By Michelle Bruno

Brandon Klein believes that meetings and conferences aren’t nearly as productive as they could be. Organizers bring people together—widely considered to be a powerful way to deliver education and provide in-person networking opportunities—but after that, they drop the ball. Education is primarily a “sit and get” proposition, he says. And although some organizers attempt to improve participant networking using various technologies and meeting formats, most leave it to serendipity. Klein has a better way to design conference experiences.

Ten women in a room won’t get you a baby.

Many factors erode meeting effectiveness. “We still put together events that aren’t personalized to each individual, that are driven by the agenda of the organizer without really understanding what each participant wants. They are counterintuitive to the way that our brains work and to the way that we talk to and communicate with other fellow humans and how any work gets done after the events,” Klein says. In other words, just convening a group of people around a topic is a weak strategy for influencing participant experiences and outcomes.

Engineered collaboration is the missing ingredient in a meeting that endeavors to impact attendees, change behaviors, increase participant retention, and build peer-to-peer relationships. Because most individuals who attend conferences have specific questions, Klein explains, collaborating—not just networking—with people who are most likely to provide the answers is more productive. The firm he co-founded, Collaboration.Ai, uses network science, attendee data, and artificial intelligence to create teams of individuals who can help each other.

Network science and data

Klein’s firm works with conferences, such as the World Economic Forum, to create intelligent teams. Fast Company describes his software as a tool that “melds social network analysis and machine learning techniques to probe for hidden interests and connections between people, and then uses that information to generate new teams.” He encourages conference organizers to replace the “dimly-lit” PowerPoint sessions and unstructured networking opportunities with team-based collaboration for at least fifty percent of the program. Engineered teams are a hundred times more effective in helping team members, he says.

Data is crucial to developing effective teams. Collaboration.Ai uses information gleaned from multiple sources, including participants’ social networks (“the social trail that people leave online is frequently quite extensive,” Klein says) and customized questionnaires completed at registration. Some event organizers also provide data purchased from third parties. But more data doesn’t necessarily deliver better team composition. “What we usually find is [the best data] varies quite a bit by the type of conference and what the conference is trying to achieve,” he explains.

The role of diversity in teams

Even with the best data and highest algorithm rendered compatibility scores, small groups can still fail to collaborate successfully.  Klein uses a study conducted by Brian Uzzi and Jarrett Spiro to illustrate the role of diversity in effective teams. The researchers studied why some Broadway plays become hits, and some don’t. They found Klein says, “Production teams that stay together from play to play almost never produce a hit. All new teams that come together almost never produce a hit. Success comes when an existing team replaces part of the old team with new talent, skill or demographics.”

Collaboration.Ai runs models that peg optimal team diversity at twenty to thirty percent—a sweet spot for collaboration, Klein says. For example, “If, at a banking conference, you put ten bankers together in a group, the collaboration is usually pretty unsuccessful. If you bring together fifty percent bankers and fifty percent artists, the collaboration fails at about the same rate as one hundred percent bankers. But with seventy percent bankers and a thirty percent mix of all types of diverse individuals, the success rates start to shoot up dramatically,” he explains.

Redesigning conferences around collaboration

There are multiple ways to implement Collaboration.Ai. Conference organizers can use it as a standalone platform or technology partners can leverage its capabilities via an application-programming interface (API). Pre-determined groups can be seated together during learning and networking experiences, or a visual map of how everyone in the room links to one another is used to stimulate conversations and collaboration. “I call it the ‘fall off your chair effect’ when people see how the entire conference room is connected through people and interests,” Klein says.

Corporate conferences can take advantage of Collaboration.Ai software too. The platform can match individuals seeking solutions with sales reps or product engineers. Collaboration can take place in more natural settings, such as part of a work stream or learning track that the customer has chosen. “It’s an opportunity for more of a human dialogue instead of a sales dialogue,” Klein suggests. The tool can also match solution seekers with customer advocates who have already solved their problems using the conference host’s products—an indirect, but highly efficient way for organizations to meet conference objectives.

Engineering experiences

Conferences have the potential to be more effective. It requires aligning the right data, technology, and mix of individuals with specific participant and organizer objectives and embedding the practice of engineered collaboration into the fabric of the conference. Collaboration.AI brings conference-goers who are typically three to four connections away from each other to within one connection. In doing so, it rewrites the rules for creating more impactful and personalized group experiences. It’s the Six Degrees of Kevin Bacon game on steroids.

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