By – Heidi K.Gardner
How can you transform a competitive, star-driven culture into a collaborative one? Companies struggle with that question, particularly when knowledge work is the offering. Here’s the underlying tension: Their subject-matter experts must deeply immerse themselves in their disciplines to stay on the cutting edge. And yet that expertise must be integrated across fields and areas of practice to solve the complex problems that today’s customer face.
Can you have it both ways? Yes—by engaging in smart collaboration, which is about producing results, not just playing nicely together in the sandbox (though that’s also important). By pooling their know-how and resources across boundaries, organizations can solve problems more creatively, increase their productivity, and reap higher profits—achievements that attract top-caliber talent and keep the stars engaged. Collaboration is not easy, given how time-pressed managers are, how reluctant they are to cede control over projects and relationships, and how tough it is for them to stop working in silos when they’ve been doing so for ages. Those rewards make it worth the effort.
I’ve observed all this in my empirical research with more than a dozen professional services firms over the past decade. Through further surveys and interviews with hundreds of executives and employees across organizations and industries, I’ve discovered similar themes in pharmaceutical companies, commercial real estate firms, financial institutions, and other knowledge-based settings.
Specialists resist collaboration when their careers are built on niche expertise.
Take Big Science, where networks of researchers and labs come together around large-scale problems of national or international concern. Examples range from the Manhattan Project, which developed the first nuclear weapons in the 1940s, to the Human Genome Project, launched in 1990. In contrast to traditional science, in which individuals or small teams investigate theories that explain observed phenomena, Big Science attempts to bridge the gap between basic research (pure theory) and translational research (application to people’s lives). The ultimate goal is to deliver a real-world solution, such as a warhead or a medical therapy. Initiatives like these tend to receive government or philanthropic support, and researchers often team up with industry to increase scale and impact.
Within that context, public-minded organizations like the Boston-based Dana-Farber Cancer Institute use collaboration in sophisticated ways to reconcile their experts’ need to stay on top of their respective fields with the larger aim of bringing institutional knowledge to bear on societal challenges. When I worked with Edo Bedzra and Shereef M. Elnahal, a couple of my students in Harvard’s dual MD-MBA degree program, to write a case study about Dana-Farber, I saw firsthand how difficult it was for the institution to move away from a star-based system to one that gets researchers working together across specialties and facilities. Here we’ll look at how that played out—and at the benefits the organization eventually reaped.
Why Dana-Farber Speaks to Businesses
Dana-Farber started off, in 1947, as the Children’s Cancer Research Foundation. It has since evolved into one of the world’s leading institutions that care for adult and pediatric patients in addition to conducting research. It also serves as the lead institution for the Dana-Farber/Harvard Cancer Center, a partnership of five hospitals and two schools that together oversee some 700 clinical trials a year. The institute provides a valuable example for anyone trying to foster smart collaboration in a knowledge-based organization, for two reasons.
First, knowledge in business is changing so rapidly that, like scientists, professionals across nearly every domain must narrow their focus to keep current and break ground. At Dana-Farber skilled practitioners evolve quickly from generalists to specialists—intellectual visionaries who identify and tackle some of the toughest problems related to cancer detection and treatment. Edward Benz, the institute’s president and CEO, explains, “We now have a deeply detailed understanding of what cancer is and how it arises, thanks in large part to talented individuals who pursued undirected research goals.”
You may be thinking, Really? No direction? And that works? It does—on the “staying on the cutting edge” side of the equation. Over the years, Dana-Farber’s system has been honed to encourage creative, talented scientists to study what interests them. Faculty stars have run essentially freestanding laboratories filled with postdoctoral researchers, and they’ve typically had complete control over their daily lab activities. Until recently, the institute has provided little or no guidance on project selection. “Scientists had a tremendous amount of autonomy over how they used institutional resources,” says Dana-Farber’s chief scientific officer, Barrett Rollins. “As long as they published their work in high-impact journals and advanced whatever field they chose, it didn’t matter what they studied. As a result, some faculty members here do research with implications for diabetes treatment rather than cancer.”
Allowing such autonomy seems a reasonable price to pay for developing world-class specialists. As the principal investigators on research programs, those stars have played a critical role in winning grants from the National Institutes of Health and other funders. That funding, in turn, has fueled a virtuous cycle: More money enables better research, which gets published in prominent journals, which brings greater prestige to the institution and its researchers, which attracts high-caliber new talent, which increases the likelihood that the next grant applications will be approved. At the same time, the institution’s fundraisers market those stars and their successes to potential donors, generating another welcome source of revenue.
Conversely, not allowing sufficient autonomy could be dangerous. The experts’ skills, intellectual property, and research funding are highly portable. Scientists might well start disappearing if other world-class organizations offered the same advantages Dana-Farber does, but with fewer constraints.
The parallels with business are clear: Stars are a crucial source of innovation and competitive advantage for companies. This is especially true in certain industries, or at certain stages in a company’s evolution. “In the engineering/consulting industry,” noted one contributor to my web-based forum, “iconic practitioners are often critical to start new practices or move an existing practice towards ‘blue chip’ status. A cluster of icons in a practice or service area can have transformational effects.” And, of course, having a star poached can be just as disastrous for a business as it is for Dana-Farber.
But there’s a problem here. What if the challenge at hand grows bigger than the capabilities of one brilliant star? What if what’s needed is a constellation?
That brings us to the second reason for looking so closely at Dana-Farber: Its challenges are as complex as those faced by almost any knowledge-based company today. About a decade ago, Benz, Rollins, and other leaders at the institute concluded that preventing and treating cancer effectively required a multidisciplinary effort. “Cancer” is, in fact, an umbrella term for a multidimensional phenomenon. Its causes and effects must be addressed on a number of fronts, including disease biology, imaging and diagnostics, drug discovery, treatment administration, population monitoring, and research methodology. Given cancer’s pathogenic intricacy and the range of spheres across which it can be confronted as a clinical and public health issue, translational research efforts can’t focus on applying the findings of deep, narrow studies; they must add up to a much broader, more integrated program. Indeed, as Benz and Rollins began to rethink Dana-Farber’s approach, it became increasingly clear that the most promising breakthroughs in translational research were coming from all-new categories that cut across traditional disciplines, such as bioinformatics and biomedical imaging.
Again, consider the parallels with knowledge-based companies. A 2016 PwC study of more than 1,400 CEOs across 83 countries found that they were more worried than at any point in the previous five years about the increasingly complicated business landscape. Business leaders must respond to diverse constituencies—customers, regulators, competitors, employees, shareholders, local communities, and more—often across the globe. They must navigate dense thickets of regulation that have sprouted up in recent years and try (against worsening odds) to guarantee cybersecurity for their companies and customers. Those are just a few of the major challenges that require multidisciplinary expertise.
Overcoming Obstacles to Change
Let’s assume that the leaders of a knowledge-based organization perceive the need for smart collaboration. It can still be hard to get specialists on board when they have built their careers and reputations around their niche expertise and are continuing to reap the benefits of that investment.
Dana-Farber’s experts liked designing their own research programs and calling the shots in their labs. But given the growing complexity of the problems they’re charged with solving, their fiefdoms couldn’t last forever. Benz, Rollins, and their senior colleagues knew they needed to find ways to persuade the powerful and highly autonomous stars to change their behavior before their competitive, siloed ways of working made them obsolete.
To create a sense of urgency and facilitate buy-in among the specialists, the top team asked Dana-Farber’s faculty stars to work together to drive strategic planning for the entire institute. Collectively, they had the institute’s deepest expertise about cancer and the best sense of where research could be rapidly and effectively applied. They identified 10 areas as having the greatest potential. One was oncological genomics, which focuses on detecting genetic alterations in tumors. What became clear, in this field and others, was that many experts had to contribute. Applying genomics techniques to cancer research, for example, requires the best thinking of a host of specialists, including geneticists, genome scientists, technical experts in DNA sequencing, and bioinformaticists.
Unrestricted financial support had motivated stars to compete, not collaborate.
As a result of the strategic planning process, Dana-Farber created the first 10 of its Integrative Research Centers, and Benz asked Rollins to lead their launch. Rollins saw his new role as twofold: implementing the faculty’s most promising ideas, and ensuring accountability. He viewed the program as an opportunity to transform the institute’s culture from primarily celebrating individual achievements to also cherishing collaboration, with the aim of hastening diagnostic and therapeutic progress.
Still, Rollins knew that he faced a number of big constraints. The most important of these lay in the paradoxical nature of the assignment: While fostering smart collaboration, he also had to maintain Dana-Farber’s entrepreneurial approach to research, which continued to make breakthroughs. The entire structure of research funding, particularly through the National Institutes of Health and other government agencies, was still based on this proven model for innovation. Could that model somehow be augmented?
On a more practical level, Rollins lacked some of the tools that would have been available to him in a traditional corporate context. Because the researchers in question were members of the Harvard faculty, he couldn’t influence promotion criteria or reward people differentially through compensation. But as an accomplished scientist in his own right, he understood how his colleagues thought, and he knew what other levers he could pull.
Rollins’s first step was to build a new organizational structure on top of the existing one. Individual laboratories would continue to conduct the research, and the 10 Integrative Research Centers would be an administrative overlay. The centers would be led by faculty members who had the appropriate content expertise, but anyone who wanted to engage in relevant research would be eligible for center membership, regardless of departmental home. This was a radical departure for scientists accustomed to “owning” their resources, operating as principal investigators, and even deriving their status partly from the physical size and location of their labs.
Next Rollins mitigated the negative effects of the old incentives. The architects of the strategic plan recognized that the traditional model (supplying unrestricted financial support to brilliant individuals to pursue their own goals) had motivated experts to compete rather than collaborate. That basic funding model remained in place, but as a counterbalance, Dana-Farber offered a separate set of financial incentives. In exchange for agreeing to lead a fully collaborative center, faculty members received extremely generous seed funding—10 to 20 times as much as they were accustomed to getting for individual projects.
Of course, this largesse came with very significant strings attached. Faculty leaders who accepted funding under these terms would have to apply basic project-management principles and tools to their activities. Most business practitioners are familiar with these, but scientists tend not to be. Each prospective center leader was asked to prepare a detailed business plan for his or her center, outlining intended activities and their rationale and estimating expenses for the first five years. Though each center would begin its work with the institutional seed funding, it would need to be self-sustaining within five years, relying on revenues generated from grants or commercial contracts.
Ultimately, the true measure of a center’s success would be its impact on patients with cancer. But since that is an extremely long-term goal, Dana-Farber needed to define interim objectives against which to measure centers’ progress. Some of them were standard academic metrics—such as the number of papers published in high-quality journals by center scientists and the amount of outside funding received—because those reflect peer groups’ assessment of the quality of the research being conducted. But new metrics came into play as well. Collaboration was one of the main goals of the strategic plan, so center leaders were asked to provide a quantitative accounting of collaborative interactions with other laboratories and centers, at Dana-Farber and elsewhere, and to track the related research outcomes. In addition, scientists needed to submit project budgets and plans with concrete milestones. All these checkpoints ensured that when a project was approved, its contribution to the center’s mission and to the bottom line had been reviewed and documented.
Rollins was responsible for monitoring center performance and had the authority to mandate changes within centers—or even to eliminate them—if they were deemed to be underperforming. An external advisory board would evaluate each center’s scientific progress independently and provide annual reports. Each center would also be evaluated annually by its own oversight committee, composed of faculty members and administrative staffers not directly associated with the center. Those reviewers would determine how well the centers capitalized on their opportunities for collaboration and hold them accountable for their financial performance and progress against milestones.
Because center leaders weren’t accustomed to this level of scrutiny, some initially chafed at the new rules. Rollins learned that he needed to meet with them individually, often multiple times, not only to sell them on the merits of a formal process but also to explain the consequences of failing to meet their objectives. Over time, Rollins reports, those conversations became easier, as more scientists benefited from the world-class technologies and resources that were available only because other centers had worked collaboratively and operated with financial discipline.
The centers were also required to use a new web-based platform to disclose each project’s goals and progress to all Dana-Farber faculty members. This encouraged more scientists to join the efforts—and to sign up in the right frame of mind. The increased information flow, which kept the individual centers’ work in line with Dana-Farber’s overall strategy, allowed researchers to say to one another, “Hey, I saw your proposal. We’re doing something over here that may be useful to you. Should we put our heads together?”
Throughout this change process, Rollins knew he had to support Dana-Farber’s leader-experts and let them play to their strengths. Because most of the academics were unfamiliar with developing detailed business plans, the institute’s Department of Strategic Planning worked closely with them, providing templates and financial spreadsheets and frequently reviewing drafts. Over time, externally recruited research scientists with significant pharmaceutical or biotech experience took on more and more operational duties in the centers so that the faculty leaders could focus on contributing their expertise and scientific vision. After all, Rollins realized, great people want to do great work—it’s highly motivating.
As for the new recruits, Rollins and the centers’ directors looked for candidates who would happily forgo high commercial salaries in exchange for intellectual excitement, an inspiring mission, and a community of stimulating peers. “We now have over 120 of these people who are on the nontenure research scientist track,” Rollins noted at the end of 2015. They can advance through six “ranks” and earn salary increases as they gain experience, publish papers, and acquire letters of recommendation. Tenured faculty members turn to these research scientists regularly for help and advice. “It’s a respected track,” Rollins says, “a vibrant way for people to have a career here.”
Within a few years, smart collaboration at Dana-Farber had already borne fruit. Consider, for example, the results at the Center for Cancer Genome Discovery (CCGD), a research and development group that supports precision medicine efforts at Dana-Farber, Brigham and Women’s Hospital, and Boston Children’s Hospital. Its mission is to “advance precision cancer medicine by developing new technologies for the analysis of cancer genomes and to provide basic, translational, and clinical investigators with access to these technologies.” Since its founding, in 2008, the center has worked on 257 projects, all of which have involved collaboration among multiple faculty members, and several of which have spawned new partnerships with outside academic and commercial institutions. With its multidisciplinary skill sets and cutting-edge technology, the CCGD allows scientists to perform analyses they would otherwise be unable to do—resulting in 103 groundbreaking publications since 2008.
Smart collaboration isn’t just nice—it’s a strategic response to external change.
The CCGD has also helped Dana-Farber launch Profile, a large-scale cohort research study aimed at collecting and synthesizing as much genomic information as possible about every type of cancer. The goal is to learn more about the genetic and molecular alterations that cause cancer to develop and progress—and then to create better treatments and prevention strategies. Since August 2011 more than 43,000 patients have participated in Profile, and more than 15,000 tumor profiles have been generated. And with the help of a next-generation sequencing tool developed at Dana-Farber and applied by the Department of Pathology at Brigham and Women’s Hospital, roughly 60% of the study participants have had “clinically actionable” test results—that is, results their doctors can use to select the right therapies and understand the hereditary risks so that family members can be assessed. Bioinformatics, a joint team of the CCGD and Profile, boosts the CCGD’s efforts by developing sophisticated analytical tools that mine and translate the sequence data for researchers and clinicians.
Of course, some prominent Dana-Farber researchers had achieved great success under the prior system. They were the most likely to feel threatened by this major institutional change. But Dana-Farber has evolved its approach to win over the skeptics. These days, well before launching a new center, the leader of the proposed initiative holds meetings with faculty colleagues to gather their insights about the best use of the platform. It’s a “listening tour” that helps shape the business plan—but also an opportunity to explain how the stars’ research can benefit through shared resources.
From Big Science to Business
We’ve seen how smart collaboration works in Big Science, but how does it work in business? If you step back from Dana-Farber’s story, you can see elements of McKinsey’s classic model of influence—levers of change that executives can pull in a wide range of organizations:
First, sell your stars on collaboration by giving them quantitative evidence that it works.
You need to show people that smart collaboration is not just a nice-to-have. It’s a strategic response to external change. So make a business case for it. Early in the game, use analytics to find pockets of excellence in the organization, and hold them up to illustrate potential gains in other areas. In one organization I worked with, for example, we used data from its time-reporting system to map how employees’ project-based networks had gradually changed and then examined how those changes had affected individuals’ ability to bring in revenue and meet other business objectives. Demonstrating that highly collaborative employees consistently achieved stronger results made a difference. People could see that their own colleagues had been able to make the desired behavioral changes, which helped reassure the “It’s not possible here!” skeptics, who also couldn’t argue with better performance.
In crafting your message about smart collaboration, spotlight benefits that may appeal to a range of people in your company. Because it’s a collective effort, you’ll need widespread buy-in across levels, roles, and functions. Many people will value the prospect of becoming higher achievers. In surveys of thousands of employees worldwide, I’ve also found that the camaraderie of collaboration, the intellectual challenge associated with more-complex work, and the ability to learn from others can be crucial motivators. And those who are approaching the end of their careers often report wanting to leave a legacy of juniors who have apprenticed with them.
Sometimes telling a compelling story requires proof of concept. Consider testing a small-scale version of the collaboration initiative you’re envisioning. In retrospect, Rollins admits that he and his senior colleagues at Dana-Farber were naive to launch 10 integrative research centers all at once, without any piloting or prototyping. Prototyping establishes a visible change that speaks for itself—and small wins motivate people to keep going.
Change your stars’ behavior by tweaking systems and structures.
Don’t treat compensation as a magic bullet. Granted, it plays a large part in shaping employees’ behavior and probably explains why some companies are, on average, more collaborative than others. But it doesn’t account for differences in collaboration between people who operate under the same compensation system. In one firm I worked with closely, about a third of the practice leaders almost never collaborate, whereas a quarter of the leaders make it a core part of their work. This degree of variation exists in most companies I’ve studied—even those with “lockstep” systems, where partners’ compensation is a tenure-based share of overall profits. You’d think that in such organizations, each partner would be motivated to share work across geographies and practices to optimize the client experience and grow accounts profitably. But at a lockstep firm I studied in depth, people hoarded work locally. Some account leaders involved only partners and associates from their own office, even when those professionals weren’t best suited for the work. And sometimes the lead partner had an opportunity to involve a world-class expert from an office not only in the same country but within driving distance—and didn’t. The result was predictable: Those accounts grew more slowly than others.
Clearly, money isn’t everything when it comes to influencing collaboration. Indeed, most psychologists (and, increasingly, economists) argue that performance metrics, whether or not they’re linked to compensation, drive behavior. So turn your attention to your company’s performance management system. Every relevant lever, when pulled, should work in support of collaboration—and nothing can actively work against it. But beware: The outcomes of smart collaboration are long-term, whereas the investment has to happen up front.
Have your stars provide business plans of their own, and then track progress against concrete, quantifiable goals. When evaluating people, focus on ends that are best achieved through collaboration, such as growth in the number of customers served substantively by different parts of your organization. Relying too heavily on metrics that track initiation but not follow-through, such as the number of cross-unit referrals or multidivisional projects, may leave the system open to being gamed—for example, by employees who introduce colleagues to clients that are unlikely to place an order. To counter this risk, measure the broader outcomes you want collaboration to improve.
Use technology to make the business objectives of individuals and groups transparent to your whole organization. If those objectives are searchable, colleagues can build on them by offering their expertise or seeking help on similar projects. Managers should be able to access their plans easily (from a smartphone, for instance) so that they can post brief updates when their teams make progress toward their goals. That makes the plans feel like living, breathing parts of the workday and helps people stay focused and motivated.
It’s also important to create an organizational structure that supports collaboration. That may be less about changing existing, formal reporting lines and more about creating an overlay, as was done at Dana-Farber. Such overlays are relatively common in large professional services firms, which are now moving toward a sector-based approach that mirrors their clients’ industries and helps align internal experts with the needs of those industries. But plenty of other types of companies are bringing together employees from various divisions or functions in creative, informal ways to promote innovation or serve customers better.
For example, Cisco had the goal of selling end-to-end technology solutions but found that the experts who needed to create these complex systems were scattered across multiple, globally dispersed business functions, and many of them were cut off from customer-facing staff members. To foster the kinds of collaboration necessary for addressing client problems and driving innovation, Cisco created its Architecture Center of Excellence—a technology-enabled workspace for sharing content and tools, backed up with incentives for participation.
As you’re working on organizational structure, make sure role descriptions are clear. Think about how various roles support one another—that will push you to attach specific behaviors to each one. You’ll need to revisit these descriptions periodically to reflect what’s needed—and what’s actually happening—in the changing organization. (Recall that at Dana-Farber, people were eventually brought in from outside to take on a lot of the operational work so that the research stars could go back to doing what they did best.) At the same time, identify and deploy project managers to keep things on track. Consultants can play a critical role too, but relying heavily on outsiders sends a message that change is temporary. So invest in internal change agents who have the capabilities and authority to sustain what you’ve started.
Shore up your stars’ collaborative skills.
For collaboration to work over the long term, leaders must invest one-on-one time with the key implementers of the strategy and support them with adequate training and coaching. Like Rollins’s multiple meetings with prospective center leaders, these sessions are critical for ensuring technical capabilities and modeling collaborative behavior.
We’ve long known that adults rarely learn merely by listening to instructions; they must also absorb the new information, use it experimentally, and integrate it with their existing knowledge. In practice this takes time, even for those who are willing. To get started, try building and using a personalized competency grid—a tool I learned about in my time at McKinsey. Work with your professional development staff to define the core capabilities—say, six of them—that are necessary for success in each role in the organization. Then sit down (or have the team leader sit down) with each person and agree on which areas to prioritize. Typically, everyone should meet a minimum standard in all six areas but develop what McKinsey calls a spike in one or two. In effect, you’re pushing individuals to build on their strengths.
This is a particularly effective way to foster smart collaboration, because it highlights and values the various contributions required for effective cross-boundary work. For example, someone who spikes on functional specialist knowledge can be legitimately recognized as a strong contributor, even if he or she isn’t great at closing deals.
Ideally—and yes, this is an especially long-term prescription—start developing the right capabilities in your junior ranks. McKinsey, for example, provides associates with highly visible collaborative leadership opportunities (such as heading recruiting teams or planning business-unit retreats) early in their time with the firm. Day to day, McKinsey project managers—who typically have three to five years’ experience at the firm—oversee many aspects of client relationships. Build in collaboration at this level, and you are building for the future.
If you can’t be the change, point to it.
Perhaps most important, employees want to see you and your senior colleagues behaving in the prescribed way. Model the kinds of collaboration you want to see take root. Take some calculated risks by making visible investments (time, money, reputation) in new kinds of teams. Lead those teams, as Rollins did at Dana-Farber.
Above a certain level in the organization, your administrative responsibilities may keep you off the front lines of collaboration. If that’s your situation, remember that your actions and words still convey what types of behavior are truly valued. But you can also rely on other powerful leaders within the organization who—like you—want to push toward smart collaboration. Call it role modeling by proxy.
Your most effective proxies aren’t always the most obvious ones. At one public relations firm I worked with, the core change leader turned out to be a relatively junior professional who was passionate about collaboration. He first educated himself so that he could deeply understand and articulate the business case—going so far as to devote all his vacation time in a given year to attending an executive development program.
Drawing on the knowledge he gained there and through his prior collaborative efforts, he crafted a rationale that persuaded senior leaders in the firm to invest in collaboration. He also organized workshops and focus groups to bring his peers along, and got a budget to hire outside experts to support the initiative. Being the pathfinder can be arduous—after all, there’s no beaten path—but also exhilarating. Work with those collaborative individuals who are ready to be challenged and exhilarated.
And finally, don’t forget storytelling. At one company I worked with, the senior leaders were reluctant to publicly praise small-scale examples of success, on the grounds that such a granular celebration might seem to show favoritism or otherwise skew the organization’s value system. But then, at a series of town hall meetings, the CEO told a story about a team that had collaborated effectively, and the response was almost universally positive. People liked seeing someone on more or less their level getting public recognition for collaborating. And they liked hearing an “accessible” story. It got them thinking, That could be me.