What’s In A Title?—Are You Hiring The Right AI Leader?
What the AI Leader’s Title Signals to the Organization
Recently, several CEOs have asked me for advice on hiring the most senior AI leader in their company, from shaping the job description and recommending the best title for the role to interviewing candidates. While each company's situation is unique, the central idea remains: the AI leader’s title signals the role’s remit and limitations. But the variations of titles and associated nuances make it easy to select one that sounds right, but has just a partial view or scope of what is actually needed. But what are the most common AI leadership titles, and where do they fall short?
Common Titles and Where They Fall Short
Historically, there have been several titles based on their functional scope or the technology their teams primarily work with, such as Head of Data Science or Head of Machine Analytics. However, with the growing scope of AI and driving business transformation, additional roles have been introduced. They are easy to spot: Take an IT role and replace the two letters with “AI,” such as AI Governance, AI Innovation, and such. However, for an AI leader to be successful, it requires more than a nice job title. The following suggestions assume that the company is looking for a leader to drive AI end-to-end across all business functions and lead the company’s AI transformation.
Here is a selection of the most common job titles, and what they actually mean:
Head of AI Strategy ⮕ defines, but doesn’t own the execution
Focuses on aligning the technology strategy with the business strategy, advising on key partnerships and technology platforms, and takes a consultative approach. To scale AI across the organization, there’s more change management and influence needed, and taking a product mindset.
Head of AI ⮕ researches and builds, but is disconnected from the business
Has deep data science and research experience and has led teams in these areas. However, to productize and scale AI adoption, their skills need to go beyond models and technology.
Head of AI Innovation ⮕ experiments and explores, but little gets implemented
Scouts the tech landscape for new technologies, products, and other innovations and seeks to bring them into the organization. Few leaders are able to adopt it beyond a proof of concept, and few organizations are ready to adopt it.
Head of AI Adoption ⮕ scales AI usage, but doesn't have the teeth
Is tasked with scaling AI to increase ROI and productivity. But their skillset aligns more with taking what’s there and scaling it. But they often don’t have the mandate or skills to influence other, more senior, leaders across the business.
Head of AI Governance ⮕ tries to keep their company out of trouble. Tries.
Defines guidelines and frameworks for how employees should use AI and the types of scenarios in which the company will (or won’t) use AI, but defining guidelines and rolling them out is a different skillset than what’s needed to find the most valuable AI scenarios to pursue in alignment with the business strategy, etc.
Head of Agentic AI ⮕ buzzword-loaded title, but the organization doesn’t understand what it actually needs
sounds innovative, but again, most likely only has a partial (tech-centered) skillset and likely misses the change management and C-suite influence aspects.
So, what is needed then to lead AI in a cross-company effort?
Re-introducing the Chief AI Officer
The Chief AI Officer (CAIO) is the most senior leader in a business who has an end-to-end purview of AI across the entire business. Ideally, they report directly to the CEO or divisional president as any attempts to tuck the CAIO underneath the CIO or CTO will leave them constrained to the strategy and KPIs.
CAIOs typically have four key responsibilities in a business (more details in the AI Leadership Handbook):
- Knowledge ⮕ stay on top of AI trends and disseminate what’s useful for the company.
- Enablement ⮕ drive learning and change across the organization through strong partnerships with business function.
- Governance ⮕ define structure and processes that deliver high-quality outcomes
- Vision ⮕ collaborate with peers for maximum impact and relevance of AI scenarios
Among all, the CAIO is the only title and role with the full remit to work across the organization, spanning technology, IT, HR, other business functions, and productization. It signals the significance of AI for the entire company, especially when reporting directly to the CEO.
If you’re a CEO looking to hire an AI leader, or if you’re applying for an AI leadership role, it is imports to understand the difference and the role and its challenges.
Summary
Lately, an increasing number of companies are looking for their first AI Leadership hire. The future title speaks volumes about the likely situation the new AI leader will be in, and the type of work they’ll be doing. The CAIO ideally reports directly to the CEO to maximize their impact.
Bonus:
Head of Machine Learning -> uses proven methods to deliver measurable results, because their business doesn’t fall for shiny objects
…baut this is just the gehinning.
If you’re looking to hire your company’s first Chief AI Officer and don’t know quite where to start, send me a note.
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