The CEO as digital translator

Why AI literacy is now a core leadership capability

The CEO does not need to become an AI engineer. But the CEO can no longer treat AI as a specialist topic to be delegated downwards. The leadership requirement has changed: CEOs now need enough AI literacy to translate between technology, strategy, risk, operations, finance, customers and people.

That translation role matters because AI is no longer just another digital tool. Accenture describes AI as “the new digital” because it is both a technology and “a new way of working”. Yet the gap between adoption and impact remains wide: Accenture’s 2025 Technology Vision found that only 36% of executives say their organisations have scaled generative AI solutions, and only 13% report significant enterprise-level impact. It also found that 77% of executives believe AI’s true benefits will only be unlocked on a foundation of trust.

For South African CEOs, this is not an abstract global trend. BCG’s AI Radar found that 72% of executives in South Africa, Nigeria and Morocco ranked AI or GenAI as a top-three strategic priority for 2025. In the same African sample, 37% said AI was already delivering significant value, while 60% said it was promising but had not yet delivered full value. The South African Reserve Bank and FSCA’s work on AI in the financial sector also points to the same leadership challenge: adoption is increasing, but so are concerns around data privacy, bias, discrimination, explainability, governance, skilled talent and consumer protection.

The issue is not whether AI will matter. It is whether the CEO can make it matter commercially, responsibly and at scale.

AI literacy is not technical fluency. It is leadership fluency.

The AI-literate CEO does not need to know how to train a model. But they do need to know what questions to ask before the organisation automates a decision, launches an AI agent, redesigns a process or exposes customer data to a third-party model.

AI literacy at CEO level has five dimensions.

First, strategic literacy: understanding where AI can change the basis of competition, not just where it can reduce cost. McKinsey’s 2025 AI survey found that 88% of respondents say their organisations regularly use AI in at least one business function, but only about one-third report that their companies have begun to scale AI programmes. The highest performers are more likely to use AI for growth and innovation as well as efficiency.

Second, operating literacy: knowing that value comes from workflow redesign, not isolated tools. McKinsey found that high-performing AI organisations are almost three times more likely than others to have fundamentally redesigned individual workflows.

Third, commercial literacy: insisting that AI has a value case. BCG’s AI Radar found that 60% of companies are failing to define and monitor financial KPIs related to AI value creation. This is where the CEO’s role becomes critical: to move the conversation from experimentation to measurable business outcomes.

Fourth, risk literacy: understanding that AI risk is not only a technology risk. It is a reputational, customer, regulatory, cyber, conduct and governance risk. ISO/IEC 42001, the first international AI management system standard, frames AI governance around ethics, accountability, transparency and data privacy. NIST’s Generative AI Profile similarly helps organisations identify and manage risks unique to generative AI.

Fifth, human literacy: recognising that AI adoption changes work, power, skills, identity and trust. Deloitte’s 2025 AI ROI research found that successful organisations are more likely to treat AI fluency as a non-negotiable core competency; among AI ROI leaders, 40% mandate AI training.

The CEO’s job is to translate AI into decisions

The most dangerous AI conversation in the boardroom is the vague one: “We need to do something with AI.” It sounds urgent, but it does not force choices.

The CEO’s role is to translate that urgency into five practical questions.

1. From technology promise to strategic choice
The question is not “What can AI do?” It is: “Where could AI change our economics, customer proposition or operating model?” For a bank, this may be credit decisioning, fraud detection or service personalisation. For a retailer, it may be demand forecasting, margin optimisation or store labour planning. For a mining company, it may be predictive maintenance, safety analytics or energy efficiency.

2. From use cases to workflow redesign
An AI pilot can be impressive in a demo and irrelevant in the business. The real question is whether it changes how work flows across functions. BCG’s 10-20-70 principle is useful here: 10% of value creation is algorithms, 20% is technology, and 70% is people and processes. BCG also found that two in three companies struggle to reimagine workflows, shift incentives, change culture and upskill their workforce.

3. From experimentation to capital discipline
AI spend should not escape normal investment discipline simply because the technology is exciting. Deloitte found that 85% of organisations increased AI investment in the previous 12 months and 91% planned to increase it again, yet only 6% reported payback in under a year on a typical AI use case. CEOs need to ask: Which AI investments defend today’s economics? Which build tomorrow’s advantage? Which are expensive distractions?

4. From technical risk to enterprise trust
PwC’s 28th Annual Global CEO Survey found that about half of CEOs believe GenAI will increase profitability in the year ahead, yet only 33% have a high degree of trust in having AI embedded into key processes. PwC’s phrase “bounded optimism” is the right leadership stance: neither blind enthusiasm nor uninformed pessimism.

5. From workforce anxiety to a new performance contract
Employees do not only ask whether AI works. They ask what AI means for their role, status, learning curve and job security. The CEO must set a credible narrative: where AI will automate, where it will augment, what skills matter, how performance will be measured, and how people will be supported to adapt.

Why this cannot sit only with the CIO

The CIO, CTO or Chief Data Officer can lead technology enablement. But only the CEO can resolve the cross-functional trade-offs.

AI decisions cut across capital allocation, customer trust, organisational design, legal exposure, cyber resilience, labour relations, data governance and brand promise. IBM’s 2025 CEO study found that CEOs expect the growth rate of AI investments to more than double over two years, but 50% say rapid investment has left their organisation with disconnected, piecemeal technology. That is a classic CEO problem: not technology adoption, but enterprise coherence.

KPMG’s 2025 Global CEO Outlook reinforces the point. CEOs are backing AI investment and high-potential talent, but KPMG also identifies agility, transparent communication and the ability to identify, prioritise and manage risks as key leadership capabilities. AI literacy is now part of that leadership toolkit.

A practical CEO agenda for AI literacy

A CEO who wants to raise the organisation’s AI maturity should start with a simple operating rhythm.

Run a board and Exco AI literacy session focused not on tools, but on strategic implications, risk appetite, use-case economics and governance.

Create an AI value map that ranks opportunities by strategic relevance, financial value, execution difficulty, data readiness and risk.

Separate productivity AI from transformation AI. Productivity use cases may improve speed and efficiency. Transformation use cases redesign processes, roles and business models.

Mandate AI fluency for leaders. Every senior leader should be able to explain where AI affects their function, what risks it introduces and what value metrics they are accountable for.

Build governance into the work, not after the fact. AI governance should include model risk, data lineage, human oversight, explainability, cyber controls, vendor risk and customer impact.

Measure what matters. Track adoption, productivity, cycle time, customer outcomes, error rates, risk incidents, employee capability and financial impact.

The leadership test

BCG CEO Christoph Schweizer has said that many companies have an opportunity to “close the gap between their ambitions and reality.” That is the essence of the CEO’s AI role.

The AI-literate CEO does not chase every tool. They translate possibility into priorities. They translate pilots into operating-model change. They translate risk into trust. They translate workforce anxiety into capability building. And they translate technology investment into competitive advantage.

The next phase of AI will not be won by the companies with the most experiments. It will be won by the companies whose leaders can make AI understandable, governable, commercially relevant and humanly credible.

That is why the CEO is now the organisation’s most important digital translator.