The balance sheet still looks the same. Everything underneath it has moved. Payments settle in seconds rather than days, trade finance runs on digital platforms that did not exist a decade ago, and artificial intelligence (AI) now shapes decisions that used to sit squarely with the finance function. Nancy Amert, founder and principal of Agility Educators and author of Leading the Intelligent Enterprise, argues that this amounts to something more fundamental than a technology upgrade. Money has become data, and most executive teams are still managing it as though it were cash sitting in an account somewhere waiting to be counted. That gap between how capital behaves and how leadership thinks it behaves is where margin quietly disappears, and where, for the companies paying attention, it reappears.
Treat Money As Data, Not As A Balance
The practical consequence of money moving as data is that value no longer accumulates where the ledger suggests. Capital flows through systems, platforms, and partners in real time, which means the opportunity sits in the movement rather than the resting position. Amert’s framing is deliberately uncomfortable for finance leaders trained to manage stocks rather than flows: the question is not how much capital the company holds, but how it travels and who touches it along the way. “Knowing how money travels through your business, you will find value others missed,” she says. The inefficiencies are structural and invisible on a standard report, sitting in the handoffs between entities, currencies, and counterparties.
Amert has the operational receipts for this view. At Seagate, she helped lead and launch a global cross-border supply chain finance program that saved the company more than $100 million a year. That number did not come from cutting costs in the conventional sense or renegotiating supplier terms into the ground. “That value came from managing how capital moved across borders and partners,” she says. The distinction matters because it reframes treasury and finance operations from a control function into a source of recurring profit. Executives who map the path that capital takes through their business, including every delay and intermediary, tend to find that the map looks nothing like the organizational chart they assumed governed it.
Count The Real Cost Before You Fund It
The current enthusiasm for financial technology, AI very much included, has produced a familiar pattern of investment decisions made against the wrong number. Leadership teams evaluate the software price and approve the spend. Amert’s warning is that the software price is close to the least significant line in the calculation. The real cost sits in infrastructure, data readiness, cybersecurity, integration, governance, and adoption, and those categories routinely dwarf the license fee. Her instruction is blunt: “Map the full profit and loss first.” A business case built on anything less is not a business case; it is a hope expressed in a spreadsheet.
What follows from that discipline is a different funding posture. Rather than committing to enterprise-wide rollouts on the strength of a vendor demonstration, Amert argues for funding what proves itself and withdrawing from what does not. “Then what earns the right to scale is what you’re going to fund. And stop what you can’t prove has value,” she says. The second half of that sentence is the harder one in practice, because organizations are far better at starting initiatives than at killing them. Sunk cost, internal sponsorship, and reputational exposure all conspire to keep underperforming programs alive long past their usefulness. Treating scale as something a project earns rather than something it is granted at launch is a governance choice as much as a financial one, and it is what separates portfolios that compound from portfolios that bleed.
Build Governance That Creates Speed
Governance has a reputation problem among executives who associate it with committees, sign-offs, and delay. Amert inverts the assumption, and her evidence is the kind that regulated industries rarely produce. Working most recently with a large bank, she helped deploy over 450 features in a regulated environment. That volume is not what most people expect from a sector defined by compliance constraints, and the reason it was possible has nothing to do with lowering the bar on risk. It has to do with who was in the room, and when.
“When risk, IT, finance, data, and the workforce work as one system from day one, you can scale with confidence,” Amert says. The phrase “from day one” carries the weight here. In the conventional sequence, a business unit designs a solution, builds it, and then submits it to risk and compliance for review, at which point the rework begins and the timeline collapses. Treating those functions as a single operating system from the outset removes the rework rather than the rigor. The strategic payoff extends past delivery speed. An organization that can move quickly inside regulatory constraints has an advantage over competitors who treat every new control requirement as a reason to pause, and that advantage grows as the rules around digital finance keep tightening. Amert’s final point is the one executives cannot delegate: AI promises productivity, but accountability for profit stays at the top. Understand how money moves, count what it genuinely costs, and govern for speed. The financial system is becoming more intelligent whether or not leadership keeps pace with it.
Follow Nancy Amert on LinkedIn for more insights on enterprise transformation, digital finance, and leading the intelligent enterprise.
