The must-reads MIT SMR editors are reading this week, including: How working couples are hacking inequality at home, the future of work won’t be utopian, and why you should kill your favorite meeting.

The combined power of social, mobile, analytics, and cloud technologies has now become part of the technology foundation for organizations. In the looming “post-digital” era, adopting these technologies isn’t going to offer a competitive advantage — it will be a necessity for doing business.

The must-reads MIT SMR editors are most excited about this week, including: Why emotion is a key ingredient for getting customer experiences to stick, what we can learn from Germany’s platform economy, the best leader for your digital transformation effort might not be the obvious candidate, and more.

How should organizations organize for and implement artificial intelligence?

A key driver of AI’s role in the global economy will be how cities deal with technological developments. Many cities plan to become “smart cities” armed with AI-driven processes, like AI-based traffic control systems. But simply adopting these new technologies won’t be enough to guarantee their success. Like organizations and education experts, cities need to assess and prepare for AI-related skills gaps.

As companies pour resources into designing the next generation of tools and products powered by AI, many are failing to simultaneously examine the question of who is ethically and legally responsible for the societal backlash if these systems go awry. Over 80% of Americans now believe that robots and/or AI should be carefully managed. Because there are no clear-cut answers or solutions, the talk of regulations — and, more lightly, standards — is getting louder.

To win over skeptics, champions of AI-driven customer service need to show more benefits than just saving money. There are economic, technical, political, and cultural barriers that savvy managers must overcome in order to successfully integrate AI into their company’s customer service systems.

Microsoft has been active in advocating for an ethical perspective on artificial intelligence, and in 2018 it appointed its first general manager for AI policy and ethics. Tim O’Brien, who had been with the company for 15 years, says his activities as “AI ethics advocate” include extending the community of people who are focused on the ethics topic, meeting with Microsoft customers, and leading a research effort to develop a global perspective on tech ethics.

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?