The skills gap in artificial intelligence is real. A recent study from Randstad, a recruitment company, showed that jobs referencing productive AI skills have increased by 2000% since March. It is the third most in-demand skills set and the shortest in supply.
The logical step for large companies is to hire a Chief Artificial Intelligence Officer (CAIO) to kickstart their efforts. Earlier this year, Dylan Fox wrote an opinion piece claiming that every Fortune 500 company needs a Chief Artificial Intelligence Officer.
“Companies that do not integrate AI into their products, processes, and business strategy will struggle to remain competitive — and will be left behind by those that do,” Fox wrote.
It’s a compelling argument aimed at large companies. But what about everyone else? Startups and emerging companies urgently need to integrate AI — especially if they are trying to raise funding in this current moment for artificial intelligence. However, they often lack the resources or organizational structure to support a full-time executive dedicated exclusively to AI.
Here comes the role of the fractional AI officer. Fractional leadership is a recent trend in the labor market: experienced executives in a specific field work across two or more clients simultaneously, where they lend their expertise to rapidly growing companies that cannot afford to hire them full-time.
And here’s the key point: having a fractional AI officer is better than hiring full-time at a critical juncture. AI — especially productive AI — is such a new technology that the broad experience across multiple companies gives fractional managers an edge over their full-time counterparts.
Three Stages of AI Adoption
While productive AI promises significant benefits, it is challenging for companies to establish a reliable return on investment metric in the early stages of the adoption curve, especially in an environment where companies are expected to be more conservative in their spending.
Increasing productivity and workflow efficiency will likely be the number one driver of AI adoption.
Stage One: Workflow Efficiency + Productivity
Given market challenges, companies are looking for ways to free up cash and reduce spending to maintain stable budgets in 2024. This is why increasing productivity and workflow efficiency will likely be the number one driver of AI adoption. A recent study by BCG found that productive AI can drive significant improvements in workflows, processes, and internal tools — participants using GPT-4 completed tasks 12% of the time with an average speed 25% faster than the control group without GPT-4. This is where we will first see the return on investment. Let’s call it Perspective 1.
Perspective 2: Customer Experience
This is a great step towards the next stage of adopting productive AI: enhancing customer experience. These days, customers expect significant improvements — and more personalized digital experiences. They will turn to your competitor if you do not remember who they are or anticipate their needs. Productive AI can bring personalization to your digital experiences.
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