David Kiron, Executive Editor, MIT Sloan Management Review
Michael Schrage, Research Fellow, MIT Center for Digital Business
From MIT Sloan Management Review
Machine learning (ML) is changing how leaders use metrics to drive business performance, customer experience, and growth. A small but growing group of companies is investing in ML to augment strategic decision-making with key performance indicators (KPIs). Our research,1 based on a global survey and more than a dozen interviews with executives and academics, suggests that ML is literally, and figuratively, redefining how businesses create and measure value.
KPIs traditionally have had a retrospective, reporting bias, but by surfacing hidden variables that anticipate “key performance,” machine learning is making KPIs more predictive and prescriptive. With more forward-looking KPIs, progressive leaders can treat strategic measures as high-octane data fuel for training machine-learning algorithms to optimize business processes. Our survey and interviews suggest that this flip ― transforming KPIs from analytic outputs to data inputs ― is at an early, albeit promising, stage.
Those companies that are already taking action on machine learning ― investing in ML and actively using it to engage customers ― differ radically from companies that are not yet investing in ML. They are far more likely to:
- Develop a single, integrated view of their target customer.
- Have the ability to drill down to see underlying KPI data.
- Check their KPI reports frequently.
These differences all depend on treating data as a valuable corporate asset. We see a strong correlation between companies that embrace ML and data-driven decision-making.
MIT Sloan Visiting Lecturer Irving Wladawsky-Berger
From The Wall Street Journal
People have long feared that machines are coming for our jobs. Throughout the Industrial Revolution there were periodic panics about the impact of automation on work, going back to the so-called Luddites, textile workers who in the 1810s smashed the new machines that were threatening their jobs.
Automation anxieties have understandably accelerated in recent years, as our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans. But on balance, such fears appear to be unfounded, noted the World Bank in a comprehensive recent report on The Changing Nature of Work. Our problem is not that there won’t be enough work in the future. Our key problem is that, in many countries, the workforce is not prepared for our fast unfolding future.
Matias Adam, Lecturer at MIT Sloan School of Management
From The Hill
As companies collect increasing amounts of data about customers, a key challenge is connecting that information to customize the customer experience and boost sales. The customer journey begins long before the actual sale.
It starts with online searches, store visits, conversations and emails. Companies need to connect all of these touch points to identify potential customers and turn research and exploration into sales.
While business-to-consumer (B2C) markets have been deploying customer data platforms to consolidate the customer experience and improve marketing personalization, this has been a bigger challenge in the business-to-business (B2B) markets.
This is due to the complexity of B2B, where each customer has multiple decision-makers and users that are not always identified in the early stages, and the entire sales cycle is longer and relies on fewer leads, prospects, opportunities and sales than in B2C space.
MIT Sloan Professor Thomas Kochan
From The Conversation
The technologies driving artificial intelligence are expanding exponentially, leading many technology experts and futurists to predict machines will soon be doing many of the jobs that humans do today. Some even predict humans could lose control over their future.
While we agree about the seismic changes afoot, we don’t believe this is the right way to think about it. Approaching the challenge this way assumes society has to be passive about how tomorrow’s technologies are designed and implemented. The truth is there is no absolute law that determines the shape and consequences of innovation. We can all influence where it takes us.
Thus, the question society should be asking is: “How can we direct the development of future technologies so that robots complement rather than replace us?”
The Japanese have an apt phrase for this: “giving wisdom to the machines.” And the wisdom comes from workers and an integrated approach to technology design, as our research shows.
MIT Center for Digital Business Research Fellow Michael Schrage
From Harvard Business Review
Software doesn’t always end up being the productivity panacea that it promises to be. As its victims know all too well, “death by PowerPoint,” the poor use of the presentation software, sucks the life and energy out of far too many meetings. And audit after enterprise audit reveals spreadsheets rife with errors and macro miscalculations. Email and chat facilitate similar dysfunction; inbox overload demonstrably hurts managerial performance and morale. No surprises here — this is sadly a global reality that we’re all too familiar with.
So what makes artificial intelligence/machine learning (AI/ML) champions confident that their technologies will be immune to comparably counterproductive outcomes? They shouldn’t be so sure. Digital empowerment all too frequently leads to organizational mismanagement and abuse. The enterprise history of personal productivity tools offers plenty of unhappy litanies of unintended consequences. For too many managers, the technology’s costs often rival its benefits.
It’s precisely because machine learning and artificial intelligence platforms are supposed to be “smart” that they pose uniquely challenging organizational risks. They are likelier to inspire false and/or misplaced confidence in their findings; to amplify or further entrench data-based biases; and to reinforce — or even exacerbate — the very human flaws of the people who deploy them.
The problem is not that these innovative technologies don’t work; it’s that users will inadvertently make choices and take chances that undermine colleagues and customers. Ostensibly smarter software could perversely convert yesterday’s “death by Powerpoint” into tomorrow’s “murder by machine learning.” Nobody wants to produce boring presentations that waste everybody’s time, but they do; nobody wants to train machine learning algorithms that produce misleading predictions, but they will. The intelligent networks to counter-productivity hell are wired with good intentions. Read More