As they integrate AI into their processes, CFOs need to help employees see it as a collaborator, not a threat.
A growing number of CFOs are exploring AI technologies in an effort to yield greater business insights, enhance financial accuracy and predictability, and reduce labor-intensive processes in the finance function.
In fact, “AI’s ‘early adopter’ phase is ending,” according to the recently published third edition of Deloitte’s State of AI in the Enterprise report. The survey, which collected responses from nearly 2,750 IT and line-of-business executives, found that almost identical proportions of respondents identified themselves as either starters (27%) or seasoned (26%). But about half, 47%, categorized their AI efforts as skilled, meaning their companies had launched multiple AI systems but lagged in terms of their number of implementations or their AI expertise—or both. More than half (53%) of adopters have invested more than $20 million during the past year on AI and talent.
Given where machine learning (ML), an AI-fueled technology, is likely to have its biggest impact, CFOs should also prepare to encounter organizational resistance. For members of finance leadership, part of AI’s promise may be the role it can play in transforming the function from serving in a supporting role—consolidating and reporting financial results—to becoming a strategic partner for the business.
Others may see the emergence of machines that can improve the accuracy and efficiency of the accrual process and speed up account reconciliation—ideally, eliminating any traces of human bias—as threatening. By deciding to automate traditionally manual, labor-intensive finance processes, CFOs may inadvertently send a message to some employees that they could be expendable.
Of course, the notion of intelligent machines replacing—and overpowering—humans is largely the province of blockbuster movies. Still, CFOs should evaluate their current operations and identify opportunities to update the operating model, ensuring that the right people and capabilities are in place to maintain an AI-powered finance function. Employees working on transactional tasks may need to retool themselves, developing stronger analytical skills. The finance function’s overall skillset may shift—but not necessarily its population of full-time equivalents. Humans with the right training need to manage AI systems, leveraging the appropriate capabilities.
Artificial Intelligentsia: Humans Working With Machines
In the past, many CFOs have led campaigns to incorporate analytics, particularly in financial planning and analysis. But the stakes and rewards involved in championing ML are much higher, given its potential use across the enterprise. And while uncertainty surrounding the pandemic continues, the fact that ML is not rules-based—there’s no limit to its ability to evolve—means it can change and adapt as the next normal takes shape.
Similarly, finance leaders can lay the groundwork for the technology by taking several steps:
Inventory internal capabilities. AI technology is function-agnostic, so before CFOs plant their flag, they ought to check to make sure other functions haven’t preceded them. Marketing may have begun using it to understand how to better retain customers in a virtual environment. Supply chain departments may already be relying on it to help anticipate production or inventory disruptions. If significant internal capability exists, finance can get a jump-start by piggybacking on those efforts.
Choose a data strategy. ML benefits from receiving a complex set of inputs and analyzing how that data fits together. Data often ends up residing in functional silos, where it isn’t rich enough to inform the model and help with decisions. Finance needs to come up with a strategy for breaking down those silos—or design a limited pilot project that maximizes the data within a silo, such as classifying sales data for use by finance and commercial teams.
Establish a cross-functional task force. Since just about all functions can leverage ML, it’s best treated as a collaborative, iterative process. It’s vital that whatever initial issues ML is deployed to solve can produce results that will have a strong impact on the business. Furthermore, any such tests should be on the radar of all members of the senior leadership team. Experimentation may result in success or failure—or something in between, depending on the different perspectives that are brought to bear.
Build an AI Center of Excellence. Depending on the company’s AI maturity level and the breadth of its capabilities across the organization, it may choose to establish a Finance AI Center of Excellence. Such a resource can help spread the technology throughout the business, evangelizing for AI, deploying resources for pilot projects, leading training, and advising on hiring. Such a center of excellence could be launched as a subset of an existing continuous improvement function within finance or within an innovation group that sits in the chief technology officer’s office.
Confront fear of AI. Finance executives may instinctively resist the appeal of ML, as some have previously challenged other mammoth IT projects. They may feel they aren’t knowledgeable enough to engage their peers in a dialogue about AI. But through cross-functional partnering, CFOs will gain fluency on the topic, which will translate into confidence and a regular seat at the table when it comes to tech strategy.
No matter how they initially react, most CFOs can learn to appreciate what ML can do. Even successful implementations may include some failures, likely as a result of misalignment and data quality issues. That’s why it’s so important for CFOs to approach AI projects with humility regarding the company’s talent and technology. Presumably, that’s still an emotion that distinguishes humans from even the most intelligent machines.