The Modern AI Leader: Why most organisations start with the wrong question

Over the past 18 months, one of the most common questions from private equity investors, operating partners and portfolio company leadership teams has been: what does an effective AI leader look like?

AI has moved rapidly up the executive agenda, but unlike roles such as CFO, CIO and CTO there is still little consensus around what an AI leader is expected to do.

Before defining the scope of the role, therefore, organisations need to decide what problem they’re solving for. Too often, they start with the title before defining the outcome, leading to confusion about the role, inconsistent stakeholder expectations and ultimately disappointing hiring decisions.

One title, multiple mandates

When an organisation hires into other technology leadership roles there is usually broad agreement about what the role is responsible for. Given that most businesses are still in the early stages of adopting AI and many are unclear on the value it can generate, the same cannot yet be said for the AI leader hire. As a result, organisations are often starting with the person before they’ve properly defined the problem that person is there to solve. This is where AI leadership hires go wrong.

Across AI leadership searches, five core mandates typically emerge:

• The Product Builder – embeds AI within products and services, creates new customer value and drives growth.

• The Transformation Leader – improves productivity, redesigns processes and accelerates operational change.
• The Strategist – assesses opportunities, prioritises investment and builds an AI roadmap linked to commercial objectives.
• The Scale Operator – establishes governance, capabilities and the operating model needed to move beyond experimentation.
• The Technical Leader – provides the technical vision, expertise and leadership required to build, deploy and scale AI capabilities across the organisation.

The same title can represent very different mandates. A Chief AI Officer in a software business may be expected to build AI-enabled products and revenue streams; in manufacturing, the focus may be productivity and process redesign; in a professional services business, it may be identifying and prioritising opportunities to improve delivery and client value.

In practice, these profiles are rarely mutually exclusive. Many successful AI leaders combine elements of multiple mandates, with one typically emerging as the dominant requirement. A Product Builder may also be a capable Scale Operator, while a Transformation Leader may bring strong strategic capabilities. The objective is not to find someone who embodies every profile equally, but to identify the combination of capabilities most closely aligned to the value the organisation is seeking to create.

They also come from different talent pools:

• The Product Builder focuses on using AI to create new products, services and revenue streams. Many come from technology, software and AI-enabled businesses where they have successfully taken emerging technologies from concept to commercial reality.
• The Transformation Leader bridges strategy and execution. They understand that AI transformation is as much about people, process and culture as technology. Many come from consulting, PE operating teams, transformation functions or PE-backed businesses where they have led significant change.
• The Strategist focuses on identifying where AI can create the greatest business value. Often coming from consulting, corporate strategy or PE value creation backgrounds, they excel at prioritising opportunities and aligning investment with commercial outcomes.
• The Scale Operator focuses on turning AI ambition into an organisational capability. Often coming from technology, data or engineering leadership roles, they build the governance, operating models and foundations needed to scale successfully.
• The Technical Leader focuses on the technology, data and engineering capabilities required to build and scale AI. Often coming from AI, data science, engineering or technology leadership backgrounds, they bridge technical complexity and business objectives while providing the expertise needed to turn AI ambition into reality.

Understanding these distinctions is essential. Organisations are often comparing candidates with impressive track records without recognising that each has been successful at solving a different problem. The challenge is that all five profiles can credibly present as an AI leader on paper, despite bringing fundamentally different experiences, strengths and priorities.

While these backgrounds can provide relevant experience, the strongest AI leaders will typically bring a broader blend of capabilities. Technical depth may be critical in one organisation, while commercial judgement, strategic thinking, communication and stakeholder management may be equally important in another. The ability to translate between technical and commercial audiences, build alignment across the organisation and turn AI opportunities into measurable business outcomes is often what differentiates an effective AI leader from a strong functional specialist.

A simple framework for defining the need

Before launching an AI leadership search, leadership teams should align on a small number of questions:

1. Are we primarily looking to create new revenue opportunities or improve existing operations?
2. Is our challenge identifying opportunities or executing against already-defined priorities?
3. Do we need someone to build, coordinate or deliver capability?
4. Is AI a strategic differentiator for our business or a productivity enabler?
5. Is this a permanent leadership requirement or a transformation mandate with a defined end point?
6. Do we need deep technical expertise, commercial leadership, transformation experience or a blend of these?

The answers often reveal that organisations need very different things. Some organisations will conclude they need a dedicated AI leader, while others may need a transformation executive, technology leader, product innovator or interim specialist with strong AI capability. In many cases, the answer is not a new title, but a clearer definition of the mandate.

As a guide:

• If the priority is creating new products, services or revenue streams, you may need a Product Builder.
• If the priority is adoption, productivity and organisational change, you may need a Transformation Leader.
• If the priority is identifying opportunities, prioritising investment and shaping direction, you may need a Strategist.
• If the priority is governance, operating models and scaling capability across the organisation, you may need a Scale Operator.
• If the priority is building technical, data and AI foundations, you may need a Technical Leader.

The goal is not to select a title. The goal is to identify the capability required to create value.

The better question

Rather than asking “What does a great AI leader look like?”, organisations should start with this question: Do we actually need a dedicated AI leader?

And if the answer is yes: What do we need that person to achieve?

Are we trying to build AI-enabled products, improve productivity, identify and prioritise opportunities, drive value creation across a portfolio, or establish the capabilities and governance needed to scale? Once those questions are answered, the leadership profile usually becomes much clearer.

AI leadership is increasingly treated as though it’s a single executive role. In reality, it’s a collection of different mandates that currently share the same title. The organisations that get these hires right won’t necessarily be those that hire the most technically capable AI leader, but those that hire the leader with the right blend of capabilities for their mandate. They’ll be the ones that are clearest about the outcome they want that leader to deliver. 

For PE funds specifically, there’s an additional consideration: where should AI capability sit? While many firms are exploring central AI functions, most of the value will ultimately be created within portfolio companies rather than at fund level. Funds can help accelerate adoption through expertise, frameworks and shared resources, but AI succeeds when it’s embedded into products, operations and day-to-day decision making. In most cases, the winning model is likely to be a combination of central enablement and local ownership.

And perhaps that’s the biggest shift in thinking required. Too many organisations start with the person before they’ve defined the problem. The organisations that get these hires right do the opposite: they align on the outcome first, then determine the capability required to deliver it.

One title. Multiple mandates. The challenge isn’t finding an AI leader. It’s defining the problem first. Once that’s clear, the leadership capability required often becomes obvious.