The Executive Briefings for the Summer 2019 issue of MIT SMR give a snapshot of the issue’s close look at the challenges, culture shifts, and opportunities that adopting AI will bring to businesses. Plus: How to make M&A successful; does management style change with age?

Leisure time does two important jobs for us. Recharging is the obvious one. But it can also heighten our powers of creativity, given the cognitive benefits associated with letting our minds wander — and that gives us an edge over AI in the battle for jobs. Kellogg professor Adam Waytz makes this research-based argument in “Leisure Is Our Killer App,” the lead article in MIT SMR’s package on talent in a digital age. Check it out, along with the other pieces, in the fall issue of the magazine.

Thoughtful adoption of intelligent technologies will be essential to survival for many companies. But simply implementing the latest technologies and automation tools won’t be enough. Success will depend on whether organizations use them to innovate in their operations and in their products and services—and whether they acquire and develop the human capital to do so.

AI spares us from many mundane, time-consuming, nerve-wracking annoyances. The problem is, such annoyances play a key adaptive function by helping us learn to adjust our conduct in relation to the world around us. But AI designers can tackle that problem through system enhancements. By incorporating cognitive speed bumps, they can prompt users to engage in reflective thought rather than “outsourcing” cognitive, emotional, and ethical labor to software.

Executives intent on exploiting AI to enhance processes or products tend to focus on having a strategy for AI. But creating strategy with AI can matter as much or even more. In a machine-learning era, enterprise strategy is defined by the KPIs that leaders choose to optimize —the measures organizations use to create value, accountability, and competitive advantage. AI can help determine what KPIs are measured, how they are measured, and how best to prioritize them.

Companies can turn AI hype into operational hay by developing their capacity for enterprise cognitive computing. This capacity entails five capabilities: data science competence, business domain proficiency, enterprise architecture expertise, an operational IT backbone, and digital inquisitiveness. The capabilities shape and are shaped by four practices: identifying use cases, managing application learning, cocreating applications, and thinking “cognitive.”

As AI moves from the hype stage to implementation within organizations, retailers and marketers have new competitive opportunities with customer centricity. AI enables companies to apply data about their customers’ wants, needs, and preferences to customize their offerings, create personalized shopping experiences, and make the purchase process simpler and more convenient.

The future of customer service will allow bots and humans to focus on what they do best. For chatbots and virtual agents, that is handling routine cases quickly and efficiently. For the human supervisors, that is solving more complex problems, using empathy and emotional intelligence.

All bubbles are different. Bubbles occur when the market value of assets decouple from their intrinsic value and expectations of rising valuations generate investor demand. Many ambitious infrastructure projects that produced canals, railways, and telecom networks were fueled by bubbles. Unlike the housing bubble, the effects of a bursting AI bubble wouldn’t cause great harm.

While AI is brilliantly placed to solve decisions that are concrete and well-defined, in other contexts it can fail spectacularly, showing connections between facts or events but stumbling when the need to disentangle cause from correlation arises. Human input in the form of subject matter knowledge and common sense are often needed to complement AI. And executives must understand which challenges are right for these new technologies to address.

With customized and continuous data-driven feedback becoming a new normal, managers are revisiting the role they should play in delivering, facilitating, and curating face-to-face employee feedback. Does direct managerial involvement complement or compete with data-determined performance reviews?

How can we avoid being automated out of our jobs? When recommending areas for development, experts tend to focus on two broad classes of skills that distinguish people from machines: sociability and variability. But homing in on those areas can lead to burnout, leaving us even more vulnerable to obsolescence. Leisure can mitigate these effects. Beyond reducing burnout, leisure is a uniquely human activity that robots cannot perform, and it might actually make us better thinkers and workers.

It can be easy to get caught up in all the uncertainty and speculation around the future of automation, but when we focus on the future, we may fail to see what’s happening right in front of us. In this audio interview, MIT SMR editor in chief Paul Michelman revisits the work of Accenture researchers Paul Daugherty and H. James Wilson to discuss what’s changed in the AI landscape and what leaders should be thinking about next.

Recent efforts and concerns to protect the privacy of users’ data, while admirable, do not go far enough. Innovators and leaders must consider how new technology will be monitored to protect against potential harmful and irreversible effects.

How can companies and employees find common ground when it comes to skill development and investment in AI capabilities? To start, senior executives should seek clarity around capability gaps and determine which skills their people need. From there, leaders should take an approach that advances those skills for human-AI collaboration.

B2B Marketing departments never have enough time, manpower, or money. AI solutions such as automated emails and predictive analytics can help companies push back against these constraints. AI marketing products can act as a force multiplier. Imagine filling your headquarters with thousands of brilliant marketers, expertly analyzing data and providing actionable insights that increase the productivity of your existing marketing team.

According to a 2019 NewVantage Partners survey, fear of being disrupted is a leading factor for executives making heavy investments in AI and big data. While many are already seeing measurable results, companies that invest in their people and processes in tandem with technology may see the highest adoption.