Firms need a distinct AI business strategy because machine learning-based AI has unique self-learning working mechanisms that differ fundamentally from traditional IT. A digital business strategy typically treats AI like a regular IT application, ignoring these differences. Strategic adoption of self-learning AI creates new challenges and opportunities that require a strategy explicitly built on AI's unique characteristics.

Today's AI is predominantly based on probabilistic machine learning, which enables algorithms to autonomously learn task knowledge from data. This self-learning ability largely decouples AI systems from manual coding, allowing them to identify patterns that human experts may not see. In contrast, traditional information systems depend on pre-defined algorithmic rules written by humans and cannot learn independently. Because a digital business strategy usually treats AI as just another IT application, it fails to account for these fundamental differences. The strategic adoption of self-learning AI involves unique challenges in areas such as identifying use cases, system development, implementation, and company-wide scaling. These novel challenges and competitive opportunities make it necessary to develop an AI business strategy that explicitly builds on the inherent working mechanisms of machine learning rather than relying on a general digital strategy.

Key points

Machine learning-based AI self-learns from data, unlike traditional IT systems that rely on manually coded rules.
A digital business strategy typically treats AI as a regular IT application, missing what makes AI different.
AI's probabilistic, self-learning nature creates new strategic challenges and opportunities not covered by digital strategy approaches.
An AI business strategy must explicitly build on the unique characteristics of machine learning.

According to the chapter, why do firms need a new type of strategy for AI rather than just a digital business strategy?

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