Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
As technology spending accelerates across the business world, senior artificial intelligence (AI) leadership is critical to building successful AI programs and implementing them in organizations across industries.
In fact, the global AI market size is estimated to have grown to $432.8 billion in 2022, according to IDC. But this rapid growth has left a white space for strong AI leadership. Companies are wrestling over how to organize management of AI teams, where AI teams fit within the broader corporate structure, and who should sit in the AI leader role.
Although AI departments and programs have proliferated across business sectors over the past decade, AI remains a relatively new field that corporations are still trying to understand and integrate into their businesses and operations. To keep pushing the field forward and avoid remaining stagnant, many need to take a new approach to AI leadership to maximize their program’s potential.
>>Don’t miss our special issue: The CIO agenda: The 2023 roadmap for IT leaders.<<
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
In most organizations today, the person driving the AI strategy is usually a technology executive with a senior data scientist background and a career in analytics, data and AI. That person typically possesses a deep knowledge of AI tooling, AI governance, and management processes for AI development, among other specialties.
While this technical acumen is useful for understanding and developing AI, many organizations lack the role of a senior AI leader with technical expertise who can also focus on the business value that the technology can unlock, encourage the organization to invest wisely, and ensure that they have the resources in place to roll out successful AI programs.
To position themselves on the front foot in this new age of AI, organizations must consider creating a structure with two AI leadership roles — a Chief AI Officer (CAIO) and a Vice President of AI — to address the business and technical needs separately so that organizations can more effectively harness the value of AI.
Putting the right people in the right roles
AI’s widespread use raises the stakes for companies to have an effective AI leadership structure. Many organizations beyond tech companies use the technology in some capacity for marketing products and services to the right potential customers, calibrating supply chains to meet fluctuating demand, and monitoring their in-house technology.
A successful and optimally running AI function will help companies operate more efficiently and compete more effectively. Therefore, placing the right people in the right senior leadership roles is critical to unleash the program’s potential within the organization.
While the race for tech talent continues, it’s nearly impossible to find a single senior AI professional who is proficient in both a business and technical capacity. However, having two leadership roles that perform these duties separately can solve the problem.
Building AI leadership
So, how should an organization begin dividing up these roles and increase focus on AI strategy? Start with the CAIO. Similar to how a company’s Chief Technology Officer (CTO) sits at the executive table and the VP of engineering makes sure everything gets built, the CAIO and the VP of AI can play similar roles.
A strategic CAIO needs to be strong in business decisions and outcomes and not solely focused on implementation. From their position in the C-suite, the CAIO can pay close attention to the organization’s higher business focus and operations and what’s happening in the market. From this C-suite table, they can focus on funding and resources for their organization and building a strategic plan while having an influential voice for other executives.
Alternatively, the VP of AI needs to be a technical expert respected by the company’s data scientists and AI engineers. They’ve typically risen through the ranks of an organization’s AI structure — from a data scientist to a senior data scientist, to a manager of data science and then to the VP level.
The VP of AI focuses on delivering products and projects on schedule. They are also responsible for talent recruitment and hiring. Finally, they oversee the organization’s technical infrastructure, determining the right mix of on-premises versus cloud capacity.
Where to start
AI programs in most companies today would either fall under the purview of the Chief Information Officer (CIO) in a technical organization — where the company would house analytics and possibly a data science team — or within the digital, innovation or transformation divisions, where it would serve a more business-oriented function.
When setting up a company’s new AI division, many infer that the most strategic approach would be to place it in an IT department. However, in doing so, a company is subjected to limiting AI’s capabilities solely to that department rather than holistically incorporating it throughout the organization.
For example, an organization such as a consumer goods giant or a publisher with a data science team might decide to elevate its Director of Data Science to a newly created Director of Data Science and AI position without fully understanding what that new role would entail. In this scenario, it would then be difficult for the new Director of Data Science and AI to develop a strategic plan for the CEO or CTO, as their role to this point would have been exclusively technical.
But a two-pronged AI leadership structure presents a solution. Companies can follow two paths to implementing this new structure, depending on whether they haven’t set up an AI program yet, or whether they have set one up, but it’s not working effectively.
Evolving AI leadership
The first path applies to companies lacking an AI program. To begin, these organizations need to hire their leadership talent in data science and engineering, provide funding and allocate internal resources. They can start by hiring the CAIO and charging that person with finding a VP of AI — to act as his or her technical leader — and focusing on building a business plan for the broader AI function.
The second path applies to businesses with underperforming AI programs. Again, the problem often stems from a substandard leadership structure that lacks an AI business head at the top. In this case, companies should hire a CAIO and have them restructure the organization with a VP of AI. And if the business has fragmented its AI talent across the company, the organization needs to aggregate them under this new leadership structure.
Indisputably, AI and data science departments have grown in complexity and importance to many companies’ operations and earnings. Therefore, corporate structures should expand — by establishing a CAIO and a VP of AI — to reflect their rising prominence within organizations and better realize AI’s evolving potential.
Rodrigo Madanes is global AI leader at EY.
The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!