The AI IQ Scorecard · Powered by Team-X
Every executive is being asked to show that AI transformation is working. Almost none have the measurement infrastructure to answer. The AI IQ Scorecard turns one short survey of your workforce into direct, evidence-backed answers to the ten questions boards and C-suites are asking right now. Use Radar mode to keep up with the progress your workforce makes as they rapidly learn.
The Measurement Gap
Organizations can track what AI costs, but very few can show what it's changing. The result is transformation programs that run on anecdotes, tool procurement that substitutes for strategy, and boards that never see an AI value discussion.
Not because the technology fails — because nobody defined what success looks like. Fewer than half of organizations have formal AI KPIs in place. (State of the CIO, 2026)
Executives are investing without the measurement infrastructure to connect AI activity to business outcomes — and investors are starting to ask. (Deloitte, 2026)
"AI added" is not "AI transformed." Tools layered onto unchanged processes deliver incremental speed, not competitive advantage. (Deloitte, 2026)
The Top 10
Every answer below comes from a real baseline assessment: 51 respondents across 14 teams at a global organization mid-transformation. Click any question to see how the data answers it — including what employees said in their own words.
By analyzing patterns in who shares wins — and what they call out as interesting — we can tell who is energized, who is quietly confused, and who is resistant. The sophistication of the examples people cite reveals actual skill maturity, not self-reported confidence. In the sample baseline: 69% of staff shared 50 concrete wins, while maturity ranged across all five levels — with 27.5% still at ad-hoc stages.
"AI has reached a level of adoption where it's becoming the 'new normal'... the real opportunity now isn't just finding individuals doing impressive work, but creating more visibility into how different teams are applying these tools." — ML Engineer, sample baseline
Most executives assume they have leading and lagging departments. Our variance analysis of the sample baseline shows 92% of maturity variation sits within teams, not between them — meaning targeted "fix the lagging team" programs will fail, and org-wide enablement plus peer learning will work. No off-the-shelf survey gives an executive that reframe, and it changes where the money goes.
"No team-wide best practices and processes. Most of the efforts are coming from siloed individual use of AI." — Team lead, sample baseline
We map career stage, motivation, and AI Persona for every respondent — so you see not just who can transform, but who has the time and energy to. In the sample, 43% were deep experts with 10+ years in their field, yet "no time to learn during work hours" surfaced again and again. That's a capacity problem, not a skills problem — and it has a different fix.
"I built my own agent to help me manage my emails. However, I did it after working hours — at work, there is no time I can spend on learning and building with AI." — Staff member, sample baseline
The Scorecard collects and categorizes concrete wins — mapped to teams, themes, and tools. In the sample: 50 verified wins (54% writing & docs, 48% research & insights, 40% prototyping), plus a wishlist showing where employees say the next wave of value is (automation & agents, 27% of asks). That's a ready-made board narrative: where value is materializing today, and what to fund next.
"With this AI-backed procurement team, we were able to support a country to reduce the implementation of procurement to just 6 weeks — from the usual 4–5 months." — Procurement lead, sample baseline
The Scorecard is the measurement infrastructure: a baseline AI IQ score, five research-backed enablement dimensions (Identity & Roles, Effective Teaming, Process Orientation, Social Learning, Visible Results), and wins tied to speed, quality, and cost. Re-survey each quarter and you have a trend line the board can act on — the bridge from experimentation to investment discipline.
"I have already validated the ROI of these tools for our projects, but I've reached the ceiling of what personal-tier setups can handle." — Data scientist, sample baseline
Ranked blocker data from the sample — budget cited by 71% of teams, governance and policy by 57%, tool access by 43% — combined with Process Orientation scoring lowest of the five dimensions (2.84/6) tells a clear story: the constraint is organizational, not technical. That mirrors Deloitte's finding that 48% of organizations deployed AI without redesigning any workflows — but the Scorecard localizes it to your org, with quotes.
"Very simply, I run out of tokens from my $20 plan that I pay for myself — the organization could provide us with a higher tier." — Analyst, sample baseline
Shadow AI concentrates where individual maturity is high and sanctioned access is low — and the free-response data surfaces it directly. In the sample baseline, respondent after respondent described mission-critical work running on personally-funded subscriptions, outside policy, with no security review. The Scorecard maps that policy-versus-practice gap team by team, so you can fix it with enablement instead of discovering it in an audit.
"We are encouraged to use all AI tools, but formally everything except the sanctioned tool is forbidden by policy. This makes it very awkward and stressful." — Finance staff, sample baseline
The sample org's persona distribution showed 82% Shapeshifters and Experimenters — and zero Optimizers, zero Observers. All discovery instinct, no governance instinct. With 69% of enterprises stuck in conservative AI-autonomy postures because accountability is unclear, knowing your persona mix tells you whether you can safely expand autonomy — and exactly which instincts you need to recruit, develop, or borrow.
"I am concerned that some colleagues may not challenge AI responses enough and use them as ready-to-go. This can lead to traps." — Audit specialist, sample baseline
You tell us which teams are stuck — we tell you why, based on the AI Personas on each team. A team of all Experimenters generates endless pilots and finishes none; a team with no Integrator never spreads what works. Then our Accelerators are matched to each team's specific gap to unstick them. In the sample, the pattern was unmistakable: brilliant individual work, nothing built to spread.
"Time and task management, knowledge bases, pilot apps, performance benchmarking... and many more. But all individual, and not scalable yet, since on personal accounts." — Team lead, sample baseline
Wins and wishlist data show exactly where work is already shifting — from admin and ops toward strategic, creative, and orchestration roles — and where employees themselves say the next redesign should happen. That gives leaders an evidence-based starting map for role evolution: which tasks to automate, which skills to grow, and which people are already halfway there.
"I want to shift my role from a developer of tools to an architect of automated pipelines." — ML Engineer, sample baseline
And When You're Ready to Go Deeper
The Scorecard is the baseline. For organizations pursuing visible, sustained results, our services engagements extend the same evidence-first approach to the questions that take more than a survey to answer:
One short survey. Ten executive questions answered with your own data... benchmarked and ready for the board.