Beyond the Hype: How machine learning will transform business decisions
Machine learning can improve our decision-making and transform our organizations, but a change in mindset and culture is required to realize the potential.
Artificial intelligence (AI), previously the preserve of fiction writers and film-makers, has hit the mainstream. AI has been an area of research for over fifty years but the rapid progress over the last five years has sparked unprecedented interest, capturing the public’s attention and imagination. Everyone — businesses, politicians, and the broader public — wants to understand how AI will change the world and our lives.
Frankly, most of the current predictions still sound more like fiction than reality to me. However, this time, we know not to underestimate them. We did this in the 1980s when the idea of a Smart Home was dismissed as fanciful. Now it is reality. The way I see it, AI as scientific discipline has already moved beyond the hype and has real potential to revolutionize the way we do business. Here, I’ll explain why I view machine learning (ML) as one of the most active and exciting areas of AI today, and why it has the power to transform our organizations.
ML is a branch of AI that deals with the design of algorithms and improves their performance by using empirical data. What does that really mean? Simply put, computers will maximize their performance at a given task just by being exposed to data, without the need to be programmed with predefined, explicit instructions for an improvement. They will learn from the data they analyze and the patterns they discover and they will be able to make predictions that could serve multiple purposes. ML applications range from pattern recognition to robotics, from computer vision and bioinformatics to computer linguistics.
The field is attracting serious investment. According to a recent report by the McKinsey Global Institute (MGI), in 2016 alone, private equity and venture capital firms invested 8–12 billion dollars in the AI sector. ML investments attracted 60% of that total.
Machine Learning can provide us with insights for better decision-making and help us transform our organizations by incorporating and learning from a vastly expanded set of data. It is no wonder that professional investors are so excited. But how will this happen?
Mining the treasure
Valuable business data can be mined from our own transactions and market movements as well as other, often overlooked, sources. Most organizations already hold large amounts of structured and unstructured data that sits uselessly in the company archives. Establishing meaningful patterns in that data is not easy, so it generally isn’t done. Machine learning will change that. This previously unmined data will likely unlock a treasure trove of valuable information that can guide decisions.
As well as mining information within our organizations, machine learning will give us useful data from multiple but very different sources. This will be a game changer, allowing us to supplement and contextualize our own information, using data available from public sources such as social, geolocation, traffic, and other services.
The real potential of ML doesn’t just lie in processing data and providing context. ML will be able to generate results that will allow the automation of standard decisions. This will create a huge potential for efficiency gains.
Change your mindset, and the rest will follow
I like to consider the ways ML can make our business more efficient and sustainable. It’s clear to me that before we apply ML in our business, our mindsets and corporate culture must change. For that reason, change that embraces ML should be top down.
Such a change is coming to many firms, but perhaps more slowly than you might have thought. In the MGI report above, they conducted a survey of 3,000 C-suite executives, representing companies in 10 different countries and 14 different sectors. While these executives were aware of AI, the survey found that ‘only 20 percent said they currently use any AI related technology at scale or in a core part of their businesses.’ This shows that the commercial application of AI and ML is still very nascent. It also indicates that there is a huge potential for growth and transformation.
To make full use of this potential, we need to start seeing ML as more than an IT tool. Introducing ML into the business decision-making process requires us to profoundly change the way we understand and use data: we must accept data and ML as a foundation for business strategy. We must move from static and reactive ways of reporting and learn to incorporate much larger collections of real-time data. We should look for ways to automate standard business decisions and, through that, realize those potential efficiencies.
A good place to start on this journey would be to employ the right people to drive the change. A solid digital transformation pipeline will also prepare companies for data-driven decision-making processes. This will mean restructuring organizations, so they can conduct a large-scale change over time. Once ML and data-driven strategies and capabilities are integrated into the core of our businesses, we should find ways to extend these capabilities to every area of enterprise. We also should reflect these capabilities at the customer and vendor level as well as to strategic business partners. We must become a reference that others can use and create platforms for innovation.
Finally, the application of ML in business decisions is not a one-off exercise that is planned, executed, and forgotten. It’s a continuous process that needs constant attention and will surely evolve as quickly as the technology itself.