Not only has Artificial Intelligence (AI) been a hot topic over the past few years, but it has also driven a large number of acquisitions.
AI is a broad term that is applied to machines being able to mimic (or surpass) many human-like capabilities. These include learning, reasoning, planning, communicating (natural language processing) and perceiving - as well as applying combinations of these abilities to solve problems.
In the past 24 months, there have been over 144 acquisitions that have involved some aspect of machine learning, collectively valued at over $21 billion.
Machine learning requires data, and often a large amount of it. This is because machine learning most often entails providing a computer with a “teaching set” of data so that it can learn to identify what a solution to a problem looks like. For example, a set of atmospheric symptoms that predict a known weather disturbance such as a severe storm. Every storm that the computer identifies, whether correctly and incorrectly, is added to the teaching set and as a result the program gets better at identifying potential storms over time.
This begs the question of, what are the use cases that are driving all of this M&A activity?
A recent article in Forbes (September 2016) did an excellent job of identifying some of the high-value uses that make the application of AI so compelling. These include:
· Data & Personal Security - enhancements in predicting potential security breaches and combatting new malware threats, as well as improvements in screening procedures for identifying potential bad actors.
· Financial Trading - execution of high speed, high volume and high probability trades, including arbitrage.
· Healthcare - computer-assisted diagnosis.
· Omni-channel sales personalization - true behavioral micro-targeting including the delivery of various personality-based emotional “buy” signals. Machine learning algorithms can analyze your activity and compare it to the millions of other users to determine what you’re most likely to buy or binge watch next.
· Fraud detection – machines are getting better at spotting potential cases of fraud across many different environments.
· Online search - for example, every time you execute a search on Google, the program learns how you respond to the results so it can deliver a better result the next time. This capability is increasingly being tied to the delivery of omni-channel sales opportunities.
· Natural Language Processing (NLP) - NLP is being used across many disciplines. Machine learning algorithms can already substitute for some customer service agents and more quickly route customers to the information they need.
· Smarter Transportation - from smart cars that learn about their owners’ preferences and their own operating environments to mass transportation that learns from sensors tied into the Internet of Things.
· Smarter Applications – for example, Google just announced that it will be using AI to beef up Google Drive, Docs, Sheets, Slides and Calendar.
All of these use cases are examples of what Corum terms “AI Enablement”, and in subsequent blog posts we’ll look at some of these in more depth.
It’s clear that for the foreseeable future, AI Enablement will continue to create many more M&A opportunities – especially for smaller, highly innovative companies delivering new capabilities in these areas.