The skills gap in artificial intelligence is real. A recent study by Randstad Recruitment showed that the number of job postings indicating productive AI skills has risen by 2000% since March. It is the third most demanded 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 kickstart their efforts in this area. Earlier this year, Dylan Fox wrote an article claiming that every company on the Fortune 500 list needs a Chief Artificial Intelligence Officer.
“Companies that do not integrate AI into their products, processes, and business strategies will struggle to stay competitive – and will fall behind those that do,” Fox wrote.
It’s a compelling argument that touches companies at an enterprise level. But what about everyone else? Startups and small businesses urgently need to integrate AI – especially if they are trying to raise funding in the current climate. However, these companies often lack the resources or organizational structure to support hiring a senior executive focused solely on AI.
Part-Time AI Officer
Here’s where the part-time AI officer comes in. Partial leadership is a modern trend in the job market: experienced executives work in a specific field across two or more clients at the same time, providing their skills to rapidly growing companies that cannot afford them full-time.
Here’s the exciting part: having a part-time AI officer surpasses hiring a full-time employee in one critical aspect. AI – especially productive AI – is such a new technology that the multiple experience across various competitive companies gives part-time executives an edge over their full-time counterparts.
Three Stages of AI Adoption
Although the promises of productive AI are significant, it is challenging for companies to create a reliable return on investment scale early in the adoption curve, especially in an environment where companies are expected to be more conservative in spending.
Increased productivity and workflow efficiency are likely to be the primary drivers of productive AI adoption. A recent study by BCG showed that productive AI can achieve significant improvements in workflows, processes, and internal tools – participants who used GPT-4 completed 12% of tasks 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 this future 1.
Future 2: Customer Experience
This is a critical step in the next stage of productive AI adoption: enhancing the 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. Productive AI can bring personalization to your digital experiences.
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