When we discuss AI and machine learning, we often think of a sci-fi future in which super intelligent technology helps to unlock the central mysteries of existence in more ways than humans could ever hope to do on their own.

SwissCognitiveThat probably won’t be what happens. The way AI and machine learning will get the most usage and traction in everyday life is by helping us with the tasks and jobs that are necessary but mundane, monotonous, and time-consuming.

Several fundamental challenges must be addressed that will be a source of differentiation and competitive advantage in the use of AI and ML:

Creating a data supply chain that delivers all the needed data to the AI and ML systems. Companies that power their AI with better data will get superior results.

Designing an automation architecture so that the AI and ML are addressing tasks that have a good fit for their capabilities and create business impact. Successful companies will redesign processes to make better use of AI and ML.

Learning from exceptions. When the AI or ML systems reach their limits and people have to step in, there should be a mechanism to provide feedback to improve the models and algorithms.

These are fine general recommendations. But making them work in practice is the way business value is created.

For insight into how to make AI and ML work, I’ve been looking to enterprise software vendors, who are generally able to move faster to implement these capabilities that the bulk of business users. We are now in an era where almost every enterprise product is essentially a platform of capabilities. To make use of these increasingly complex platforms, vendors are implementing AI, ML, and advanced analytics, which make it much simpler to get desired business results without having to employ a caste of expensive high priests, like the DBAs of the past, to make the products work. The primary way that most companies will use AI and ML will be as part of enterprise products.

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