The skills gap in the field of artificial intelligence is real. A recent study by Randstad, a recruitment company, showed that jobs indicating productivity AI skills have increased 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 kickstart their efforts in this area. Earlier this year, Dylan Fox wrote an article advocating for a CAIO in every Fortune 500 company.
“Companies that do not integrate AI into their products, operations, and business strategies will struggle to stay competitive – and will fall behind those that do,” Fox wrote.
It’s a compelling argument that hints at the corporate side. But what about startups and growth-stage companies? They urgently need to integrate AI – especially if they are looking to raise funding in this AI-dominant period. However, they often lack the resources or organizational structure to support an executive focused exclusively on AI.
This is where a fractional AI officer comes into play. Fractional leadership is a modern trend in the labor market: experienced executives work across multiple clients at once, offering their skills to fast-growing startups that need their specific expertise but cannot afford them full-time.
And here’s the crucial point: having a fractional AI officer is better than hiring a full-time employee at one critical juncture. AI – especially productivity AI – is such a new technology that the extensive experience of working with various companies gives fractional executives an edge over their full-time counterparts.
Three Stages of AI Adoption
Although the promise of productivity AI is significant, it is challenging for companies to create a reliable ROI metric early in the adoption curve, particularly in an environment where companies are expected to be more conservative in spending.
Increasing productivity and workflow efficiency will likely be the primary driver of AI adoption.
Stage 1: Workflow Efficiency + Productivity
Given market challenges, companies are looking for ways to free up cash and reduce spending to keep budgets stable in 2024. For this reason, increasing productivity and workflow efficiency will likely be the primary driver of AI adoption. A recent study by BCG showed that productivity AI can drive significant improvements in workflow, processes, and internal tools – participants using GPT-4 completed 12% of tasks with an average of 25% faster than the control group without GPT-4. This is where we will first see a return on investment. Let’s call it Timeline 1.
Stage 2: Customer Experience
This is a crucial step in the next phase of productivity AI adoption: 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. Productivity AI can bring personalization to your digital experiences.
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