Your organisation spent on AI training. Do you know if it worked?
Completion rates are not capability. Most AI upskilling programmes measure who attended — not who can now do the job differently with AI. Anthropos maps AI skills, builds them with role-specific content, and verifies them through simulations. So you can show what changed.
Why most AI upskilling programmes don’t stick
The pressure to “do something about AI” drove a wave of training spend. The results have been hard to measure — because the wrong things got measured.
Generic training doesn’t transfer to real work
“Intro to ChatGPT” teaches your team about AI. It doesn’t teach your sales team how to use AI in a discovery call, or your engineers how to review AI-generated code. Role-specific application is where the value is — and where most programmes stop short.
Completion rates aren’t capability signals
You know 87% of employees completed the AI literacy module. You don’t know who changed their workflow, who still avoids AI tools, and who is using them in ways that create compliance risk. Training completion and capability development are different things.
Technical and business AI skills need different approaches
A software engineer using GitHub Copilot in production has different AI skill needs than a finance analyst using AI for modelling. One programme can’t serve both. But most organisations don’t have the taxonomy or content depth to differentiate — so they default to the same generic course.
Map, develop, and verify — in a loop
Effective AI upskilling isn’t a one-time programme. It’s a cycle: understand where people are, build targeted capability, then verify it actually developed. Repeat as AI evolves.
Map the AI capability baseline
Before spending on development, understand what AI skills your workforce already has — and where the gaps are. Anthropos maps AI skills across the 60K+ taxonomy, by role and by team, so investment goes where it’s actually needed.
Develop with role-specific AI content
Target AI Skill Paths and AI Labs to the specific roles and skill gaps the mapping revealed. Engineers get AI-assisted coding simulations. Business teams get prompt engineering and GenAI workflow content. Everyone gets what they actually need.
Verify — don’t assume — capability developed
After development, assign an AI Simulation to confirm the skill actually transferred. The employee works through a realistic scenario using real AI tools. You get an objective score — not a self-rating, not a quiz result. Evidence of capability.
Structured learning journeys built for AI skills
30+ AI-specific Skill Paths — covering everything from foundational AI literacy to advanced role-specific application. Curated content in video, document, exercise, and quiz format.
Practise with real AI tools — in a scored simulation
AI Labs are AI Simulations where employees work with real AI tools — Copilot, Claude, ChatGPT — on realistic job tasks. Not a quiz about AI. Actual use, objectively scored.
Prompt Engineering
Design and iterate prompts to complete realistic business tasks with defined quality criteria.
AI-Assisted Coding
Write, review, and refactor code using AI pair programmers — evaluated on output quality and AI judgement.
GenAI Workflow
Build and execute an AI-assisted business workflow from prompt design through output validation.
AI Output Evaluation
Assess AI-generated text, code, or analysis for accuracy, bias, and suitability for use.
Training that ends at completion. Upskilling that ends at verified capability.
Most platforms tell you who completed the course. Anthropos tells you who can now do the job with AI — and who still can’t.
Scored, not just completed
Every AI Lab and AI Simulation produces an objective score — 0 to 5 — with cited evidence from the session. HR gets data. Employees get feedback. Managers get a capability picture.
Role-specific, not generic
A data engineer and a sales manager need different AI skills. Anthropos maps the gap by role, delivers content matched to that gap, and verifies with a simulation built for that role — not a one-size programme.
Continuous, not a one-off event
AI capabilities evolve. Skills decay. Anthropos supports periodic reassessment — so you track whether AI capability is growing or regressing across your workforce over time, not just at programme launch.
Skills mapping feeds development. Development feeds verification. Verification closes the loop.
Each phase informs the next — creating a continuous improvement loop, not a one-time programme.
Map
Auto-extract AI skills from CVs and HRIS. Self-evaluation adds proficiency context. You see the baseline.
Develop
Assign targeted AI Skill Paths and AI Labs to individuals and teams, based on mapped gaps — by role.
Verify
AI Simulation confirms the skill transferred. Score + evidence updates the profile. Gaps that remain are visible.
Built for every function navigating the AI transition
AI upskilling isn’t only a technical problem. Every function in the organisation needs to understand and apply AI — at different depths, in different contexts.
Engineering & technical teams
- AI-assisted coding with Copilot and Claude Code
- Code review for AI-generated output
- AI system design and architecture principles
- Responsible AI deployment and risk assessment
Business & commercial roles
- Prompt engineering for sales and analysis tasks
- GenAI workflow design and automation
- AI output validation and critical review
- Data-driven decision-making with AI assistance
HR & L&D leaders
- Workforce AI readiness baseline and reporting
- Targeted upskilling programmes by function
- Before/after capability measurement
- Board-level AI skills coverage reporting
Trusted by leading enterprises
Frequently asked
What HR and L&D leaders ask when planning an AI upskilling programme.
Know who can work with AI — not just who completed the training
Book a demo and see how Anthropos maps your current AI capability baseline, builds role-specific development, and verifies the skills that develop — with evidence.