Leaders Score Highest on AI [Motivation Data]

Leaders who take an AI fluency assessment for leadership-related reasons outscore every other motivation group. That's the headline from AISA's motivation-segment data: the leadership cohort (n=50) averages a composite score of 51.4 out of 100, compared to 48.8 for personal interest, 45.5 for certification seekers, 43.2 for professional development, and 36.8 for career changers.

But before anyone declares victory for the C-suite, consider this: 51.4 still lands squarely in the Developing tier (28–59). Leaders are the best of a population that, on average, scores 45.9. They're ahead, but not by as much as most leadership teams would assume.

This post breaks down the full motivation leaderboard, explores why leadership-motivated assessees outperform, identifies the dimensions where even leaders struggle, and outlines what L&D teams should do with this data.

The Finding: Leadership-Motivated Assessees Outscore Every Group

People who cite leadership as their primary reason for taking AISA score 51.4 on average — 5.5 points above the population mean of 45.9 and 14.6 points above career changers. This is the widest gap between any two motivation segments in the dataset.

What the Motivation Leaderboard Looks Like

Here's the full breakdown across all five motivation segments:

Every group falls within the Developing tier (28–59). The spread from top to bottom is 14.6 points — meaningful, but nobody is breaking into Proficient territory on average.

Two Hypotheses for the Leadership Premium

The data doesn't tell us why leaders score higher, but two explanations are plausible:

1. Self-selection. People in leadership roles have more exposure to AI strategy discussions, vendor evaluations, and adoption decisions. They've accumulated practical knowledge through context, not necessarily through deliberate study.
2. Strategic thinking transfers. The AISA rubric weights Critical Thinking at 22% and Workflow & Application at 25%. Leaders who routinely evaluate trade-offs, assess risk, and design processes may carry those skills into AI conversations.

Both explanations can be true simultaneously. The point is that leadership experience appears to correlate with higher AI fluency — but correlation isn't competence.

Full Motivation Leaderboard: Where Each Group Stands

The motivation leaderboard reveals patterns that matter for anyone designing AI upskilling programs. Each segment tells a different story about readiness and intent.

Leadership (51.4): Strategic Context Helps

The leadership cohort's 51.4 average sits 5.5 points above the population mean. These are people who explicitly selected "leadership" as their reason for assessment — suggesting they're already thinking about AI at an organisational level. Their scores likely benefit from exposure to decision frameworks and cross-functional AI discussions.

Personal Interest (48.8): Curiosity Correlates with Competence

The largest segment by volume (n=334), personal-interest assessees score 48.8. This group is self-motivated and likely experimenting with AI tools on their own time. Their scores track close to the population median of 46, suggesting they represent the broad middle of the AI fluency distribution.

Certification and Professional Development (45.5 and 43.2): Intent Doesn't Equal Skill

Certification seekers (45.5) and professional development assessees (43.2) both score below the population mean. This is counterintuitive — you'd expect people actively pursuing credentials or growth to perform well. Instead, these groups may be taking the assessment because they recognise a gap. The assessment is functioning as a diagnostic, not a victory lap.

Career Transition (36.8): The Widest Gap

Career changers score 36.8 — the lowest of any motivation group and 14.6 points below leaders. This aligns with broader patterns we've observed: people entering new fields tend to overestimate their AI readiness. As documented in our analysis of career changers' prediction gaps, this group faces the steepest climb.

Why This Matters: Leaders Who Understand AI Make Better Decisions

Leaders don't need to write prompts all day. But they do need enough AI fluency to evaluate vendor claims, set realistic adoption timelines, allocate budgets, and — critically — know when their teams are overselling or underselling AI capabilities.

McKinsey's 2024 State of AI report found that 72% of organisations had adopted AI in at least one business function, up from 55% the prior year. That adoption is being greenlit by leaders. The question is whether those leaders have the fluency to distinguish a sound AI investment from a hype-driven one.

The Cost of Low-Fluency Leadership

When leaders lack AI fluency, three things tend to happen:

• Over-investment in tools, under-investment in capability. Budgets flow to licenses and platforms while the team's ability to use them effectively goes unmeasured.
• Misaligned expectations. Leaders promise stakeholders outcomes that current AI can't reliably deliver, or they dismiss capabilities that are already production-ready.
• Safety blind spots. Without understanding AI governance frameworks, leaders may approve deployments that expose the organisation to regulatory, reputational, or operational risk — particularly as the EU AI Act enforcement timelines approach.

The World Economic Forum's 2025 Future of Jobs Report estimated that 39% of workers' core skills will change by 2030. For leaders, the relevant skill shift isn't learning to code — it's developing the judgment to steer AI adoption responsibly.

What Founders and Product Leaders Already Show Us

AISA's role-based data provides a useful comparison. Founders (n=201) average 55.3, and Product professionals (n=122) average 55.7. Both groups outperform the leadership-motivation cohort's 51.4. This suggests that hands-on product and company-building experience — where AI decisions have immediate, visible consequences — builds fluency faster than strategic oversight alone.

The Flip Side: 51.4 Is Barely Above the Median

Let's be direct: a composite score of 51.4 places the leadership cohort in the middle of the Developing tier (28–59). The population median is 46. Leaders are ahead, but they're not in a different league.

To reach the Proficient tier (60–79), the average leader would need to improve by at least 8.6 points. That's not a minor gap — it represents meaningful growth across multiple dimensions.

What "Developing" Actually Means

In AISA's scoring framework, a Developing-tier score indicates someone who:

• Can use AI tools for basic tasks but struggles with complex, multi-step workflows
• Recognises some AI limitations but doesn't consistently verify outputs
• Has surface-level understanding of how models work, without the depth to make informed technical trade-offs
• May not systematically consider safety, bias, or data privacy implications

For an individual contributor, Developing is a reasonable starting point. For someone making AI adoption decisions that affect an entire organisation, it's a risk factor.

The Prediction Gap Compounds the Problem

Across the full AISA population (n=1,183 who made predictions), people overestimate their scores by an average of 19.5 points — predicting 62.9 but scoring 43.4. We don't have prediction data broken out specifically for the leadership-motivation segment, but the Founders role group (n=94) shows an 8.2-point gap (predicting 62, scoring 53.8). Even the most calibrated groups overestimate.

If leaders believe they're Proficient when they're actually Developing, they're less likely to invest in their own upskilling — and less likely to recognise when their teams need support.

What Leaders Still Get Wrong: Dimension-Level Gaps

The population-level dimension averages reveal where most people — leaders included — underperform. These five dimensions paint a clear picture of systemic weaknesses.

Technical Understanding: The Deepest Deficit

Across all 2,093 AISA assessees, Technical Understanding averages 38.8 — the lowest of any dimension. This covers knowledge of how models work, token economics, context windows, and the practical implications of model architecture choices.

For leaders, this gap matters because technical understanding shapes vendor evaluation. A leader who doesn't understand context windows can't assess whether a proposed RAG implementation will actually work at scale. A leader who doesn't grasp token economics can't evaluate whether an AI deployment's unit economics make sense.

Safety & Responsibility: The Riskiest Blind Spot

Safety & Responsibility averages 40.8 across the population. This dimension covers data privacy, bias awareness, responsible deployment, and regulatory compliance. Even among Founders — a group with strong overall scores (55.3) — Safety scores only 49.6.

With the EU AI Act's Article 4 AI literacy obligations now in effect, this isn't an abstract concern. Leaders who can't articulate their organisation's AI risk posture are exposed. Our EU AI Act training checklist covers the specific requirements.

Critical Thinking: The Verification Gap

Critical Thinking averages 42.2 across the population. This dimension measures whether people verify AI outputs, check for hallucinations, and apply appropriate scepticism. Leaders who skip verification — or who don't build verification into their team's workflows — propagate errors at organisational scale.

Founders outperform the population average on every dimension, but the rank order of weaknesses is nearly identical: Technical Understanding and Safety sit at the bottom for both groups. The pattern is structural, not role-specific.

What L&D Teams Should Do With This Data

If you're responsible for leadership development or AI upskilling, this data gives you a concrete starting point. Here's how to act on it.

Step 1: Baseline Your Leadership Cohort

You can't improve what you haven't measured. Use a team AI assessment to establish where your leaders actually stand — not where they think they stand. AISA's evidence-linked scoring means every score is tied to what the candidate actually said, not self-reported confidence. The published rubric at /resources/the-aisa-rubric shows exactly what's being measured.

Step 2: Focus on the Two Weakest Dimensions

The data points clearly to Technical Understanding and Safety & Responsibility as the areas where leaders — and the broader population — need the most development. Design targeted learning around:

• How models actually work (not at a PhD level, but enough to evaluate trade-offs)
• Data privacy and PII handling in AI workflows
• Regulatory requirements, particularly the EU AI Act
• Hallucination detection and output verification processes

Step 3: Close the Prediction Gap

Leaders who overestimate their AI fluency won't prioritise learning. Share individual AISA reports — which include dimension-level scores and a free certificate — with your leadership team. The gap between predicted and actual scores is often the most powerful motivator for behaviour change.

Step 4: Build an AI Competency Framework

Motivation-segment data is useful for benchmarking, but sustainable improvement requires a structured AI competency framework. Define what Proficient looks like for each leadership role, map it to AISA's dimensions, and track progress over time. AISA's test-retest stability — 98% of retakers land in the same or adjacent tier — means you can trust longitudinal comparisons.

Step 5: Don't Stop at Leaders

The leadership cohort's 51.4 average is the ceiling of the motivation segments. Career changers sit at 36.8. Professional development seekers at 43.2. If your organisation is hiring or reskilling across these groups, you need visibility into the full distribution, not just the top.

For executive-specific strategies, our analysis of what leaders get right — and wrong — on AI skills goes deeper into the dimension-level patterns.

---

Related reading: Career Changers Overestimate AI by 31 Pts — why the lowest-scoring motivation group also has the widest prediction gap.

Related reading: AI Fluency Benchmarks: 9 Personas [2026] — how AISA's persona model maps to real score distributions.

Related reading: AI Skills for Executives: What Leaders Get Right — dimension-level analysis of the leadership and founder cohorts.

---

Frequently Asked Questions

Do leaders need AI skills?

Yes. Leaders make decisions about AI adoption, vendor selection, budget allocation, and risk management. AISA data shows that leadership-motivated assessees score 51.4 on average — the highest of any motivation group — but this still falls in the Developing tier (28–59). Leaders don't need to build models, but they need enough fluency to evaluate trade-offs, set realistic expectations, and ensure responsible deployment.

What AI score should executives aim for?

Executives should target the Proficient tier (60–79) as a minimum. This level indicates the ability to integrate AI into complex workflows, critically evaluate outputs, and understand technical and safety trade-offs. The current leadership-motivation average of 51.4 falls short of this threshold by 8.6 points, suggesting most leaders have meaningful room to grow.

How do I assess my leadership team's AI fluency?

Use a structured AI fluency assessment that provides evidence-linked, dimension-level scores rather than self-reported surveys. AISA's conversational format measures actual demonstrated knowledge across Prompting, Critical Thinking, Technical Understanding, Workflow, and Safety. Each score is backed by specific evidence from the candidate's responses, and the published rubric ensures transparency. Results are stable on retake — 98% of people land in the same or adjacent tier — so you can trust the baseline and track progress over time.