deliver a gaps report with concrete recommendations.
6 to 8-week accompaniment
We train your team with guided accompaniment, with hands-on sessions on their real challenges. We close with a re-measurement (T1) and a delta report across the four metric families.
Methodology: measure before and after, at team level
The program is built on a simple premise: if we don't move the needle, the data shows it. That's why we use a T0 → T1 measurement system covering four metric families, captured on your real team.
Four metric families
Delivery
DORA metrics: lead time, deploy frequency, time to change, and failure rate, captured on the team's real workflows.
Adoption
SPACE metrics: satisfaction, performance, activity, communication, and efficiency, captured at team level, with no individual ranking.
Competence
Technical assessment of AI tool use per squad, focused on prompting, review, and governance, compared against the initial assessment.
Business
Business indicators your team already tracks: hours per feature, cycle time, backlog burn-down. Same method, your metrics.
We measure the system and the team, not individuals.
We train on your way of working, not generic exercises
Sessions and the closing project operate on the team's real challenges, with the same stack and AI tools already running in production. It works with Java, .NET, Python, TypeScript, polyglot stacks, and any modern stack.
Three formats depending on where your team is
One methodology, three ways to start. You choose the depth, and we adjust it after the diagnosis.
Initial assessment
1 to 2 weeks
We capture the T0 baseline for the team and deliver a gaps report with concrete recommendations. Useful to understand where you stand before committing to a full program.
Full program
6 to 8 weeks
Full accompaniment, hands-on sessions on the team's challenges, methodology for prompting, review, governance, and delivery. Closes with a T1 re-measurement and a delta report per metric family.
Ongoing reinforcement
Quarterly
Ongoing accompaniment, continuous measurement, and office hours to sustain the methodology over time. For teams that have completed Craft and want to keep the consistency.
How we work, the principles that apply to this service
Methodology, not tool
The program is agnostic to the AI tool your team already uses. It works with Copilot, Cursor, Claude Code, Codeium, or any combination. The value is in the methodology, not the vendor.
Team accompaniment, not individuals
We work with full teams with guided accompaniment. Shared accountability is what sustains the change.
Measure first, promise later
We start by measuring. We close by measuring. We don't guarantee uplift, we publish the delta as it stands. If we don't move the needle, the data shows it.
Frequently asked questions
Is this a course or a consulting engagement?
It's an accompaniment-based training program with hands-on components on the team's way of working. It's not a generic course and it's not an implementation consulting engagement: the main deliverable is that your team internalizes the methodology.
Do we need a specific AI tool?
No. The methodology is tool-agnostic. We work with Copilot, Cursor, Claude Code, Codeium, or whatever stack the team already uses. The key is that the team is already experimenting with AI, this is not an introductory program.
How long is the full program?
Craft runs 6 to 8 weeks, with sessions distributed across that period. The initial assessment runs 1 to 2 weeks. Craft+ is a quarterly reinforcement scheme for teams that have already completed Craft.
When do we see the first results?
First signals appear between weeks 4 and 5, in the middle of the program. The formal T1 re-measurement happens at the close of Craft. Stabilization and business-attributable ROI are observed in the 2 to 3 months that follow.
How do you measure impact without ranking my team?
We measure the system and the team, not individuals. DORA and SPACE metrics are captured at the squad and team level. The competence assessment is anonymized and delivered in aggregate. We publish the delta as it stands, without rankings.
Do you work on our stack and our tools?
Yes. Sessions and the closing project run on the team's real challenges, with the same stack and AI tools already in use. Java, .NET, Python, TypeScript, polyglot, all supported.
23people AI Engineering Training vs Udemy / Platzi / Internal L&D
An honest comparison of how an adoption methodology differs from tool tutorials and internal L&D programs.
| Dimension | 23people | Udemy / Platzi / Coursera | Internal L&D / Academy |
|---|---|---|---|
| Focus | Adoption methodology (how the team works with AI) | Per-tool tutorials (Copilot, Cursor, Claude) | Mixed, depends on the internal program |
| Format | Instructor-led accompaniment (2 to 16 weeks) | Self-paced, individual courses | Mixed, workshops, MOOCs, mentoring |
| Client's work | Yes, the accompaniment works on the client's real way of working | No, generic examples or sandbox | Sometimes, depends on scope |
| Outcome measurement | T0 / T1 across 4 metric families (delivery, adoption, AI usage, competency) | Certificate of course completion | Variable, typically no comparable metrics |
| Methodological depth | AI engineering: context, prompts, evaluation, guardrails, deployment, monitoring | Per-tool UI tutorials (what each product shows) | Variable, typically broad coverage, low depth |
| Sustainability of change | Team keeps measuring and improving after the accompaniment ends | Courses consumed, no continued application | Depends on internal sponsorship |
| Language and timezone | Spanish or English, LATAM timezone (overlap with US East / West) | Global platform, variable instructors | Limited to the client's internal team |
23people AIET is not a MOOC and does not replace tool-specific courses. It is an adoption program for the client's team, with comparable before-and-after measurement. If your only goal is to certify your team in using a specific tool (e.g., GitHub Copilot), a self-paced platform may be sufficient. If your goal is to change how the team delivers with AI, AIET is the better option.
Recommended reading
Recommended reading on the same theme: AI as an amplifier of capabilities, not a replacement.
Related services
Your team needs more than training, it needs the right team. These services complement AI Engineering Training.
Does your team already use AI? Can you prove it improves delivery?
We assess how your dev teams use AI, where adoption gets stuck, and what to improve, and give you concrete suggestions.
- Request your diagnosis
- Let's talk for 30 minutes
- No commitment
- Concrete suggestions for your teams