In today’s big data world, AI and machine learning applications already analyze massive amounts of structured and unstructured data and produce insights in a fraction of the time and at a fraction of the cost of consultants in the financial markets. Moreover, machine learning algorithms are capable of building computer models that make sense of complex phenomena by detecting patterns and inferring rules from data — a process that is very difficult for even the largest and smartest consulting teams. Perhaps sooner than we think, CEOs could be asking, “Alexa, what is my product line profitability?” or “Which customers should I target, and how?” rather than calling on elite consultants.
The booming growth of machine learning and artificial intelligence (AI), like most transformational technologies, is both exciting and scary. It’s exciting to consider all the ways our lives may improve, from managing our calendars to making medical diagnoses, but it’s scary to consider the social and personal implications.
Automation, from robotic process automation to artificial intelligence, is transforming every function of every business in every industry. Despite the many indicators of a transforming marketplace, almost all legacy leaders and board members still hesitate to apply artificial intelligence to corporate strategy. Leaders of businesses that don’t move quickly to capitalize on the power of AI will be left behind. Adopting an AI powered strategy is the natural next step. No matter the application, the process is similar. Here are the four steps of AI powered strategy.
In a data-driven business world it’s clear that machines are beginning to play, and will play, an ever-larger role in C-suite decision making. The best leaders of today and tomorrow will no longer rely on the instincts of a few decision-makers and will instead use insight driven by machine and deep learning solutions. With new competitors changing the market at rapid pace, companies seeking to achieve ‘superstar’ status and dominate the top of the profit and value ladder will need AI to guide the way forward.
Leaders who figure out how to leverage increasing data trove to improve their decisions and outcomes will produce superior returns, just like the best investors do that have long relied on machines and “quants.” Failing to make use of the growing surge of data will mean a significant handicap for any leader and their team just like it does in the financial markets. The answer is for corporate leaders to use artificial intelligence to facilitate and speed up the steps above and in the process, make faster, better decisions.
Machine learning is already becoming a commodity. Companies racing to simultaneously define and implement machine learning are finding, to their surprise, that implementing the algorithms used to make machines intelligent about a data set or problem is the easy part. There is a robust cohort of plug-and-play solutions to painlessly accomplish the heavy programmatic lifting, from the open-source machine learning framework. What’s not becoming commoditized, though, is data. Instead, data is emerging as the key differentiator in the machine learning race. This is because good data is uncommon.
Regardless of industry, companies all over the world are shifting to new business models based on technology and platforms, rather than the products and services of the industrial age—and those that make this shift and leap the digital divide are rewarded with dramatically higher market valuations and corresponding price-to-sales ratios. If you want to know what the market really thinks about a company, there’s one pretty simple way to tell: just look at its price to sales ratio. This one little number encapsulates performance, value, and trajectory, and it’s a lot harder to manipulate than price to earnings ratio.