A pragmatic approach to generative AI

Eighteen months into the generative AI boom, some may wonder if the shine is wearing off. In April, Axios called gen AI a “solution in search of a problem.” A month later, a Gartner survey across the U.S., U.K. and Germany found that about half of all respondents expressed difficulty assessing AI’s organizational value despite generative solutions being the No. 1 form of deployment. Meanwhile, Apple and Meta are reportedly withholding key AI features from Europe over compliance concerns.

Between the regulatory hangups and ROI questions, it’s tempting to wonder whether generative AI may turn out to be the tech industry’s latest bauble– more NFT than Netflix, if you will. But the problem isn’t the technology; it’s the mindset. What we need is an alternative approach.

Not all AI is the same. We have a bandwagon problem with companies jumping on the AI train, particularly for generative use cases. Practitioners will only unlock the true potential of AI – including generative applications – when they prioritize an engineering-first mindset and cultivate the expertise to add domain knowledge. Then, and only then, can we build a roadmap for concrete, long-term value.

Not all AI is the same

Broadly speaking, Enterprise AI splits into generative and analytical applications. Generative has received all the recent attention thanks to its uncanny ability to create written content, computer code, realistic images, and even video in response to user prompts. AI for analytics meanwhile, has been commercialized for far longer. It’s the AI that enterprises use to help run operations, drawing trends and informing decisions based on large pools of data.

Analytical and generative AI can overlap, of course. Within a given stack, you might find all sorts of integrated usages – a generative solution on the front end, for example, that surfaces ‘traditional’ AI-powered analytics to provide data visualization for the answer. Still, the two sides are fundamentally different. Analytics AI helps…