The leaders who create the most value from AI will not necessarily be the most technical. They will be the ones who can connect technology to strategy, redesign work, develop people and remain accountable for the outcome.
The question in many leadership meetings is no longer, “Should we use AI?”
It is becoming, “Where should AI assist, where should it make recommendations and where should it stay out of the decision?”
That is a much harder question. It cannot be answered by purchasing another tool or asking the technology team to run a pilot.
According to the Stanford AI Index Report 2026, 88% of surveyed organizations used AI in 2025, and 70% used generative AI in at least one business function. Yet AI-agent deployment remained in the single digits across nearly all functions.
In other words, access is widespread, but mature implementation is still developing.
This is why AI readiness has become an executive capability. Leaders do not need to build models themselves, but they do need enough understanding to make sound decisions about value, risk, people and accountability.
AI literacy is not the same as coding
The idea that every senior leader now needs to become a programmer is misleading.
The OECD’s 2026 review of AI and skills found that fewer than 1% of workers are likely to need advanced AI-specific skills such as programming or model development. Most people will instead need broader digital capability, the ability to use and interpret data, and human skills such as management, problem-solving, creativity and innovation.
For an executive, AI literacy means being able to ask informed questions:
-
What business problem are we trying to solve?
-
What data does the system use, and are we permitted to use it?
-
How will we test the quality of its output?
-
Where is human review required?
-
Who is accountable if the system is wrong?
-
How will we know whether it is creating value?
An AI-ready executive may not know how to train a model, but should understand that an answer can sound confident and still be inaccurate. They should recognize the difference between a useful experiment and a system that is ready to influence customers, employees, investments or other important decisions.
Technical depth will remain essential in specialist roles. At the executive level, however, the differentiator is judgment.
Start with the business problem, not the tool
AI initiatives often begin in the wrong place.
A new tool is introduced, teams are encouraged to experiment, and leaders then look for ways to justify the investment. This can create activity without producing meaningful improvement.
The stronger approach begins with a specific problem. Where is work slow, repetitive, inconsistent or difficult to scale? Which decisions could benefit from better information? Which customer or employee experience needs to improve?
Only then should the organization consider whether AI is the right answer.
The same Stanford report notes that the largest documented productivity gains tend to appear in structured, measurable work where outputs are relatively easy to monitor. Gains are smaller in tasks requiring deeper reasoning, while heavy reliance on AI may also carry longer-term learning costs.
This gives executives a practical starting point. Early use cases should usually have a clear owner, an observable baseline, a measurable outcome and a sensible way for people to review the result.
“Use more AI” is not a strategy. “Reduce the time required to prepare a first draft while maintaining accuracy and confidentiality” is much closer to one.
Redesign the work, not just the task
The biggest value from AI may not come from doing the same work slightly faster. It may come from changing how the work is organized.
The International Labour Organization’s 2025 global study found that one in four jobs is potentially exposed to generative AI. However, because most occupations still include tasks that require human input, the ILO concluded that job transformation is more likely than full replacement.
That distinction matters.
Leaders should examine roles at the task level and decide what should be:
-
Automated because it is routine and low risk
-
Accelerated by AI but reviewed by a person
-
Kept human because it depends on trust, context, empathy or accountability
-
Redesigned entirely because an old process no longer makes sense
Consider recruitment. AI can help organize information, prepare research or support an initial draft. It should not become an excuse to send impersonal outreach, repeat unverified claims or make consequential decisions without human judgment.
The same principle applies in finance, healthcare, manufacturing, professional services and other sectors. The tool may change, but responsibility does not disappear.
Make governance part of the workflow
Governance is sometimes treated as the part that slows innovation down. In practice, clear boundaries often make responsible experimentation easier.
People are more likely to use AI productively when they know which tools are approved, what information may be entered, when output must be checked and where to report a problem.
The US National Institute of Standards and Technology organizes AI risk management around four functions: govern, map, measure and manage. Its Generative AI Profile recommends clear responsibilities, testing before deployment, ongoing evaluation, incident processes and policies covering data privacy, intellectual property and information security.
For organizations operating in Japan, the government’s updated AI Guidelines for Business, Version 1.2, published in March 2026, also sets out unified principles for the safe and secure use of AI.
Executives do not need to own every control, but they do need to establish who does. They must also set the organization’s risk tolerance and make sure commercial pressure does not quietly override it.
At a minimum, leaders should be able to answer:
-
Which AI systems are being used across the organisation?
-
Which use cases could materially affect people or the business?
-
What information is restricted?
-
What testing and human approval are required?
-
Who has the authority to pause or stop a system?
If those answers are unclear, the organization may be experimenting faster than it is learning.
Build capability across the organization
AI readiness cannot sit with one innovation team.
Employees need role-specific guidance, practice and feedback. A legal team, a salesperson and a software engineer will use AI differently and face different risks. Giving everyone the same general training is unlikely to be enough.
The World Economic Forum’s Future of Jobs Report 2025 found that AI and big data are among the fastest-growing skills. At the same time, analytical thinking remained the most sought-after core skill, while leadership and social influence recorded the largest increase in importance compared with the report’s 2023 edition.
This combination is important. Organizations need technical capability, but they also need people who can question an output, explain a decision, influence colleagues and learn continuously.
An AI-ready executive creates room for responsible experimentation. They encourage employees to share where AI works and where it fails. They reward improvements in quality and customer outcomes, not just speed. They also model the behavior they expect by checking important outputs, disclosing AI use when appropriate and refusing to present generated material as unquestioned fact.
Lead the people through the change
AI transformation is also a human transition.
Employees may be curious, excited, sceptical or worried about what the technology means for their role. Silence from leadership rarely removes that uncertainty. It usually allows rumors and fear to fill the gap.
Executives do not need to promise that every role will remain unchanged. They should be honest about what is known, what is still being tested and how decisions will be made.
Good change leadership includes:
-
Explaining the purpose of an AI initiative in practical terms
-
Involving the people who understand the work today
-
Providing training before expecting adoption
-
Creating a safe way to question unreliable or inappropriate output
-
Showing how responsibilities and career paths may evolve
-
Measuring the effect on quality, workload and customers, not only cost
People are more likely to support change when they can see how they fit into it.
What should companies look for in an AI-ready executive?
AI readiness should not be assessed by asking whether a candidate uses a particular chatbot.
Tools change quickly. Leadership evidence is more durable.
Employers should look for executives who can demonstrate that they have:
-
Connected technology investment to a clear business priority
-
Led change across technology, operations, risk and commercial teams
-
Improved a workflow rather than simply adding a tool
-
Established appropriate controls and accountability
-
Developed the confidence and capability of their teams
-
Measured results and adjusted course when the evidence changed
For candidates, this also changes how AI experience should be presented.
“Experienced with generative AI” says very little. A stronger example explains the problem, the executive’s role, the safeguards used and the outcome achieved. The most credible candidates will also be able to discuss what did not work and what they learned.
A simple test for leaders
Before approving an AI initiative, ask seven questions:
-
What specific problem are we solving?
-
What result will tell us it worked?
-
Is the data appropriate, secure and permitted for this use?
-
What are the likely failure modes?
-
Where is human judgment required?
-
Who owns the outcome?
-
How will we train people, monitor performance and improve the process?
If a leadership team cannot answer these questions clearly, it is probably not ready to scale the initiative.
Where specialist insight can help
As AI changes roles across technology, financial services, healthcare, life sciences and other sectors, organizations need to define leadership requirements with greater precision.
Ascent Global Partners works with employers and senior professionals across Japan and Asia to understand how these expectations are evolving. For employers, that can mean identifying leaders who combine functional expertise with change management, commercial judgment and responsible AI literacy. For candidates, it can mean presenting technology experience through the business outcomes, team development and decisions behind it.
The aim is not to add “AI” to every job description. It is to understand where AI capability genuinely changes what effective leadership looks like.
Leadership still matters most
AI will not make leadership less important. It may make the consequences of poor leadership appear faster and spread further.
The strongest executives will not be those who chase every new tool or speak most confidently about the technology. They will be the ones who ask better questions, choose the right problems, build capable teams, establish sensible guardrails and remain accountable for the result.
You do not need to code to become an AI-ready executive.
But you do need to lead.
