The CFO’s Playbook for the Human + AI Workforce

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Artificial intelligence (AI) has joined your finance team. It doesn’t need its own nameplate or a hybrid work schedule, but to become a valuable part of the team, AI will require a visionary strategy from the chief financial officer (CFO)—starting today.

Most CFOs have anticipated AI’s arrival, of course. But for many, the urgent question now is,: “What’s next?” Harnessing AI—impactfully, responsibly—presents finance leaders with complex challenges unlike any they have ever seen.

By its nature, AI is dynamic and constantly evolving. Its impact on finance will unfold in stages—reshaping how work is done now, altering roles and career paths over the next few years, and driving a very different finance operating model in the near future.

Each of these stages—how finance works today, tomorrow, and in that fast-approaching future—demands a distinct approach from leaders. They’ll need strategies that are flexible enough to adapt as the technology, the workforce, and the enterprise continue to change.

In our work with clients building AI strategies and deploying AI-enabled operations at scale across finance, we’ve identified several critical areas of focus. Here are four essential considerations that should be part of every CFO’s playbook.

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#1. Reimagine Roles

AI is already absorbing much of the transactional work that once filled finance job descriptions, creating more opportunities for people to accelerate decisions and improve outcomes. But making that shift stick will require deliberate planning from CFOs. Leaders need to redesign responsibilities with the expectation that agents will steadily take on high-volume transactions, while humans move into higher-order responsibilities: interpreting results, advising the business, accelerating decisions across all functions, and guiding and managing AI itself.

Roles will start to intersect and in many cases shift outside the boundaries of “finance” as we’ve known it. The opportunity for CFOs is to map that evolution now, not react to it after the fact.

The playbook

Plan for convergence. An accounts payable/accounts receivable accountant today may focus on matching invoices and reconciling accounts, but as agents take on that work, the role shifts into exception management.

Promote judgment. With much of reconciliations and reporting automated, humans need to be redeployed into positions that demand interpretation: explaining anomalies, shaping forecasts, or translating insights into decisions. For example, a financial analyst—freed from gathering and reconciling data—will have more time to strategically partner with commercial teams to drive great value.

Design roles that span functions. Instead of staying siloed in finance, new roles will serve as connectors across disciplines—finance working with product teams to model profitability on new offerings, for instance, or teaming with marketing to dig deeper on returns from digital campaigns.

The KPMG POV

Traditional corporate functions will start to blend together, resulting in smaller teams with bigger outputs.

#2. Choreograph The Agents

AI agents are expanding across finance, but they can’t simply be switched on and left to run in a silo. Like people, they require oversight, monitoring, and continuous improvement. CFOs need to set standards for accuracy, define when exceptions need to escalate to human reviews, and establish accountability for agent performance. Treating agents as part of the workforce—not just as software—will be essential to maintaining both trust and results.

Managing this shift will require new thinking and hands-on oversight. Finance leaders should carefully consider how agents are supervised, how they work in tandem with humans, and the guardrails required to keep agents reliable across every process they touch.

The playbook

Create an “AI resources” function.

Just as human resources manages human employees, finance will need a dedicated capability to oversee agents—tracking performance, retraining models, and ensuring ethical use. This group becomes the hub for monitoring agent reliability.

Pair agents with humans for critical workflows.

For example, a collections agent may generate automated reminders and escalate overdue invoices, while a human analyst manages exceptions and sensitive customer conversations after escalation. This combination achieves scale while improving the speed and quality of responses.

Assign clear accountability.

Every agent output—whether a reconciliation, forecast, or anomaly alert—should have a named human reviewer. This prevents over-reliance on automation and reinforces agents as contributors, not decision-makers.

The KPMG POV

Within three years, 80% of finance tasks will be automated.

The traditional finance career started with transaction-heavy work—posting entries, reconciling accounts, producing reports—and built upward: analyst to manager to director. But as agents steadily absorb the mundane, transactional tasks, the old entry points disappear. CFOs need to design new career models that give professionals a foundation in digital skills and new through-lanes into higher-value roles.

The result will look less like a ladder and more like a lattice—careers that zigzag across functions rather than following one-way streets. Professionals progress by broadening capabilities across finance, operations, and technology, not climbing titles within a function. This creates a workforce that is both increasingly resilient and relevant in an enterprise where boundaries are blurring.

The playbook

1 | Build rotations across functions.

Instead of spending a decade in accounting, an accounting analyst might do a rotation in procurement, while a financial planning analyst spends time in commercial operations. These moves give staff a deeper view of business priorities and expand finance’s influence.

2 | Open paths into AI design.

Some finance professionals will grow into roles that help configure, train, and refine AI tools. A controller might partner with data scientists to design workflows or validate models—connecting domain knowledge with technology execution.

3 | Invest in hybrid skills.

Data storytelling, AI fluency, and cross-functional collaboration should be core parts of development programs. A financial analyst, for example, could rotate onto projects with information technology (IT) and data teams to learn how to explain and translate models into actionable business insights.

The KPMG POV

Three in four workers believe AI and automation can provide new career opportunities.

#4. Strengthen Governance and Human Judgment

As AI becomes embedded in finance, governance moves to the center of the CFO’s mandate. Finance leaders must establish the standards that keep automation accurate, compliant, and ethical so the enterprise can scale AI with confidence. Governance sets the boundaries, ensures transparency, and reinforces trust in the outputs that leaders depend on.

A big challenge here will be balance: Agents can produce reconciliations, forecasts, and risk alerts in seconds, but people must decide when to act, how to weigh trade-offs, and what aligns with business priorities. Human judgment becomes the anchor of trust, ensuring AI works both quickly and accurately.

The playbook:

Set enterprise-wide policies. Establish uniform standards for how agents are deployed, monitored, and validated. For example, define common approval thresholds across business units so that automation doesn’t create inconsistent risk profiles.

Audit AI like you audit finance. Treat models and agents as auditable entities, subject to periodic reviews for accuracy, bias, and regulatory compliance. This elevates governance from an IT activity to a finance-led discipline.

Make accountability visible. Require every critical AI-enabled process—tax filings, disclosures, capital planning—to have a named human owner who signs off on outcomes. Clear accountability reassures boards, regulators, and stakeholders that the CFO’s fiduciary role extends to automation.

The KPMG POV

AI will become the enterprise-wide operating system.

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