Armed with lessons from the pandemic, business leaders gained perspective on the use of AI, and how it can help overcome operational disruption. Questions that emerge in the aftermath include where to deploy it within the broader tech stack and how to scale throughout the company.

Among enterprise leaders there's more exploration and further prioritization of how AI can help sustain an organization through disruption, said Dave Parsin, VP, North America at conversational AI technology company Artificial Solutions. 

"However robust they thought their business continuity plans were six months ago, I think many have realized they were inadequate," said Parsin, speaking at the Virtual AI Summit Silicon Valley Wednesday.

AI rose to the level of a strategic initiative for businesses that adopted it in order to ease operational pressure points in the pandemic. Now, in building a tech stack that's able to withstand future disruption, AI is becoming part of business continuity planning that can scale throughout an organization. 

Businesses are relying on AI to redefine what their organization does, and how that product is delivered. Tech chiefs lead automation processes that are closest to where a customer interacts with an organization.

For BMO Financial Group, AI served as a tool to provide customers with insight into their financial health at a time when thousands grappled with loss of income.

In the spring, the company launched a tool that uses deep learning to predict customers' potential cash flow shortfalls up to seven days in advance, said Yevgeniy Vahlis, head of artificial intelligence capabilities at BMO Financial Group, speaking on the panel. Next year, the financial institution plans to continue expanding deep learning throughout the organization. 

"We're going to scale horizontally," said Vahlis, speaking on the panel. "We'll develop additional tools and services to allow any data science and analytics team and technology team in the bank to leverage these more advanced methods" in order to improve efficiency, create additional products or generate new revenue streams.

At BMO, the cycle of ideation to funding and deployment to production of digital initiatives shortened, Vahlis said. 

"That timeframe over the past few months got compressed, in a very significant way," said Vahlis. The team rolled out several initiatives into production in a few months, a rhythm of production that's "unheard of for a financial institution."

From building to adopting

Most financial sector leaders view AI deployments as a key factor of success, but they're also mindful of return on investment. Technology deployments that can have companywide impact become more valuable given the potential for multiplying their value.

In the coming year, BMO will focus on natural language processing and deep learning technology providing forecasting solutions for the organization. 

Businesses struggling to adopt AI often point to challenges in access to knowledge and difficulties accessing data due to silos or overly-complex systems.

But having a team within the company that is familiar with the science and project management requirements of an AI endeavor helps bridge the gap for parts of the organization without experience in AI deployment, said Vahlis. "The team is able to contribute meaningfully at every stage of the project."

In the enterprise realm, leaders "are reconsidering" how to take advantage of virtual and digital workforces that can supplement human workers, said Parsin. These changes in perspective respond to preparations made for "if and when there are other spikes in the future.