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As finance leaders, we've learned to be skeptical of hype. For years, artificial intelligence promised transformation, but too often delivered incremental gains. That's changing.
According to Genpact's enterprise AI research, 71% of the 45 finance leaders surveyed say they're satisfied with the value they're getting from AI investments. The data in this report confirms what many CFOs are now seeing firsthand: from forecasting and decision support to productivity and control, AI is beginning to move beyond experimentation and into execution.
But that momentum hides a harder truth. Only 12% of organizations qualify as true AI leaders when we assessed their maturity across six key dimensions: adoption effectiveness, decision autonomy, governance rigor, operational enablement, regulatory compliance, and technical capability. Our data shows that most are still operating in assist mode – using AI to inform decisions rather than fundamentally reshape how work gets done.
That gap matters. Because finance doesn't just measure value, it's responsible for realizing it.
What stands out to me most in these findings is not the technology story, but the operating model story. The biggest barriers to scaling AI are no longer data or algorithms. They are skills gaps, fragmented ownership, governance limitations, and the difficulty of integrating AI into real workflows. These are familiar finance challenges – and they require finance leadership.
Finance is deeply involved – even when it isn't 'in charge'
Most AI programs are still formally led by CIOs, CTOs, or CDOs. Yet finance is deeply involved (see Figure 1).
Figure 1: How surveyed finance leaders describe their involvement with AI initiatives at their organization (n = 45)
This reflects a ground reality: the moment AI influences forecasting, controls, compliance, or capital allocation, it becomes a CFO issue. In other words, AI may start in technology, but it scales (or stalls) in finance.
Where finance leaders are seeing value today
Finance leaders report that AI value today is coming from grounded, execution‑oriented use cases, not moonshots (see the table below).
Technology | Usage (%) | Rated moderately to very effective (%) |
|---|---|---|
| Decision support systems | 82 | 78 |
| Generative AI | 80 | 80 |
Process automation | 78 | 78 |
Monitoring and quality control | 76 | 75 |
| Conversational AI | 76 | 75 |
Table 1: Top AI use cases in finance and their effectiveness as rated by 45 finance leaders
These applications work because they fit naturally into structured finance processes. They augment human judgment rather than attempt to replace it.
Where confidence drops off is with autonomous agents. Only 40% of finance leaders rate them as effective, and 20% say they're not even in use yet. This data tells us something important: the technology may be advancing quickly, but the operating model hasn't caught up.
The real blockers aren't data and tech – they're organizational
Data quality is often blamed for AI underperformance. Finance leaders see a different picture.
The top barriers to scaling AI in finance are:
Figure 2: Top barriers to scaling AI in finance
Taken together, these findings point to a clear conclusion: AI adoption has outpaced operating‑model redesign. It's not failing to deliver impact because the technology is weak. It's failing because many organizations have not rewired how work gets done. Nearly half of all finance respondents report that their organization requires moderate restructuring of governance, team structures, or decision-making to accommodate autonomous or agentic systems.
Organizational inertia is also at play. For example, while skill gaps were reported as a top challenge, only 38% of finance leaders said their organization was offering personalized AI learning paths for employees. Without deliberate investment in AI fluency and role redesign, AI remains an assistant, not a partner.
Caution is a feature, not a flaw
Finance stands out for its disciplined approach to AI autonomy.
Figure 3: How finance leaders responded to the question "How are the following decisions and processes typically carried out-and how much autonomy do AI systems have today?" (n = 45)
This isn't resistance. It's responsibility.
But it also explains why AI value often plateaus. AI is doing more analysis but not enough execution. Without redesigning how decisions are made, AI remains advisory rather than transformative.
This is where governance comes in. For CFOs, governance is no longer a brake on innovation but the lever that enables AI to scale safely and confidently. Clear guardrails, accountability, and auditability help CFOs lay the groundwork for AI to move beyond assist mode and unlock the next wave of value, where AI becomes an integrated, transformational force.
The good news is that most finance organizations already favor structured models, with 59% using a federated approach and 38% relying on centralized enterprise oversight. Very few use ad hoc or decentralized methods.
This focus on structure is vital. Why? Because an overwhelming 99% of executives said they don't have adequate governance models and structures in place for autonomous or agentic AI systems and associated risks. CFOs are leading the charge to change that.
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What CFOs should focus on next
The research makes one thing clear: the next phase of AI value will not come from deploying more tools. It will come from re‑architecting how finance operates as organizations move toward the autonomous enterprise.
The leaders in AI maturity offer a framework for CFOs to help their organizations succeed with AI:
Redesign finance processes to embed AI: AI must be integral to how work gets done, not an overlay. This means building process intelligence to identify processes ripe for transformation – high-effort ones, such as accounts payable and accounts receivable. And as the organization's AI adoption accelerates, CFOs must insist on deploying orchestration models that enable AI agents to work seamlessly with the finance team as well as with each other
Close the skills gap: Every finance professional will need the fluency to interpret AI outputs, challenge recommendations, and intervene when judgment is required. And as routine tasks are increasingly automated, finance roles must evolve toward higher‑value decision‑making, oversight, and orchestration
Manage change thoughtfully: Robust change management practices – anchored by clear vision, leadership buy-in, and a focus on both technical and human factors – will be the linchpin that enables organizations to accelerate AI adoption. This involves helping teams understand AI's benefits, adapt to new workflows, and build confidence in their new roles. Finance leaders must involve stakeholders early to address concerns about job security, data integrity, and accountability
Push for architectures that support near‑time insight: Static reporting limits AI's value. Decision velocity matters. Finance leaders should push for architectures that support scale, traceability, and responsiveness, especially as AI systems begin to act and not just analyze
Lead AI governance proactively: For CFOs, governance is no longer defensive. It's a strategic enabler. They must embed strong governance into workflows, architectures, and decision rights before regulators or auditors force the issue
Our data show that AI is already beginning to deliver value in finance. The next wave of advantage will come from how boldly CFOs reshape processes, cultivate new capabilities, and embed robust governance at the core of their organizations. The choices made today will define success for years to come.
