AI Adoption & Preparedness — sample organisational insights
SAMPLE — DEMONSTRATION ONLY. Sample output. Fictional data, shown to illustrate the format only. No real participant, organisation or result appears here.
AI readiness insights
Group results from the Employee AI Adoption and Preparedness Survey. Individual answers are never shown.
Organisation
Break down by
Group readiness
out of 100
Readiness band
Strong Readiness
Developmental banding for the group.
These are developmental pilot bands used to guide learning and support. They are not normative benchmarks and are not a comparison with other employees.
AI Literacy & Understanding
72AI Capability & Confidence
64Adoption Mindset & Openness
58Using AI at Work
69Safe & Responsible Use of AI
77Learning Agility for AI
61How often do you currently use AI tools in your work?
- Daily25%
- Weekly38%
- Occasionally27%
- Never10%
48 responses
Sample Organisation · 48 respondents · generated 15/01/2026, 09:00:00
Executive summary
Employees report a positive view of AI and reasonable foundational understanding, but day-to-day use and confidence in safe use lag behind, and organisational enablement is rated the weakest element overall.
Strongest enablers
- Positive adoption mindset across the group, so change resistance is not the main constraint.
- Reasonable foundational understanding of what AI tools can and cannot do.
Key barriers
- Low reported use of AI in real work tasks despite willingness.
- Limited confidence about what safe and acceptable use looks like.
- Organisational enablement — guidance, tool access and training — rated lowest.
Top priorities
- Publish approved tools and acceptable-use guidance so willingness converts into use.
- Run role-based, hands-on enablement rather than further general awareness sessions.
- Equip managers to sponsor and supervise AI use within their teams.
Current-state readiness
Group readiness sits in the middle of the developmental range. Understanding and attitude are ahead of applied capability, which indicates an enablement gap rather than a motivation gap.
Perceived organisational support is the lowest-rated element. People report that guidance, access and training have not kept pace with their willingness to use AI.
- AI Literacy & Understanding: Adequate base; reinforce rather than rebuild.
- Practical AI Capability & Confidence: Main capability gap; needs supervised practice.
- Current AI Usage: Well behind mindset; treat as an access and use-case issue.
- Safe & Responsible Use: Confidence is thin; sequence guardrails with skills.
Priority adoption themes
Convert willingness into everyday use
Adoption mindset is materially ahead of reported usage, so persuasion is not required; access and permitted use cases are.
- Publish a short list of approved tools with the tasks each may be used for.
- Give every function two or three sanctioned starter use cases.
- Remove access friction for the roles with the largest usage gap.
Build confidence through supervised practice
Practical capability is the weakest readiness dimension while understanding is adequate.
- Run role-based working sessions on live tasks rather than generic demonstrations.
- Pair less confident users with an early adopter for a fixed period.
Make safe use obvious
Confidence in safe and responsible use is low, which suppresses use even where tools are available.
- Issue plain-language do and do-not guidance with worked examples.
- Define an escalation route for uncertain cases.
Employee segments and adoption needs
Willing but inactive users
Positive attitude alongside low reported usage.
Approved tools, starter use cases and permission to experiment.
Active but uncertain users
Regular usage alongside low confidence in safe use.
Clear guardrails, review points and an escalation route.
Non-users
No current usage and limited practical exposure.
Foundational, task-anchored introduction with hands-on support.
Priority AI use-case opportunities
Drafting and summarising
Routine document drafting and meeting summarisation in operations and finance.
Validate: Confirm data-handling rules and measure time saved on a small pilot before scaling.
Knowledge retrieval
Faster access to internal policy and process information.
Validate: Check source accuracy and access permissions before opening this to all staff.
Learning and capability plan
Learning topics
- What good prompting looks like for our own tasks
- Acceptable and unacceptable use, with worked examples
- Checking and correcting AI output
Audiences
- Non-users
- Willing but inactive users
- People managers
Formats
- Short hands-on clinics
- Role-based working sessions
- Reference guides at the desk
Manager enablement
- How to set expectations for AI use in their team
- How to review AI-assisted work
Practice activities
- Apply one approved use case to a live task each week for a month
- Team retrospective on what worked and what to stop
Champion network
- Identify two champions per function from current active users
- Give champions a light, time-boxed remit and a feedback channel
Governance and responsible AI actions
- Publish an acceptable-use position covering confidential and personal data.
- Define which tools are approved and who authorises new ones.
- Set a review point for incidents, questions and emerging risks.
Leadership and change actions
- State the purpose of AI adoption and what it is not intended to do.
- Have leaders describe their own use openly to normalise it.
- Fund protected time for practice rather than expecting it on top of workload.
90-day action plan
Days 1–30
- Publish approved tools and acceptable-use guidance.
- Brief managers on expectations and escalation.
Days 31–60
- Run role-based clinics for the lowest-usage functions.
- Launch two sanctioned starter use cases per function.
Days 61–90
- Review pilot use cases and retire those with no measurable benefit.
- Extend champion support to the remaining functions.
6–12 month roadmap
Months 1–3: foundations
- Guidance, access and manager briefing
- First role-based enablement
Months 4–6: applied use
- Scale validated use cases
- Embed review of AI-assisted work
Months 7–12: consolidation
- Re-measure readiness
- Integrate AI expectations into role standards
KPIs and measures
Survey indicators to re-measure
- Practical capability and confidence average
- Current usage average
- Perceived organisational enablement average
Adoption metrics
- Proportion of staff using an approved tool weekly
- Number of sanctioned use cases live per function
Business outcomes to track
- Cycle time on the specific tasks targeted by each pilot
- Quality or rework rate on AI-assisted outputs
This strategy is generated from aggregate employee-readiness and organisational-enablement data and is intended as a decision-support starting point. It should be validated against business priorities, technology constraints and governance requirements.