With so many digital tools in the workplace, collaboration has gone omnichannel. Given how hyperconnected people are, the authors set out to explore the implications for organizations and teams. In their research, they discovered that always-on connectivity was good for fact finding and information sharing but not for problem-solving, as we tend to assume. For tasks that require imagination, it’s better to alternate between connectivity and quiet focus. Leaders must help establish a good rhythm.

For managers, it can be easy to fall into the trap of assuming that simply engaging in high-level product and business discussion is “being strategic.” But with this approach, leaders may be neglecting the core problems their organizations need to address most. By investing more time in three key activities, new and experienced managers alike can become better strategic leaders.

The traditional method for selling — typically framed around asking the customers’ needs — is not enough. Why? Because often customers don’t recognize their core problem. To increase sales, managers should direct their teams on taking a problem-centric approach.

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.

Leaders in a digital world have to navigate more complexity than ever before, where a problem that arises in one node of such network work can spread easily, with widespread adverse impact. But complexity-induced problems often have similar fundamental causes — and similar solutions. Leaders can ameliorate the effects of complexity by developing broader, not just deeper, perspectives; learning to think in terms of scenarios; and being clear about strategic intent.

Finding the right metrics to track innovation is by no means straightforward. To steer clear of common mistakes, executives need a holistic perspective on their company’s innovation process.

Differentiating between things that are complex versus those that are merely complicated may sound like an exercise in semantics, but it is an essential first step in solving difficult problems. Solutions for complicated issues rarely work for problems that are complex.

As busy as they are, leaders need to find ways to observe fundamental work processes in their organizations. When they do, they usually discover that there are gaps between theory and reality in how works get done. Michael Morales’ experience — in which identifying and addressing such gaps led to his company saving $50,000 in just 60 days — is a case in point.

Few questions in business are more powerful than “What problem are you trying to solve?” Leaders who can formulate clear problem statements get more done with less effort and move more rapidly than their less-focused counterparts. But stopping to ask this question doesn’t come naturally — managers must put conscious effort into learning a structured approach.

The ability of artificial intelligence to sift through mountains of data and identify patterns — and problems — in real time is its key value for business. Using AI to predict failures and take action to prevent them will become commonplace in the very near future. But it can also offer insights into human behavior to help managers improve customer service and employee relations.

The ability to innovate and rapidly respond to changes in the business environment is critical to competitiveness and success. Improvisation and experimentation combined with focus and flexibility are needed to identify new business opportunities and effectively execute projects. But while improvisation may seem to be spontaneous, managers can foster it through the deliberate development of certain processes and capabilities in an organization’s culture, team structure, and management practices.

Many companies have maintained a competitive advantage through analytics for many years — even decades. Those companies include Wal-Mart, ABB Electric, Procter & Gamble, American Airlines, and Amazon. Peter C. Bell (Ivey Business School) writes that “research over a 30-year period suggests that there have been five basic ways in which companies have sustained an advantage generated through analytics.” Tactics include keeping your company’s analytics secret and applying analytics to the right problems.

A new book by Thomas H. Davenport and Jinho Kim says that if companies can’t turn all the data they’re swimming in into better decision making through quantitative analytics, they’re “probably creating suboptimal performance.” The book, Keeping Up with the Quants: Your Guide to Understanding and Using Analytics, is geared toward executives who are not analytics experts but whose jobs require them to deal with those who have such expertise, both inside and outside their organizations.

Accepting five inconvenient truths about project status reporting can greatly reduce the chance of being blindsided by unpleasant surprises. For instance, many employees tend to put a positive spin on anything they report to senior management. And when employees do report bad news, senior executives often ignore it. Overconfidence is an occupational hazard in the executive suite, and executives need to examine their own assumptions and beliefs about project status reporting.

Asking why you’re embarking on a project before you begin raises the project’s chance of success. But “to our continuing surprise, we often discover these teams have not even discussed, let alone agreed on, why they are pursuing the project,” write Karen A. Brown, Nancy Lea Hyer and Richard Ettenson. But producing a good “why” statement often requires both a lot of work and heated debate.

Top data scientists often share three characteristics: they are creative, they are curious and they are competitive. Anthony Goldbloom, CEO of Kaggle, a company that hosts data prediction competitions, has figured out how to tap all three of these characteristics to help companies crowdsource their analytics problems.

The next wave of Enterprise 2.0, says MIT Sloan’s Andrew McAfee, will center around the concept of harvesting expertise, solutions and knowledge — not just from within the organization, but from anywhere that expertise can be identified and gathered.

“When faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution,” writes psychologist and Nobel Prize winner Daniel Kahneman, in a new book.

Social networking inside Tata Consultancy Services helps people access the full breadth of the organization’s collective intelligence in lightening speed, says K. Ananth Krishnan, TCS’s chief technology officer.

Al Roth, expert in game theory, experimental economics, and market design (and Harvard Business School professor), is one of the big names in the field of matching markets — building efficient systems that match, for instance, new doctors to their first hospital jobs out of medical school.