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.

CEOs worry about ensuring that their companies have the right skills mix to thrive in the age of AI and automation, and they’re smart to be thinking about talent at a strategic level. But the external labor market can do only so much to address the anticipated shifts in demand. So companies should double down on retraining the people they have, with an emphasis on lifelong learning and adaptability.

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.

Even a highly skilled, knowledge-based industry like management consulting — one worth $250 billion and filled with some of the smartest people on the planet — is vulnerable to the same market forces that are disrupting services everywhere, including AI.

Technology changes faster than society can keep up, a pattern now playing out with artificial intelligence. Many CEOs are taking a wait-and-see approach to AI, while others are anxious to barrel forward. In both cases, there’s little conversation about AI’s human costs. Incremental adaption makes it more likely that AI algorithms shared across organizations and geography are spreading their shortcomings. Leaders must act to mitigate these challenges if AI is to benefit society.

Modest questions about how today’s problems could be solved more effectively lead to applications of technology with easily foreseeable gains. But when people start asking bigger, bolder questions that challenge basic assumptions about how a problem has been framed, they open up space for breakthrough innovations. That’s been the pattern in many digital realms, including cybersecurity, artificial intelligence, and the internet of things.

The robots are coming! But counter to popular belief, it’s not just low-paying jobs that are at risk of automation. According to research by Scott Latham and Beth Humberd, predicting which jobs are vulnerable requires analyzing the type of value job holders deliver and the skills they use to deliver it. Workers must understand four paths of job evolution — and factors behind each path — if they hope to adapt.

The fear of robots eclipsing human power in society, in particular the workforce, has persisted for decades despite the moderate progress of artificial intelligence systems. For professor and robotics researcher Ken Goldberg, a hybrid human-machine workforce is much more likely to take shape, and in many industries, it has already begun.

Not many companies are there yet, but there’s a developing framework for what it takes to lead an AI-driven company. Leaders at the forefront of AI have seven key attributes: They learn the technologies; establish clear business objectives; set an appropriate level of ambition; look beyond pilots and proofs of concept; prepare people for the journey; get the necessary data; and orchestrate collaborative organizations.

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.