What IT Leaders Need to Know About Collaborative Intelligence

Collaborative intelligence, a concept being adopted by a growing number of IT organizations, is the group intelligence that emerges from the collaboration, collective efforts, and competition of multiple parties.

Collaborative intelligence is the process of engaging with others, particularly parties offering different perspectives, to reach a specific goal informed by the power of multiple intelligence sources, explains Ola Chowning, a partner with global technology research and advisory firm ISG. “Essentially, more brains and experiences are better than one,” she notes. “Often, the participants have different skills, capabilities, backgrounds, priorities, and roles, and may even have different tasks within the solution process.”

Casting a Broad Net

In IT, innovation often results from casting the broadest possible net in an effort to capture ideas. “Solutions and goals can also benefit from a similar broad net casting in terms of the intelligence needed,” Chowning says. Collaborative intelligence aims to generate a large swath of insightful knowledge supplied by multiple participants. “It also strengthens teamwork while building up the emotional intelligence — EQ — of team members as they learn to become more self-aware and self-controlled [with] more empathy,” she adds. “The result is better solutions.”

Collaborative intelligence teams don't have to be exclusively human. “Augmenting the strengths and intellect of humans with the accuracy and speed of AI machines can vastly improve performance, productivity, employee-customer experience, and free humans for higher value tasks,” explains Gary Arora, co-dean of the Deloitte Cloud Institute and managing director of Deloitte Consulting.

AI-connected collaborative intelligence can be used in many different ways, including streamlining internal IT operations. “Hours of complex diagnosis and troubleshooting can be accelerated through an intelligent guided process, making sense of the logs, patterns, and unstructured data, and dynamically learning from previous outages, predicting the next best action,” Arora says.

Getting Started

Getting started with collaborative intelligence is relatively easy, says Kimberley Tyler-Smith, a former McKinsey & Company analyst, currently strategist at career tech service company Resume Worded. On the human side, she recommends creating an environment that's conducive to collaboration, providing ample opportunities for team members to interact with each other and share ideas. “Collaboration is more likely to occur when everyone is enjoying themselves,” Tyler-Smith notes.

Both top-line and team leaders are necessary to establish a collaborative intelligence team's basic intent, processes, and goals. Leader education is an utmost priority, Chowning says. Meanwhile, commonly available collaboration processes and tools can help teams understand how to both effectively collaborate and measure collaboration performance. She also suggests giving teams the freedom to build and use their own collaboration measurements in order to conduct their own self-improvement activities.

Arora advises potential adopters to look for business processes where AI-supported collaborative intelligence can add value, such as by allowing better decision making, increased product and service personalization, or greater operational reliability at scale. “Such human-led, machine-assisted, operations include guiding humans through a complex multi-step process, or proactively identifying the next best action,” he explains. “Then find the tools needed for execution and initiate a pilot.”

Drawbacks to Collaborative Intelligence

While collaborative intelligence offers many advantages, there's also a handful of drawbacks that potential adopters should keep in mind. It may be difficult, for instance, to achieve consensus among team members, Tyler-Smith warns. “There's also the risk that one individual may attempt to take all the credit for a group effort,” she adds.

Although collaborative intelligence is sometimes perceived as a relatively slow-moving process, since it takes time to unite all of the participants and get them to agree on a consensus, most studies have found that the final result is well worth any throughput hinderance, Chowning says. Whenever speed is a priority, such as when there's an immediate need to solve a specific problem, enterprises can step away from collaboration to create a short-term work-around while allowing the collaborative process to continue focusing on the long-term solution.

Parting Thoughts

Ultimately, collaborative intelligence is only as good as the people — and AI technology –involved. “It can backfire if the understanding of what should be automated and what should remain a human task isn’t clear,” Arora warns. “It requires re-testing, reassessment, continuous improvement, time, and commitment.”

A critical success factor for collaborative intelligence would be clarity in roles and accountability for humans and AI, says Sandhya Balakrishnan, US region head, analytics, at global technology consulting and technology services company Brillio. “This is still an evolving space and upfront and thoughtful definition of the process, human involvement, and tech integration of human inputs for AI would help accelerate the journey.”

Overall, collaborative intelligence is a powerful tool that can bring about multiple benefits for IT teams. “When used correctly, it has the potential to promote creativity, innovation, and teamwork while also helping organizations save time and resources,” Tyler-Smith says.

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