The AI skills gap is real. A recent study from Randstad, a recruitment company, showed that jobs referring to generative 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 AI Officer (CAIO) to kickstart their efforts. Earlier this year, Dylan Fox wrote an opinion piece claiming that every company on the Fortune 500 list needs a Chief AI Officer.
“Companies that do not integrate artificial intelligence into their products, processes, and business strategy will struggle to remain competitive – and will fall behind those that do,” Fox wrote.
It’s a compelling argument aimed at large companies. But what about everyone else? Startups and emerging businesses need to heavily integrate AI – especially if they are trying to raise funding in this critical moment for AI. However, they often lack the resources or organizational structure to support a senior executive focused exclusively on AI.
Here comes the role of the fractional AI officer. Fractional leadership is a recent trend in the labor market: experienced executives in a particular field work across two or more clients simultaneously, offering their talent to rapidly growing companies that need their specific skill set but cannot afford them full-time.
And here’s the key point: having a fractional AI officer surpasses hiring a full-time one at a critical juncture. AI – especially generative AI – is such an extremely new technology that the broad experience across multiple companies gives fractional managers an edge over their full-time counterparts.
Three Phases of AI Adoption
While generative AI promises greatly, it’s challenging for companies to establish a reliable return on investment at an early stage of the adoption curve, especially in an environment where companies are expected to be more conservative in spending.
Increased productivity and workflow efficiency will likely be the primary driver of generative AI adoption.
Horizon 1: Workflow Efficiency + Productivity
Given market challenges, companies are looking for ways to free up cash and reduce spending to keep budgets intact in 2024. This is why increased productivity and workflow efficiency will likely be the primary driver of generative AI adoption. A recent study from BCG found that generative AI can drive significant improvements in workflows, operations, and internal tools – participants using GPT-4 completed 12% of tasks on average 25% faster than a comparison group without GPT-4. Here we will see ROI first. Let’s call this Horizon 1.
Horizon 2: Customer Experience
This is a great step toward the next phase of generative AI adoption: improving customer experience. These days, customers expect significant enhancements – and more personalized digital experiences. They will turn to your competitor if you do not remember who they are or anticipate their needs. Generative AI can bring personalization to your digital experiences.
Source: https://techcrunch.com/page/2/
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