What are the biggest challenges to AI implementation? MIT SMR readers share their thoughts in a recent online discussion.

How should organizations organize for and implement artificial intelligence?

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.

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.

AI has considerable potential as a crystal ball to predict, but prediction is inherently more difficult than detection — and may have a much lower ROI for businesses.

Companies are looking to artificial intelligence to create business value, and as MIT Sloan Management Review’s 2018 Global Executive Study and Research Report on AI shows, Pioneer organizations are pulling ahead of their counterparts. By deepening their commitment to AI and focusing on revenue-generating applications over cost savings, these early implementers are positioning themselves to reap the benefits of AI at scale.

A 2018 research survey found that companies see competitors gearing up for big data and AI success — and that’s fueling their investment in similar capabilities.

To deliver ROI and productivity, firms must overcome the barriers to implementing AI technology.

AI provides access to unstructured information and enables organizations to learn faster and offer new services.

AI’s largest and most enduring contributions will be in non-technology sectors, and many of them will come from China. Buoyed by the country’s latest five-year plan and enabled by centralized data, Chinese companies are investing aggressively in AI and adapting their business models to make the most of AI’s potential, but unclear business cases and bottlenecks due to lacking technical capabilities hinder adoption.

Businesses should use AI technology as a tool to improve customer service and processes, not just to imitate humans.

AI can do much more than augment human performance in the workplace.

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.

According to a 2018 NewVantage Partners survey, executives now see a direct correlation between big data capabilities and AI initiatives. For the first time, large corporations report having direct access to meaningful volumes and sources of data that can feed AI algorithms to produce a range of business benefits from real-time consumer credit approval to new product offers.

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.