Why Executive Fluency is Your Boardroom's Biggest Asset

AI fluency for executives goes beyond simply understanding what AI can or cannot do. It encompasses the ability to ask the right questions, assess how AI creates value for your organisation, make informed decisions, and confidently guide the organisation through inherent ambiguity and risk. It requires the capacity to cut through the noise and challenge the hype without letting caution lead to complacency or delaying necessary action. You don't need to master every technical detail, but you need enough understanding to lead with confidence.

Strategic decision-making in the age of AI spans across almost all functions of an organisation, from Legal and Marketing to IT and HR. Consider these potential dilemmas:

• Unapproved internal use of new generative AI tools, causing conflict between legal and marketing, highlighting the need for clear governance and guardrails.
• Evaluating the acquisition of an AI-native competitor with opaque technology and unclear IP, requiring collaboration across M&A, data, and IT teams.
• Discovering bias in an AI hiring algorithm, necessitating strong ethics, bias assessments, and transparent communication.
• A major customer requesting data sharing for co-developing an AI solution, leading to hesitation from legal/IT while commercial teams push forward, requiring a nuanced perspective and potentially creative solutions like synthetic data sets.

Responding effectively to such scenarios requires a sophisticated understanding of AI's potential value, risks, and implications across the business.

A growing body of research indicates that AI fluency within the boardroom or executive leadership team is one of the strongest predictors of AI success in businesses. This isn't merely anecdotal; studies, such as one by MIT researchers, demonstrate that top management teams with higher AI literacy are significantly better at steering their organisations towards AI-driven opportunities and implementing AI initiatives more effectively. This collective literacy within the top management team (TMT AI literacy) is crucial because navigating AI involves not just technology but also risk assessments, people considerations, and other factors too numerous for one person to master.

AI implementation isn't just a technology challenge; it's an organisational transformation and a value creation opportunity that demands strategic direction and ownership from the top. Relying solely on hiring data scientists or machine learning engineers is a critical misconception; no amount of technical talent can compensate for a leadership team that lacks understanding of how AI creates value.

Research measuring TMT AI literacy (defined as the share of executives on the TMT possessing at least one AI skill or competency from a taxonomy) has found it positively affects key organisation characteristics essential for value generation:

• AI Orientation: An organisation's overall strategic direction and goals for introducing and applying AI.
• HR-related AI Implementation Ability: An organisation's capacity to implement IT systems with an AI component, particularly focusing on human resources aspects like hiring decisions and attracting talent.

Furthermore, this research highlights that AI orientation positively impacts HR-related AI implementation ability, and AI orientation partially mediates the effect of TMT AI literacy on HR-related AI implementation ability. This indicates that TMT AI literacy both directly influences the ability to build teams and resources for AI implementation and indirectly influences it by fostering a clear strategic direction that guides implementation efforts.

Interestingly, the context of the organisation matters. The effect of TMT AI literacy on HR-related AI implementation ability is stronger in startup organisations compared to incumbent organisations. Startups' inherent flexibility, speed, and agile culture may allow TMT decisions regarding AI talent acquisition to spread and translate into implementation ability more rapidly. However, organisation type did not significantly moderate the effect of TMT AI literacy on AI orientation, suggesting that defining a strategic AI direction is less dependent on the organisation's established structure than the ability to implement it.

Translating potential into performance requires both identifying AI value (AI orientation) and being able to realise it (AI implementation ability). To secure investment and demonstrate value, especially when exact ROI is hard to predict, executives must:

• Translate AI potential into business language, framing it in terms of tangible outcomes like cost savings, revenue growth, customer experience improvements, or risk reduction, rather than just technical capabilities.
• Use proxies, benchmarks, or scenarios to illustrate credible value potential and underlying assumptions.
• Start with high-visibility, low-risk pilots to demonstrate quick wins, build credibility, and unlock further investment.
• Highlight compounding value, explaining that AI solutions often improve over time through learning, data reuse, and automation gains, and may require a reasonable time frame to show full impact.

Prioritisation of AI initiatives should be driven by clear business goals, with AI serving as an enabler, not simply the reason for the project. Involving cross-functional teams early is essential to assessing real operational impact, and leaders must have the courage to review their AI portfolio and stop low-impact initiatives.

Building AI fluency at the executive level doesn't happen by chance. It's a shared mandate across key leadership roles:

• The CEO: Sets the tone, champions top-level upskilling, and ensures AI fluency is on the strategic agenda as a core leadership competency.
• The CHRO: Integrates AI into executive development, builds peer learning initiatives, and updates leadership competency models.
• The CIO/CDO/CAIO: Provides insights, translates AI concepts into business-relevant language, co-designs learning content, and facilitates strategic AI conversations.
• The Board: Ensures oversight, champions executive education, and aligns governance structures for AI risks and opportunities.

If this shared responsibility is not clearly defined and actively led, AI fluency remains a gap, leading to poor decisions, stalled initiatives, and missed opportunities.

Since the field is constantly evolving, continuous learning is key. Executives don't need to become AI engineers, but they must build enough understanding to lead confidently. Practical steps include:

• Subscribing to curated AI newsletters.

• Reading short explainers or articles from reputable sources.

• Attending summits or events where peers share perspectives.

• Carving out some time to get hands on and use the tools in a practical application

• Using frameworks or asking specific questions within executive meetings to assess the team's collective ability to respond to AI-related challenges and identify gaps.

In conclusion, AI fluency is rapidly becoming an essential leadership capability, not just a 'nice to have'. It is a strong predictor of success in turning AI potential into realised business value. By fostering this collective understanding within the top management team, defining clear ownership, and committing to continuous learning, executives can better navigate the complexities of the AI era and position their organisations for competitive advantage.

• The GenAI Divide STATE OF AI IN BUSINESS 2025

https://www.artificialintelligence-news.com/wp-content/uploads/2025/08/ai_report_2025.pdf

• Pinski, M., Hofmann, T. & Benlian, A. AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability. Electron Markets 34, 24 (2024).

https://doi.org/10.1007/s12525-024-00707-1