Overview
The platform and the AI infrastructure
The Human Layer Graph
One living model of your workforce
Anima, the engine
Ingest, analyse, transition, traction
Agents, MCP and CLI
Let agents drive the platform
Signals and Integrations
Data in, intelligence out, read-only
Security and TrustEXT
Trust Center
Featured programmes
The four flagship ways in
All programmes
The full library, twelve across three phases
Discovery programmes
Baseline the work, role by role
Transform programmes
Redesign roles, stack and process
Traction programmes
Measure adoption from live systems
Our approach
How AI reshapes work, and how we tackle it
Our experts
AI and domain experts who run engagements
How engagements work
First call to live measurement
Forward-deployed alongside your team
Week by week, and exactly what you see
Why partner
An engine to lead transformations
How it works
You in front, the platform behind
Partner tooling
The MCP and CLI layer, partner-framed
The partner journey, application to renewal
Your agents. Every client's graph.
Insights
Frameworks, research and points of view
Roles
Task-level analysis across roles
Industries
AI impact by sector
By team and leader
Built for every leader
Compare
See how the model stacks up
Trust CenterEXT
trust.inc
Your AI transformation is firing your greatest asset
What is AI workforce transformation?
The challenges you face
AI is changing work faster than organizations can adapt. Sound familiar?
Backward-looking dashboards do not answer the question
Execs want to know how AI will reshape the workforce in 12 to 24 months. Your current analytics stack only shows what already happened.
Industry averages are useless for your org
Generic reports say 30% of tasks will automate. But which 30%? For which roles? In your specific context? You need analysis tailored to your actual workforce.
You are stuck in reporting mode
You have the analytical depth to drive workforce strategy, but without forward-looking intelligence tools, you are producing dashboards instead of earning a seat at the strategy table.
Task-level data simply does not exist in your stack
You know AI impacts tasks, not job titles. But your HRIS, your ATS, and your BI tools were all built around job architecture, not task decomposition.
You need to trust the methodology
Black-box AI predictions are worse than no predictions. You need to see the data sources, the confidence intervals, and the assumptions behind every projection.
Adding another tool to the stack is painful
Your analytics infrastructure is already complex. Any new platform needs to integrate with your existing data sources, not create another silo.
How Human Layer Lab helps
Move from reactive firefighting to proactive workforce strategy
Six-layer task decomposition and scoring
Every role is broken into tasks, scored for AI impact, projected forward at 6, 12, 18, and 24 months, then synthesized into actionable outputs. The granular data you have been asking for, finally.
Evidence-grounded signal intelligence
Analysis draws from government labor data, job market trends, AI tool maturity tracking, and regulatory signals. Plus org-specific data from Jira, Linear, Toggl, and your HRIS. Every source is visible.
On-demand Researcher agent
APEX includes a Researcher agent for on-demand intelligence queries. Ask it about emerging skills, market benchmarks, or AI adoption patterns and get answers grounded in real data, not generic summaries.
Benchmarking against the Roles Library
Compare your org's role analysis side-by-side against our curated Platform Roles Library spanning multiple industries. Defend your projections with market context, not just internal data.
Roles we analyze for you
Task-level analysis of the roles that matter most to your organization
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