Many managers are excited about smart machines but are struggling to apply machines’ limited intelligence. Indeed, computers can process data just fine, but to generate competitive advantage from machine learning applications, organizations must upgrade their employees’ skills. Companies will also need to redesign employee accountabilities to empower and motivate them to deploy smart machines when doing so will enhance outcomes.

On March 15, 2017, MIT SMR held a webinar to share insights from our report, “Analytics as a Source of Business Innovation.” Many participants asked questions during the webinar that we didn’t have time for, so we decided to answer them in blog format instead. This post is the second set of responses.

On March 15, 2017, MIT SMR held a webinar to share insights from our report, “Analytics as a Source of Business Innovation,” which summarizes our findings about the increased ability to innovate with analytics and its benefits across industries. Many participants asked questions during the webinar that we didn’t have time for, so we’ll answer some of them in blog format instead.

As busy as they are, leaders need to find ways to observe fundamental work processes in their organizations. When they do, they usually discover that there are gaps between theory and reality in how works get done. Michael Morales’ experience — in which identifying and addressing such gaps led to his company saving $50,000 in just 60 days — is a case in point.

Managers should make it a priority to identify and understand the components of process success rather than waiting to dissect a failure.

If you want to lead your organization’s technology transition, the first step is grasping the realities of digital transformation — rather than getting seduced by the hype.

Though the current state of AI falls short of its promise, managers should find ways to incorporate it into business practices now. Using pragmatic, thoughtful experiments and being transparent with customers and suppliers, organizations can learn and develop their own capabilities as AI continues to rapidly evolve.

Companies can monetize their data by improving internal business processes and decisions, wrapping information around core products and services, and selling information offerings to new and existing markets. Adopting any of these approaches, however, requires management commitment to specific organizational changes and targeted technology and data management upgrades.

Market leaders have many advantages when adopting new technologies such as e-business, but they don’t always make the move. Why? It’s partly because new technology can be leveraged along a chain of related companies only if business partners also make the leap to adopt these changes. And research reveals that when large companies are significantly concerned about customer adjustment costs of new innovations, these powerful and otherwise highly capable organizations often resist change.

Although it’s unlikely that a single system will be able to handle all strategic decisions, the narrow intelligence that computers display today is already sufficient to handle specific strategic problems.

Using data and analytics to understand the complexities of modern business has become not only common, but essential. Gahl Berkooz joined Ford Motor Co. in 2004, eventually becoming head of data and governance and a member of the company’s global data insights and analytics skill team. Berkooz became acutely aware of how important analytics is to the company’s ability to thrive in the global marketplace. “What it boils down to,” he told MIT SMR’s Michael Fitzgerald, “is that we know how to make decisions. It’s about finding the opportunities to bring data and analytics to make better decisions.”

Peter Drucker defined the work of business leaders by three principal tasks: delivering financial results, making work and workers productive, and managing a company’s social impacts. Technological advances have transformed — and continue to transform — the world in myriad ways since Drucker published that definition in 1974. But technology hasn’t changed Drucker’s tasks. Instead, it is giving rise to new and better ways of executing them. This new column aims to help you identify big ideas and new tactics at the intersection of technology and management.

With the emergence of a digital economy over the course of the past two decades, leading companies have learned that they must act faster to respond to customer needs and competitive dynamics. The fourth annual Big Data Executive Survey confirms that Fortune 1000 firms recognize that faster time-to-insight correlates with success and will be the driving force behind Big Data investment for the years ahead.

The Internet of Things is on the brink of transforming business, but most businesses aren’t ready for the changes to the marketplace that the IoT will bring. There is very little time for companies to prepare for the changes coming as data-collecting devices proliferate. The good news is that by recognizing certain challenges, organizations can begin the possible, albeit difficult, process of getting ready.

Most large corporations are saddled with fragmented analytical processes, limiting their ability to operate with agility, flexibility, and insight. As a result, larger firms are often challenged when it comes to innovation and responsiveness. But Big Data approaches that enabled the flexibility and rapid growth of newer, smaller firms are being adopted by mainstream corporations. The goal: overcome legacy challenges and introduce greater corporate speed.

Problems with data quality come from a lot of sources — short-term solutions, mergers or acquisitions, or even the mundane complications of living in a complex society. The “stench” that develops when data quality declines can create serious issues for data-driven business. If a foul odor is emanating from your data, one solution might lie in refactoring analytics processes.

Hong Kong’s premier airline is using a blend of data and know-how to guide its daily operations. In an interview with MIT Sloan Management Review, Cathay Pacific CIO Joe Locandro describes how the airline uses analytics to make decisions that balance data with what it knows from the field. “Analytics will give you statistical spreads, give you training, but you still need to have this thing called experience and insight,” he says.

GRI is an international organization based in Amsterdam with offices around the world. It produces a set of standards used by organizations in over 90 countries and has become the global standard-setter for sustainability reporting. But as the organization’s Chief Executive, Michael Meehan, explains, sustainability reporting is not about writing a report; it’s the process by which organizations identify their risks related to important issues, like human rights, the environment, labor and other social issues.

You’ve figured out how to get the data, and how to make sure it’s good quality. You’ve hired the right people to put your data through the analytics wringer. Now you’ve got the results in your hands &mdash and you may not be sure what to do next. Consuming analytics effectively — and getting business value out of your analytics — is a challenge for many companies, and executives must get creative to increase their comfort level.

Analytics acts as an amplifier for business processes. In business, as in music, “louder” does not always mean “better,” so companies seeking to increase their analytics capacity should keep in mind four principles that underscore its limitations for business.