Cisco's 2025 AI Readiness Index surveyed 8,000 senior IT and business leaders across 30 markets. Only about 1 in 8 of their organisations qualified as fully ready for AI, and that top group has held at the same share in all three years of the study.
An AI adoption readiness assessment is a structured check of whether an organisation's people will actually use AI in their daily work. At The Decision Lab, we run these assessments with our SPROUT framework, which we developed and validated with data from more than 20,000 employees.
An enterprise AI readiness assessment typically evaluates six areas: strategy, data, technology, governance, talent, and culture. Our assessment focuses on the behavioral conditions that decide whether employees turn those investments into consistent use. It complements a technical readiness assessment rather than replacing one.
The five dimensions of AI adoption readiness
The SPROUT framework evaluates five dimensions:
- Social Readiness: whether team dynamics and peer norms make AI use acceptable.
- Psychological Readiness: how open individuals feel toward AI, including whether they fear being replaced.
- Role Fit: whether AI feels relevant and useful for day-to-day work.
- Organizational Enablement: whether policies, leadership, incentives, and culture support AI use.
- Understanding and Technical Capability: whether employees have the confidence and skills to use AI well, from basic literacy to effective prompting.
Technically ready organisations can still stall on adoption
Cisco's most AI-ready organisations are four times more likely than others to move AI pilots into production. Cisco's own profile of them includes comprehensive change management alongside roadmaps and infrastructure.
We have run behavioral diagnostics for organisations ranging from the World Bank to Fortune 500 companies, and the gap between having tools and using them comes up repeatedly. A Fortune 500 HR technology company of 20,000 employees came to us after deploying AI tools across the organisation, backed by training and internal communications. The tools worked and employees knew about them, yet usage stayed low.
We combined a 48-source review of the adoption literature with workforce interviews, surveys, and expert consultation. That work identified five barriers: time constraints, workflow integration, unclear usage guidelines, low trust in AI accuracy, and perceived complexity. All of them were behavioral.
What each SPROUT pillar measures
Social Readiness
In an AI readiness assessment, low Social Readiness shows up when employees expect AI use to damage how colleagues judge their competence or effort.
The research supports that concern. A social evaluation penalty is a drop in how capable or motivated others judge someone to be, based on their method rather than their results. Jessica Reif, Richard Larrick, and Jack Soll, at Duke University's Fuqua School of Business, reported four preregistered experiments with 4,439 participants in the Proceedings of the National Academy of Sciences in 2025. People who used AI at work both expected and received lower ratings of their competence and motivation.
Psychological Readiness
Low Psychological Readiness shows up when employees are wary of AI or worried about what it means for their job, often before they have much experience with it.
Algorithm aversion is the tendency to abandon an automated tool after seeing it make an error, even when the tool outperforms people. Berkeley Dietvorst, Joseph Simmons, and Cade Massey documented it in 2015. At our Fortune 500 client, low trust in AI accuracy was one of the five barriers, and a skeptical segment of employees stalled at first contact.
Role Fit
Low Role Fit shows up when employees cannot name a recurring task where AI improves speed or quality without adding friction to their workflow.
Workflow integration and time constraints were both barriers at our client. The programmes that moved adoption were built into real tasks, from hands-on exercises inside existing workflows to a mission-based challenge series run in Slack.
Organizational Enablement
Low Organizational Enablement shows up when employees are unsure what is permitted, or when leaders and incentives signal that AI use is not really expected.
Unclear usage guidelines were another barrier at our client. When rules are unclear, people tend to keep using AI and stop mentioning it. The 2026 PagerDuty Shadow AI Survey of 1,250 office professionals at companies with at least $500 million in annual revenue found that 2 in 3 had used an AI tool at work they believed policy did not permit.
Leadership clarity belongs here too. With BDC, Canada's bank for entrepreneurs, we worked alongside the Senior Management Committee and AI Council to specify ten AI capabilities in a phased roadmap. Each capability keeps human judgment from account managers and community partners as the point of trust.
Understanding and Technical Capability
Low scores on this pillar show up as employees who find AI complicated, or who have only tried it for simple tasks.
Perceived complexity was the fifth barrier at our client, and confidence turned out to move quickly. Over the one-month pilot described below, confidence in exploring AI tools rose 41% in the pilot group and fell 26% in the control group.
How to read SPROUT results
The diagnostic returns a score for each pillar and a ranked list of the team's top barriers, each paired with a recommended opportunity. An AI-generated summary turns the results into a written account the team can act on. The scores are designed to pinpoint a team's structural strengths and its weakest links, so they are most useful read against each other.
Start with the ranked barriers, since the diagnostic has already put them in priority order. A single weak pillar can hold back use even when the others score well: our Fortune 500 client had working tools and full awareness, and usage still stalled. When several pillars score low, we would start with Organizational Enablement, because a team cannot judge Role Fit until leadership has said what the tools are for.
Each diagnostic covers one team. Comparing segments means running it separately for each group, including separate runs for employees, managers, and executives when readiness may differ by level.
Results from our Fortune 500 pilot
Our client's workforce split into two groups with opposite failure modes. Enthusiasts were already experimenting but kept running into friction, while skeptics stalled at first contact. An organisation-wide average would blend the two into a middling score that describes neither group.
We designed 20 candidate programmes, each targeting a specific barrier for a specific segment, and tested eight in a one-month controlled pilot with more than 100 employees, using before-and-after measures of ease and confidence. The published case study reports a 35% rise in organisation-wide AI adoption and a 23% rise in use of AI in employees' core responsibilities. The programmes are now scaling to the full 20,000-person workforce.
These are results from one proprietary pilot. The case study does not publish:
- Starting levels
- How core-responsibility use was measured
- A breakdown by programme or segment
- Whether the gains lasted beyond the pilot month
Read the figures as what moved in one organisation rather than as a benchmark.
Running the assessment
Setting up our free AI Adoption Diagnostic takes a few minutes. You enter company details and team size, then share a unique link. Team members answer 17 anonymous questions about their experience with AI and the barriers they face. No names or emails are collected, results appear only after at least five responses, and the organiser can delete all data at any time.
Four practices sharpen what the diagnostic tells you:
- Compare anonymous answers about unreported AI use against logged usage data.
- Run separate diagnostics for teams you expect to differ, and compare their weakest pillars.
- Rerun the diagnostic after any change to AI policy, and check whether Organizational Enablement improves before usage does.
- Retest after 90 days against a team that received no intervention.
Cisco's index is built from senior leaders' answers, while SPROUT asks employees anonymously. Running both views on the same teams would show where leaders and employees disagree about readiness, and those disagreements are probably where adoption support should go first.
Frequently asked questions
What is an AI readiness assessment?
An AI readiness assessment evaluates whether an organisation can deploy AI successfully, usually across strategy, data, technology, governance, talent, and culture. Cisco's index, one widely cited example, sorts organisations into four stages: Pacesetters, Chasers, Followers, and Laggards. Behavioral assessments such as SPROUT add whether employees will actually use what gets deployed.
What does an AI readiness assessment measure?
Technical assessments measure factors such as infrastructure, data quality, security, and governance maturity. Behavioral assessments measure how people respond to AI. SPROUT covers peer norms, individual attitudes, task relevance, organisational support, and user capability. Most organisations need both views, since strong infrastructure does not guarantee daily use.
How do you assess employee readiness for AI adoption?
Use an anonymous survey, because many employees will not report unapproved AI use under their own name. Our diagnostic asks 17 questions and shows results only once five or more people have responded, so no individual answer can be identified. Comparing the results with logged usage shows how much AI use goes unreported.
What are the five dimensions of AI adoption readiness?
In The Decision Lab's SPROUT framework, the five dimensions are Social Readiness, Psychological Readiness, Role Fit, Organizational Enablement, and Understanding and Technical Capability. Their first letters spell the framework's name, with U and T sharing the final pillar. Each dimension is scored separately, so a team can see exactly where it is held back.
Why can technically ready organisations struggle with AI adoption?
The obstacles that remain after deployment are mostly behavioral. At our Fortune 500 client, the tools worked and employees knew about them, yet usage stayed low because of time pressure, poor workflow fit, unclear rules, low trust, and perceived complexity. None of those show up in an infrastructure audit.
How often should an organisation reassess AI readiness?
Reassess after any significant change, such as a new tool or a policy update, and at least every 90 days during an active rollout. Readiness moves faster than most organisations expect: in our pilot, confidence fell 26% in one month among employees who received no support.
What is the difference between AI readiness and AI adoption readiness?
AI readiness describes an organisation's capacity to deploy AI, including its data, infrastructure, and governance. AI adoption readiness describes whether its people will use AI once it is deployed. An organisation can score highly on the first and still see low usage if the second has not been assessed.
How can organisations measure AI readiness by employee segment?
Run the assessment separately for each group you want to compare, such as departments, roles, or seniority levels. Averages can hide segments with opposite problems: at our Fortune 500 client, enthusiasts hit friction while skeptics stalled at first contact, and each group needed a different programme.
