Machine learning is re-engineering corporate decisionmaking

Remember when Kodak dominated the consumer film industry? When digital came along, Kodak’s leadership made the fateful decision to double down on celluloid film instead of going all-in on digital. With the benefits of hindsight, we know now that that decision was the beginning of the end for the company’s dominance. Think of all the one-off decisions that led up to that grand strategic misfire. 

When you run a business or manage a team, decisionmaking comes with the territory – who to hire, which ideas to pursue, how much money to allocate to each department, which products to greenlight. Difficult choices are par for the course for many professionals. Unfortunately, the right calls are rarely obvious. Even after months of planning and analysis, it’s entirely possible to go wrong when making challenging choices.  

The challenges of decisionmaking today

In one study, 78% of professionals responsible for making decisions said they struggle to find the right answers in the constantly-changing business landscape. Fear of making the wrong decisions can prevent action, which in some cases is just as bad as choosing incorrectly. 

Fortunately, business leaders today have access to unprecedented amounts of data as well as artificial intelligence platforms that enable them to make better decisions about their companies’ futures. If Kodak executives had had a better grasp of changing market trends, they might have chosen a different strategic focus when shaping their business. 

One promise of AI automation technologies is that these systems allow humans to reach their potential more fully. Managers and other decisionmakers in particular will be relieved of administrative duties, empowering them to spend more time on high-level tasks like strategy and talent development. AI platforms will also generate data and provide analyses that help leaders make smarter, more informed decisions. Clearly the better we understand our own industry and market, the better we can serve them. 

But becoming a better decisionmaker takes more than simply hooking up new AI tools and relying on their outputs. As one business expert said, “If one of the potential promises of machine learning is the ability to help make decisions, then we should think of technology as being intended to support [managers].” Many leaders are already coming around to this redefined management style. In fact, nearly 80% of managers surveyed by Accenture “believe that they will trust the advice of intelligent systems in making business decisions in the future,” according to the researchers. 

To truly leverage the potential of these new tools, professionals must hone their judgment skills and view AI as a powerful new complement to existing business processes. 

Big data, big decisions 

Managing the personnel and processes around smart decisions is a complex challenge. In part because our understanding of how people make decisions evolves with new discoveries in neuroscience and psychology. 

In 2007, cognitive and business researchers put forth a business-focused decisionmaking framework based on the concept of contextual responsiveness. They wrote that there are five contexts in which professionals make decisions: simple, complicated, complex, chaotic, and disorder. To effectively steer their companies, leaders need to understand when to apply wisdom from past experiences, when to enlist expert counsel, and when to let patterns reveal themselves before deciding how to address a crisis. 

Such a framework provides a powerful guide to thinking about business challenges and making decisions in a data-rich world. Data is everything, and there’s more of it than we humans can handle on our own. By 2020, 1.7 megabytes of data per person will be generated each second. Even if data analysts worked 24/7, there’s no way they could keep up without the aid of tools like artificial intelligence.  

The massive quantity of data is a good thing for decisionmakers. More data means the potential for more accurate predictions about consumer preferences and market trends. Instead of relying on past market events or customer behaviors, data allows businesses to use real-time information and increasingly accurate forecasts to shape their decisions. 

Machine learning improves business processes

Machine learning will play a major role in business decisionmaking because it can be used to adjust resource allocation based on real-time measurements and demands, and can personalize customer experiences. The more data a system gathers about different users’ preferences, the more accurately it can predict what types of products and services they’ll gravitate toward in the future. 

What better way to keep people engaged on a company’s website than to offer constant promotions on exactly the types of goods and services that interest each individual user? The consumer data analyzed by AI algorithms can help create more effective product strategies and marketing campaigns, not to mention mitigate the fallout from supply chain disruptions and other business crises.  

The implications for improved business processes through machine learning and other forms of AI are far-reaching. With AI algorithms becoming smarter all the time, managers will be able to automate customer personalization, resource allocation, and even fraud detection in cybersecurity. They can then focus on bigger picture questions and complex problems that machines simply cannot solve. 

Making decisions is one of the most challenging aspects of business leadership. AI is making that process both easier and more flexible. By providing deep insights into industries, customers, and markets, AI is giving leaders more options and capabilities than ever before. Now it’s up to us to make smarter decisions.