Beth Stackpole

As organizations push forward with AI, they should put individuals at the center of the design process. Here’s a roadmap for making that happen. As companies double down on business initiatives built around technologies like predictive analytics, machine learning, and cognitive computing, there’s one element they ignore at their peril — humans. That was the message from a pair of experts at a recent MIT Sloan Management Review webinar, “People-Centered Principles for AI Implementation.” As organizations push forward on their artificial intelligence  journey, they should strive to put individuals at the center of the design process, the experts advised.

A natural progression

For all the hype surrounding AI, the technology is hardly a newcomer. Early derivatives surfaced in the 1970s and 1980s in the form of decision-support and expert systems. Today, organizations are progressing from a foundation of big data and predictive analytics to machine learning, neural networks, and eventually to fully autonomous AI.Fueled by the rise of cloud computing, there is now ample memory, storage, and computational horsepower to handle sophisticated algorithms that were developed in the past but not put to use due to the limitations of technology, said Bray, who is also a senior fellow at the Florida Institute for Human & Machine Cognition.

Companies have been collecting data and are now in the process of transforming that data into insights that will empower more informed decision-making. “We go from data to information flows to insight, and then [from] insight to action,” he explained. “That’s the data-decision paradigm we’re aiming for.” While AI has cycled through periods of heated interest and winters of stagnation, the time has come for all to get serious about advancing people-centered AI initiatives to stay abreast of the competition, said Bray and his co-presenter, R. “Ray” Wang, CEO of Constellation Research. The pair provided this roadmap for getting started:

• Classify what you’re trying to accomplish with AI
• Embrace three guiding principles
• Establish data advocates
• Practice “mindful monitoring”
• Ground your expectations

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