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.

MIT Sloan Management Review and MIT Sloan School of Management will chat on Twitter (#MITSMRChat) about the intersection of business and sports analytics. Participants will learn how insights from the sports industry can help companies in other industries excel at performance measurement.

What if, instead of perpetuating harmful biases, AI helped us overcome them? What if our systems were taught to ignore data about race, gender, sexual orientation, and other characteristics that aren’t relevant to the decisions at hand? They can do all that — with guidance from the human experts who create, train, and refine them.

Not many companies are there yet, but there’s a developing framework for what it takes to lead an AI-driven company. Leaders at the forefront of AI have seven key attributes: They learn the technologies; establish clear business objectives; set an appropriate level of ambition; look beyond pilots and proofs of concept; prepare people for the journey; get the necessary data; and orchestrate collaborative organizations.

Empirical evidence demonstrates the value of feedback and ratings for performance. But HR is moving away from traditional performance reviews because managers and employees say they don’t like them. It’s a mistake that will backfire.

In a Q&A, Sydney Finkelstein, the author of Superbosses: How Exceptional Leaders Master the Flow of Talent, notes that employees entering the workforce today have technological capabilities unmatched by any workforce before them. That’s changing the way leaders must operate. Today’s best leaders embrace technology as a management tool but retain a human touch, creating opportunities for the employees they manage and enabling flexible work practices.

Less than one in five organizations has integrated AI into some of their processes and offerings. Among the barriers to AI adoption, says MIT SMR executive editor David Kiron in his recent keynote at AI World: lack of access to talent and usable data.

Digital transformation is top of mind for many organizations. But many flounder by focusing more on developing the next technology breakthrough — when a core component of a winning digital transformation strategy is cultivating the talent who will lead the effort.

With digital skills in short supply, companies must reimagine both where and when work gets done and who is going to do it. New research identifies four distinct approaches companies are taking to manage their digital talent — and why they aren’t all equally effective.

Most of us view our jobs as specialized or somehow differentiated, but the world of business and management increasingly feels otherwise. For many organizations today, the next big driver of job commoditization is automation driven by smart machines. Simply put, if a job is viewed as a commodity, it won’t be long before it’s automated. The key for workers whose jobs have traditionally seemed safe: Highlight the tasks that require a human touch.

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.

An infographic illustrates how digitally maturing businesses achieve success by increasing collaboration, scaling innovation, and revamping their approach to talent.

Many executives believe they are good at identifying leadership talent. However, when asked how they make their decisions, they often cite intuition or “gut” instincts. Social science research, on the other hand, suggests that individuals are often prone to cognitive biases in such decisions. Rather than just relying on the subjective opinions of executives, some companies are using assessment tools to identify high-potential talent.

Many western multinationals have a tough time finding local talent in East Asia — a problem that global companies originating in East Asia don’t seem to face. One problem: The cultural values and expectations of those doing the hiring and those seeking the jobs are at odds.

Competition for digitally savvy talent has never been higher, but companies’ methods for acquiring and keeping the skilled employees they need are outmoded. Whether they want to develop capabilities in employees or tap on-demand talent markets — or some mix of both — human resources directors need to experiment with new talent management models.

As demand for big data technologies grows, so does the problem of finding sufficient skills. Result: Talent shortages could limit the rate of productivity growth. Research shows that labor-market factors have shaped early returns on investment in big data technologies such as Hadoop, a framework for distributed processing of large data sets. It turns out that when know-how is scarce, organizations that invest in new IT or R&D derive significant benefits from the related investments of other organizations.

Managers should be skeptical consumers of external strategy advice. External strategy advice can be costly — and wrong. The best sources of insight about strategy tailored for your company can lie dormant within the company itself, in its employees. Ironically, companies often expend significant resources on obtaining flawed external advice while the employees with the best strategy ideas are ignored — and thus may walk out the door.

By addressing the less-recognized causes of gender and racial pay inequality in the workplace, companies can attract a more diverse workforce and better leverage it in the quest to attain sustained competitive advantage.

When employees find their work meaningful, there are myriad benefits for their productivity — and for their employers. Managers who support meaningful work are more likely to attract, retain, and motivate the talent they need to ensure future growth. But can companies ensure this experience for their employees? A groundbreaking study identifies five factors that support meaningful work — and the seven management sins that can destroy it.

Many managers think they can create better products just by improving the development process or adding new tools. But it’s skilled people, not processes, that create great products. So-called “lean” organizations invest heavily and continuously in the skills of product developers, and rather than developing single products, they think in terms of streams of products. By making people the backbone of the product development system, companies can achieve a triple win: increased innovation, faster time to market, and lower costs.