Picking the Right Approach to Digital Collaboration

Many software solutions promise to facilitate teamwork — but what suits close-knit colleagues may not help those who need to make connections across the organization.

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Consider this paradox about digital change: Although it increases the need for collaboration in organizations, it also makes collaborating more difficult. In my research and consulting work, I’ve observed that this happens for three key reasons.

First, it becomes harder to identify the right internal partners. In many organizations in the thick of transformation, particularly agile work environments, employees are given greater latitude to make important decisions on the ground. But when they need help completing tasks or solving problems to execute those decisions, they often aren’t sure where to turn for support, because they lack a broad understanding of who has what expertise in the organization. Thanks to technology, people can connect with coworkers across an array of specialties. However, research shows that they tend to focus on the information, ideas, and skills held by the colleagues around them — those in their work groups, for instance, or those who sit in close physical proximity.1 That may be evidence of an attempt to rein in an overwhelming field of potential collaborators because employees have no clear sense of which colleagues know what.

When people narrow their attention in that way, it undermines the benefits of digital connectivity, but it’s understandable. Given how frequently and fluidly people move from project team to project team (possibly from week to week), they don’t often build the relationships that would allow them to map out the expertise in their companies. And failing to find the right experts can easily lead to work duplication and missed opportunities for efficiency and innovation.2

Second, it’s harder to get coworkers to say yes to requests to collaborate — even experts who would be ideal collaborators if they had the time, energy, and resources to commit to yet another emergent team. In a dispersed, agile workplace, persuasion and influence are essential to securing needed resources. But it’s tough to persuade people to join your project if you have never worked together closely and have not developed trust.

Third, given that lack of close connection and established trust, it’s also harder to develop the kind of common ground that facilitates productive interaction. The issues that people care about, the technical languages they speak, their modes of problem-solving, and their goals tend to diverge greatly when they work in different locations, specialize in different domains, and are responsible for different outcomes. It’s particularly challenging to bridge the gaps in understanding if they don’t know many people in common. The less employees know about each other’s motives and knowledge bases, research shows, the less inclined they are to share knowledge with each other.3 This can lead to more mistakes, slower project completion, and, in many cases, less innovative outcomes.

When digital change makes collaboration more difficult, companies become more siloed. But we’re not talking about yesterday’s “top-down” silos, where formal organizational boundaries divided employees into separate functions or business units. The silos in transforming companies often arise as people do their work, and they are based on their differences in knowledge, geography, and goals. You can bust the top-down silos with cross-functional teams or a matrixed reporting structure, but those organizational solutions don’t apply to silos that emerge from on-the-ground work.

To bust those, you’re better off using digital collaboration tools. Unlike email, chat, videoconferencing, and data repositories — channels through which people communicate — digital collaboration tools are platforms upon which employees use various channels to interact, watch others interact, and gain a deeper understanding of where knowledge lies. (Common platform examples include Basecamp, Microsoft Teams, Slack, Jive, Chatter, and Workplace.) Such tools are designed to help people work together and learn from one another by creating threads of conversation and places to exchange information. My research shows that those platforms’ primary benefit for collaboration goes beyond knowledge sharing: They provide a window into who knows and does what in the organization, and into how people make decisions and do their work.4

Over the past decade, I’ve studied and consulted at more than two dozen companies that have reaped this benefit — many of them unexpectedly — as they’ve turned to digital collaboration tools to streamline operations, integrate knowledge, and enable remote work. I’ve observed two basic types of collaboration needs inside these organizations: collaboration among coworkers who interact frequently on teams or in other ways (the regular collaborators in one’s inner loop) and among coworkers who are dispersed across the company (the sporadic collaborators in one’s outer loop). These sets of needs require different types of tools. We’ll discuss why, but first let’s step back and think about how digital collaboration can make expertise more visible in an organization, since that’s really the “killer feature” they provide.

Making Expertise Visible

A common obstacle to collaboration among specialists or geographically dispersed colleagues is that their work is often invisible to one another. We typically see the outputs of others’ work — models, prototypes, reports — but not all the thinking and decision-making that went into producing those outputs. At best, we end up guessing at what was done (if we give it that much thought). At worst, we fail to see the richness of insight and expertise that our colleagues have and that could be available to us if we only knew to reach out.

Digital collaboration tools not only provide a space for employees to work together but also make that work and its history visible to other people within the company. Third-party observers can pick up on bits and pieces to re-create the context in which the original interactions occurred, see which colleagues offered their expertise to solve problems, follow the logic of the decision-making, and better understand how and why things were done.

That depth of insight is much harder to gain if you see only the output of people’s work and try to reverse-engineer it by making assumptions about why it came together as it did. I’ve found that when employees can participate vicariously — through social tools, after the fact — in the construction of the model, prototype, or report, their ability to learn from their colleagues and make that knowledge actionable increases dramatically.

Connecting Two Types of Collaborators

Digital collaboration tools serve two main constituencies within a company. Regular collaborators interact frequently, often on projects, and rely on one another to complete their day-to-day work. Typically, employees know their regular collaborators relatively well. These colleagues can be team members, project sponsors, managers, or mentors. Sporadic collaborators seldom interact and do not know one another well. They are not teammates but may have knowledge or information that is directly applicable to one another’s projects. An employee’s sporadic collaborators might include coworkers who occupy similar positions in different business units or people who work on similar problems in different regions or functions.

The best tools for solving the collaboration problems created by digital change will depend on the type of collaborators involved.

Teamwork platforms for regular collaborators. Since people tend to work on many project teams (often several at once), they’re continually collaborating with a variety of coworkers on myriad tasks. It can be hard to keep everything straight. So working effectively with one’s regular collaborators requires a teamwork platform that allows employees to engage in persistent chat about tasks and goals, to look at or work on documents simultaneously, and to tap colleagues’ knowledge and resources when problems arise. Such platforms — which include task management tools as well as social enterprise tools — help people follow the streams of action on their teams and leverage networks across projects.

Following streams of action. To collaborate productively with people in their inner loop, employees need to stay up to speed on what these coworkers are doing day to day and why they have made certain decisions. Otherwise, it’s difficult to develop an accurate sense of what teammates bring to the table, to get people to say yes to requests, and to establish common ground.

Take this example: At a large research laboratory in the central U.S., a network engineer we’ll call Cindy was working simultaneously on three project teams, each of which included coworkers from across the laboratory.5 Most of her teammates were in functions different from her own, and none had an office in her building. One problem Cindy struggled with was losing focus on project A when some task from project B or C demanded her attention. “I just could never keep up with where the teams were,” she told me. “It would take me two days to get back into the swing of things after coming back from an emergency on another project, and my team would be mad at me. I was slowing them down, and I wasn’t being a good contributor.”

Once she began using her organization’s new teamwork platform, Cindy found that she could more easily catch up on the interactions and conversations that her coworkers had in her absence. “I am able to reconstruct the context of what went on,” she said. “I can see why they decided to go in one direction rather than another, and I can see what the arguments and objections were that led to that decision. That means I can ask the right questions. … It allows me to jump right back into the action and be a strong contributor.”

No one can be everywhere they are needed all the time. But by following the various streams of action on their projects — the tasks, conversations, and decisions that have occurred, in sequence — employees who aren’t present in real time for important activities can step back into the flow later and engage productively. The live archive of action streams is also a script of sorts; it helps people figure out when and how to engage most successfully in their ongoing collaborations. They can see what worked and what didn’t and tailor their own actions accordingly.

By following the streams of interaction in which her coworkers were enmeshed, Cindy better understood who had expertise in which areas. This knowledge enabled her to make targeted requests for ideas and advice when needed. Because she was asking coworkers for help with issues they felt they could easily solve, they were more likely to say yes to her requests (and to the requests of others who also closely followed streams of action on the company’s teamwork platform).

Executives at Cindy’s organization reported to me that using the teamwork platform led to more than a 20% increase in on-time project completions, which translated into more than $50 million in additional revenue.

Leveraging networks across projects. A second major advantage of teamwork platforms is that employees can build and leverage networks of regular collaborators across project teams. Several recent studies have shown that making such connections is critical for innovation and efficiency.6 Team performance hinges not only on how productively members work together but also on how much information and knowledge from outside the team they can bring in and apply to the project at hand.

Suresh, an engineer at a global automotive company, discovered this when he tapped his cross-team network to address some problems with simulations and testing. Suresh worked with four product teams to increase the crashworthiness of the company’s bestselling SUV. (Each team focused on a different brand variant.) When the organization began using a teamwork platform to help employees collaborate on projects, he was able to follow colleagues’ interactions across teams in real time.

That’s when he noticed that coworkers on two of the teams were having the same difficulties getting his simulation model to correlate with the results of physical tests. So Suresh connected those coworkers with Tim, an engineer who worked on a team that was not having the correlation problems. Tim was willing to talk to the other two engineers because Suresh was able to explain the problem in detail, having closely followed these engineers’ streams of action on the teamwork platform. Based on Suresh’s explanation, and after reviewing the teamwork platform himself, Tim realized that his work shared many commonalities with that of the other engineers and that his insights would be useful to them. In short, he found common ground with them. Using the collaboration platform, members from the three teams discussed the issues and together developed a solution to fix them. To Suresh’s surprise, a colleague from the fourth team then entered the conversation, saying he had been experiencing the same difficulties and was delighted to see that the other teams had worked out a solution. With the problem fixed on the three teams that were struggling with it, the company estimated that it saved more than $10 million in design and testing costs.

If networks are critical to the movement of knowledge and information in the digital age, teamwork platforms may be the ultimate facilitators for regular collaborators: By allowing individuals to connect people in their inner loop who would otherwise not come into contact, these platforms make it easier to coordinate knowledge and action, bringing collaboration’s many benefits within closer reach.

“Broadcast” platforms for sporadic collaborators. Employees in most large organizations do not have many meaningful relationships with colleagues beyond their regular collaborators, aside from a few ties with friends or mentors in other parts of the company. And relationships have become even more insular because of the silos that have formed as byproducts of digital change. A second category of digital collaboration tools — platforms that essentially “broadcast” information about employees’ expertise and internal resources — can address that problem by allowing people to create a roster of distant connections that can be activated as needed. Employees who use such tools also advertise their own expertise, showcase their projects, and promulgate corporate norms simply by doing their work within a forum that’s accessible to colleagues across the company. Broadcast platforms help people overcome collaboration problems in two ways: by enabling them to develop metaknowledge (knowledge about who knows what and whom in the company), and by creating “social lubricant” so they can more readily approach colleagues they don’t know well — or at all — for support.

Developing metaknowledge. Companies have tried many tactics over the past half-century to help employees develop metaknowledge about outer-loop colleagues. Some of those tactics have been technological, such as creating employee directories or adopting knowledge management systems. Others have been organizational, such as building communities of practice or establishing rotation programs that move people from division to division to broaden their knowledge of the company. But the critical problem with both kinds of approaches is that they place an undue burden on the people who have knowledge that others need. They must take time out of their work to codify their expertise in a way that might be useful to someone else and then disseminate that to those who may be interested. Since all of that involves a fair amount of guesswork, it’s not terribly surprising that the evidence suggests that these strategies for helping employees learn who knows what and who knows whom don’t work well.7

Broadcast platforms change the game by making tasks and projects visible beyond one’s regular collaborators. When regular collaborators communicate in the course of doing their work, they generate clues about what they are doing and how. That puts the onus on sporadic collaborators to interpret those clues to find the experts they need.

Consider this experiment at a large financial services firm. Two large groups of employees — the marketing and operations divisions — were given a survey to assess their metaknowledge about coworkers in their respective divisions. Both groups scored horribly. On average, employees in marketing could accurately identify only 4% of what their marketing coworkers knew and only 2% of who they knew. The numbers were nearly identical in the operations group. Marketing was given a broadcast tool to use for six months. During this time, employees communicated with their regular collaborators on the platform. They weren’t codifying their knowledge; their communications represented the normal discussions that they had in the course of their project work. Because those interactions took place on a broadcast platform rather than through email or private instant messages, they were available for employees across the division to see. Colleagues who weren’t involved directly in the work began to respond to these posts with their own questions, ideas, insights, and stories. Over this same period, the operations division didn’t use the platform.

At the conclusion of those six months, employees in the two groups took the survey on metaknowledge again. Those in marketing improved their ability to identify “who knows what” by 33% and “who knows whom” by a whopping 88%. There was no improvement in the operations group. What happened in marketing? As one employee observed, “I just started to see the kinds of things people were doing and I got a sense for the kinds of knowledge they had based on their work. I also saw who responded to them and what kinds of things those people said, so I now have a better sense of who the people those people talk with are.”

This increase in metaknowledge across the marketing division led to reductions in work duplication (for instance, by enabling employees to reuse code and to leverage existing analysis by consultants instead of re-creating it themselves). It also boosted innovation (for example, by facilitating the exchange of new ideas for products and the development of more efficient organizational processes). These are exactly the types of outcomes one would hope for when tapping into the knowledge of sporadic collaborators.

Creating social lubricant. Knowing what and whom other people know is not enough to make an outer loop useful. Employees must be able to acquire knowledge and resources from their sporadic collaborators by getting them to devote time and energy to help them. How can they do this if they don’t know them or don’t interact with them regularly? Here again, broadcast platforms come in handy because they provide material that facilitates new social relationships. This social lubricant comes in two forms: work-related and non-work-related information.

When employees read posts on broadcast platforms, they learn about the kinds of work others do in the company. Mario, a staff member in the financial planning and analysis department of a large software-as-a-service company, recounted how he used information about the work done by a sporadic collaborator to get some financial figures he needed. As Mario explained, for several months he had seen posts by a colleague in a business unit located in another country: “Every post she made referenced this acquisition project the unit was working on. When I was asked to provide some inputs into a possible acquisition, I thought she could be helpful, but I didn’t know her at all. So I sent her a message saying that I’d been following her posts about the acquisition in her unit and I was impressed by how much she seemed to know about it and wondered if she might be able to help me. She responded in a few seconds and we got on the phone. It was immensely valuable.”

What Mario picked up from those posts was information he could use to start a conversation with someone he didn’t know at all who worked in a very different part of the company. That initial conversation led Mario to follow this coworkers’ posts even more closely. Today, he says, “I definitely feel like she is someone I can turn to if I need help, even though we don’t interact regularly.”

Using broadcast platforms to share and learn non-work-related information can also turn distant colleagues into sporadic collaborators. Starting new relationships is hard. Think about going to a party: It’s much harder to strike up a meaningful conversation with someone if you know nothing about them; it’s much easier if the host tells you that the two of you are both soccer fans and love the same team. Broadcast platforms serve the function of the attentive host in the digital workplace.

Senior leaders at a large multinational telecommunications company understood that principle. They encouraged employees from various divisions to regularly post interesting facts and updates from their personal lives on the company’s broadcast platform — as they would normally do on Facebook or Instagram. The idea was that if employees could find coworkers with shared interests or similar backgrounds outside of work, they might feel more comfortable reaching out to those people about work-related matters. Although employees initially felt odd posting what they called “Facebook-like” content on a workplace tool, management encouraged and even modeled the behavior. Soon, the broadcast platform’s algorithm started linking people who had similar nonwork interests. The company found that when employees had discussions about food, sports teams, movies, and fitness, they became more likely to ask one another work-related questions too. As one employee who had connected with a coworker over their shared love of independent movies noted, “Talking about movies with her made me feel comfortable to ask her for advice on some tricky work matters.”

Many times, employees need a little conversational support to approach a distant colleague and turn that person into a sporadic collaborator. Both work- and non-work-related content on broadcast tools can provide that social lubricant to make such conversations happen. Importantly, employees are able to maintain lightweight relationships with their sporadic collaborators by following their posts and commenting on them at times when they don’t need their help. Doing so makes reaching out when they do need something feel less transactional and more organic. In large organizations in particular, this also helps employees feel connected to their organization and to see themselves as members of that community.

Although the two types of digital collaboration tools — teamwork platforms and broadcast platforms — make it easier to overcome obstacles to collaboration with immediate and sporadic collaborators, not all employees will see such value from day one. It takes time for records of people’s interactions to accumulate on both types of platforms, so employees who work in companies that are launching them will not reap their benefits immediately. Employees must keep using these platforms, though, so that the content will grow rapidly and become useful for the organization.

Of course, their employers have work to do as well. With prodigious amounts of data generated on both teamwork and broadcast platforms, organizations can begin to use algorithms that code employee communication and behavior patterns into particular categories of action, sort those categories, and perform complex computations that link categories together. It is only in the past several years that the computational power and development of mathematics undergirding AI and machine learning algorithms have progressed far enough to make meaningful analyses of such vast amounts of employee data. Companies could benefit more from such analyses by using AI to combine these patterns with new data sets (like employee turnover or performance data) to test assumptions about certain relationships, learn autonomously from these tests, and make predictions about future employee behavior.

Yet as organizations increasingly use algorithms to sort through the glut of digital data on worker interactions, it is essential to remember the risks associated with collecting, storing, and analyzing all that information. For one thing, employees can be unfairly advantaged or disadvantaged by the way AI turns such data into predictions. For another, the organizations adopting digital collaboration platforms aren’t the only ones that can profit from analyzing the data generated: Most of these tools are cloud-based applications. Contract rights give vendors access to some or all of the digital exhaust produced. The vendors can then use their own algorithmic modeling to create macro-level digital footprints of their business customers and the people who work for them — and sell those predictions to other companies. They can also use the data to improve their own technologies in ways that allow them to collect even more digital exhaust.8

This means it’s incumbent upon companies to determine what level of vendor access they will build into their contracts. One could decide that allowing vendors to access data improves the product and is in the best interest of all users. Or one could decide that organizations paying for the tools and services should be entitled to own their own data and metadata so that vendors are not profiting off them twice. With either choice, companies must be transparent with their employees about who has access to their data and how it will be used.

As with the introduction of any new initiative, digital collaboration tools need to be implemented carefully by senior leadership. But the effort is more than worthwhile. These platforms enable employees to find the right partners for their work, persuade those partners to help, and establish the common ground necessary to make their collaborations run smoothly. Getting people to use the tools so they will see the benefits in practice and deploying advanced algorithms and AI to discover any fruitful collaboration patterns that emerge are critical management tasks in this dawning age of digital business.

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References

1. P.H. Christensen and T. Pedersen, “The Dual Influences of Proximity on Knowledge Sharing,” Journal of Knowledge Management 22, no. 8 (December 2018): 1782-1802; and M.R. Tagliaventi and E. Mattarelli, “The Role of Networks of Practice, Value Sharing, and Operational Proximity in Knowledge Flows Between Professional Groups,” Human Relations 59, no. 3 (March 2006): 291-319.

2. P.M. Leonardi, “Social Media, Knowledge Sharing, and Innovation: Toward a Theory of Communication Visibility,” Information Systems Research 25, no. 4 (December 2014): 796-816.

3. L. Argote and Y. Ren, “Transactive Memory Systems: A Microfoundation of Dynamic Capabilities,” Journal of Management Studies 49, no. 8 (December 2012): 1375-1382.

4. P.M. Leonardi, “Ambient Awareness and Knowledge Acquisition: Using Social Media to Learn ‘Who Knows What’ and ‘Who Knows Whom,’” MIS Quarterly 39, no. 4 (December 2015): 747-762; P.M. Leonardi, “Social Media and the Development of Shared Cognition: The Roles of Network Expansion, Content Integration, and Triggered Recalling,” Organization Science 29, no. 4 (June 2018): 547-568; P.M. Leonardi and S.R. Meyer, “Social Media as Social Lubricant: How Ambient Awareness Eases Knowledge Transfer,” American Behavioral Scientist 59, no. 1 (January 2015): 10-34; and T.B. Neeley and P.M. Leonardi, “Enacting Knowledge Strategy Through Social Media: Passable Trust and the Paradox of Nonwork Interactions,” Strategic Management Journal 39, no. 3 (March 2018): 922-946.

5. Names in this article have been changed to ensure individuals’ and companies’ anonymity.

6. J. Cummings and C. Pletcher, “Why Project Networks Beat Project Teams,” MIT Sloan Management Review 52, no. 3 (spring 2011): 75-80; and N.B. Ellison, J.L. Gibbs, and M.S. Weber, “The Use of Enterprise Social Network Sites for Knowledge Sharing in Distributed Organizations: The Role of Organizational Affordances,” American Behavioral Scientist 59, no. 1 (January 2015): 103-123.

7. P.M. Leonardi, “The Social Media Revolution: Sharing and Learning in the Age of Leaky Knowledge,” Information and Organization 27, no. 1 (March 2017): 47-59.

8. Management professor Shoshana Zuboff has written eloquently about how vendors monetize digital exhaust and use it to construct digital footprints that predict and shape our behavior. See S. Zuboff, “The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power” (New York: PublicAffairs, 2019).

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