Diagnostic
AI Adoption Assessment
A structured view of whether the organisation is ready to turn AI ambition into sustainable change.
Please consider each statement and select the response that most accurately reflects the organisation today, rather than where you intend it to be.
Try not to reflect for too long. Your first reaction will often provide the clearest indication of the organisation’s current AI adoption maturity.
Your results
AI Adoption Readiness
Overall readiness
The results indicate that your organisation has strong foundations for adopting AI successfully. AI initiatives are generally connected to clear business needs, supported by leaders and governed through appropriate decision-making and risk controls.
Employees appear to be involved in shaping how AI will affect their work, and the organisation has established many of the capabilities, communications and support mechanisms required for adoption. Benefits and usage are also being measured, increasing the likelihood that AI investment will translate into sustained business value.
The priority should now be to maintain consistency across the organisation, address any lower-scoring individual categories and ensure that effective practices can be repeated as the number and complexity of AI initiatives increase.
The results indicate that your organisation has established some of the foundations required for AI adoption, but these are not yet applied consistently.
There is likely to be a broad understanding of the potential value of AI, with some leadership support, governance and employee engagement in place. However, gaps between categories suggest that the organisation may be better at initiating AI activity than embedding it into everyday operations.
Without greater consistency, AI initiatives could deliver isolated successes without creating sustainable organisational value. Your organisation should focus on strengthening its lowest-scoring categories, clarifying accountability and developing a repeatable approach that connects business value, responsible delivery and workforce adoption.
The results indicate that your organisation faces significant barriers to successful AI adoption.
AI initiatives may be progressing without sufficiently clear business outcomes, active sponsorship, operational involvement or the capabilities required to embed new ways of working. Governance and benefits tracking may also be underdeveloped, making it difficult to manage risk or determine whether AI investment is delivering meaningful value.
The organisation should avoid rapidly scaling AI activity until these foundations have been strengthened. Priority should be given to establishing clear ownership, selecting a small number of credible use cases, involving affected employees and creating a structured approach to adoption, governance and value realisation.
The results indicate that your organisation does not currently have the organisational foundations required to adopt AI safely and successfully at scale.
AI activity may be fragmented, technology-led or disconnected from clearly defined business problems. Leadership accountability, governance, workforce readiness and benefits measurement are likely to be limited or absent. This creates a high risk of wasted investment, low employee adoption, unmanaged consequences and declining trust in AI.
A structured readiness intervention is recommended before further significant investment. Your organisation should establish its AI ambition, decision rights, risk controls, priority business opportunities and workforce adoption approach before moving into wider implementation.
Category Scores
Detailed Results
Business Value
The results indicate that your organisation takes a commercially grounded approach to AI. Initiatives are generally linked to defined business problems, measurable outcomes and strategic priorities, rather than being pursued because the technology is available.
The organisation also appears to consider alternative interventions and the full cost of implementation. This should help prevent overinvestment in low-value AI use cases.
Recommended focus: Maintain consistent value cases, validate assumptions during delivery and periodically review whether AI remains the most appropriate intervention.
The results indicate that your organisation usually considers the business value of AI, but the quality and consistency of this assessment may vary between initiatives.
Some projects may have clear objectives, while others are supported by broad productivity assumptions or loosely defined benefits. Full lifecycle costs may not always be understood.
Recommended focus: Introduce a standard AI opportunity assessment, define measurable baselines and require a proportionate value case before investment decisions are made.
The results suggest that AI initiatives within your organisation are not consistently connected to defined business needs or measurable outcomes.
There is a risk that technology is being selected before the problem is fully understood. Costs, operational implications and alternative interventions may also be underestimated.
Recommended focus: Review the current AI portfolio, clarify the problem behind each use case and pause initiatives that cannot demonstrate credible strategic or operational value.
The results indicate that your organisation lacks a reliable process for determining where AI can create genuine business value.
Investment may be driven by market pressure, individual enthusiasm or technology availability rather than evidence. This creates a significant risk of wasted investment and poorly targeted implementation.
Recommended focus: Begin with business problems rather than AI solutions. Establish an opportunity pipeline, assessment criteria, baseline measures and clear investment thresholds.
Sponsorship
The results indicate that senior leaders within your organisation understand their responsibilities for AI adoption. Sponsors are visible, accountable and able to explain the outcomes that AI initiatives are expected to achieve.
This active leadership should help remove barriers, accelerate decisions and reinforce that AI adoption is a business responsibility rather than solely a technology initiative.
Recommended focus: Maintain visible sponsorship and develop a broader network of leaders capable of sponsoring increasingly complex AI-enabled change.
The results suggest that executive sponsorship exists for AI, but the level of active involvement may be inconsistent.
Sponsors may support the strategic case but become less visible during implementation, leaving project or technology teams to resolve organisational barriers.
Recommended focus: Clarify sponsor expectations, establish regular sponsor interventions and provide leaders with practical guidance on leading AI-enabled change.
The results indicate that AI sponsorship within your organisation is weak or largely symbolic.
Leaders may approve investment without actively communicating the purpose of AI, resolving barriers or taking accountability for adoption and outcomes. This can undermine employee confidence and slow decision-making.
Recommended focus: Assign one accountable executive sponsor to each major initiative and define clear responsibilities for direction, communication, decisions and benefit realisation.
The results indicate that AI initiatives lack meaningful executive ownership.
Without visible and accountable sponsorship, AI may be perceived as a technology experiment rather than a strategic business priority. Decisions may be delayed, risks may remain unresolved and employees may receive inconsistent messages.
Recommended focus: Do not scale significant AI initiatives until accountable sponsors have been appointed and equipped to lead the organisational change required.
Governance
The results indicate that your organisation has established clear governance for AI decision-making, prioritisation and risk management.
Appropriate consideration appears to be given to privacy, security, accuracy, bias, explainability and human oversight. The organisation is also prepared to stop or redirect initiatives where value or responsible use cannot be demonstrated.
Recommended focus: Keep controls proportionate to risk, monitor emerging issues and regularly test whether governance remains effective as AI use expands.
The results suggest that AI governance is emerging but may not yet be consistently understood or applied.
Some initiatives may receive robust scrutiny, while lower-profile or employee-led uses of AI operate with less oversight. Decision rights and escalation routes may also remain unclear.
Recommended focus: Define a common governance framework, classify use cases by risk and establish clear approval, monitoring and escalation requirements.
The results indicate that your organisation does not yet have a sufficiently consistent approach to governing AI.
Responsibilities may be fragmented across technology, data, legal and business teams, creating gaps in accountability. Risks may be considered late or only after a solution has been selected.
Recommended focus: Establish an AI governance forum, define decision rights and create minimum controls covering data, model outputs, human oversight and ongoing monitoring.
The results indicate that AI is being explored or used without adequate governance.
This exposes your organisation to significant operational, legal, ethical, security and reputational risk. The organisation may also be unable to identify where AI is already being used.
Recommended focus: Conduct an immediate AI usage review, introduce interim controls and define accountable ownership before additional high-risk use cases proceed.
Operational Engagement
The results indicate that employees, process owners and operational specialists are meaningfully involved in shaping AI initiatives.
Solutions are therefore more likely to address genuine operational problems, fit existing workflows and reflect the practical implications for roles, decisions and customer outcomes.
Recommended focus: Continue involving operational teams throughout discovery, design, testing and post-implementation improvement — not only during initial consultation.
The results suggest that operational employees are involved in AI initiatives, but often after the use case or solution has already been substantially defined.
Feedback may improve implementation without materially influencing the original decision or design.
Recommended focus: Bring process owners and affected employees into opportunity identification and early design, and create clear routes for their feedback to change decisions.
The results indicate that AI initiatives are being designed with limited involvement from the people who understand or perform the work.
This creates a risk that solutions address assumed problems, disrupt effective practices or fail to reflect operational complexity. Employee resistance may therefore be a rational response to poor design rather than a reluctance to change.
Recommended focus: Conduct operational discovery, map affected workflows and involve representative users before further solution decisions are made.
The results indicate that AI is largely being introduced to employees rather than developed with them.
Affected teams may have little opportunity to influence the solution, raise concerns or understand how their roles will change. This creates a high risk of rejection, workarounds and unintended consequences.
Recommended focus: Pause implementation where necessary and undertake structured employee engagement, process discovery and impact assessment before proceeding.
Change Capacity
The results indicate that your organisation considers organisational capacity when prioritising and implementing AI.
Employees appear to receive sufficient time and support to learn new ways of working, while leaders recognise the impact of existing workload, transformation activity and change fatigue.
Recommended focus: Continue monitoring cumulative change impacts and protect the capacity required to embed AI after technical implementation.
The results suggest that change capacity is considered, but delivery pressures may still override readiness concerns.
Teams may receive initial support but lack the protected time, reinforcement or management attention needed to sustain adoption.
Recommended focus: Introduce capacity and change-impact assessments during prioritisation and ensure adoption activity is included in delivery plans and resource estimates.
The results indicate that AI initiatives are adding pressure to an already stretched organisation.
Employees may be expected to adopt new tools alongside existing workloads and competing change programmes. This is likely to reduce learning, increase workarounds and weaken adoption.
Recommended focus: Review the AI portfolio against available capacity, sequence initiatives more carefully and explicitly resource the time required for learning and transition.
The results indicate that your organisation does not currently have sufficient organisational capacity to absorb additional AI-enabled change successfully.
AI may be introduced without considering workload, operational pressures or existing transformation demands. Even technically effective solutions are unlikely to become embedded under these conditions.
Recommended focus: Pause or rephase lower-priority initiatives, reduce competing demands and create a realistic adoption plan before further implementation.
Communications
The results indicate that your organisation communicates AI initiatives openly and credibly.
Employees understand why AI is being introduced, what outcomes are expected and how it may affect their work. Opportunities to ask questions and hear about both successes and lessons learned appear to be well established.
Recommended focus: Maintain transparency as plans evolve and ensure communications remain specific to different employee groups rather than relying on broad corporate messaging.
The results suggest that communications explain the broad ambition for AI but may not provide enough practical detail for affected employees.
Messaging may focus on opportunity while giving less attention to role impacts, limitations, uncertainty or lessons from implementation.
Recommended focus: Develop an audience-based communication plan, strengthen two-way forums and provide managers with clear, consistent information for team conversations.
The results indicate that employees do not receive sufficiently clear or trusted information about AI.
Communications may be irregular, overly technical or focused on positive outcomes without addressing legitimate concerns. This can create rumours, fear and declining trust.
Recommended focus: Create a clear AI narrative covering purpose, boundaries, workforce implications and decision-making. Establish accessible channels for questions and feedback.
The results indicate a serious communication gap around AI.
Employees may be unaware of how AI is being used, why decisions have been made or how their work could be affected. In the absence of credible information, assumptions and misinformation are likely to fill the gap.
Recommended focus: Introduce immediate, transparent communication led by accountable leaders and create a visible mechanism for employees to raise questions and receive honest answers.
Capability
The results indicate that your organisation has invested in the skills required to use, manage and sustain AI effectively.
Employees appear to understand both the practical application and limitations of relevant tools, while managers and specialist teams are equipped to support adoption.
Recommended focus: Refresh learning as tools evolve, develop advanced role-specific capability and monitor whether training translates into safe and effective workplace behaviour.
The results suggest that AI learning and support are available, but capability may vary considerably between teams and roles.
General awareness training may be stronger than the practical, role-specific learning required to use AI confidently and responsibly.
Recommended focus: Complete a capability assessment, define role-based learning pathways and equip managers to coach employees through changing responsibilities and workflows.
The results indicate that employees and managers are not yet sufficiently equipped to adopt AI effectively.
Training may focus on how tools work without covering judgement, limitations, data handling, process changes or appropriate human oversight. Specialist delivery capability may also be overstretched.
Recommended focus: Undertake a training needs analysis, define priority competencies and provide practical learning linked directly to real use cases and job roles.
The results indicate that your organisation has a substantial AI capability gap.
Employees may be expected to use tools without sufficient guidance, while managers and leaders may lack the knowledge needed to oversee their use. This increases the likelihood of errors, inappropriate use and low confidence.
Recommended focus: Establish minimum AI literacy and responsible-use training before scaling access, supported by targeted development for leaders, managers and specialist roles.
AI Adoption and Sustained Use
The results indicate that your organisation has a strong approach to monitoring and sustaining AI adoption.
The organisation appears to track whether intended users are engaging with AI tools and, importantly, whether those tools are being used meaningfully within relevant processes and activities. Adoption data is supported by employee feedback and operational insight, enabling the organisation to identify where usage is falling away and understand why.
Your organisation also recognises that usage alone does not demonstrate success. It considers whether adoption is contributing to measurable improvements in productivity, quality, customer outcomes, employee experience or risk.
The priority should now be to maintain this discipline as AI use scales and to ensure that adoption measures continue to reflect effective and responsible use rather than simply volume of activity.
Recommended actions
- Maintain role- and team-level adoption dashboards.
- Monitor sustained use rather than initial uptake alone.
- Compare adoption data with operational and benefit measures.
- Share lessons from high-adoption teams across the organisation.
- Review whether increased usage is resulting in improved outcomes.
The results indicate that your organisation has started to monitor AI adoption, but the approach may not yet be consistent or sufficiently detailed.
The organisation may track basic measures such as licences, logins, active users or prompt volumes. However, these measures do not necessarily demonstrate that AI is being used effectively, appropriately or as part of day-to-day work.
There may also be limited understanding of why some employees adopt AI while others do not. Without combining usage data with employee feedback, process insight and outcome measures, your organisation may struggle to distinguish genuine adoption from occasional experimentation.
The organisation should strengthen its approach by defining what meaningful adoption looks like for each use case and linking usage data to business outcomes.
Recommended actions
- Define meaningful adoption for each AI solution.
- Move beyond licence and login statistics.
- Segment adoption data by role, team and use case.
- Investigate where usage is lower or less sustained than expected.
- Link adoption measures to quality, efficiency and business outcomes.
The results indicate that your organisation has limited visibility of whether its AI solutions are being adopted effectively.
AI tools may have been deployed without clear adoption measures, or success may be based primarily on access, licences issued or initial user activity. This creates a risk that low or superficial use remains hidden.
Where adoption is not tracked, the organisation cannot reliably identify whether employees are avoiding the tool, using it inconsistently, applying it outside intended processes or creating workarounds. It may also be unclear whether low adoption is caused by capability gaps, lack of trust, weak process integration, limited relevance or poor solution performance.
Your organisation should establish a structured adoption measurement approach before scaling further AI investment.
Recommended actions
- Establish baseline measures for current ways of working.
- Define expected user groups, activities and adoption behaviours.
- Introduce regular usage and adoption reporting.
- Conduct interviews or surveys to understand barriers.
- Review whether the solution is sufficiently useful and integrated into work.
- Avoid presenting usage figures as proof of realised value.
The results indicate that your organisation does not currently have a reliable way of determining whether AI tools are being adopted.
Solutions may have been launched without tracking usage, sustained engagement or changes in employee behaviour. The organisation may therefore be unable to tell whether AI is being used, how it is being used or whether employees have reverted to previous ways of working.
Where usage data is available, it may be treated as proof of success without evidence that business performance has improved. This creates a significant risk that ineffective or unused tools continue to receive investment while genuine barriers to adoption remain unresolved.
A structured adoption measurement and intervention plan should be introduced as a priority.
Recommended actions
- Identify which AI tools are currently available and who is expected to use them.
- Establish basic usage and active-user reporting.
- Define what effective use looks like for each tool and process.
- Engage employees to understand resistance, confusion and workarounds.
- Introduce adoption owners with responsibility for monitoring and intervention.
- Connect adoption measures to tangible business and workforce outcomes.
Value Realisation
The results indicate that your organisation has a disciplined approach to measuring AI performance and value.
Baselines, benefits and accountable owners are generally established, with attention given to adoption, customer outcomes, quality, employee experience and unintended consequences — not just technical deployment.
Recommended focus: Continue reviewing benefits over time and use evidence from live initiatives to improve investment decisions and future use-case selection.
The results suggest that benefits are identified for AI initiatives, but measurement may be inconsistent or weighted towards easily available technical metrics.
Adoption, quality, employee impact or sustained business outcomes may receive less attention.
Recommended focus: Introduce a standard benefits framework, assign owners and establish baseline, adoption and outcome measures before implementation.
The results indicate that your organisation cannot consistently demonstrate whether AI investment is delivering meaningful value.
Success may be defined by completing a pilot, deploying a tool or recording user access rather than improving measurable business outcomes.
Recommended focus: Review current initiatives, establish baselines and agree a balanced set of financial, operational, customer, workforce and risk measures.
The results indicate that AI benefits are not being systematically defined, owned or tracked.
The organisation may therefore be unable to distinguish valuable AI applications from costly experimentation. Poor outcomes may continue without intervention, while credible successes remain unproven.
Recommended focus: Require every AI initiative to have measurable outcomes, accountable benefit owners and agreed review points before additional investment is approved.
Readiness gaps are fixable — and far cheaper to fix before scaling than after.