There may be plenty of experimentation and several successful pilots, yet the impact on revenue, cost, productivity or customer experience remains unclear. This is becoming a familiar situation as companies move from trying AI tools to figuring out how AI should actually become part of the way the business operates.

Recent research reflects the same challenge. Organisations are increasing their AI investment, while many are still working through issues around workflow redesign, governance, employee capability and return on investment. McKinsey’s research has found that redesigning workflows is strongly associated with greater business impact from generative AI, while Deloitte’s research continues to highlight the difficulty many organisations face in converting AI investment into measurable returns.

For CEOs, this puts AI firmly on the business agenda.

Before committing more money, people and management attention to AI, there are five areas that deserve serious attention.

1. Start with the business problem

The easiest way to begin an AI programme is to look at what the technology can do, but that can quickly lead to a long list of experiments without a clear business purpose.

AI can analyse information, summarise documents, generate content, identify trends, automate repetitive work and support decisions. The CEO needs to connect these capabilities to a specific business priority, whether that means reducing customer response time, improving sales productivity, reducing operational errors or helping finance teams work more efficiently.

That business problem should guide the AI use case. Current research suggests that organisations are capturing more value when AI initiatives are linked to specific business outcomes, with McKinsey also highlighting workflow redesign and leadership involvement as important factors in achieving impact from generative AI.

VentureBean explores this relationship in AI in Business Strategy: Why Human Insight Still Matters ,which looks at how AI can support analysis and decision-making while business leaders continue to provide context and judgement.

The starting point should therefore be a business need, followed by the technology that can help address it.

2. Look at the process before adding AI

Imagine a company using AI to prepare a customer proposal. The first draft is ready in minutes, creating an obvious productivity gain. The proposal then goes through four internal reviews, waits two days for pricing approval, returns to the sales team for changes and eventually reaches the customer a week later.

The AI has made one part of the process faster while the overall process remains slow.

This is where many AI programmes can lose their potential. If the workflow contains unnecessary approvals, manual handoffs, duplicated information or unclear responsibilities, introducing AI into one part of it will have limited effect.

McKinsey’s research has found that workflow redesign has one of the strongest relationships with reported business impact from generative AI, yet many organisations continue to introduce AI into existing processes without reconsidering how the work should be organised

CEOs therefore need to examine the process as a whole and identify where work slows down, which activities require human judgement, which decisions need senior approval, what can be automated and where information is being duplicated. These operating-model decisions have a direct bearing on the value AI can create.

VentureBean’s Strategy vs Execution: Why Great Plans Still Fail  is relevant here because an AI strategy only produces results when it translates into changes in how people, processes and systems actually work.

3. Put ownership and governance around AI

AI can influence decisions with significant consequences for a business, from customer offers and fraud detection to recruitment, financial analysis and service prioritisation. 

As organisations move towards AI agents that can perform multiple tasks with limited human intervention, CEOs need clear ownership and accountability around how these systems are used. This includes deciding who owns the use case and data, who reviews AI output, which decisions require human approval, what happens when the system makes an error and how risk will be monitored.

These are business governance decisions, not simply technical matters. McKinsey’s research has also found an association between CEO oversight of AI governance and greater reported business impact from generative AI.

Good governance should give teams enough room to experiment while keeping responsibility clear, particularly as AI becomes part of customer-facing, financial and operational processes.

4. Prepare people for the change in their work

AI adoption can look very different from the employee’s desk. Management may see productivity gains, while employees may see a new system to learn, uncertainty about changing responsibilities or concerns about job security and the reliability of AI output. 

Some employees will welcome AI because it removes repetitive work. Others may be concerned about job security or may not trust the output produced by the system. Some may use AI extensively without understanding the risks involved.

Deloitte’s research continues to identify workforce capability, training, trust and organisational change as important factors in successful enterprise AI adoption. 

The CEO therefore has an important role in explaining what the organisation is trying to achieve.

People need to understand which parts of their work will change, which responsibilities will remain with them and what support they will receive. They also need opportunities to learn how to use AI appropriately within their roles.

When senior leaders use AI themselves, discuss its limitations and demonstrate how it can support their work, employees receive a much clearer message than they would from another corporate communication.

VentureBean’s How to Effectively Navigate and Manage Change in Business  provides a useful perspective on communicating change, involving people and helping teams adapt to new ways of working.

5. Decide how AI will be measured before it is implemented

This is where many AI discussions become vague.

A team may report that an AI tool has saved 500 hours. That sounds positive. The CEO still needs to know what those 500 hours produced.

• Did the sales team contact more prospects?
• Did customer service resolve more cases?
• Did the finance team close the books faster?
• Did operating costs fall?
• Did customers receive better service?
• Did revenue increase?
• Those measures connect AI activity to business performance.

Deloitte’s research shows that many organisations are taking longer than expected to achieve satisfactory returns from AI investments, which makes disciplined measurement even more important. 

A CEO should establish the expected business outcome before approving a significant AI initiative. The relevant measure could be revenue, margin, productivity, cycle time, customer retention, error rates, conversion rates or another metric that matters to the organisation.

AI usage is useful information.Business impact is what ultimately matters.

VentureBean’s Business Consulting approach focuses on measurable outcomes across strategy, performance, operational efficiency, scalability, governance and execution. The same discipline can be applied when evaluating AI investments.

AI adoption needs a CEO’s attention

AI is moving too quickly for organisations to wait until every uncertainty has been resolved.

Experimentation has its place. It allows teams to understand what the technology can and cannot do. The challenge comes when experiments continue without a clear decision about where AI should become part of the business.

The organisations that gain the most from AI are likely to be those that connect technology with business priorities, redesign work where necessary, prepare their people, establish accountability and measure outcomes.

That requires CEO involvement because AI affects much more than the technology function.

It can change how decisions are made, how employees work, how customers are served, how information moves through the organisation and how leaders manage performance.

VentureBean’s Executive Coaching in the AI Era: How Leaders Stay Strategic While Technology Evolves looks at the leadership side of this challenge and the need for senior leaders to remain focused on business priorities while technology continues to evolve.

For a CEO, the opportunity is bigger than finding another task that AI can perform.

It is an opportunity to reconsider how work gets done, where decisions should sit and where technology can improve the economics of the business.

AI can provide the capability.The CEO still has to decide where that capability belongs, how it should be used and what result the business expects from it.

FAQs

1. What are the biggest challenges CEOs face with AI adoption?
Key challenges include unclear AI strategy, limited leadership readiness, weak business alignment, poor measurement of value, and difficulty getting employees to adopt new ways of working.

2. How can CEOs create a successful AI strategy?
CEOs can create an effective AI strategy by connecting AI initiatives to business goals, measurable outcomes, operational priorities, and long-term transformation plans.

3. How can AI create business value?
AI can create business value by improving productivity, operational efficiency, decision-making, customer experience, cost management, and opportunities for business growth.

4. Why does AI transformation require leadership?
AI transformation changes how people work, make decisions, and collaborate, making strong leadership essential for managing organisational change and achieving sustainable adoption.

5. How can executive coaching help CEOs with AI transformation?
Executive coaching can help CEOs strengthen strategic thinking, decision-making, change leadership, and the confidence needed to navigate complex AI-driven transformation