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\n\t\t\t\t\t\t\tRaphael Ouzan<\/span> The AI skills<\/span> gap is real. A recent study from Randstad<\/a>, the recruitment company, found that job posts referencing generative AI skills have risen by 2,000% since March. It\u2019s the third most sought-after skill set and one of the shortest in supply.<\/p>\n The logical step for enterprise companies is to appoint a chief AI officer (CAIO) to kickstart their efforts. Earlier this year, Dylan Fox penned an opinion piece<\/a> arguing that every Fortune 500 business needs a CAIO.<\/p>\n \u201cCompanies that do not integrate AI into their product, operations, and business strategy will struggle to remain competitive \u2014 and fall behind those that do,\u201d Fox wrote.<\/p>\n It\u2019s a compelling argument that makes sense at the enterprise level. But what about everyone else? Startups and scale-ups need to integrate AI just as badly \u2014 especially if they\u2019re trying to fundraise in this AI moment. However, they often don\u2019t have the resources or the organizational structure to support a senior executive focused exclusively on AI.<\/p>\n This is where a fractional AI officer comes in. Fractional leadership is a recent workforce trend: seasoned executives with subject matter expertise working across two or more clients simultaneously, lending their talents to rapidly growing companies that need their specific skill set but can\u2019t afford it full-time.<\/p>\n Here\u2019s the kicker: Having a fractional AI officer is superior to hiring full-time in one crucial respect. AI \u2014 especially generative AI \u2014 is such a new technology that breadth of experience across multiple companies gives fractional executives an edge over their full-time counterparts.<\/p>\n While the promise of generative AI is significant, it\u2019s hard for companies to establish a reliable ROI metric early in the adoption curve, especially in an environment where companies are expected to be more conservative in spending.<\/p>\n \t<\/p>\n \n\t\t\tIncreasing productivity and workflow efficiency will likely be the No. 1 driver for generative AI adoption.\t\t\t\t\t<\/p><\/blockquote><\/div>\n \t <\/span><\/p>\n Due to the market challenges, companies are looking for ways to free up cash and lower spending to keep budgets flat in 2024. That\u2019s why increasing productivity and workflow efficiency will likely be the No. 1 driver for generative AI adoption. A recent BCG study<\/a> found that generative AI can drive significant improvements in workflows, operations, and internal tooling \u2014 participants who used GPT-4 completed 12% more tasks on average and 25% quicker than the control group without GPT-4. This is where we will see ROI first. Let\u2019s call that Horizon 1.<\/p>\n This is a great steppingstone into the next stage of generative AI adoption: improving customer experience. These days, customers expect drastically better \u2014 and more personalized \u2014 digital experiences. They\u2019ll switch to your competitor if you don\u2019t remember who they are or anticipate their needs. Generative AI can bring personalization to your digital experiences.<\/p>\n<\/p><\/div>\n
\n\t\t\t\t\t\tContributor<\/span>\n\t\t<\/p>\n<\/p><\/div>\n\n
The three stages of AI adoption<\/h2>\n
Horizon 1: Workflow efficiency + productivity<\/h3>\n
Horizon 2: Customer experience<\/h3>\n