A Blog by Jonathan Low

 

Oct 6, 2021

How Corporate Tech Leaders Make AI Implementation Successful

The key to assure corporate support - and continued funding - is to demonstrate how AI and machine learning investments successfully impact financial outcomes. 

To do so, devoting time and resources to preparation is essential, especially with regard to securing and cleaning the relevant data. As with most technological innovations, there is no such thing as plug and play. Forethought and effort is required for optimizing results. JL

Jonathan Vanian reports in Fortune:

Technologists and corporate finance are often at odds because it’s difficult for the tech side to convey the value of A.I. to accounting. It can be a challenge to directly tie benefits to a financial outcome like improved sales or lower operating expenses. (For instance) a 2% improvement in fuel use by airliners, achieved through a machine-learning model, equates to a “massive cost savings” because 25% of an airline’s operating costs are fuel-related. Another hurdle for companies is cleaning and prepping the necessary data at the beginning of an A.I. project. Doing so lets companies more easily develop A.I. initiatives for less money.Companies are eager to kickstart artificial intelligence projects, but they must ensure that these cutting-edge initiatives actually help boost sales or cut costs.

That’s one of the takeaways from a Fortune Brainstorm A.I. online event earlier this week about using data to lift business operations. Experts discussed the need for company data scientists or managers to educate executives about their A.I. projects and to explain how those projects can benefit the company financially.

As Fortune previously reported, technologists and corporate finance teams are often at odds because it’s difficult for the tech side to convey the value of A.I. to accounting teams. Machine learning, for example, can be a powerful tool for improving a corporate app's performance while also burnishing a company’s tech credentials. But it can be a challenge to directly tie those benefits to a specific financial outcome like improved sales or lower operating expenses.

Accenture global managing director and data-led transformation lead Joe Depa said a key to a successful A.I. project is for its team members to engage with the C-suite, like chief financial officers, at the onset. That way, executives can ensure that the project stays on track.

“You got to make sure that there are use cases that are driving revenue or cost savings along the way so they continue to have the C-level sponsorship that you need to make these programs successful,” Depa said.

Sandra Nudelman, Wells Fargo’s head of consumer data and engagement platforms, explained that her company looks to use A.I. in specific ways that can lower costs, such as reducing credit card fraud. A major hurdle for companies, however, is spending enough time cleaning and prepping the necessary data at the beginning of an A.I. project. Doing so builds a “foundational layer”, she said, that lets companies more easily develop new A.I. initiatives for less money.

“If you don't have that, getting that initial investment to set it up your data situation up front is hard,” Nudelman said. “If you already have your data set up relatively well, it's relatively easy to pilot and not a whole lot of money out of pocket to do it.” 

Bonnie Titone, the chief information officer of Duke Energy, said that her company has a set method for evaluating A.I. projects so they don’t waste money. It allows for eight-week experiments to determine whether an imitative has potential, and if not, the company shelves the project and goes on to the next one.

“I think it’s a safe-to-try model that also helps with innovation so that you're not squashing ideas, because you don't have a full business case and you don't want to go ask for millions of dollars in funding,” Titone said. 

Ultimately, a successful A.I. project can lead to huge corporate financial gains, even if an initiative only modestly improves operations.

The Boeing Company chief information officer Susan Doniz explained that even a minor rise in a machine-learning model's accuracy could have a major financial impact. A 2% improvement in fuel use by airliners, achieved through a machine-learning model, equates to a “massive cost savings to the bottom line” because 25% of an airline’s operating costs are fuel-related.

“And also it helps with [our] sustainability agenda as well,” Doniz added. “More fuel optimization also means less fuel emissions as well.”

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