Steering AI Is Now Part of the Executive’s Job

AI fluency has moved from optional skill to executive responsibility. This episode looks at why leaders must steer AI as a business system, setting direction, design, guardrails, and proof.

It explores the gap between AI ambition, leadership readiness, workforce reality, and governance expectations.

TLDR / At a Glance

• Executive AI fluency
 • System steering
 • Direction, design, guardrails, proof
 • Talent readiness gaps
 • Hidden employee AI adoption
 • Leadership modelling and legal duty

The key takeaway is that AI value depends on leaders who can govern, redesign, and measure AI across the organisation. 

Transcript

Steering AI Becomes The Job

SPEAKER_00 0:00

Steering AI is now part of the executive's job. When AI fluency reaches the executive agenda, it usually arrives dressed as training, a prompt workshop, a tool demo, a session on writing better instructions to a chatbot. The implication is that a leader becomes AI fluent by becoming a more capable user of AI. That framing is comfortable because it turns a hard leadership question into a familiar skills exercise, and it quietly lets the real work go undone. This article argues that executive AI fluency is now part of the job, but not the part most training assumes. The fluency leaders actually need is the ability to steer AI as a business system, not to operate it as a personal tool. It sets out where leadership readiness is falling behind strategic ambition, why prompt skills sit well below the real executive task, and what a more useful model of fluency looks like. The

Prompt Skill Versus Leadership Judgment

SPEAKER_00 0:58

fluency that gets mistaken for a skill. There is nothing wrong with executives learning to use AI well. The mistake is assuming that personal proficiency is the same thing as leadership fluency. A leader who writes excellent prompts but cannot judge where AI belongs in the operating model, how decisions should change around it, or whether the investment is paying off has learned to drive without being able to navigate. The expertise gap at the top is wider than most leadership teams admit. Gartner found that only 5% of C-suite executives have enough expertise to evaluate and navigate AI effectively, even as expectations of it run high. That is not a prompt problem, and no amount of prompt training will close it. It is a fluency problem, and fluency here means something closer to judgment than to technique.

Ambition Outruns Operational Readiness

SPEAKER_00 1:50

The executive job has changed shape. The reason this matters now is that AI is reshaping the executive job itself, not only the work beneath it. Leaders are being asked to decide where to automate and where to keep humans firmly in charge, how to redesign work rather than simply equip it, how to govern data and risk, and how to turn scattered experiments into value. These are not questions a specialist can answer on a leader's behalf, because they are leadership questions wearing technical clothing. Seen this way, the useful distinction is between using AI and steering it. Using AI is operating the tool, prompting, drafting, summarizing the things any capable employee can now do. Steering AI is directing it as a system across the organization, choosing where it is aimed, how work is rebuilt around it, what keeps it safe, and whether it is actually working. The first is a personal skill, the second is the job. That the second is unfamiliar is clear from how leaders describe their own readiness. Microsoft found that while 79% of leaders believe their company must adopt AI to stay competitive, 60% worry their organization's leadership lacks a plan and a vision to do it. The appetite is there, the steering capacity is not, and the space between the two is where value leaks away. Ambition is outrunning readiness. The clearest symptom of weak steering is the distance between how ready leaders feel about strategy and how ready they are about everything strategy depends on. It is comparatively easy to form a confident view that AI matters. It is much harder to prepare the talent, data, and workflows that turn that view into results. Deloitte's research captures the imbalance well. While 42% of companies said their strategy was highly prepared for AI adoption, only 20% said the same about their talent. A leadership team can be strategically certain and operationally unready at the same time, and that combination is more dangerous than open doubt, because it funds ambition without building the capability to deliver it.

Employees Adopt AI Under The Radar

SPEAKER_00 4:10

Leaders are often the last to know. A second symptom is more uncomfortable. Leaders frequently misread what is already happening in their own organizations. AIUs tends to run ahead of permission, with employees adopting tools quietly because official provision is slow or the guardrails are unclear. A leader who is not fluent cannot see this clearly, and so plans for a future that has, in part, already arrived. The scale of the misjudgment is documented. McKinsey found that employees are roughly three times more likely to be using generative AI for a meaningful share of their daily work than their leaders assume. That gap is not just a curiosity. It means risk, value, and behavior are all being shaped below the waterline, where an unfluent leadership team cannot govern them, and steering becomes guesswork.

Why Prompt Training Falls Short

SPEAKER_00 5:06

Why prompt training is not enough? Prompt training earns its place. Teaching people to write better instructions improves how individuals use the tool, and an organization whose people cannot prompt well is starting from behind. The problem is not that it is wrong, but that it is only the first layer. It does almost nothing about where the tool is aimed, how the work around it is organized, or whether the result creates value, which are the questions that actually determine impact. The neglected work is visible in the numbers. Deloitte found that 84% of companies have not redesigned jobs around AI capabilities, even as they invest in tools and training. That is the tell. Organizations are layering AI onto yesterday's operating model and hoping for transformation. Prompts are the easy part, and redesigning the work, the decisions, and the accountabilities around AI is the executive part that is largely being skipped. The pattern holds across the strongest performers and the rest. Companies that treat AI as a tool to distribute tend to see scattered local gains that never reach the income statement. Those that treat it as a reason to rethink how work is structured are the ones that convert activity into advantage. The difference is not the technology, which is broadly available to both. It is whether leadership did the redesign work that only leadership can authorize.

Four Dials For System Fluency

SPEAKER_00 6:35

A model for steering not using. If steering is the job, it helps to be precise about what it involves. I find it useful to separate two kinds of fluency. Tool fluency is the ability to use AI well yourself. System fluency is the ability to direct AI as a business system, and it is the executive's real mandate. System fluency rests on four control points, the dials a leader actually holds. The first is direction. This is the choice of where AI is aimed, which few value pools and use cases matter most, where it creates advantage and where it does not, and whether the organization should build, buy or partner. Direction is also where vendor challenge lives because aiming well depends on seeing through a supplier's claims to the model underneath. The second is design. This is the redesign of work, decisions and roles around AI, which tasks change, which decisions can be augmented, which must stay decisively human, and where formal override rules belong. Design is the layer most companies neglect, and it is where the operating model is either rebuilt or quietly left untouched. The third is guardrails. This is the governance of risk, accountability and compliance, bias, privacy, security, explainability, regulatory exposure, and the question of who is answerable when AI is involved in a recommendation or a decision. The fourth is proof, the discipline of measuring real financial and operational value rather than counting pilots, and recalibrating investment when the evidence is thin. Direction, design, guardrails, and proof are not technical specialisms. They are the leadership work that no specialist can do for you.

Leaders Make Fluency Contagious

SPEAKER_00 8:24

Fluency is contagious when leaders model it. There is one more reason fluency belongs at the top rather than being delegated downward. Leadership behavior is one of the strongest predictors of whether AI adoption succeeds at all. When leaders use AI visibly and well, they license experimentation, set the standard for judgment, and make it safe for employees to bring their use into the open rather than hiding it. The effect is measurable. BCG found that employee positivity about generative AI rises from 15% to 55% when leadership support is strong, a swing too large to ignore. A fluent leader is not only making better decisions about AI, they are changing the conditions under which everyone else uses it. Microsoft's research points the same way. It found that when managers actively model AI use, their teams report markedly higher confidence in its value, sharper critical thinking about when to rely on it, and greater trust in more autonomous systems. The mechanism is not mysterious. People take their cues about what is acceptable and what is expected from how their leaders behave. Fluency, in other words, is contagious and so is its absence.

AI Literacy Becomes A Duty

SPEAKER_00 9:45

Fluency is becoming a duty, not a choice. What was once a matter of competitive advantage is also becoming a matter of obligation. The EU AI Act now places an explicit duty on providers and deployers to ensure a sufficient level of AI literacy among the people operating AI on their behalf, which pulls executive and workforce fluency into the realm of legal expectation rather than optional development. Fluency is starting to carry fiduciary weight, not just strategic value. The risk of inattention is concrete. KPMG's global study found that around two-thirds of workers rely on AI output without checking its accuracy, a habit that turns ungoverned AI use into a steady source of error. A leadership team that cannot see, shape, and assure how AI is used is not only leaving value on the table, it is accumulating exposure it does not understand. Steering here is simply another word for control.

Checks Leaders Must Answer Now

SPEAKER_00 10:50

What this means for leaders. The temptation is to meet the AI moment with a training calendar, workshops, certificates, a push to get everyone prompting. That is not wrong, but it is not the work. The work is to treat fluency as a leadership capability and to ask whether the senior team can actually steer AI as a business system, rather than just use it as individuals. In practice, that means a few honest checks. Can your leaders name the handful of use cases that matter and challenge a vendor on what sits beneath the product? Have you redesigned the work and the decision rights around AI, or only the tools? Do you know how much unsanctioned use is already happening? Who owns the risk, and how value is being measured rather than assumed? And are your managers modeling the behavior you want the organization to adopt? Those questions map directly onto direction, design, guardrails, and proof. Financial fluency, strategic fluency, and digital fluency all became part of the executive job in earlier waves, not because leaders ran the spreadsheets or wrote the code, but because they had to steer what those capabilities made possible. AI is now the same kind of test. The technology creates the possibility. It is the leadership system that turns it into value, and fluency is what lets leaders do the steering.

Closing And Where To Read More

SPEAKER_00 12:15

This concludes the article. You can also read this article on my LinkedIn page where I share regular insights on AI, strategy, and emerging technologies.