Four Bold Moves to Reimagine Finance in the Age of AI

Finance used to measure what happened. Now it has to shape what happens next. The function that long centered on precision and reporting is fast becoming the engine of enterprise strategy—powered by data, AI agents, and human judgment working in unison.

Before finance can help redesign the enterprise, though, it has to reimagine itself. The decades-old finance operating model—transactional, control-focused, and anchored to predictable reporting cycles—no longer fits a business world that moves continuously and is being upended by AI and automation. Incremental changes won’t cut it.

Across industries, finance’s playbook is being rewritten in real time—and the boldest leaders aren’t waiting for the next edition. They’re taking action now to rebuild their operating model, step by bold step, and recalibrating people, processes, and technologies for the decade ahead.

Here are four model-defining moves every chief financial officer (CFO) should consider as they chart their organization’s path to the future of finance.

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The old model

Finance roles traditionally have been defined by hierarchy and repetition. Analysts gather data, managers reconcile results, and directors review reports. Each level performs a step in a process rather than contributing to a shared outcome. The model prioritizes stability over flexibility.

The new approach

AI and automation are changing how work gets done and who—or what—does it. AI agents are assuming much of the transactional activity, from reconciliations to variance checks, which enables people to spend more time on insights and decision support. The modern finance organization is built around capabilities, not titles: data stewardship, insight generation, scenario design, and strategic advising. Teams will become smaller but more productive, blending human judgment with digital precision to flex with business needs.

Making the switch

Map roles to capabilities: Replace static job descriptions with adaptable capability frameworks. For example, start by focusing on four clusters: decision support, scenario planning, AI oversight, and data governance.
Redeploy talent for value: Use automation to shift people from low-value transactions to high-value analysis. Instead of reconciling data, for instance, analysts might model pricing elasticity or simulate supply shocks using data automation and AI-supported insights.
Build hybrid roles: Create new paths for roles such as AI model stewards, data translators, and scenario interpreters who can bridge technology and business strategy. This also ensures that every AI initiative has a human architect to govern it.

The old model

For most companies, the finance tech stack is a complex architecture built around the ERP. Reporting tools, planning platforms, and other point solutions orbit around it—but rarely in sync. Each system captures a partial view of the business, and stitching them together requires integrations, upgrades, and long IT roadmaps.

The new approach

AI is quickly becoming finance’s operating system. It’s the connective layer that makes a future-ready tech stack work, not another tool in orbit. AI and automation link data, workflows, and systems in real time, drawing information from multiple sources without needing to build or maintain massive integrations. Legacy platforms can be enhanced, rather than replaced. The ERP becomes a component within an AI-driven ecosystem that connects, learns, and adapts. In this model, finance no longer works around its systems—the systems work around finance.

Making the switch

Lead with strategy, not architecture: Define how AI will interact with your most critical workflows—forecasting, reporting, and planning—and then align your systems to support that vision.
Wrap, don’t rip: Rather than rebuilding from scratch, use AI and automation to “wrap” existing platforms, connecting ERP, analytics, and data tools through APIs, AI agents, and orchestration layers that allow them to operate as a single, truly connected ecosystem.
Empower real-time intelligence: Leverage AI to continuously pull and process live data across systems—for example, cash flow from ERP, risk exposure from compliance, and margin data from sales. AI tools can create an automated layer that works with data on the fly instead of relying solely on static databases.

The old model

Operational silos prevail. Finance, for example, is typically a stand-alone function, managing budgets, forecasts, and reports, while other core areas (operations, supply chain, sales) focus on execution. And each department has its own data, metrics, and processes, so information moves in handoffs, not in real time.

The new approach

AI and connected data are breaking down those boundaries. Instead of separate systems and scorecards, CFOs can enable shared visibility across functions through unified data models and AI-driven insights. Finance shifts from being a back-end control center to a side-by-side partner with other business leaders. The goal is collaboration and cocreation: designing strategies, forecasts, and actions together based on a single, live view of enterprise performance. The CFO becomes the organization’s chief connector, linking data, people, and decisions across the business.

Making the switch

Unify and share data: Use AI and cloud analytics to connect finance, operations, and commercial systems so everyone works from the same live performance data. Shared insights eliminate reconciliation delays and enable faster, better-informed decisions.
Embed finance talent in the business: Place finance professionals directly within operating teams—like supply chain, sales, or marketing—so they become co-owners of performance, not just post-event reporters. For instance, an embedded finance partner might work with procurement to model supplier risks and payment terms in real time.
Redefine success metrics: Replace siloed key performance indicators (KPIs) with cross-functional ones that reflect enterprise goals—margin by customer segment, for example, or cost-to-serve across the value chain.
Use AI to connect decisions: Deploy AI agents that surface patterns—pricing pressures, working-capital inefficiencies, or margin erosion—spanning multiple departments, then feed those insights into joint planning and forecasting cycles.

The old model

For the most part, finance operations continue to define value through efficiency: control costs, hit targets, protect margins. Success is measured more by what’s in the ledger and less by what drives long-term enterprise growth.

The new approach

“Finance as the new value creator” has been talked about for years. But an AI-led operating model can make that contemplated promise a reality. With real-time insight into performance drivers—customers, products, channels, and investments—CFOs can steer the business toward profitable growth and smarter capital allocation. The finance agenda expands from protecting value to architecting it, powered by predictive analytics, dynamic resource allocation, and forward-looking performance metrics. In this model, finance becomes the launchpad for growth.

Making the switch

Recast performance metrics: Move beyond efficiency and cost measures to forward-looking indicators such as customer lifetime value and returns on innovation investment. The entire organization needs KPIs that capture long-term growth.
Use AI to model future value: Deploy AI to simulate financial and operational trade-offs—testing pricing changes, product mixes, or capital allocations—to guide strategic decisions before they hit the P&L.
Integrate finance into growth planning: Place finance leads alongside commercial, product, and operations teams to rapidly model the business impact of investments and new initiatives. When finance is part of the design phase, value creation becomes continuous, not episodic.

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