As artificial intelligence continues to reshape industries, most executive leadership teams are grappling with the burning question of how best to integrate AI across their organization. This is a weighty consideration complicated by all kinds of variables involving organizational strategy, structure, digital maturity, timing and overall resources. What many companies are finding is a growing need for a specified AI leader explicitly suited to their own evolving AI journey.
Discovering what kind of AI leader is needed begins with clarifying the organization’s AI strategy and maturity. If a company is primarily focused on leveraging off-the-shelf AI solutions, their existing data or technology team might be sufficient. However, if they are developing proprietary models or seeking to drive a comprehensive AI transformation, a dedicated AI leader will likely be essential.
With AI leadership roles on the rise, we are increasingly supporting our clients through this process of determining what type of AI leader they need. Below, we break down the three main archetypes of AI leaders to help identify which aligns best with an organization’s needs.
The Three AI Leader Archetypes
1. AI Industry Shapers
These are visionary leaders who focus on the broader impact and future implications of AI. They are typically thought leaders, often seen as trailblazers in the space, pushing organizations to explore AI’s potential beyond immediate applications. Industry Shapers are ideal for organizations aiming to stay ahead in AI innovation, where strategic foresight and influence are crucial.
- Why they matter: Industry Shapers challenge conventional thinking and help executive teams navigate AI’s future. They are often instrumental in building partnerships and advocating for responsible AI practices. They can bring value in board or advisory roles, contributing to discussions on AI’s long-term impact.
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Attractions and considerations: Shapers are drawn to organizations focused on innovation and societal impact rather than just commercial gains. However, they may not align well with companies primarily motivated by short-term financial performance, as they prioritize pioneering and advocacy over operational realities.
2. AI Builders
These leaders are deeply technical, often with backgrounds in technology research and development, and they are passionate about creating cutting-edge AI solutions. Builders are therefore the right fit for companies developing proprietary AI models and products, requiring technical expertise to innovate at scale.
- Why they matter: Builders are the architects behind disruptive AI products. Having them on board can establish your organization as an innovation hub, attracting similar technical talent and enhancing R&D capabilities.
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Attractions and considerations: AI Builders are typically motivated by the freedom to innovate and work with advanced technology. However, they may struggle with organizational politics and often are best suited for environments that value experimentation over immediate KPIs.
3. AI Transformers
Transformers can have two tracks, the Tech Drivers—well-rounded business leaders who are still technologists at heart; and the Business Drivers—well-rounded, tech-savvy business leaders who are analytical. They are both committed to driving the value that AI can create for their business. Which type of AI transformer is chosen can largely depend upon the best overall cultural fit as well as the one that best complements and enhances the strategic direction and expertise of the board and ExCo.
Transformers bridge the gap between technical AI knowledge and business execution. These leaders excel at translating AI capabilities into real-world applications that augment business value. Companies seeking to operationalize AI across departments, and those with established data infrastructures, benefit most from this archetype.
- Why they matter: Transformers are execution-oriented leaders who manage cross-functional teams to deliver AI-driven business results. They possess the influence and communication skills to align AI initiatives with corporate goals, making them pivotal in ensuring ROI on AI investments.
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Attractions and considerations: Transformers are attracted to roles where they can oversee the integration of AI into core business functions, this often requires a breadth of leadership influence across teams and resources in order to drive initiatives. Given the cross-functional nature of their work, they may seek roles with direct reporting lines to the CEO for greater impact and influence.
Matching the Right Archetype with Your Company’s Maturity Level
A company’s readiness for AI is often assessed through its data maturity level, which determines the type of AI leader that would be most effective. Here’s a high-level approach to identifying where your organization stands:
- Focus on the data pipeline: Companies at the foundational stage are often implementing data governance practices and setting up basic data infrastructure. Here, a Transformer may be ideal to help build data capabilities that can support AI down the line.
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Small-scale experimentation: Organizations experimenting with descriptive and diagnostic analytics can benefit from an AI Builder who can be tech-savvy, innovative and mentor PhDs and can help expand these projects and apply predictive models to create tangible value. -
Scale-up and narrow-AI experimentation: For companies in the process of scaling up their use cases across the enterprise, this would most likely always require a Transformer who can provide the right mix of technical knowledge and execution capability. -
AI at scale: With more mature AI programs—often digital natives or tech innovators—will likely need a Transformer to sustain their leadership in the AI space and keep exploring cutting-edge advancements.
Should You Hire Internally or Externally?
Once you’ve identified the best archetype for your organization, the next decision is whether you can find the right leaders internally or have the need to hire someone externally. Both paths present distinct benefits and considerations:
- Internal candidates: If an existing leader in your organization shows promise, they may be the most effective leader since they are deeply familiar with the existing culture and the extent of (or limits to) the internal knowledge necessary to champion AI initiatives effectively. However, the lack of prior explicit AI experience can certainly be a risk to be measured as it could slow the adoption process or lead to missing opportunities for innovation. The other question to ask will be if the organization structure is giving the opportunity for the leader to flourish with the AI program, or do you need to make changes to empower the internal leader by changing the organization or appointing a sponsor.
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External candidates: External hires, especially those with extensive experience in AI leadership, bring proven expertise and a fresh perspective. They are often best positioned to accelerate AI adoption and bring in new competencies. At the same time, external candidates may need time to adapt to your company’s culture and may command high compensation due to market demand.
How We Can Help You Evaluate AI Leadership Potential
At Egon Zehnder, our assessment process looks at several key factors to gauge a candidate’s fit and readiness for AI leadership, including:
- Technical proficiency and business acumen: We evaluate a candidate’s depth in AI and data science, ensuring they can manage or mentor technical teams effectively. But not only that—they should also demonstrate the business acumen for selecting and scaling the right use cases.
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Communication skills: AI leaders must be able to communicate complex technical concepts to non-technical stakeholders, making this a critical capability that often distinguishes candidates. -
Strategic vision: An effective AI leader should have the vision to align AI initiatives with business objectives to create measurable value. -
Leadership and influence: We assess candidates for their ability to inspire and lead across functions, especially in overcoming the fears and internal resistance often associated with AI adoption. -
Adaptability and resilience: AI is an evolving, quickly changing field, so its leaders must be flexible, resilient change agents capable of smoothly adapting and leading others to do the same in this climate.
By identifying these attributes, we can help your organization determine whether an internal candidate has the potential to succeed, or if an external hire would provide a faster path to success.
The Talent Market for AI Leaders
Demand for AI and machine learning talent has surged, resulting in a competitive market where qualified leaders can be selective about roles and command above-market compensation. It is commonly misperceived that the best AI talent is concentrated in digital native and tech companies, but these individuals often lack experience with industry-specific compliance, legacy systems, or traditional corporate structures. The best AI leaders can often be found where their technological knowledge intersects with industry knowledge—people who can develop and scale the right use cases that are unique to this specific industry.
There is a growing pool of business-savvy, tech-driven leaders emerging in sectors like consulting, financial services, industrials and retail, equipped with the storytelling, influence, and operational skills to drive AI transformations effectively. These leaders are increasingly drawn to opportunities where they can make a long-term impact, especially on a global scale.
Identifying the right AI leader requires a strategic approach that considers your organization’s current data maturity, business goals, and cultural alignment. Whether hiring internally or externally, AI leadership is a pivotal investment that will shape your organization’s ability to harness AI’s potential in the years ahead. We are here to support you in making this transformative choice.
