The skills gap in artificial intelligence is real. A recent study by Randstad Recruitment showed that the number of job postings requiring production AI skills has risen by 2000% since March. It is the third most in-demand skill set and the shortest in supply.
The Logical Step for Large Companies
The logical step for large companies is to appoint a Chief Artificial Intelligence Officer (CAIO) to spearhead their efforts. Earlier this year, Dylan Fox wrote an article advocating for the necessity of a Chief AI Officer in every Fortune 500 company.
“Companies that do not integrate AI into their products, processes, and business strategy will struggle to stay competitive and will fall behind those that do,” Fox wrote.
It’s a compelling argument that finds justification at the level of large corporations. But what about startups and scaling businesses? They need to embrace AI just as urgently – especially if they are trying to raise funding in this current period of rapid AI advancement. However, they often lack the resources or organizational structure to support a high-level executive dedicated exclusively to AI.
Enter the fractional AI officer. Fractional leadership is a recent trend in the labor market: experienced executives work in a specific field across two or more clients simultaneously, offering their talents to rapidly growing startups that require their unique skill set but cannot afford them full-time.
And here’s the surprise: having a fractional AI officer trumps hiring full-time in one critical aspect. AI – especially production AI – is such a new technology that extensive experience across multiple companies gives fractional executives an edge over their full-time counterparts.
Three Phases of AI Adoption
While production AI promises significant benefits, companies find it challenging to establish a reliable ROI metric in the early stages 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 AI adoption.
Phase One: Workflow Efficiency + Productivity
Given the market challenges, companies are looking for ways to free up cash and reduce spending to keep budgets stable in 2024. For this reason, increased productivity and workflow efficiency will likely be the primary driver of AI adoption. A recent study by BCG found that production AI can drive significant improvements in workflows, processes, and internal tools – participants using GPT-4 completed 12% more tasks at an average of 25% faster than the control group without GPT-4. This is where we will first see ROI. Let’s call that the first time frame.
Phase Two: Customer Experience
This is an important step in the next phase of production AI adoption: enhancing customer experience. These days, customers expect radical improvements – and more personalized digital experiences. They will turn to your competitor if you do not remember who they are or anticipate their needs. Production AI can bring personalization to your digital experiences.
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