To effectively implement AI, organizations will need to use human-centered AI processes that motivate and retrain workers, which shifts the focus from automation to collaboration between humans and machines. To test that idea, an experiment was designed to see how human workers might augment an existing AI system and embrace their new roles as AI trainers — resulting in a symbiotic system that enabled humans and AI to each work to their strengths.

By finding smart ways to make the most of in-house potential, tapping into the growing global ecosystem of expertise, and being open to automation and other tools that might help bridge gaps, leading companies are drilling new sources of advanced analytics talent.

Leading teams and organizations today means honing strategic and digital skills, hiring and mentoring diverse employees, and being agile and adaptive in the face of constant change. With this collection of MIT Sloan Management Review articles, readers will benefit from decades of research from academics and practitioners on the skills, processes, and frameworks that can help managers lead through times of uncertainty, change, and disruption.

To make gender equality a reality, organizations need to shift their thinking about what it means to them — that is, to make it part of their company’s values and a permanent driver of culture rather than a mere priority that might change in time. Lessons can be learned from the energy sector, which saw a cultural shift take place around another crucial workplace issue: safety.

Elite soccer is a multibillion-dollar business, and top clubs are constantly looking for the next promising young player that they can develop into a superstar. Youth academies are one way for clubs to do this, but they have to find the players with potential before they can work with them. Counterpoints talks with Chelsea Football Club’s head of research and innovation, Ben Smith, who is on an analytics-driven hunt for star material.

The future of work will entail thinking not just analytically, but also algorithmically — so companies need to retrain workers for writing code, not formulas. Organizations that manage to make code the natural language for diffusing analysis across their organizations can often grow and innovate faster than their peers.

It can be easy to get caught up in all the uncertainty and speculation around the future of automation, but when we focus on the future, we may fail to see what’s happening right in front of us. In this audio interview, MIT SMR editor in chief Paul Michelman revisits the work of Accenture researchers Paul Daugherty and H. James Wilson to discuss what’s changed in the AI landscape and what leaders should be thinking about next.

The workforce is changing, with more and more skilled workers electing to work for themselves or become entrepreneurs. As the competition for talent heightens, intuition is no longer adequate to identify and attract — not to mention keep — the best potential employees. In this webinar, ManpowerGroup’s chief talent scientist Tomas Chamorro-Premuzic discusses the current workplace dynamic and the innovative methods to solve the talent problem, including digital tools for talent assessment.

In this episode of the sports analytics podcast, Counterpoints looks at the unusual case of Larry Murphy, a right-handed hockey defenseman whose support for Hall of Fame lefthanders helped two teams win the Stanley Cup in the 1990s. Was this outcome due to a unique quality Murphy brought to the game, or does a more general strategy of finding complementary talents improve team performance?

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.

This episode of the sports analytics podcast Counterpoints shows that the greatest legal advantage in sports is a good night’s sleep. Using wearable devices to monitor athletes’ sleep, physiologists have shown that at least 8 hours of sleep can greatly improve performance — with implications not just for sports, but all areas of business and daily life.

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.

While most organizations still rely on traditional methods such as résumé screenings, job interviews, and psychometric tests to find the right people and match them with the right roles, three new approaches to talent identification are quickly gaining traction. Gamified assessments, digital interviews, and candidate data mining have the potential to make hiring more precise and less biased, as long as they’re used responsibly.

A winning record seems like it would help teams draw more fans to their games, yet there’s plenty of evidence that even losing teams can be profitable — sometimes more so than winners. This episode of the sports analytics podcast Counterpoints looks at the problem of selling a product with unpredictable performance by focusing on baseball.

In this episode of the Counterpoints podcast, we look at how sports analytics measures “basketball IQ” — and how this metric explains the improbable success of 8-time NBA All Star point guard Steve Nash.

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.