The AI skills gap is real. A recent study from Randstad, a recruiting company, showed that job postings indicating productivity AI skills have increased by 2000% since March. It’s the third most in-demand skill 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 op-ed claiming that every company on the Fortune 500 list needs a CAIO.
“Companies that do not integrate AI 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 and logical argument at the level of large corporations. But what about everyone else? Startups and emerging businesses urgently need to integrate AI – especially if they are trying to raise funding at this critical moment for AI. However, they often do not have the resources or organizational structure to support a senior executive focused exclusively on AI.
This is where a fractional AI officer comes in. Fractional leadership is a modern trend in the job market: experienced executives in a particular field work across two or more clients at the same time, providing their talents to rapidly growing companies that cannot afford 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 productivity AI – is such a new technology that the extensive experience of working with different companies gives fractional executives an advantage over their full-time counterparts.
Three Stages of AI Adoption
While the promise of productivity AI is great, it is difficult for companies to create a reliable return on investment gauge at an early stage of the adoption curve, especially in an environment where companies are expected to be more cautious with spending.
Productivity gains and workflow efficiency are likely to be the primary drivers of AI adoption.
Given the market challenges, companies are looking for ways to free up cash and cut spending to keep budgets stable in 2024. For this reason, productivity gains and workflow efficiency are likely to be the primary drivers of AI adoption. A recent study from BCG found that productivity AI can achieve significant improvements in workflows, processes, and internal tools – with participants using GPT-4 completing 12% more tasks at an average of 25% faster than the control group without GPT-4. This is where we will see the return on investment first. Let’s call this Future 1.
Future 2: Customer Experience
This is a great step towards the next phase of productivity AI adoption: enhancing customer experience. These days, customers expect radical improvements – and more personalized digital experiences. They will switch to your competitor if you don’t remember who they are or anticipate their needs. Productivity AI can bring personalization to your digital experiences.
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