
The five levels of AI readiness — and why most companies are at level two
Before choosing a use case, know where your data, processes and people actually are.
Read more →Role-based training for leaders, managers and staff: how to use AI tools safely and productively, how to write prompts and evaluate output, and how to run AI projects.
Executive briefing
Role-based workshops
Hands-on labs with your documents
Prompt and evaluation playbooks
Internal champions programme
30-day follow-up and measurement
Licences without adoption.
Sensitive data pasted into public tools.
Buying decisions without criteria.
Sustaining adoption after the training.
Five-level maturity map, data and process review, prioritised use cases.
Scope, data set, owners, guardrails and acceptance criteria.
Run in parallel with the current process; measure quality and time.
Roll out with training, monitoring and a governance cadence.
Admin dashboards, programme reporting and planning data for public bodies and agri-trade.
E-commerce, cross-department ERP, AI assistants and field apps for tour operators.
Newsroom CRM, Telegram AI bots and content operations for media teams.
Membership academies, learning platforms and training programmes.
MVPs, internal tools, market research and go-to-market for tech teams.
Industrial IoT, monitoring and forecasting — capability profiles and pilots.
No — labs use your documents and processes.
Yes — our event team runs the programme logistics.
Pre/post skills assessment and usage data at 30 days.
Both.
Bring us the problem nobody on your team has time to own. We will scope it, price it and start with a pilot you can measure.

Before choosing a use case, know where your data, processes and people actually are.
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Two design rules make AI assistants deployable in real operations: every answer has a source, every action has an approver.
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Most repetitive work is rule-shaped. Use a model only where documents or judgement vary.
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