How can we avoid being automated out of our jobs? When recommending areas for development, experts tend to focus on two broad classes of skills that distinguish people from machines: sociability and variability. But homing in on those areas can lead to burnout, leaving us even more vulnerable to obsolescence. Leisure can mitigate these effects. Beyond reducing burnout, leisure is a uniquely human activity that robots cannot perform, and it might actually make us better thinkers and workers.

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.

Recent efforts and concerns to protect the privacy of users’ data, while admirable, do not go far enough. Innovators and leaders must consider how new technology will be monitored to protect against potential harmful and irreversible effects.

How can companies and employees find common ground when it comes to skill development and investment in AI capabilities? To start, senior executives should seek clarity around capability gaps and determine which skills their people need. From there, leaders should take an approach that advances those skills for human-AI collaboration.

B2B Marketing departments never have enough time, manpower, or money. AI solutions such as automated emails and predictive analytics can help companies push back against these constraints. AI marketing products can act as a force multiplier. Imagine filling your headquarters with thousands of brilliant marketers, expertly analyzing data and providing actionable insights that increase the productivity of your existing marketing team.

According to a 2019 NewVantage Partners survey, fear of being disrupted is a leading factor for executives making heavy investments in AI and big data. While many are already seeing measurable results, companies that invest in their people and processes in tandem with technology may see the highest adoption.

From what “being digital” really means for organizations to how developing strategy can feel akin to finding cadence in jazz, there was no shortage of insightful comments from our readers in 2018.

In 2018, MIT Sloan Management Review readers gravitated toward new articles that will help them prepare for the future of work, including topics as varied as skills needed in the age of AI and digital communication tools for virtual teams. The most popular article of the year focused on an area high on the list of most CEOs’ agendas: how to transform your organization successfully.

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.

Companies are racing to apply machine learning to important business decisions, only to realize that the data they need doesn’t even exist yet. In essence, the fancy new AI systems are being asked to apply new techniques to the same old material. The result is a visible arms race as companies bring on machine learning coders and kick off AI initiatives alongside a behind-the-scenes, panicked race for new and different data.

As AI develops better decision-making skills, leaders may feel threatened and push back, resisting the imperative to leave their biggest and most critical decisions to mechanical minds. And that might be a mistake, says psychologist Daniel Kahneman, given AI’s potential to reduce the bias in human thought processes.

Digital customer service agents (known as virtual assistants, chatbots, or softbots) are typically used to sift through and process only the most straightforward customer inquiries, such as requests for basic information. At most companies, complex issues get passed along to human agents. In that regard, public sector agencies in Australia are ahead of the curve: They are using digital agents to handle complex inquiries from citizens, and businesses stand to learn much from these applications.

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.

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.

In this webinar, AI experts and authors Philipp Gerbert and Julia Kirby discuss the findings from the report, “Artificial Intelligence in Business Gets Real” and dispel several myths around scaling and reaping the benefits of AI.

Banks and financial startups were some of the earliest adopters of mobile-first strategies and now they continue to innovate with AI. Learn about the four pillars driving change and AI adoption in banking and how these strategies can apply to organizations across any industry.

AI devices that help stimulate psychological and physiological human connection are serving a population whose loneliness is at an all-time high, but they also raise societal concerns.

Many people don’t trust decisions, answers, or recommendations from artificial intelligence. To address that problem, makers of AI applications and systems should stop overpromising, be more transparent about how systems are used, and consider third-party certification.