The AI-First Company: How to Compete and Win with Artificial Intelligence

Free-riding copycats need both the technology and the data.

capture the data “exhaust” (anything recorded when customers perform operations in an application, such as clicking buttons and changing values) from existing systems of record (the “single source of truth” for a business function), and build AI on top.

take a real-world workflow, develop software around it, and then add further data and features is why I believe that all business software will one day be intelligent.

Indeed, we’re still in the era of building such workflow apps for many industries—a huge opportunity for software developers willing to learn how specific industries get things done.

This table summarizes who to hire and when to hire them. role background cost outsourceable? hiring sequence Data analyst Business Low Yes First Data scientist Statistics Low Partially Second Data engineer Databases Medium Yes Third Machine learning engineer Computer science Medium No Fourth Data product manager Product management Medium No Fifth D

Here’s the formula: data learning effects = economies of scale to data + data processing capabilities + data network effects. Put in a sentence: get lots of data, process it into something useful in terms of making a decision, and create a system that automatically generates more useful data.

appropriate customers from incumbent suppliers by selling a specialized product to a niche segment of customers at a fraction of the cost of the incumbent’s product.

speak to potential customers and distill what they say into a few problems to solve with statistics.

There are some promising transfer and semisupervised learning techniques that may provide alternatives to gathering a great deal of labeled data,