AI readiness assessment framework
Our assessment method helps organisations review potential AI use cases, readiness and governance requirements before wider adoption.
- Type
- Glyz method
- Services
- AI strategy, readiness and governance
- Area
- AI and digital transformation
Context
The wider situation.
Interest in AI has spread quickly across most organisations. Individual teams often try tools on their own, and pilots start in different departments without a common way of judging whether they are worth continuing. The result is a collection of experiments that are hard to compare, with little agreement on which ones should receive investment or how they connect to the work the organisation actually needs to do.
Readiness is rarely only a question of technology. Useful AI depends on data that is accessible and of suitable quality, on systems that can be connected, and on teams who understand how to use the outputs responsibly. Governance has also become a practical requirement, as organisations need clear rules on review, approval and data protection, and must take account of regulation such as the EU AI Act when deciding where and how AI is used.
The challenge
What needed to change.
Many organisations have AI tools and pilots but no shared view of where AI creates value, how ready their data and teams are, or how it should be governed.
A shared assessment can help teams compare proposals, define priorities and agree practical next steps.
What we did
Our approach.
Review of workflows, data, systems and team readiness
Use cases mapped to real work and prioritised by value and readiness
A governance model covering review, approval, data protection and the EU AI Act
A phased roadmap with measures of success, aligned to the Glyz AI Framework
As a Glyz method rather than a single client project, the assessment follows a defined sequence. It begins with a review of workflows, data, systems and team readiness. Potential use cases are then mapped to real work and prioritised by value and readiness. A governance model is set out covering review, approval, data protection and the EU AI Act. The assessment concludes with a phased roadmap and measures of success, aligned to the Glyz AI Framework, giving leadership and delivery teams a documented view of priorities, dependencies and next steps.
What the assessment provides
A documented view of priorities, dependencies and next steps for leadership and delivery teams.
Planning similar work
What similar organisations should consider.
Start from real work
Use cases are easier to assess when they are described in terms of a specific workflow and the people who carry it out. Starting from real work, rather than from a list of tools, keeps attention on the problems that matter. It also makes it clearer what would need to change for a use case to succeed in practice.
Assess readiness honestly
A promising use case may still depend on data that is incomplete, systems that cannot yet be connected or teams who need further support. Reviewing workflows, data, systems and team readiness together shows where preparation is needed before adoption. An honest view at this stage prevents investment in work that cannot yet deliver value.
Prioritise by value and readiness
Not every idea should be pursued at once. Comparing proposals on both the value they could create and how ready the organisation is to support them helps leadership choose a manageable starting point. Early work that is both valuable and achievable builds confidence and experience for the more demanding use cases that follow.
Put governance in place before scaling
Governance is easier to establish while AI use is still limited. Defining how use cases are reviewed and approved, how data is protected and how regulatory obligations are met gives teams clear boundaries. A phased roadmap with agreed measures of success then allows progress to be tracked and decisions to be revisited as experience grows.
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