Starting from zero
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AI Readiness Curriculum
Readiness is not a score. It is a shared view of the conditions that make a useful first move possible.
A practical baseline for organizations that want to understand the conditions around AI before asking people to change workflows or commit to a new tool.
Programme architecture
A curriculum that meets people where they are.
- For
- Cross-functional teams spanning leadership, operations, people, technology, and risk.
- Format
- Interviews, working sessions, self-assessment, and an evidence-led baseline
- Typical shape
- Five readiness lenses plus a prioritisation workshop
Already experimenting
Connect informal use to clearer workflow, information, review, and accountability practices.
Preparing to scale
Identify the operating conditions that need to be dependable before wider implementation.
Sessions and working outcomes
The work is designed to leave something useful behind.
Purpose and priorities
Clarify the business outcomes, teams, and decisions that matter before reviewing tools.
Working outcome: A shared readiness question.People and capability
Understand confidence, role needs, manager support, and the learning conditions people need.
Working outcome: A capability and support view.Workflows and information
Map handoffs, quality expectations, information access, and the constraints around useful experimentation.
Working outcome: A workflow and information baseline.Governance and care
Make responsibilities, review points, escalation, and responsible-use boundaries visible in the context of work.
Working outcome: A practical governance baseline.Priorities and first move
Decide what can begin now, what should be strengthened first, and how the organization will learn.
Working outcome: A prioritised readiness plan.What the organization keeps
Tools, decisions, and practices that can travel into the next phase.
- Readiness baseline
- People and capability view
- Workflow and information map
- Responsible-use starter guide
- Prioritised action plan
Find the right entry point
Start with a short practical check, then decide the right level of support.
The learning path
Four stages, designed to build on each other.
See
Understand current use, confidence, priorities, workflow visibility, and governance concerns.
Name
Make constraints and enabling conditions explicit across people, information, process, and decision-making.
Strengthen
Choose the smallest changes that make a contained experiment safer and more useful.
Review
Create a living baseline that can be revisited as capability and implementation grow.
What participants leave with
Work that can continue after the session.
- A clearer view of strengths and gaps
- A language for cross-functional readiness conversations
- A prioritised baseline for the first phase of work
- Practical guidance on what should wait and what can begin
The thesis
The AI Readiness Thesis
Read the point of view behind this curriculum, the decisions it is designed to support, and the practices that make the work durable.
Download thesis outlineFind the right starting point