The AI officer

The past

New innovations have always required companies to adapt by introducing (or eliminating) roles. Consider the cybersecurity field, which led to the creation of multiple management positions over the past two decades. This industry gained traction following the first Worm attacks in the 1990s, with mainstream adoption occurring in the early 2000s as the internet and interconnected systems became widespread. Roles such as the CIO, CISO and CDO were introduced to protect organizational infrastructure, data and operations. Ironically, even today, there are SMEs that pay little attention to cybersecurity (source).

The organisational effects and adoption of AI are vastly different. Currently, over 75% of staff already use accessible tools like GPT on a daily basis (source). Unlike cybersecurity, which is tied to fear and disruption, AI at the moment of writing is associated with unlocking growth and efficiency. Its utility is advancing at an incredible pace. By the end of 2024, AI is generating - often not so original - content and establishing good supporting systems. By the end of 2025, many human-supervised AI agents will have emerged.

Most organisations lack a dedicated role capable of turning AI implementations into a daily reality. Simply put, it is challenging to:

• Keep up with the rapid introduction of useful innovations.
• Understand the possibilities and limitations of off-the-shelf tools, automation, AI (agents), data (science) and IT infrastructure.
• Possess management power to drive improvements that directly impact existing business operations.

Transitioning to an AI-embedded organisation will require significant effort. Fortunately, the approach and key activities to achieve AI adoption are relatively clear. However, the human-function/role to facilitate this does not yet exist.

The Role

These are the functional requirements for the AI officer:

• IT and business experience: Ability to understand information systems and reason from a business perspective.
• Innovation experience: Experience managing new projects or implementations within an existing organization.
• Management and corporate experience: Capability to align organizational strategy with the AI innovation roadmap, make tough decisions, and navigate company politics.
• Tech experience: Knowledge of automation, (basic) programming, databases, ETL, IT infrastructure and so on, to avoid being misled by hype.

Currently, the Chief Technology Officer (CTO) is the most likely candidate for the AI officer role without the need for much additional education. However, CTOs most likely have more on their plate. The next best candidates are tech leads or (technical) product owners with the ability to grow into a management position.

The first 100 days

To a man with a hammer, everything looks like a nail. Having spent over 15 years in innovation-related jobs, I’m probably the man with the hammer. But the approach that the AI officers should take seems clear to me: build an Innovation Portfolio Management framework. Originating from Alexander Osterwalder’s corporate innovation practices, this framework is well-suited to initiating and tracking AI-related initiatives. 

Suggested steps for the first 100 days:

1. Set organization-wide objectives: Collaborate with the management team to define objectives and make them tangible using Key Results (or KPIs).
2. Identify core team members: Determine the who’s:some text
   • Who decides and funds
   • Who ideates
   • Who validates
   • Who creates
   • Who implements
   • Who tracks
3. Build a framework: Create a structured funnel for ideation and execution:some text
   • Establish ideation and “Investment Committee” mechanisms to review initiatives.
   • Execute a set number of initiatives within a specific timeframe (e.g., three active projects per quarter).
   • Monitor progress, capture learnings, and decide to kill, pivot, persevere, or promote projects.
4. Fill the AI idea funnel: Regularly introduce new projects to maintain a balanced pipeline.

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Note: balancing initiatives

Similar to the Three Horizon’ theory of McKinsey, there should be a balanced focus between two types of initiatives:

• Improve and optimize: Allocate at least 50% of resources to enhance existing operations.
• Explore and create: Dedicate the remaining effort to new opportunities.

There is a fundamental difference between the two types of initiatives: risk, budgets, roles, approach and results. Depending on the capabilities and risk appetite of the organization, the Explore & Create is something that is relatively easy to outsource to external teams. While Improve & Optimize is often intertwined with the parent organization, which means organizational-politics are an important factor. 

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Final thoughts

The AI Officer is the catalyst for embedding AI across operations, fostering growth, and driving efficiency. This strategic hire bridges the gap between innovation and execution, ensuring that organizations are well-equipped to thrive in the future. As AI adoption accelerates, the gap between early adopters and laggards will only widen, leaving organizations that delay at risk of falling significantly behind their competitors.

The perspective shared above is based on my experience and what I believe is the most likely scenario. However, there is an alternative possibility worth considering: instead of centralizing organizational AI efforts around a specific role, these initiatives could be structured around a program. This is the approach that most organisations took in their Digital Transformation journey the past decade(s) 

That said, I suspect many people involved in digital transformation efforts might argue that programs should have been built around a dedicated expert from the start, rather than assembling a team of 'experts' around the program.

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