AI Skills for Executives: What Leaders Get Right

Leaders who take an executive AI assessment for leadership reasons outscore every other motivation group in our dataset. With an average composite of 51.4 against an overall mean of 46.3, the leadership-motivated cohort sits firmly at the top — but that headline number hides a more complicated skill profile underneath. AI skills for executives aren't uniformly strong. They cluster in predictable places and leave predictable gaps.

This post breaks down what 1,937 completed AISA assessments tell us about how leaders actually perform across five AI fluency dimensions, where founders (our closest proxy for executive leadership) excel, and where even the strongest leadership cohort falls short.

Leadership-Motivated Assessors Outscore Every Other Group

People who select "leadership" as their motivation for taking the assessment score an average of 51.4 — the highest of any motivation cohort. This puts them ahead of personal interest (48.8), certification seekers (45.5), professional development (43.2), and career changers (36.8).

This isn't surprising. Leaders who seek out an AI fluency assessment tend to be people already using AI tools in their work. They're not exploring out of curiosity or checking a box for compliance. They're trying to understand where they stand so they can make better decisions about their teams and organisations.

What the Motivation Breakdown Tells Us

The leadership cohort (n=50) is smaller than the others, but the pattern is consistent with what we see in role-level data. Founders — the role most analogous to executive leadership — score 55.4 on average across 192 assessments, which places them in the Developing-to-Proficient transition zone.

Selection Bias Is Real, But the Signal Still Matters

You could argue that leaders who voluntarily take an AI assessment are already more engaged than the average executive. That's probably true. But even accounting for selection effects, the data shows that leadership motivation correlates with stronger performance across multiple dimensions. These aren't people who just talk about AI strategy in board meetings — they demonstrate working knowledge when assessed conversationally.

The more interesting question isn't whether leaders score higher. It's where they score higher, and where they don't.

How 51.4 Compares to the Overall Average

The overall average composite across all 1,937 AISA assessments is 46.3, with a median of 46. The leadership-motivated cohort's 51.4 sits 5.1 points above that mean — meaningful, but not a massive gap. It places them solidly in the Developing tier (28-59), not yet crossing into Proficient (60-79).

To put this in context: Engineering roles average 54.6, Product roles average 54.9, and Founders average 55.4. The leadership motivation cohort's 51.4 trails all three of these role-based averages. This makes sense — the motivation cohort includes people from various roles, not just technical or product leaders.

The Prediction Gap Is Smaller for Leaders

One of the most telling data points in our dataset is the gap between predicted and actual scores. Across all assessors who made a prediction (n=1,027), the average predicted score was 62.4 against an actual of 43.7 — a gap of 18.7 points.

Founders, our leadership proxy, show a much tighter gap: predicted 61.7, actual 54.0, gap of just 7.7. That's the smallest prediction gap of any role group we track. Compare that to Students (31.7 gap) or even Engineering (13.8 gap). Leaders aren't just more skilled — they're more calibrated. They have a more accurate mental model of what they know and don't know.

This calibration matters enormously for executive decision-making. A leader who overestimates their team's AI capabilities by 30 points will make very different investment and hiring decisions than one who's off by 8. As Stanford's 2024 AI Index Report noted, organisations where leadership accurately assesses internal AI capability are significantly more likely to capture value from AI investments.

Where 51.4 Sits in the Persona Map

An average score of 51.4 maps roughly to the Enthusiast persona (average score 52.0, representing 21.6% of all assessors). Enthusiasts are engaged and curious, with solid prompting instincts, but they typically lack the systematic workflow and application patterns that distinguish Tacticians (59.9) and Builders (71.1). For executives, this means the typical leadership-motivated assessor knows enough to be dangerous — in both the good and bad senses.

Where Leaders Still Fall Short: The Safety Blind Spot

The universal weak spot across our entire dataset is the Safety & Responsibility dimension, which averages just 41.1 across all 1,937 assessments. This dimension covers AI ethics awareness, data privacy practices, bias recognition, and responsible deployment — exactly the areas where executive judgment has the highest organisational impact.

Safety Scores by Role

Founders lead on safety at 49.7, but that's still below 50 — squarely in the Developing band. For a dimension weighted at 10% of the composite, this might seem like a minor issue. It isn't. Safety & Responsibility is the dimension most likely to create organisational risk when executives get it wrong.

Consider the Plugin4Shell vulnerability disclosed this month, which affected Claude Code, Codex, GitHub Copilot, and Gemini CLI with a zero-click remote code execution exploit. Executives who don't understand prompt injection risks, data classification requirements, or the basics of AI security can't make informed decisions about which tools to approve for their organisations. A 49.7 safety score suggests founders understand the concept of AI risk but struggle with the specifics.

Why Safety Lags Every Other Dimension

Patterns suggest several reasons safety consistently underperforms:

1. Training materials focus on productivity. Most executive AI education emphasises what AI can do, not what can go wrong.
2. Safety knowledge is harder to acquire through casual use. You can improve your prompting by using ChatGPT daily. You don't naturally learn about data retention policies or bias detection through normal workflows.
3. The EU AI Act is still new. Article 4's AI literacy requirements are driving awareness, but many leaders haven't yet translated compliance obligations into personal competency. (For more on this, see our EU AI Act training checklist.)

McKinsey's 2024 State of AI report found that only 21% of organisations using AI had implemented risk governance processes — a number that tracks with the low safety scores we observe across all roles.

The Founder Profile: Strong Workflow, Technical Gaps

Founders are the closest role proxy we have for executive leadership in our dataset (n=192). Their dimension profile reveals a distinctive pattern: strong on application, weaker on underlying mechanics.

Founder Dimension Breakdown

Founders beat the overall average on every dimension, with the largest absolute gap in Technical Understanding (+10.3) and Workflow (+9.7). But look at the relative ordering within their own profile: Workflow leads at 57.1, while Technical Understanding trails at 49.3 — a 7.8-point internal spread.

What This Pattern Means

Workflow & Application (57.1) is the founder's strongest suit. This dimension measures how well someone integrates AI into real work — task decomposition, tool selection, multi-step orchestration. Founders score here because they're pragmatists. They've figured out which AI tools save them time on investor decks, hiring pipelines, market analysis, and product specs. They're not theorising about AI; they're using it.

Technical Understanding (49.3) is where the gap shows. This dimension covers how models work — tokenisation, context windows, temperature settings, the difference between fine-tuning and RAG, why models produce certain failure modes. Founders can use AI effectively without understanding why it works, but this gap creates blind spots. A leader who doesn't understand context windows can't evaluate whether a vendor's RAG implementation is sound. A leader who doesn't understand token economics can't assess whether an AI integration will scale cost-effectively.

This mirrors what we see in the broader data. Product managers show a similar pattern: highest on Workflow (57.7) and Prompting (53.4), lowest on Technical Understanding (45.4). Application-oriented roles develop strong usage patterns but often skip the mechanical understanding that would help them evaluate tools, vendors, and architectural decisions.

The Calibration Advantage

The founder prediction gap of 7.7 points (predicted 61.7, actual 54.0) deserves emphasis. This is less than half the overall prediction gap of 18.7. Founders know what they don't know — or at least, they're closer to knowing. This self-awareness is arguably more valuable than a higher raw score. A founder who scores 54 and knows it will seek the right advisors. A mid-career professional who scores 43 but thinks they're at 62 will make confident, wrong decisions.

For a deeper look at how founders compare to other roles, see our founder-specific data analysis.

What This Means for Executive AI Upskilling

The data points to a specific upskilling strategy for executives — one that's different from what you'd prescribe for engineers or individual contributors.

Priority 1: Close the Safety Gap

Executives don't need to become AI safety researchers, but they need to move from Developing (49.7) to at least Competent (55+) on Safety & Responsibility. This means understanding:

• Data classification: What data can and can't be sent to which AI services, and why
• Bias recognition: How to spot when AI outputs reflect training data biases in hiring, lending, or customer-facing decisions
• Regulatory basics: What the EU AI Act requires, what NIST AI RMF recommends, and how these map to your organisation's AI usage
• Incident response: What to do when an AI system produces harmful outputs or is compromised (Plugin4Shell is a live example)

Deloitte's 2024 State of Generative AI in the Enterprise report found that 49% of respondents cited concerns about AI risks as a barrier to adoption — but only a fraction had formal risk frameworks. Executive safety fluency is the bottleneck.

Priority 2: Build Technical Understanding to Inform Decisions

Founders don't need to understand transformer architecture at the implementation level. But moving Technical Understanding from 49.3 toward 55+ would help them:

• Evaluate vendor claims about model capabilities and limitations
• Understand why certain AI integrations fail at scale (token limits, context degradation, hallucination patterns)
• Make informed build-vs-buy decisions for AI features
• Assess whether their engineering team's AI architecture choices are sound

This doesn't require a course on backpropagation. It requires structured exposure to concepts like context windows, model selection criteria, and the practical implications of different model architectures.

Priority 3: Measure Before You Train

The prediction gap data makes a strong case for assessment before investment. If your leadership team thinks they're at 62 but they're actually at 44, any training programme that doesn't start with a baseline measurement will be poorly targeted. You'll over-invest in areas where leaders are already competent and under-invest in their actual gaps.

A team AI assessment gives you the dimension-level breakdown needed to design targeted upskilling. Instead of sending every executive through the same generic "AI for Leaders" workshop, you can route your CFO toward safety and compliance modules while your CTO focuses on workflow optimisation patterns.

For a framework on building this kind of targeted programme, see our guide on AI competency frameworks for teams.

Priority 4: Reassess Regularly

AI capabilities shift fast. Anthropic disclosed this month that Claude now leads 26% of its own R&D work, with roughly 30,000 agents running concurrently. The skills executives need to evaluate and govern AI systems are changing quarter by quarter. A one-time assessment gives you a snapshot; periodic reassessment shows whether your upskilling investments are working and whether new gaps have emerged.

Turning Leadership AI Fluency Into Organisational Advantage

The leadership-motivated cohort's 51.4 average tells a nuanced story. Leaders are more skilled than the general population, more calibrated in their self-assessment, and strongest in the practical application of AI to real work. But they carry the same safety blind spot as everyone else, and their technical understanding — while above average — may not be sufficient for the governance and vendor evaluation decisions they're making.

The gap between a 51.4 Developing score and a 60+ Proficient score isn't enormous, but it's the difference between an executive who uses AI and one who can effectively lead an AI-enabled organisation. Closing that gap requires targeted work on the specific dimensions where leaders underperform, not generic AI literacy training.

If you're wondering where your own leadership team stands, the starting point is measurement. You can't close gaps you haven't identified. Our data consistently shows that teams who assess first make better training investments — because they're working from actual skill profiles, not assumptions.

---

Related reading: AI Skills for Founders: 192 Assessed [2026 Data] — how founders compare across all five AI fluency dimensions.

Related reading: How Good Is My Team at AI? [2026 Data] — using team-level assessment data to target upskilling investments.

Related reading: AI Competency Framework: Build One [2026] — a practical guide to building role-specific AI competency models.

Frequently Asked Questions

Do executives need hands-on AI skills?

Yes, but the type of hands-on skill matters. Our data shows founders score highest on Workflow & Application (57.1), which measures practical AI integration into real tasks. Executives don't need to write code or fine-tune models, but they need enough direct experience with AI tools to evaluate outputs critically, understand failure modes, and make informed decisions about AI investments and governance.

What AI dimensions matter most for leaders?

For executives specifically, Safety & Responsibility and Technical Understanding are the highest-priority development areas. Leaders already tend to be strong on Workflow & Application and Prompting, but safety (49.7 for founders) and technical understanding (49.3) lag behind — and these are the dimensions most critical for governance, vendor evaluation, and organisational risk management.

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

Start with a conversational AI assessment that measures performance across multiple dimensions rather than testing factual recall. AISA's team assessment provides dimension-level breakdowns for each team member, showing exactly where leaders are strong (typically workflow and prompting) and where gaps exist (typically safety and technical understanding). The prediction gap data — how accurately leaders estimate their own skills — is often as valuable as the scores themselves.

How does the executive AI assessment differ from a general AI skills test?

A general AI skills test often relies on multiple-choice questions about AI concepts. An executive AI assessment needs to evaluate judgment, not just knowledge — how leaders reason about AI trade-offs, evaluate outputs, and make decisions under uncertainty. AISA uses a conversational format where a separate AI evaluator scores independently across 11 criteria, capturing the kind of applied reasoning that matters for leadership roles rather than textbook definitions.