The Truth About Machine Learning In Enterprise Software Posted on : Jul 07 - 2021

There’s a lot of hype around machine learning, but what does it really mean in the context of enterprise software? How does it work, where is it adding business value today, and what should we expect from it in the future?

Let’s start with some definitions. Artificial intelligence (AI) is an umbrella term that includes machine learning (ML), deep learning and cognitive learning. The part most relevant to enterprise software is ML, which in this context is the ability to create automation through AI algorithms.

A lot of what ML does is really just statistical analysis: crunching numbers, measuring parameters, identifying patterns and projecting future outcomes based on past results. You don’t actually need fancy ML algorithms to do this; you can do it with standard logical programming.

The degree to which the ML itself improves business outcomes is currently marginal. The accuracy of a financial forecast, for example, is sensitive to far greater factors than whether the algorithm can refine itself slightly over time. If you haven’t got harmonized, accurate and complete data to start with, simply applying ML to it isn’t in itself going to result in better business decisions.

A Solution Looking For A Problem?

In terms of Gartner’s hype cycle, ML is currently at the peak of inflated expectations. You cannot simply throw ML at a bucket of big data and expect it to magically come up with a perfect business plan.

As so often in business, you shouldn’t start with the technology itself. Before you think about where to apply ML, you need to step back and ask: What is it we’re trying to achieve?

Look for points in your business processes where some sort of judgment or prediction is required and where any small improvement in accuracy would have a disproportionate benefit to the business. These are the potential use cases for ML. Otherwise, ML is at risk of becoming a solution looking for a problem.

For example, if you apply ML instead of conventional statistics — and you have good underlying data — you should be able to continuously enhance the accuracy of the predictions to improve, say, operational efficiency and customer experience. View More