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Of the $1.2 trillion spent globally each year on R&D across corporations and academia, 40 percent—much the largest share—pays for people. Our team interviewed and surveyed world-class researchers in academia and a range of industries to understand what drives research productivity in labs. We found that the best ones, regardless of specialty or industry, share a pattern of behavior across six key practices: talent, strategies and roles, collaboration, problem solving, portfolio and project management, and alignment with the needs of the business and the market. To understand what characterizes the best labs, we then studied 4,500 researchers in 260 laboratories in academia and research-based industries, including automotive, basic materials, high tech, and pharmaceuticals.
Our conclusion was that talent management, more than anything else, is what the best R&D operations consistently get right (Exhibit 1). While all the practices we looked at are clearly correlated with high performance in labs, talent is the most important driver of their productivity and shows the highest level of correlation. Interestingly, talent management is also the practice that has the highest opportunity for improvement. That makes this a tremendously powerful lever to improve R&D productivity, regardless of its current level (Exhibit 2). Strategy is the second most correlated practice, but here the respondents saw the least opportunity for improvement.
Top-quartile academic labs are five times more productive than bottom-quartile ones. Similar differences exist among industrial labs. Yet many research institutions don’t understand how well they are doing, because the people who work there wildly overestimate their own performance: in our survey, 12 percent of them suppose that their own lab is in the top 1 percent, and 70 percent think it is at least in the top 25 percent. Most researchers don’t know how productive great labs are or how they become great. In fact, most labs can assess how well they do only by basic output measures. A halo effect further distorts perceptions: researchers who think that their lab performs well assume that its talent-management practices are also strong.
What top labs get right
Talent management isn’t simply about hiring the best; not everyone can. It’s about managing talent appropriately through selection, recruitment, development, and rewards. Just about any lab can do so, yet many don’t. We looked at each of these areas, and while all are correlated with performance, some matter more than others (Exhibit 3).
Recruiting for potential
Managing talent appropriately starts with recruiting appropriate talent. The head of a top-ranking academic lab told us that “the most important intrinsic we look for is scientific curiosity.” Great labs such as this one evaluate the potential of researchers by appraising their basic intellectual ability, general problem-solving skills, and enthusiasm. They also test a candidate’s cultural fit, which is important to support teamwork and collaboration, which in turn drive productivity. Candidates may, for example, spend an afternoon devising answers to a specific question or working in the lab with the team. This approach helps labs assess a candidate’s social compatibility as well. Before making a decision on recruitment, the best labs also solicit the views of team members about each candidate.
Average labs typically look mostly for specific technical proficiencies—say, the ability to use a piece of equipment or to run certain tests. Specific technical capabilities are sometimes required, but even when hiring for them, top labs want people who can adapt to new roles as the research evolves. Those new roles, especially in industrial settings, should include project management and business experience—something many labs overlook.
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Wouter Aghina and Marc de Jong are principals in McKinsey’s Amsterdam office; Daniel Simon is a consultant in the London office.
The authors wish to thank Ajay Dhankhar, Michael Edwards, Mubasher Sheikh, and Tony Tramontin for their support with the research behind this article, as well Ankita Gupta, Eoin Leydon, and Kate Smietana for their help with the analytics.