Managers in less autonomous companies should learn from — and be wary of — the trend toward autonomous companies.

The danger in using artificial intelligence to expedite inefficient legacy processes is that the technology may prolong the life of systems that limit competitive advantage.

AI’s most potent, long-term economic value may lie not in the thousands of new startups, but in the ability of AI to augment the discovery and pursuit of basic scientific advances that could be the foundations of new industry.

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.

Artificial intelligence is beginning to replace many of the workplace roles that men dominate. The parts of those jobs that will have staying power are those that rely more heavily on emotional intelligence, abilities such as empathy, persuasion, and inspiration — skills in which women typically excel. In the AI economy, men won’t be as successful as women unless they embrace these differentiator skills.

Managers may wonder what sets AI apart from all the other highly touted technologies that may (or may not) live up to their hype. The key difference: We now have sufficient processing power to make good on AI’s promise.

AI’s strength is processing input from many signals quickly to build an accurate short-term estimate of what will happen. But sooner or later, AI must confront the dark side of human behavior in real-world situations — where people don’t always respond in ways that make sense. The concern: When people know what AI will do, but AI can’t predict how people may behave, there’s an opportunity to “game the system” in ways that hurt businesses that use AI.

Though AI is far from perfect, vast training data has given smart systems formidable accuracy in making independent decisions. Yet even as these decision-making capabilities improve, a Cold War history lesson reminds us that human involvement may still be needed to avoid intolerable consequences of incorrect AI decisions.

A global survey by the McKinsey Global Institute finds that AI is delivering real value to companies that use it across operations. C-level executives report that when they adopt AI at scale — meaning they deploy AI across technology groups, use AI in the most core parts of their value chains, and have the full support of their executive leadership — they are finding not just cost-cutting opportunities, but new potential for business growth, too.

Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company’s AI strategy is now. MIT Sloan Management Review has partnered with The Boston Consulting Group to provide baseline information on the strategies used by companies leading in AI, the prospects for its growth, and the steps executives need to take to develop a strategy for their business.

Detailed and data-rich insights won’t help your company if your employees don’t know where to find them — but that’s a problem AI can solve. Machine learning can enable faster organizational learning by helping each employee quickly understand what others in the organization understand — forming a knowledge distribution network.

Tom Davenport speaks at the 2017 MIT CIO Symposium, sharing the three ways businesses use artificial intelligence.

Andrew McAfee and Erik Brynjolfsson discuss the future of work and the global economy at the 2017 MIT CIO Symposium.

At the MIT Sloan School of Management’s 14th annual CIO Symposium, “The CIO Adventure: Now, Next and… Beyond,” senior IT executives came together to discuss key technologies, including how AI will transform the workplace. The goal: to help prepare these tech leaders for challenges they face, including shepherding ongoing digital transformations, building a digital organization, and managing IT talent.

Organizations have made rapid gains in their ability to generate big data sets, but the ability of managers and executives to develop insights from that data has lagged behind. Data processing by artificial intelligence offers the prospect of speeding things up — but it also risks expanding the gap, as managers lack understanding of how AI reaches its data-based conclusions.

The MIT Sloan School of Management 14th annual CIO Symposium discusses the impact artificial intelligence will have on the jobs of the future.

The growth of AI in business is likely to defy smooth, linear progression. It is difficult to build off of what has already happened to reliably determine what is likely to develop — and sooner or later, there’s a point of diminishing returns.

Tomorrow’s most productive individuals will have more and better digital versions of themselves. The vision: Individuals will be able to utilize customized software and digital tools to improve their performance and productivity, with these digital versions of themselves able to significantly outperform their average self. In this world, AI will stand for “Augmented Introspection” as well as “Artificial Intelligence.”

Deep learning and neural networks have dramatically improved in effectiveness and impact, leading to human-level performance in many aspects of vision, conversational speech, and problem-solving. But there is a backlash brewing.

AI is rapidly changing how organizations make decisions, serve customers, increase quality, and reduce costs. But the pace of change may be too fast for managers to effectively manage processes, react to new problems, and learn from data whose usefulness has a shorter and shorter lifespan.