Do You Need a Chief AI Officer? Why Every Board Should Be Asking This Question

From automation to ethics, the pace and scale of AI advancement present the most pressing dilemma facing today’s boardroom: What should we do about AI? 

With so much discussion about the impact of new technologies, every organization – large or small – must now grapple with a growing list of questions: 

• Should we aim to be in the vanguard, or adopt a more cautious approach for now? 

• Will competitors gain a strategic edge if we don’t act now? 

• How will emerging technologies affect our people and our long-term strategy? 

• Should we pivot to become a software, information, or knowledge-driven company? 

• What are the dangers of implementing AI into our operations and our data management? 

The pressure is mounting, and various questions linger on the collective world’s lips: Will AI make my job redundant? Does a company truly have a choice? How does it weigh its commitment to driving profitability and delivering shareholder value against its responsibility to its people?  

It’s up to leaders to consider how AI can benefit their organization’s pursuit of growth without undermining employee morale and motivation. The challenge is to deliver a balanced plan – one that isn’t so skewed toward shareholder advantage that it loses sight of community and human accountability. 

Employees are also looking to leadership for clear direction on when and how to use readily available tools like ChatGPT and Copilot. Team leaders, who are often experts in entirely different fields of business, must determine if these tools are safe to use and investigate ethical, legal, or governance concerns. Leaders will also need to assess how AI could potentially empower their teams without compromising performance, privacy, or trust.  

Michael de Kare-Silver, Managing Partner at Signium UK, acknowledges the challenge that AI presents:

“So much could go wrong for those who misuse or fail to use AI. But, on the flip side, so much could go right. Basically, it’s a perfect storm of innovation, possibility, and uncertainty – and someone needs to steer the ship.” 

Introducing the CAIO: A new role for a new era 

A CAIO understands the capabilities and consequences of AI and other emerging technologies. 

As AI reshapes industries at breakneck speed, boards are under increasing pressure to make fast, informed decisions on technologies most of them weren’t trained to navigate. To meet these demands, there is a growing need for a dedicated, senior voice at the table: a Chief AI Officer (CAIO).  

More than just a tech lead, the CAIO is a new executive role tasked with guiding the organization through the risks, rewards, and regulatory landmines of the AI age. This role demands not only fluency in machine learning, automation, and data governance, but also the leadership credibility to influence boardroom decisions, shape policy, and embed AI into the fabric of the business – ethically, efficiently, and at scale. 

This role must: 

• Champion innovation while safeguarding against risk. 

• Act as a primary point of contact for the Board and key stakeholders. 

• Ensure AI strategies align with corporate values and governance frameworks. 

• Deliver a roadmap that balances opportunity with responsibility. 
   

The rise in adoption: Key statistics 

What’s changing the game is the fast pace of breakthroughs in machine intelligence. “In the human context, AI feels incredibly new and unknown,” says de Kare-Silver. “And yet, it’s no longer experimental – it’s already operational, scalable, and embedded in core business functions across industries and borders. That’s a huge leap forward.” 

Gartner research emphasizes this: 

• 45% of companies are now using AI widely across their business. 

• 91% of companies are reviewing potential future investments in AI. 

• 85% of businesses have projects investigating how AI can improve customer service and reduce costs. 

Budgets on AI projects are expected to surge as organizations ramp up investment in digital transformation. Gartner’s research suggests a fivefold increase over the next three years. IBM research estimates that the total AI market size will be approximately $140 billion in 2025 and could reach $1.5 trillion annually by 2030. Further research by the International Data Corporation (IDC) indicates that the biggest area of spend is on automation and improving efficiency. Other key goals include increased innovation, new product and service development, new ways to engage customers, and accelerating time to market. 

Exploring sectors: Who’s using AI the most in 2025?  

Financial services are among the biggest innovators and users of machine learning and AI applications, and banks and insurers are expected to be the biggest group of AI tech investors. Leading retail banks like Barclays, Bank of America, and Citibank are already exploring and utilizing new tech opportunities across both front and back offices. Their plans include: 

• Transitioning from branches to fully digital banking 

• Replacing customer service representatives with conversational bots 

• Introducing facial scanning for ATM transactions (to be known as “smile to pay”) 

• Using machine learning to enhance product cross-sell, upsell, and recommendation systems 

• Automating threat intelligence and prevention systems 

• Deploying comprehensive fraud management via real-time monitoring of customer interactions 

• Implementing fully automated claims processing 

• Using predictive IT operations maintenance 

• Applying AI tools like Santander’s “Kairos” to analyse the potential impact of economic events on corporate clients and support investment and lending decisions 

• Using Bank of America’s platform “Glass” to help employees uncover hidden market patterns through consolidated market data and machine learning 

The healthcare sector is also seeing accelerated investment in AI, particularly in pharmaceuticals and primary care. Drugmakers are leveraging AI to speed up research, identify new compounds, and optimize clinical trials. In general medical practice, AI tools are increasingly being used to support diagnosis, treatment recommendations, and even prescription renewals.  

De Kare-Silver says, “While full automation is not yet widespread, these advancements are beginning to ease administrative and diagnostic workloads, signalling a shift toward more efficient, accessible models of care. In practice, this could ultimately remove the need to wait for weeks for a GP appointment, which is the current reality for many people in need of healthcare.”  

In every sector – whether front of house, back office, manufacturing, education, or government – people, processes, and investment priorities are all likely to evolve as AI technology develops. 

What does the CAIO role look like in the real world? 

If a company is to appoint a CAIO, what should it expect from that person? Is the role simply to accelerate cost-saving initiatives? Or is it something more fundamental – a chance to enhance the company’s long-term reputation, societal role, and stakeholder value? 

The CAIO needn’t be a high-cost C-level executive, but they must be a respected figure with sufficient stature, who is able to command the Board’s attention and acknowledgment. De Kare-Silver hints at what makes a person right for this role: “While expertise in technology is hugely advantageous, the ideal CAIO is a true guide and leader who inspires clarity and vision across both technical teams and executive leadership. They help to develop strategies that can be confidently embraced and effectively implemented.” 

Philippe Rambach, CAIO of Schneider Electric 

Philippe Rambach, one of the first CAIOs appointed globally, was tasked with establishing a global AI hub and centre of excellence at Schneider Electric.  

Speaking of his role, Rambach shares: “If you are a business owner who does not harness the power of data, you will have a tough time in the future economy. In that respect, we have no choice. We have to review how AI can help Schneider be a winner in the next five years, so mastery of AI is vital.” 

Examples of Schneider Electric’s AI innovation under Philippe Rambach include: 

• Leveraging IoT and AI together to transform large volumes of energy data into actionable insights, enabling continuous optimization of systems for maximum efficiency. 

• Monitoring consumption trends in real time, with the ability to automatically fine-tune energy systems without human intervention. 

• Developing a “smart building” innovation hub in Grenoble that forms part of a smart grid-ready ecosystem, capable of interfacing with neighboring buildings, tracking grid demand, and dynamically deferring energy usage during peak periods. 

• Establishing an open innovation ecosystem, grounded in the belief that meaningful AI advancement cannot happen in isolation. 

• Collaborating through alliances, joint ventures, and open AI platforms to accelerate innovation and ensure scalable, interoperable solutions. 

Notably, Rambach doesn’t have a background in technology but brings 14 years of cross-functional experience at Schneider. This has made him a well-known and respected stakeholder manager – an essential trait for aligning departments and driving collaboration. 

Kim Young, Hyundai Heavy Industries 

South Korea’s Hyundai appointed a CAIO to its Heavy Industries division, with a specific focus on applying AI and leveraging big data in shipping operations and shipbuilding.  

Under Kim Young’s leadership, the division has concentrated on developing AI-based autonomous navigation systems for unmanned vessels – astounding technology designed to enable ships to operate independently, without a human crew, while optimizing fuel efficiency. In partnership with US data firm Palantir Technologies, Hyundai has already achieved a world-first by remotely and autonomously navigating a container ship across the Pacific. 

Adrian Joseph, former Chief Data and AI Officer at BT 

Adrian Joseph became the first Chief Data and AI Officer at BT plc, and quickly encouraged the Board to adopt a bold but simple vision: “We will become an AI-led company.”  

Joseph spearheaded the launch of AI Accelerator, a dedicated platform to pioneer and oversee all AI developments driven by BT’s data community. Its objectives include: 

• Defining business priorities for AI investment, ensuring the targeted value-add and business gain are achieved. 

• Substantially reducing software development time, from prototype to production – targeting six days, down from six months – by automatically enforcing templated best practices, which the AI Accelerator platform will continuously develop. 

• Embedding governance through built-in triggers, designed to ensure that new AI use cases are properly assessed in line with guidelines on data privacy, security, and ethics principles. 

De Kare-Silver weighs in on these efforts, saying, “BT has positioned this platform as a tool not only for speed and scale, but also for safe and responsible AI deployment across the business. This all sounds very encouraging, but there’s still very little explanation or elaboration on what those  “ethics principles” in this context might be or what, if any, boundaries they represent.”  

Not an afterthought: Microsoft’s approach to responsible AI leadership 

One company that has made moves to establish responsible AI is Microsoft –  they did so by creating the Office of Responsible AI (ORAI). ORAI runs the Aether Committee (AI, Ethics, and Effects in Engineering and Research) and developed a set of core guidelines with the primary message that: “Responsibility must be a key part of AI design, not an afterthought.” 

A statement from ORAI clearly stipulates the group’s stance on the potential opportunities and pitfalls of AI: “At the start, we received an exciting new model from Open AI called ChatGPT, and straightaway we assembled a group of testers to probe the core model and understand both its capabilities and its limitations. The insights generated have helped Microsoft think about what the planned mitigations are and to bake in more safety features and controls. For example, we wanted to look at possible ‘hallucinations’ where the model may make up facts that are not true. So we have designed ChatGPT in a way that has responsible AI at its core”.  

Microsoft has now gone on to publish its Responsible AI guidelines, aiming to establish its writings as an industry standard: “Our guidelines are intended to share the group’s learnings and help our customers and partners navigate this new terrain. AI must develop as technology built by humans for humans.”  

“Yet even such good and noble intentions face real-world challenges,” says de Kare-Silver. “ChatGPT gained 100 million users within two months of launch, and over a billion users by 2025 – free to use or a subscription of just $20 per month. It’s a digital juggernaut. Continuous updates, plugins, and industry-specific applications only increase the urgency for responsible AI leadership.” 

The CAIO Profile – Finding the right leadership for a new role 

Considering all this, it’s more important than ever for every company of any scale to have an AI champion. We might expect CAIOs to most typically come from a core Technology or Data  Analytics background – for example, Adrian Joseph joined BT as a former Data & Analytics  Partner from EY. Others like Di Mayze at WPP, Sanjeevan Bala at ITV, John Giannandrea at  Apple share that technical expertise and know-how.  

However, a noticeable shift in recent appointments is the growing emphasis on leaders who know the organization well – those who have been with the company for some time, understand its people, possess strong stakeholder management skills, and can build alignment and support for navigating this new space. 

Schneider’s success with Philippe Rambach exemplifies this. Coming from a Commercial / Profit & Loss management background without any previous technology or data expertise, Rambach brought nearly 14 years of working experience in various roles and different divisions across the company. He was well-known, respected, and widely admired as an excellent key stakeholder manager.  

What Schneider realised was that this role would need to be collaborative – it would have to work across critical functions and strategic business units to get alignment around data and analytics usage, investment, governance, and controls. The organization recognized that filling this role would be the key to getting AI developed and progressing for the company.  

Not every AI leader looks the same 

Each unique company is at a different point in its AI journey. Depending on the stage of AI adoption, different leadership profiles may be needed. Some leadership dynamics could look like the following:  

• An academic could establish capability and foundational skills 

• A commercial leader may be adept at translating ideas into revenue and growth 

• Process experts would be able to drive efficiencies and improve performance 

• A collaborator can align teams and craft a strategic AI roadmap 

• A Risk/Governance leader would be valuable in creating ethical guidelines and managing AI protocol development 

De Kare-Silver comments, “Traditionally, a CFO would come from a strong financial background. A CEO would rise through years of business development. An HR executive typically built their career in recruitment and people management. While it makes sense to place an IT professional in the CAIO role, technical expertise alone isn’t enough. The broader qualities of this leader are just as critical. AI spans departments, industries, and even continents, and affects humans at every level – a CAIO must understand the scale and gravity of that impact.” 

“AI isn’t coming. It’s here. The organizations that act with clarity, vision, and responsibility will shape the future. So the question isn’t whether you need a Chief AI Officer – it’s how soon you’ll appoint one.” 

Michael de Kare-Silver