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Augmented reality sensor data creates a feedback loop for businesses to better understand how a product is being used and where opportunities for customization exist.
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Many workers value the flexibility and income that gig work provides; customers like being able to find people to do things they want done. However, the extent to which gig workers, typically self-employed individuals, should be afforded the legal rights of employees has yet to be fully resolved in many jurisdictions.
Blockchain represents more than just cryptocurrencies and digital cash. The decentralized ledger system provides a platform for smart contracts, which are digital agreements that are fast, secure, and require no third party.
Each month, the MIT SMR Strategy Forum poses a single question to our panel of experts in the fields of business, economics, and management. This month’s question asks our panel whether ride-sharing platform Uber must develop a self-driving car capability to remain viable in the market.
To deliver ROI and productivity, firms must overcome the barriers to implementing AI technology.
Virtually all human achievements have been made by groups of people, not lone individuals. As we incorporate smart technologies further into traditionally human processes, an even more powerful form of collaboration is emerging.
Companies are adopting artificial intelligence at an accelerated pace — and learning that developing and deploying AI is not like implementing a standard software program. Before diving into AI systems, companies should consider three principles that can greatly improve the chances for a successful outcome. First, they need to recognize that humans and machines are in this together. Second, they need to teach the AI systems with a lot of data. And third, they need to continually test what the systems have learned.
Machine-learning algorithms enable companies to realize new efficiencies for tasks from evaluating credit for loan applications to scanning legal contracts for errors. But they are as susceptible as any system to the “garbage in, garbage out” syndrome when it comes to biased data. Left unchecked, feeding biased data to self-learning systems can lead to unintended and sometimes dangerous outcomes.
In Part 5 of our eight-part video series, we look at production steps in digital additive manufacturing.
Researchers are exploring how to create intelligent machines that work with us better as opposed to taking our place. Robots that can express human body language can have a positive effect on their human colleagues, enabling them to be more effective at their jobs, take on higher-level tasks, and realize psychological benefits. The overall result is a more productive human-robot team.
New emotion-sensing technologies can help employees make better decisions, improve concentration, alleviate stress, and adopt healthier and more productive work styles. But companies must address important privacy issues.
For young adults accustomed to continually checking their cellphones, even a single day without access to them can be anxiety-producing. What are the implications for executives about managing this constantly connected generation – and their devices – in the workplace?
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.
Business has become too complex for boards and CEOs to make good decisions without intelligent systems. Just as artificial intelligence helps doctors use patient data to make better diagnoses and create individualized medical solutions, AI can help business leaders know more precisely which strategy and investments will provide exponential growth and value in an increasingly competitive marketplace.
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.
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.
Even when faced with evidence that an algorithm will deliver better results than human judgment, we consistently choose to follow our own minds. Why? MIT Sloan Management Review editor in chief Paul Michelman sat down with the University of Chicago’s Berkeley Dietvorst to find out.
AI will augment jobs, much as electric ignition, cruise control, and back-up cameras incrementally improved the automobile. So the question is not “Which jobs will be replaced?” but rather, “How will jobs be increasingly assisted by AI?”
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