Speed is the natural byproduct of humans and machines working in tandem. Well-trained A.I. can sort, pattern-match, and generate data at the pace of networked systems transmitting information at the speed of light. As a result, analysis that used to take weeks can now be done in less than 24 hours, as one multinational consulting firm proved when a banking crisis took root in the U.S. in early March 2023.
The work is an example of what Accenture researchers Paul Daugherty and Jim Wilson termed “collaborative intelligence” in a lengthy article published in 2018 in Harvard Business Review. Their findings were later collected in a book titled “Human + Machine: Reimagining Work in the Age of AI.”
In their work, Daugherty and Wilson defined roles humans and machines would occupy in mutual achievement. Among them: amplifying, the idea that A.I. can lead us to better decisions, faster, by providing the right data to do so at precisely the right time. This short blog post is part of an ongoing series in which we highlight observed cases of machines amplifying humans, a process we believe is best captured by the term collaborative A.I. success.
Read on to learn how a multinational consulting firm helped its banking clients get data for making decisions that would help determine the difference between solvency and catastrophic default.
24 hours on the edge
As the U.S. banking system felt the stress of a high-profile failure in early March, executive teams at both national and regional banks across the country began asking their teams to find the experts who could assess their own risk. Were they as exposed to failure as those already making headlines? Several turned to the consulting firm for answers.
The job? Sort through hundreds of thousands of workers in tens of thousands of roles to analyze the organizational structures of each client. In doing so, banking executives hoped to find and isolate the experts and key actors who knew what capital might be at risk if the crisis spread and contagion set in.
Many were acting as if the system had already become unbalanced, preparing for the worst on the presumption that even the most skilled analysts would be unable to deliver insights in time. All that worry proved premature as the firm applied GraphGrid, using A.I. trained with knowledge graphs to describe the roles and relationships in each banking client. Human analysts did the rest and answers once thought to be weeks away were instead delivered inside a day.
Building a culture of speed, insight, and improving margins
For the consulting firm and its banking clients that intense period is a high-wire illustration of the amplifying capabilities of A.I. as Daugherty and Wilson predicted back in 2018. Partners have taken notice. They’ve begun thinking expansively about how to take advantage of A.I. + graph with GraphGrid for adding value to clients in new use cases.
Speed is a virtue in the consulting business. The more work that can be automated and accelerated, the more billable hours the firm has to apply to big problems for big clients. It’s a virtuous cycle: more solutions brings more problems to solve and longer contracts. Financially, that’s a recipe for steady growth and higher margins.
A.I. + graph has also opened up at least one new business line. Specifically, the firm has established a revenue-generating partnership with a major provider of human capital management (HCM) software. Customers of the HCM suite can order on-demand reports on the hidden strengths and weaknesses in their org structures – all powered by the same A.i.-infused process used to produce fast answers for the firm’s banking clients.
Call it the A.I. evolution instead of the A.I. revolution. As business evolves, it’ll be humans and machines working together to lead the way.
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