As the AI field currently stands, deep learning is playing an increasingly critical role. As organizations begin adopting deep learning, leaders must ensure that these artificial neural networks are accurate and precise–lest they negatively affect business decisions and potentially hurt customers, products, and services.

Three common trouble points impede companies moving toward using AI: Leaders are unclear what it means to adopt AI; systems are drawing from too much junky data; and there isn’t a careful balance between customer loss of privacy and the value returned. These problems can be resolved only when leaders pay close attention to the strategic challenges of bringing AI on and approach AI as an integrated element of their processes.

In a data-rich world, company stakeholders know more than ever about the organization and possess unprecedented power to spread the word when something goes wrong. Leaders must realize that the damage to the business and its reputation will be revealed more quickly and spread faster and wider than ever before — so it’s vital that they look carefully at potentially risky decisions and practices.

There’s very little regulatory oversight for the tech industry, and this has become a problem. The status quo lacks transparency and shuts down competition — while holding no one accountable for breaches of trust. Some want big tech companies broken up. Others want stronger government oversight. They all are trying to answer the same question: What’s the best way to regulate the tech industry so that privacy and ethics concerns are addressed without stifling innovation?

The editors of MIT Sloan Management Review are pleased to announce the winner of this year’s Richard Beckhard Memorial Prize, awarded to the most outstanding MIT SMR article on planned change and organizational development. The 2019 award goes to “Building an Ethically Strong Organization,” by Catherine Bailey and Amanda Shantz.

As organizations begin adopting deep learning, leadership must ensure that artificial neural networks are accurate and precise to avoid negative impacts on business decisions that hurt customers, products, and services. A designed-centered approach helps address both these short-term concerns as well as the long-term concerns that machines might displace humans when it comes to business decision-making.

As companies pour resources into designing the next generation of tools and products powered by AI, many are failing to simultaneously examine the question of who is ethically and legally responsible for the societal backlash if these systems go awry. Over 80% of Americans now believe that robots and/or AI should be carefully managed. Because there are no clear-cut answers or solutions, the talk of regulations — and, more lightly, standards — is getting louder.

Folktales and stories from our ancestors were designed to keep new listeners from repeating the mistakes of the past. But in an era when employees move between companies at a faster pace than decades ago, leaving little time to transmit organizational mythology, are companies at risk of losing touch with the lessons of the past? In a time of great technological change, which demands reflection and clear corporate culture, this is a vital question.

To survive and thrive in today’s market, a healthy corporate culture is more important than ever. The MIT SMR/Glassdoor Culture 500 uses machine learning and human expertise to analyze culture using a data set of 1.2 million employee reviews on Glassdoor. This interactive tool offers previously untapped insights about the organizational culture of over 500 of the world’s leading companies and provides leaders with new tools for benchmarking culture in their own organizations.

Microsoft has been active in advocating for an ethical perspective on artificial intelligence, and in 2018 it appointed its first general manager for AI policy and ethics. Tim O’Brien, who had been with the company for 15 years, says his activities as “AI ethics advocate” include extending the community of people who are focused on the ethics topic, meeting with Microsoft customers, and leading a research effort to develop a global perspective on tech ethics.

AI spares us from many mundane, time-consuming, nerve-wracking annoyances. The problem is, such annoyances play a key adaptive function by helping us learn to adjust our conduct in relation to the world around us. But AI designers can tackle that problem through system enhancements. By incorporating cognitive speed bumps, they can prompt users to engage in reflective thought rather than “outsourcing” cognitive, emotional, and ethical labor to software.

An infographic explores distinctive characteristics of innovation in digitally maturing organizations.

Should there be an imperative — moral or otherwise — to consider what’s fair when making a business transaction? Many say that it’s perfectly ethical to profit from an asymmetry of information, where, for instance, one party is paying much more for an item or service than others would say it’s worth. But other people are working to integrate the business case with the ethics case. They reject a narrow, transactional view of business in favor of a more relationship-oriented approach.

Most organizations can agree on what questions to consider before making a decision about marketing, finance, or operations. But many stumble when the issue has ethical consequences. Leaders need to define what set of questions they want to consider when confronted with an issue that has ethical implications. Seven basic questions can get them started.

For companies just starting their investigation of AI’s capabilities, there’s a lot to think about. What can AI do for your company to provide value? And just as important: What can’t it do? A third consideration that many companies overlook (but shouldn’t) lies in the realm of ethics: Understanding when AI offers a capability that companies could use, but should not.

As artificial intelligence-enabled products and services enter our everyday lives, there’s a big gap between how AI can be used and how it should be used. A 2018 Deloitte survey of AI-aware executives found that 32% ranked ethical issues as one of the top three risks of AI, but most companies don’t yet have specific approaches to grapple with the challenges. Here, we list the seven actions that leaders of AI-oriented companies — regardless of their industry — should consider taking.

The room to maneuver on business and human rights has significantly expanded with the exposure of journalist Jamal Khashoggi’s brutal death. Companies must take advantage of this moment to prioritize human rights. Now is the time for business leaders and company boards to get on the right side of history — or risk becoming complicit.

Technology changes faster than society can keep up, a pattern now playing out with artificial intelligence. Many CEOs are taking a wait-and-see approach to AI, while others are anxious to barrel forward. In both cases, there’s little conversation about AI’s human costs. Incremental adaption makes it more likely that AI algorithms shared across organizations and geography are spreading their shortcomings. Leaders must act to mitigate these challenges if AI is to benefit society.

We can choose not to engage in improving the world. We can seize on every advantage available to us and our companies without thought to the consequences. We can act as if the planet and the global economy are not among our most critical stakeholders. We can join the crush of others who are just hoping to play out the string: keep our heads down, meet our numbers, collect our bonuses, and abdicate long-term responsibility to the next generation. But when we make those choices, we do violence against the future.

Unethical behavior and misconduct has been a persistent problem in the business world. A company’s ethical norms are a cumulative outcome of how daily ethical dilemmas are addressed in the workplace. Over time, these micro-level issues can evolve into a corporate ethics scandal — unless organizations work to help employees make ethical choices day to day.