COVID-19 Highlights How AI Is Reimagining Collaboration

COVID-19 has forced many businesses to kick into high gear their digital transformation plans. What was originally planned as a years-long strategy was crammed into a period of just a few months this spring and summer. Providing innovative digital experiences became a lifeline for companies that had to shut their doors for in-person business, and replace them with new digital offerings like curbside pickup and widespread remote work. But what role has AI played in the ‘new normal’ – and what role will it play in the post-pandemic aftermath?

That was the subject of a recent Fortune webinar I listened in on, “How the Pandemic is Revealing the Power of A.I.” The discussion was led by Tatsiana Maskalevich, Director of Data Science at StitchFix; Dr. Joelle Pineau, the Co-managing Director of Facebook AI Research; and Lan Guan, Global Managing Director at Accenture. And while they touched on a couple different fronts along this topic, one I found the most interesting was how COVID has highlighted the impact of AI on collaboration: collaboration between humans, between humans and machines, across an entire organization, and across the organizations and industries themselves.

A multi-layered model of collaborating, driven by AI

It was Lan who explained this multi-layered collaborative dynamic – collaboration between humans and machines, within an organization, and then across organizations and industries – as like three layers of concentric circles, one on top of the other. Imagine it like this: The innermost layer is the collaboration between people and machines. This is how we normally understand AI applications: whether it’s IT teams leveraging AI assistants to streamline their work and optimize systems, or AI leading customers to products they may be interested in and ensuring a smooth sales process.

Expanding out from there, you have the company level. A lot of large organizations are under greater pressure and urgency to not just reshape their business models to withstand COVID and the aftermath, but also find ways to derive insights into how these models are working now to inform their post-COVID strategies for later. Applying AI to evaluate how different teams are functioning and thriving (or not) within a company and collaborating with each other (or not) to drive tangible business outcomes, is key to that.

Finally, we have the outer, macro level, which takes a cross-organization/cross-industry view. Here it’s not just about how AI empowers collaboration among individuals, but how AI can activate entire constituencies as collaborators – from government, to healthcare, to manufacturing – to fuel work between larger organizational and vertical players.

The bird’s eye view of all of this is that AI is already driving a multi-layered model of collaboration, acting as a kind of living, breathing engine that augments human beings from as small-scale as the individual level to as large-scale as entire verticals.

What this looks like in practice

That sounds pretty abstract (and no doubt it is) but I also heard a lot of cool examples of how this AI-fueled model of collaboration is already being applied to real-world scenarios:

• Curbing the spread of misinformation on social media by flagging hundreds of millions of pieces of false information, far beyond what humans could manually manage.
• Enabling COVID response measures, like contact tracing, vaccine research, and monitoring social distancing patterns in public spaces.
• Deriving insightful understandings of customer behavior to help forge more personalized connections between businesses and their clients.
• Modeling COVID caseloads and infection rates, based on both epidemiological models and models of (mis)information dispersal in communities.
• Providing predictive formulas that help determine which COVID patients may need to be moved onto ventilators and/or into ICUs.
• Fostering collective scientific efforts like open-source code and academic research partnerships.

And this is all just scratching the surface of what’s possible. Because one thing that was abundantly clear is that we still don’t yet know a lot of what AI can do – not because the tech is limited or the use cases are limited, but because our own thinking around it needs to expand.

This is actually the perfect proof point for AI as a collaborative tool: human creativity and innovation enrich AI to adapt to new tasks and behaviors, which in turn augments that human creativity to drive even more applications for AI. And on and on. That collaboration ripples out from individuals to teams to companies to whole industries and governments.

There are a lot of lessons that decision makers can take away from this pandemic, but one of the key takeaways shaping the post-COVID landscape is that we need to start thinking bigger about everything – and AI, if leveraged properly, can help do that. AI provides the insights, and facilitates the collaborations, of today, that in turn will define the ways of working, learning, and living for tomorrow.