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Three Questions with BCG Fellow Philipp Gerbert About AI

"Expect AI to dwarf other technologies in terms of the transformational effects."

Seeing through the hype, where are we in terms of actual AI adoption and implementation in businesses today?

AI continues to be a technology, where adoption in very similar businesses differs widely: e.g. between insurance players in China vs US. I have not seen that in other early technology waves. Many companies continue to struggle with AI and some do not grasp the difference between AI and Big Data (for the latter, tools and data were separate, in AI they are integrated), or AI and Digital (with AI, you can fully automate processes — even when judgement is required).

What does it take for companies to go beyond pilot programs and scale AI throughout the organizations?

Scaling requires a “continuous” delivery model, operationalizing the ability to absorb new data into models, while assuring secure, industrial-scale implementation and use. This has deep organizational and process implications — within the technical team, between technical team and users and beyond. Clearly single applications have been taken to scale, but that is still relatively easy, compared to interacting company processes and applications.

This will require an accurate grasp of the ‘maintenance/management’ phase, far beyond the initial deployment of the use case. It will be challenging especially for large existing companies to achieve this, and we will likely see full realization only in businesses built or rebuilt from scratch.

What questions should leaders be asking themselves to get the most out of AI?

What are the most critical applications that make a competitive difference — and focus on those only. Where can AI shift the entire industry value pool and how can I pre-empt this dynamic and profit from it? How do I best structure the tech team/user team workflow?

We expect AI to dwarf other technologies in terms of the transformational effects on organizations, businesses, and strategies. All the implementation challenges I have mentioned are therefore well worth tackling — one might even say mandatory.

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