Advisors agree with that approach. Rick Opal, global digital leader at professional services firm BDO, advises CFOs to “take technology off the table” when embarking on an AI journey and instead focus on business friction points first. Where are the bottlenecks, growth constraints, or operational inefficiencies? From there, the goal is to identify the “one or two” problems that AI can address.
Opal points out that these projects should be quick, iterative successes rather than multi-year transformations. “We want to attack as quickly as we can. We jointly expect it won't be perfect from day one…and we work together to make sure we get over the line and get a good result.” Earning initial wins early on puts the organization on a trajectory to build upon, and repeat as capability and confidence grow.
Build a clean, connected data foundation
AI systems rely on accessing large amounts of accurate data in easily digestible formats, but many financial organizations still operate across fragmented systems with inconsistent data definitions. “The output is only as good as the input,” says Chris Gerosa, CFO of R&T Deposit Solutions. “If your data isn’t clean and complete, you can slice it up as much as you want in automated ways, but it won’t give you accurate results. So making sure your data is clean is a very important milestone to overcome before you start deploying AI.”
For many organizations, this means investing time upfront in data quality, standardization, integration, and governance. These tasks can slow down AI adoption, but ultimately determine its success. For example, before launching an AI initiative, CFOs should ensure that standardized definitions of metrics are in place across business units and that integrated systems enable automated data flow, reducing the need for manual data extraction and manipulation.
At sales enablement software company Seismic, the journey was a multi-year effort, says CFO Evan Goldstein. “We have spent the last four to five years overhauling our data structures to make them consistent, adjusting where there were deviations, and cleaning up and automating processes,” he explains. This effort has given his team confidence in their data and enabled them to leverage AI more proactively over time. “It's not a good feeling to know that if something is wrong, your team is downloading the data and manually fixing it in a spreadsheet. But we don't do that anymore.”
For companies that invest early, standardization and data quality create compounding benefits, improving reporting and decision-making today and enabling faster and more confident adoption of AI and advanced analytics in the future. “In many ways, we're back to the early days of ERP implementation, when everyone was talking about 'garbage in, garbage out,'” says Scott Lotman, president of CFO advisory at professional services firm RGP.
“Organizations need to get their internals in order before they start talking about AI. If you jump into AI and try to figure out use cases for AI when you don't have good data governance and data quality around organizational information, the AI mission will fail. You need to get that sorted out first before you start.”
Putting the right people on the right path
CFOs also emphasize the importance of having the right talent in place and working together to take AI initiatives from concept to execution. This often requires upskilling existing finance staff, hiring new talent with data and analytics expertise, and intentionally investing in dedicated roles focused on automation and AI.
External advisors can play a valuable role, but over-reliance on external vendors without considering internal accountability can lead to misalignment and missed opportunities, he adds. Gerosa appointed a member of his senior team as an internal leader responsible for overseeing R&T's automation initiatives with NetSuite AI. “This was the right important decision for us to make, because you need someone in-house to build it right as a full-time job. Your people know the business well and will work with you to build it right.”
AI initiatives can also stall if employees are hesitant to adopt tools because of how AI will change their roles and expectations. “People are worried in the financial world. Will this cost me my job? ” Denise Graziano, CEO of Graziano Associates, says that without clear communication, that concern can lead to reluctance to engage with a new system and jeopardize success.
Graziano advises addressing employee concerns head-on through transparent and consistent communication about why AI is being deployed, the problems it solves and how roles may evolve. “Leadership needs to explain how people's jobs change, and it does change, and how they upskill or reskill to keep them relevant,” she says. “The people who get it right are the ones who are honest about it. It might mean saying, 'We don't know exactly what our role will be in one year or five years, but this is our plan and here's why.'” When employees participate in decisions, they become more committed to its use, and its success becomes a team effort. ”
Developing a governance framework
Companies that move forward with AI efforts without having policies and procedures in place for its use risk running into problems down the road. Without guardrails, there are a wide range of risks, including employees disclosing intellectual property while experimenting with AI and running afoul of regulatory agencies. And once a lack of controls creates a vulnerability, it can be difficult to backfill financial controls, Gerosa says. “It’s important to have a consistent and strong policy, procedure and risk framework in place across disciplines, because if you try to add it later or run it in parallel as you build the system, it becomes very difficult.”
To address governance challenges, Seismic established a cross-functional AI Council comprised of product, legal, compliance, and information security stakeholders, with a designated leader to guide the AI implementation. In its early years, the group focused on surfacing and vetting product-oriented AI initiatives aimed at meeting customer needs, but its role expanded over time, Goldstein said. “We've now said, 'We need a governance framework within the company for how people use AI.' They'll be tasked with coming up with that and presenting it to company leadership so we can discuss it and move it forward.”
Such models can help create shared ownership, break down silos, and ensure that decisions about AI are based on multiple perspectives, not just financial or IT. It also helps companies develop clear frameworks for acceptable use, data access, model validation and escalation protocols, as well as practices for evaluating potential use cases and vetting vendor partnerships.
Identify workflows where AI can provide value
Once the fundamentals are in place, finance leaders can start exploring areas where AI has the potential to reduce friction, increase efficiency, and improve decision-making. Many people first turn to financial planning and analysis, where AI can improve the speed and quality of decision-making.
“When you start incorporating driver-based analytics, anomaly detection, predictive analytics, all of these capabilities into your processes, you not only increase efficiency, but you also put yourself in a position as a finance leader to close the books faster and get to the forecasting part sooner to drive more accurate forecasts,” said Mike Shuker, global head of solutions consulting at Wolters Kluwer. “It drives revenue, and it allows you to make bets earlier on where you put your capital and what important initiatives you drive, like supply chain and operations. It also allows you to be more nimble if you get bad news early. You have more options.”
Several CFOs report initiatives that are helping their teams streamline time-sensitive processes. Seismic's finance team leverages AI to streamline transaction approval and pricing workflows. The company has developed an AI agent that retrieves relevant data from past transactions (deal structure, approval records, pricing) to evaluate proposed deals and flag deals that are unlikely to pass the approval process.
Seismic's Goldstein says that while final approval still rests with the head of finance, the results will allow sales teams to adjust expectations early in the process and avoid bottlenecks during a crisis. “At the end of the month, the sales reps are pushing very hard, so it would be helpful if we could tell them earlier, “This doesn't look like it's going to work.'' That way we aren't paralyzed by those in charge pushing for things that can't happen. ”
AI can also fill the gap for companies that lack dedicated FP&A resources. “Right now, the VP of Finance and I are tag-teaming on that feature,” says Owens, who says Maxio has developed AI-driven analytical tools that allow users to query in plain language, effectively turning reporting dashboards into conversational interfaces. “We were so impressed with its quality that we wondered if we needed to hire dedicated resources. Once trained, the system can produce results comparable to those obtained from mid-level FP&A professionals.”
Maxio also uses AI to uncover the most likely causes of customer dissatisfaction and churn, with the aim of improving retention rates. “From Zendesk” by analyzing data across the customer lifecycle [support] Ticket to Salesforce [CRM] Interaction with Gong [conversational intelligence] “Most companies collect all of this, but they don't have a way to aggregate it and proactively use it to fix bad experiences,” Owens says. You can do that with AI, and you can do it in a less biased way than, say, asking a salesperson who might say it's a product issue, or a product person who might say it's an implementation issue. Because no one wants to blame their own functions. ”
Start experimenting and expand further
For many financial leaders, AI roadmaps are on the agenda. Start with a defined problem. Invest in the underlying infrastructure (data, processes, people) to support it. Focus on targeted use cases where AI can accelerate decision-making or reduce friction. Then build from there and repeat as your ability and confidence grows.
Success with AI, CFOs say, will come less from radical transformation efforts and more from disciplined execution based on clear business priorities, a strong data foundation, and thoughtful governance. Finally, AI implementation is not a one-time initiative, but an evolving, ongoing function, and the CFO's role must change accordingly, moving from overseeing financial reporting to coordinating how data, technology, and talent work together to improve performance.
Ultimately, in finance as in other fields, the winners of AI will not necessarily be those who act first, but those who act with purpose.
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