In this second part of our three-part series on upskilling for the AI era, we explore the distinct AI skills needed by today’s executives and how they fit into any ongoing programme of professional development.
Whether making a personal executive transition, receiving executive outplacement or driving organisational transformation, AI literacy is now an essential skill that should be considered as part of any development or change initiative. Executives who integrate AI mastery into a continuous learning agenda, spanning both personal and organisational transformation, will remain competitive and relevant in a rapidly evolving landscape.
As highlighted in our previous insight on how executives can stay ahead of the AI curve, of the $30 billion spent on AI globally, only 5% is seeing a return on investment. T his may be partly due to metrics and measurements not catching up with what success looks like, but progress is too often also impeded by executives’ glacial response as the technology accelerates exponentially in real time.
As former Cisco CEO John Chambers observed, half of executives “won’t have the skills to adjust to this new innovation economy driven by AI because they were trained to move at the speed of a five-year cycle as opposed to a 12-month cycle.”
Senior leaders therefore need to continuously reinvent themselves to stay aligned with the pace of technological evolution.
Below, we look at specific AI skills sets for executives who face distinct requirements when building AI competency. This guide provides an overview of core AI skills executives should consider acquiring and examines how training can be incorporated into broader leadership development strategies.
Skill 1: AI Strategy, Appraisal and Value Framing
Why it matters: Executives must identify where AI creates measurable return, build business cases and sequence pilots into scaled capability, recalibrating and updating according to technological advances which may otherwise outrun specific projects and lead to shareholder value erosion through misaligned investments or missed opportunities. Leaders who map use cases to financial outcomes gain competitive advantage.
Related competencies: Strategic foresight, scenario planning, critical and creative thinking.
Skill 2: AI Governance, Risk and Compliance
Why it matters: Boards and C-suites are prioritising governance, auditability and regulatory readiness amid a fragmented regulatory landscape, where inadequate oversight can expose organisations to severe fines or reputational damage from incidents such as bias scandals. Governance is a rising board agenda item, helping attract top talent through ethical practices and building resilience by managing the inherent complexities of scaling AI, while fostering ESG alignment and stakeholder trust.
Related competencies: Stakeholder collaboration, ethical decision-making, resilience.
Skill 3: Data Literacy and Decision Science
Why it matters: Executives who interpret model outputs, ask the right questions of data teams and set measurable KPIs are more effective sponsors of AI projects. This skill facilitates literacy in relation to decision frameworks, enabling navigation of volatile markets and bridging analytical gaps for informed sponsorship, particularly when aligning with UK initiatives around data protection and digital information that demand robust, privacy-conscious handling.
Related competencies: Data governance, analytical and critical thinking, cultural sensitivity.
Skill 4: Generative AI Literacy and Prompt Design
Why it matters: Executives need practical fluency with generative tools so they can assess vendor claims, pilot real workflows and set safe guardrails, unlocking productivity gains while mitigating risks such as hallucinations leading to flawed decisions or unintended outputs. Amid the rise of multimodal trends, this becomes essential for integrating tools like enterprise Copilots and scaling pilots without misuse, in line with UK recommendations for safe adoption that emphasise responsible experimentation and organisational safeguards.
Related competencies: Strategic foresight, ethical decision-making, change management.
Skill 5: People Leadership for Augmented Work
(Part three of this series will examine workforce upskilling.)
Why it matters: Adoption failures arise when leaders treat AI as a technology or tooling problem rather than one of people and process change, overlooking the human elements of redeployment and upskilling that can enhance team creativity and improve retention in blended workforces. This fosters resilience in hybrid AI-human environments, addressing the transformative shifts in job roles and skills needs, and ties into broader workforce strategies. Leadership skills supporting redeployment and upskilling are flagged in employer surveys as essential.
Related competencies: Strategic workforce foresight, stakeholder collaboration and influence.
Skill 6: Responsible AI and Ethics
Why it matters: Bias mitigation, explainability and responsible deployment are areas where executives must make trade-offs between speed and trust. Courses increasingly include practical governance frameworks to support these decisions.
Related competencies: Ethical judgement and integrity, strategic foresight and systems thinking.
Developing the above competencies requires structured and intentional learning. The next step is therefore understanding how executives can build and apply them effectively. While AI learning opportunities are widely available, their effectiveness depends on context and application. As with learning a new language, the greatest value comes not from theory alone but from practical use and cultural understanding.
A range of flexible programmes now support executives in building these capabilities. Some offer on-demand, video-based content with downloadable certification (e.g. LinkedIn Learning, Microsoft, DeepLearning.AI). Others blend live instruction with self-guided modules or in-person engagement.
However, without strategic framing, such courses may lack the nuance required to translate learning into leadership impact. Incorporating executive coaching or providing structured professional development can help align AI learning with transition goals, business transformation objectives, and broader leadership capabilities such as ethics and human-first implementation.
A wide spectrum of AI learning options is available to meet different executive needs, schedules, and learning preferences. To optimise the benefits of AI education, Rialto consultants recommend beginning with compact, high-quality micro-courses for immediate familiarity, followed by targeted intensive programmes aligned to sector or functional priorities. Ongoing micro-learning and peer discussion groups can then sustain progress.
Bite-size and micro-learning courses provide rapid, low-cost access to foundational AI literacy, typically requiring a commitment of four to twenty hours. They are particularly effective for boards and senior teams seeking immediate fluency, offering practical exposure to areas such as prompt engineering and vendor assessment. These short, modular courses, available from providers such as DeepLearning.AI and LinkedIn Learning, make learning highly accessible and inclusive. However, they generally offer limited depth in areas like governance, data architecture, and strategic trade-offs, and they tend to provide fewer networking opportunities or weaker credentials. As a result, they are best suited for establishing baseline literacy, developing tool-specific competence, or supplementing more intensive development initiatives.
For leaders seeking deeper engagement, intensive executive AI programmes offer a more comprehensive approach, often spanning three to eight weeks. These programmes address advanced themes such as AI governance, data architecture, vendor strategy, and organisational change management, while also enabling participants to build peer networks with other senior leaders. Providers such as MIT Sloan, Harvard Business School, Oxford, and Wharton offer faculty-led experiences with access to implementation playbooks and sector-specific case studies. Although these programmes require a higher time and financial investment, they provide the strategic depth and board-level perspective essential for developing AI maturity across organisations and for positioning executives for future leadership transitions.
As AI continues to redefine the leadership landscape, executives who commit to continuous, structured learning will be best placed to lead responsibly, transform their organisations, and remain relevant through disruption. AI fluency is not an isolated technical skill; it is now a cornerstone of strategic foresight, ethical leadership, and cultural adaptability. Embedding AI capability within broader professional and organisational development enables leaders to make informed, values-driven decisions that build resilience and trust in a rapidly evolving economy.
Rialto supports this journey through its programme of complimentary invitation-only events exploring AI and leadership topics. With 85 consultants operating globally, Rialto helps executives strengthen leadership capability, navigate transition, and align AI learning with strategic transformation goals.
Executives can also contact our research department for examples of leading AI learning programmes and providers—including Harvard Business School, LinkedIn, Deloitte, and others—that Rialto clients have successfully undertaken. To learn more, email research@rialtoconsultancy.com.
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