How to Assess Maturity Before Adopting an AI Tool
Introduction
Decisions to adopt AI tools are often made based on technological potential or immediate benefits. Less attention is given to how mature the organization actually is in handling the consequences.
Lack of maturity rarely becomes evident at the procurement moment, but later on – through unclear usage, informal workflows, inadequate documentation, and undefined lines of responsibility.
This article provides a structured framework for assessing maturity before adopting an AI tool, with particular focus on management decisions, governance, and pre-implementation.
What is meant by maturity in the AI context?
Maturity in the AI context is not primarily about technical capacity. It relates to the organization's ability to adopt, manage, and monitor AI in a controlled manner over time.
A mature organization is characterized by:
– Understanding why AI is desired
– Knowing which processes and decisions are affected
– Having clear roles and responsibilities
– Being able to manage risk, change, and documentation
– Having realistic expectations of both benefits and limitations
Thus, maturity is an organizational and governance issue, not a question of how advanced the technology is.
Why is maturity assessment a management responsibility?
When AI is adopted without an explicit maturity assessment, one of two things happens: either the usage is stalled by uncertainty and ad hoc adjustments, or practices develop informally without governance.
Both increase risk.
Management has the responsibility to assess:
If the organization is ready for change
AI affects work methods, assessments, and interaction – even when its use begins small.If the governance structure is adequate
Without clear owners, frameworks, and follow-up, AI quickly becomes "no one's responsibility."If the decision foundation is sufficiently broad
Technical functionality alone is a weak basis for a strategic decision.
Maturity assessment is therefore a prerequisite for informed management decisions – not an addition afterward.
Common Misunderstandings
"We can test first and evaluate maturity later"
Pilot use quickly establishes practice. When employees adapt their workday to a new tool, the change has already begun – before governance is established.
"Maturity is about digital competence"
Competence is important, but without frameworks for responsibility, usage, and follow-up, competence alone provides limited control.
"This only matters for larger AI solutions"
Small tools can have a large impact. The need for maturity is determined by the impact on workflow and decision-making foundation, not by technological size.
"The vendor can help us with this"
Vendors know the technology, but not the organization's context, lines of responsibility, or governance needs. Maturity assessment cannot be outsourced.
What should be in place in practice?
A practical maturity assessment before implementation should touch on several dimensions simultaneously. Not as a checklist, but as a structured review.
Typically, management should have clarified:
– Purpose and use case
Why should AI be used and which processes are affected?
– Impact on work and decisions
Does the tool change how assessments are made, prioritized, or documented?
– Responsibility and ownership
Who is responsible for usage, follow-up, and any changes over time?
– Competence and understanding
Do the users have sufficient insight into limitations, risks, and correct use?
– Governance, documentation, and follow-up
Are there frameworks for how use should be followed up, adjusted, and reviewed?
– Change readiness
Is the organization prepared for the usage and effects to evolve over time?
If several of these questions are unresolved, it is a signal that maturity has not been adequately assessed – regardless of how attractive the technology appears.
Actera's role in this
Actera is established to provide dental health organizations with structure around responsible use of AI.
We do not work with technology development or clinical decisions, but with governance structure, lines of responsibility, and documentation – so that AI can be used in a safe, predictable, and verifiable way.
Final remark
Assessing maturity before adopting an AI tool is not about delaying development, but about ensuring that the decision is rooted in organizational reality.
When management takes responsibility for pre-implementation, AI becomes a manageable tool – not a source of informal practices and undefined consequences. Maturity is thus not a goal in itself, but a necessary foundation for responsible use.
