The Academy now has four credentials. Three of them form a ladder: EMA-I, EMP-II and EME-III examine the same five domains at the scale of a team, an organisation and an engineering function. The fourth, the EMA AI Extension, is examined against something else entirely, and the reasons for that design say a good deal about what a credential in this area can honestly claim.
A level would have been the wrong shape
The ladder is defined by scope of judgment. Moving from EMA-I to EME-III means the decisions in the questions get bigger, from one team's sprint to an organisation's operating model. AI does not fit on that axis. A first-line manager whose team ships an AI-powered feature faces AI decisions every week, and so does a director setting an adoption policy for four hundred engineers. Scope varies; the subject stays.
So EMX-AI is an extension. You hold it alongside a ladder credential, and you need a valid EMA-I, EMP-II or EME-III when the sitting starts. The platform checks that when you order and again when you begin, because the prerequisite carries weight in the design. Competency TJ-5 of the core framework, AI-assisted engineering, is the baseline every engineering manager needs: introducing assistance with clear expectations, recognising its risks, telling real help from looking fast. Every ladder credential already certifies it. The extension assumes that baseline and goes further.
The core promises not to move. AI moves anyway.
The core standard changes its competencies at most once a year, after a public comment period. That promise is worth a lot. A credential earned in 2026 should mean the same thing in 2028, and employers who read it should not need a changelog.
AI practice cannot live under that promise. The evidence on productivity is contested and keeps shifting, regulation such as the EU AI Act is taking effect in stages, and the tools a team relies on this year may not exist in two. A standard bound to an annual cycle would end up certifying judgment about a world that has already moved on.
The EMA AI Standard therefore has its own governance. Major versions, which change competencies, weights or indicators, come at most once every six months and always after public comment. Minor versions, which correct and clarify, come quarterly. The credential pays for that freedom in two ways. It is valid for two years, against the ladder's three. And when a major version changes an indicator, every question written to that indicator is retired or rewritten before the version takes effect, so nobody is examined on a statement the current standard no longer makes.
One rule keeps the two standards from drifting apart. TJ-5 is the baseline and the AI Standard never contradicts it. If a release of the AI Standard would, TJ-5 is corrected in a minor version of the core in the same quarter, and both changelogs record it.
Six competencies, three of them technical
The EMA AI Competency Model names six competencies:
- AI-1, adoption strategy and staged bets: where AI earns its place, sizing a bet so it teaches you something, and when to stop.
- AI-2, governance, security and compliance: data flows, prompt injection and agency risks, policy in proportion to the risk.
- AI-3, AI-assisted software delivery: accepting generated code, agent permissions, review and testing when code is cheap.
- AI-4, building AI-powered products: choosing the approach, evaluations, non-determinism, cost and model dependencies.
- AI-5, operating AI systems in production: quality and cost signals, model changes treated as production changes, AI incidents.
- AI-6, capability, roles and measuring impact: building skill, protecting early careers, measuring honestly.
AI-3, AI-4 and AI-5 are the technical half, and they carry 34 of the examination's 60 questions. That weighting was a deliberate choice. A manager who leads a team building on a model has to read an architecture sketch, question an evaluation result and decide whether an agent may merge its own changes. Those are judgments about technical decisions, and a credential that skipped them would certify opinions about AI rather than the ability to lead the work.
The questions still never test engineering recall. No item asks a candidate to write code, a prompt or a configuration, or to name a vendor's product. Products and their current figures change faster than any standard can, and naming them would date the examination within a quarter.
What it looks like in practice
The fastest way to see the register is to try it. The EMX-AI sample questions are ten original items written for the sample, weighted across the six competencies the way the real outline is. One describes an assistant that keeps quoting company rules that were retired months ago and asks which change addresses the cause. Another gives two prompt versions scoring 81% and 84% on a single run of an evaluation set and asks what the team should conclude. Neither needs a line of code to answer. Both need you to understand what is happening inside the system.
The examination itself is 60 questions in 90 minutes with a pass mark of 75%, drawn at the weights published in the exam content outline. Like every EMA credential, it is earned by examination only. The Academy sells no AI course and endorses none, which is what lets the credential say something independent of whoever taught you.
Version 1.0 is open for comment
Version 1.0 of the AI Standard was released with its comment period running alongside, because it had no earlier version to keep in force while it was discussed. Comment is open until 17 November 2026 on the consultation page. Every comment is answered, and the Academy will publish a summary of what it received, what it changed and why it declined anything it declined. If you lead engineers who build with AI, the standard is better for your reading of it.