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Startups should consider hiring fractional AI officers

The AI skills gap is real. A recent study from Randstad, the recruitment company, found that job posts referencing generative AI skills have risen by 2,000% since March. It’s the third most sought-after skill set and one of the shortest in supply.

The logical step for enterprise companies is to appoint a chief AI officer (CAIO) to kickstart their efforts. Earlier this year, Dylan Fox penned an opinion piece arguing that every Fortune 500 business needs a CAIO.

“Companies that do not integrate AI into their product, operations, and business strategy will struggle to remain competitive — and fall behind those that do,” Fox wrote.

It’s a compelling argument that makes sense at the enterprise level. But what about everyone else? Startups and scale-ups need to integrate AI just as badly — especially if they’re trying to fundraise in this AI moment. However, they often don’t have the resources or the organizational structure to support a senior executive focused exclusively on AI.

This is where a fractional AI officer comes in. Fractional leadership is a recent workforce trend: seasoned executives with subject matter expertise working across two or more clients simultaneously, lending their talents to rapidly growing companies that need their specific skill set but can’t afford it full-time.

Here’s the kicker: Having a fractional AI officer is superior to hiring full-time in one crucial respect. AI — especially generative AI — is such a new technology that breadth of experience across multiple companies gives fractional executives an edge over their full-time counterparts.

The three stages of AI adoption

While the promise of generative AI is significant, it’s hard for companies to establish a reliable ROI metric early in the adoption curve, especially in an environment where companies are expected to be more conservative in spending.

Increasing productivity and workflow efficiency will likely be the No. 1 driver for generative AI adoption.

Horizon 1: Workflow efficiency + productivity

Due to the market challenges, companies are looking for ways to free up cash and lower spending to keep budgets flat in 2024. That’s why increasing productivity and workflow efficiency will likely be the No. 1 driver for generative AI adoption. A recent BCG study found that generative AI can drive significant improvements in workflows, operations, and internal tooling — participants who used GPT-4 completed 12% more tasks on average and 25% quicker than the control group without GPT-4. This is where we will see ROI first. Let’s call that Horizon 1.

Horizon 2: Customer experience

This is a great steppingstone into the next stage of generative AI adoption: improving customer experience. These days, customers expect drastically better — and more personalized — digital experiences. They’ll switch to your competitor if you don’t remember who they are or anticipate their needs. Generative AI can bring personalization to your digital experiences.

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