Services · AI Adoption
AI adoption, measured by telemetry — not surveys.
Most organisations measure AI by counting licenses, sending surveys, and tallying chatbot logins. That measures exposure — not transformation. We measure the real thing: every prompt, skill, agent, MCP tool call and multi-agent flow.
The problem, reframed
Without telemetry, AI adoption stays invisible.
How it’s usually measured
Exposure metrics
License counts, surveys, training completions. They tell you who could use AI — never who actually does.
- How many employees have access to AI?
- How many ChatGPT licenses are active?
- How many trainings were completed?
- How many people answered 'yes' in the survey?
How we measure it
Behavioral telemetry
Objective signals from the systems people actually use, model- and infrastructure-agnostic.
- Who is using AI daily — and how intensely?
- Who is building skills and reusable workflows?
- Who is orchestrating multi-agent systems?
- Where is AI creating measurable business impact?
What we track
Eight signal families. One telemetry layer.
If a user prompts a model, runs a skill, invokes a tool, calls an MCP server, or spawns an agent — that interaction generates measurable adoption data.
Prompt-level AI usage
Every model call leaves a fingerprint. Frequency, structure and context-awareness reveal who is actually thinking with AI — and who is one-lining it.
- Frequency and intensity
- Prompt structure complexity
- Context-aware interactions
- Model switching behavior
Skill and workflow execution
Sub-agents and agentic workflows
Agent teams and multi-agent systems
MCP servers and tool integration
CLI and developer AI usage
AI composition and hybrid workflows
Cross-cutting signals
From telemetry to maturity
Five levels of AI maturity.
Raw telemetry becomes structured intelligence. Each user, team and department gets scored on a five-level ladder, by behavior — not self-report.
Integrated AI workflows
Tool-connected AI is embedded into business processes — ticketing, ERP, source control, internal APIs.
Model- and infrastructure-agnostic
If it produces inference, orchestration, or tool-calling telemetry — we can measure it.
- GPT family
- Claude
- Llama
- Mistral
- Qwen
- DeepSeek
- Gemma
- Open-source LLMs
- On-prem AI
- Enterprise AI gateways
The chain
Behavior → telemetry → maturity → transformation.
True AI transformation happens when AI is embedded into workflows, composed into systems, delegated as agents and orchestrated across teams. Those behaviors generate telemetry. Telemetry reveals maturity. Maturity drives transformation.
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— Behavior
Real interactions
Embedded into workflows, composed into systems, delegated as agents, orchestrated across teams.
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— Telemetry
Objective signals
Prompts, skills, agents, tool calls, MCP traffic, model-switching, inference events — captured continuously.
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— Maturity → Transformation
Scored capability
Telemetry rolls up to L1–L5 maturity per user, team, department — the only honest measure of AI transformation.
What enterprises gain
AI adoption becomes a data discipline — not a narrative.
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Quantify real AI usage
Replace anecdotes with intensity scores per user, team, function.
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Identify power users
Surface champions and shadow practitioners worth elevating.
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Detect shadow AI
Spot unauthorised tools, models, or data flows in real time.
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Benchmark departments
Compare maturity across functions and over time.
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Track transformation
See progression L1 → L5 by team, role, and quarter.
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Measure ROI
Tie AI usage to business outcomes — output, cycle time, cost.
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Optimize enablement
Direct training where telemetry shows it actually moves the needle.
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Strengthen governance
Continuous compliance, not annual surveys.
Next step
Stop guessing. Start measuring.
45-minute readiness review: we map your AI surfaces — gateways, IDEs, MCP servers, model providers — and leave a 4-week telemetry pilot plan with a maturity baseline.
What you leave with