Finance teams are no strangers to transformation. From paper ledgers to Excel to cloud-based Enterprise Resource Planning (ERP) systems, each technological leap has redefined what it means to add value. Now, artificial intelligence (AI) is reshaping the function again. For Dave Sackett, Vice President of Finance at Persimmon Technologies Corporation, the most pressing question is how leaders prepare their teams to work alongside it.
“Technology is only valuable if it’s used correctly and if it’s used with good data,” Sackett says. “If you automate bad processes, you just get bad data faster.” In his view, intelligent automation succeeds when finance leaders combine disciplined process design, strong data governance, and deliberate talent development.
Redefining Automation As An Upgrade, Not A Threat
Finance teams, as with many other functions, tend to split into two camps when automation is introduced. Some see efficiency and opportunity, while others see risk. “You’ve got people who see it as a tool to help them be more efficient and give them skills to advance into the future,” Sackett says. “And other people feel threatened by it, that they’re no longer going to be needed.”
Sackett addresses both perspectives directly and is explicit about the importance of preserving human oversight. “We are keeping the human,” he says. In his view, AI should augment judgment; oversight, validation, and interpretation remain firmly in human hands.
That reassurance, however, can’t be rhetorical. Sackett sits down one-on-one with team members to map their career trajectories and align their growth with the department’s evolution. From there, he works backward, identifying how automation can free them from repetitive tasks and position them for more strategic responsibilities. Often, that conversation surfaces precisely where automation can create a measurable impact and where human judgment should remain front and center.
Start With Process Discipline And Clean Data
One of the most common mistakes finance leaders make is attempting to automate workflows that were never designed for automation. Organizations operating in reactive mode, relying on legacy habits or informal controls, often assume technology will fix structural inefficiencies.
“If you get at it too early, don’t think it through, have bad data, don’t trust things, you’re just going to carry that into automation,” Sackett says. Before introducing AI, he looks for bottlenecks that are measurable and rules-based. Where is the volume high? Where are the controls repetitive? Where can consistency be encoded? Automation performs best when applied to structured processes with reliable inputs.
Equally important is a willingness to redesign workflows. “They try to automate what they know and what’s been working in the past,” he notes of some finance leaders. “Maybe with automation, you have to change internal controls. You have to reroute processes.”
Target High-Volume Wins To Prove ROI
For Sackett, accounts payable delivered the clearest early return. In a manufacturing environment with three-way matching requirements, manual invoice verification is labor-intensive and prone to delay. Automation transformed that equation. “I know exactly what it costs to cut a check,” he says. “I know what it cost before, I know what it costs now. And it’s less by 50 percent or more.” The system now automatically verifies purchase orders, receipts, pricing, and quantities. What once required two employees now requires one, with the cost savings effectively funding the automation itself.
Sackett is also piloting AI in supply chain approvals and demand planning. The system flags anomalies, identifies missing purchase orders, and highlights pricing inconsistencies that would be too tedious for manual review. “Having all these rules in place that would be too tedious for people, now it can be taken over by AI,” he says. These early wins matter, as tangible Return on Investment (ROI) reduces skepticism and builds momentum for broader adoption.
Elevating Finance From Data Assembly To Action
As automation takes over data aggregation, the role of finance professionals shifts toward interpretation and execution. Sackett points to Financial Planning and Analysis as a prime example. “AI can put the data together,” he says. “Instead of coming up with comments, AI can put comments based on comparing a budget to an actual.”
Sackett believes the most valuable professionals will be those who translate analysis into execution. “The data is telling us to do X, Y, and Z. Let’s put a plan together. Track it. Have metrics against it. Make that your skill.” Technical fluency becomes table stakes, as does adaptability. Sackett draws on his own progression from paper ledgers to Excel as proof that embracing technology accelerates careers. Those who remain curious and proactive about new tools can position themselves as indispensable.
Building A Culture Of Curiosity And Accountability
Ultimately, intelligent automation is as much cultural as it is technical. Teams must be open to new ways of working and disciplined enough to hold systems accountable. “Really learn what the tools can do and work with the tools the way they were designed to be used,” Sackett advises. Forcing new technology into outdated processes limits its potential. Approaching it with curiosity unlocks it.” Finance, long viewed as a back-office function, is becoming a driver of strategic action. With the right leadership, automation can sharpen finance roles.
