AI is a powerful enabler, but it’s not a magic bullet. Companies that successfully integrate AI into their products and operations don’t just bolt on a model and call it a day—they rethink their entire approach to value creation and delivery. While some vendors make AI adoption look effortless, there’s a significant amount of work happening beneath the surface to ensure that these systems align with business objectives and drive meaningful outcomes.

As the O'Reilly article below highlights, AI adoption today is reminiscent of early custom software development. It requires new ways of thinking, disciplined execution, and a willingness to address fundamental strategic questions:

>>Like early-day custom software development, today’s AI opportunities bear the price tag of new approaches and new discipline. You can’t just cram a bunch of data scientists into an office and cross your fingers that everything works out.

Plenty of companies have tried… [L]ike their earlier software counterparts, [companies new to AI] have to address operational matters of this new technology. But before that, [companies] must perform prep work around strategy: ‘What is AI, really? What can it do in general, and what can it do for us in particular? How can incorporating AI into our products harm us or our customers or unaffiliated parties who just happen to be in the wrong place at the wrong time?’”<<

The real work of AI adoption starts long before deployment. Companies that succeed take the time to ask the right questions, reimagine their business and build the necessary operational infrastructure to support AI in a responsible and effective way.

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