Kyle Langworthy is Head of AI, ML, & Data for Riviera Partners, an executive search firm focused on tech, product, and design leadership.
As companies across tech and beyond seek to integrate AI into their processes, products and organizations, they’ll need an AI leader to harness its power effectively. However, there’s more than one type of AI leader, reflecting the varied AI needs and maturity of businesses today.
Based on our conversations and experience recruiting tech talent for AI leadership positions, we’ve identified five AI leader archetypes. Each brings a distinct set of skills and experiences to the table, making it critical you focus your search strategy on finding the proper fit.
The Five AI Leader Archetypes
1. The Research Leader
An AI leader focused on research or applied science will primarily concentrate on use cases like optimization, customization, fine-tuning and model creation, pushing the boundaries of what AI can achieve.
As a hands-on, technical leader, this person is more often a senior individual contributor than a people manager. Their deep technical expertise and strong academic background make them valuable as a founder at a seed-stage company or leading the research or science team at a late-stage company.
These are the people you want to push innovation further but not necessarily lead the commercial or business implications of their work. Similarly, they may lack experience managing large teams, overseeing complex organizational structures or scaling operations.
2. The Data Science Leader
This archetype concentrates on use cases around personalization, recommendation engines, growth initiatives, pattern recognition and signal analysis to help their organization improve business decision-making and the customer experience.
This leader is most often found at growth-stage companies in sectors like finance, marketing and e-commerce, typically managing a small team while also acting as a technical contributor. They may report to the CTO or be attached to the CX or marketing function, with their insights influencing growth strategies, product development and customer engagement.
A data science-driven leader can help your organization translate complex data into actionable business strategies. They can act as a “player-coach” to balance hands-on technical work with mentoring and management. However, they may lack the deep technical expertise or business experience to take an organizational-wide AI leadership role.
3. The Horizontal Engineering Leader
This archetype is focused on developing an enterprise-wide data strategy, building out a horizontal AI platform and creating shared services across all business departments and units.
We see this leader most often at later-stage or mature organizations—usually ones that have several acquisitions under their belt that require a unified data strategy. As such, this leader requires significant experience managing complex engineering initiatives, implementing large-scale data management strategies and connecting data infrastructure initiatives to business results.
The role typically reports to the CIO or senior tech leader in the organization. In a few cases where an organization has a significant AI remit to empower broader organizational change, this role reports directly to the CEO.
This type of leader brings deep experience in creating and implementing a comprehensive data strategy, leveraging infrastructure experiences with cross-functional leadership to drive change across disparate business units. As a result, it requires a leader with the project management skills to navigate scale and complexity, along with the ability to garner executive support and overcome resistance across different business units.
4. The Commercial-Focused Engineering Manager
This archetype specializes in proof of concepts, rapid iteration and leveraging third-party tools and services to build AI capabilities. To paraphrase the old FedEx slogan, when it absolutely, positively has to be done overnight, this is the leader for the job.
This leader thrives at growth-stage organizations with established product suites and revenue streams seeking to create quick wins. They have a strong technical foundation, along with proven commercial experience taking AI projects from concept to market, giving them the savvy and intuition required to identify which POCs are most likely to generate value.
This AI leader often reports to the head of engineering but operates in a sandbox to quickly innovate and create minimum viable products that can be scaled and incorporated by others into the broader organization if successful.
This is a leader who’s comfortable adapting on the fly and identifying the commercial applications of AI. At the same time, the role will likely have relatively fewer resources and support compared to the above leaders, requiring someone comfortable working in a position that can offer both limitations and freedom in equal measure.
5. The AI Product Strategist
This AI archetype is primarily responsible for strategic planning, identifying the types of AI products and services to be built and ensuring all initiatives align with the company’s overall long-term vision.
We often see this leader archetype at large, later-stage public companies outside of the technology sector. Although these organizations don’t sell tech products, they’re committed to leveraging AI to augment their business strategy and operations.
This strategic leader may report directly to the CTO or CEO or be part of an innovation hub within the organization.
A strategic AI leader will have the vision required to set the direction for AI initiatives, along with the acumen to understand market needs, identify unique business dynamics and collaborate with technical teams, executives and other stakeholders across the organization. As a result, their relatively limited technical know-how may limit their ability to oversee complex technical challenges while creating a dependence on technical teams for execution.
Choosing The Right AI Archetype
It takes a nuanced understanding of the organization’s specific needs and the candidate’s skills and experience to ensure the right fit. When evaluating archetype-organizational fit, evaluate your role through three lenses:
• Do we sell tech products or services as our core offering or does technology play a supporting role in the business?
• How mature is our data practice, platform, quality and compliance?
• Do we want to build AI products and services to optimize our business, or do we want to build net-new products and services?
There’s no one “correct” AI leader—an archetype that will thrive in one organization will flail in another. By understanding your needs, you can find the right leader for the job and leverage AI to its fullest potential.
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