L.E.K. Consulting’s 2025 Office of the CFO Survey: A Study of AI in the OCFO

Artificial intelligence (AI) has captured the attention of chief financial officers (CFOs), but most are still navigating its early promise with measured caution. L.E.K. Consulting’s Annual 2025 Office of the CFO (OCFO) survey, based on input from over ~100 CFOs, reveals a landscape where strategic intent far outpaces operational reality. Despite AI’s potential to reshape forecasting, automate transaction-heavy workflows and elevate finance into a more strategic function, current adoption remains modest. We conduct a comprehensive OCFO survey annually, capturing views on what’s top of mind for CFOs. This year, we captured feedback from over ~100 CFOs across multiple industries.

The next few years, however, are set to define a turning point. Early adopters are already achieving measurable efficiency gains, especially in areas like accounts payable (AP), accounts receivable (AR) and financial close. Others are looking to embed AI capabilities within platforms to do the heavy lifting as tech stacks consolidate and expectations for decision intelligence grow.

For finance software vendors, platform providers and private equity investors, the implications are significant: The opportunity lies not in evangelizing AI’s potential but in demystifying its implementation, solving for integration and proving its return on investment (ROI) in practical finance applications.

The current state of AI adoption in the OCFO

Our 2025 survey reveals that approximately 60% of CFOs believe AI is going to be one of the most impactful technologies in the OCFO over the next years (up from roughly 50% in 2024). Furthermore, 54% of CFOs think that delaying the adoption of AI will slow their organization’s ability to grow.

Yet only about 11% of CFOs are using AI within their finance functions today, while around 35% are just beginning to experiment with AI in pilots or proofs of concept (see Figure 1).

CFOs who have adopted AI are reporting strong impacts across productivity, work quality improvements and cost reduction (which remains a top priority for CFOs in 2025). AI is playing a central role in enabling these outcomes by automating time-intensive processes, improving data accuracy and freeing teams to focus on higher-value activities. In productivity, AI is accelerating critical workflows like financial close, cash flow forecasting and invoice processing. In terms of work quality, AI-driven tools are enhancing forecasting precision, improving reconciliation processes and enabling more-informed data-rich decisions.

“... [AI] flags anomalies and helps us pinpoint variances faster, which has cut down our month-end timeline and frees up our team to focus on more analytical work ...
— CFO, energy software and analytics company

On the cost side, AI is helping cut labor-intensive, repetitive tasks, particularly in AP, AR and financial planning functions. For CFOs navigating tight budgets and economic uncertainty, these efficiencies are proving not just beneficial but essential (see Figure 2).
 

Where is AI being used today?

Among those using AI, three areas stand out (see Figure 3):

• AP/AR automation: CFOs reported strong results from implementing AI to handle invoice ingestion, payment routing and reconciliation.
  “... We’ve seen a real lift in AP and AR productivity. A task that used to take three hours now takes 15 minutes. The team is no longer stuck processing invoices the whole day ...”
  — CFO, global produce distributor
• Budgeting and forecasting enhancement: CFOs are using AI to streamline budget creation and scenario planning, with tools that dynamically adjust forecasts based on shifting inputs, improving accuracy and enabling faster, more-informed decision-making.
  “... We’ve started to use the built-in AI in our financial planning and analysis (FP&A) tool to surface anomalies and guide our forecast reviews. It helps us know where to dig in, which is huge when you’re moving quickly ...”
  — CFO, energy software and analytics company
• Cash flow forecasting: Predictive AI models are helping finance teams run dynamic cash flow scenarios faster, using historical patterns and real-time inputs. This can help drive stronger growth for businesses (in particular small and midsize businesses), as better cash flow data and predictability are expected to expand access to loans and credit.
  “... For cash flow management AI has greatly increased productivity, enabled better forecasting and lowered costs …”
  — CFO, multinational packaging manufacturer

Yet these pockets of progress are the exception and not the rule. For most finance teams, AI remains in a pilot phase or limited to generic generative AI (GenAI) use cases like reading PDFs or summarizing transcripts. 
“... We’ve been experimenting with ChatGPT to summarize meeting notes and transcripts, but for accounting? We’re not anywhere near there ...”
— CFO, adtech platform company

So why is AI adoption still limited?

Despite growing enthusiasm for AI’s potential, widespread adoption within the OCFO remains uneven (see Figure 4). While early adopters report clear productivity and efficiency gains, many finance leaders are still sitting on the sidelines. Adoption remains constrained by several barriers, including:

• Awareness and education gaps: Many CFOs struggle to articulate what AI solutions are available and how capable they are, making it difficult to assess potential ROI or justify investment.
  “... We’re interested in AI, but right now it feels like we don’t know what we don’t know. It’s hard to evaluate whether these tools would provide a reasonable ROI if we’re not even sure what’s out there or what they can realistically do ...”
  — CFO, adtech platform company
   
• Integration challenges: Fragmented systems, particularly among midsize firms using multiple point solutions, make implementation difficult.
  “... Our ERP [enterprise resource planning] is a bit dated; we’d love to implement AI tools for invoice reading and reconciling, but we don’t have the ability today to do the right integrations. That’s the biggest blocker for us. It’s not the tool; it’s connectivity ...”
  — CFO, home furnishings retailer
• Risk aversion and trust barriers: CFOs are historically cautious adopters, especially of AI, citing concerns about data accuracy, explainability and the risk of hallucinated outputs in financial decision-making.
  “... For AI to really be valuable, it needs to be accurate and explainable. We’re not willing to risk using tools that work in a black box to make decisions that impact our financials ...”
  — CFO, enterprise IT expense management provider

The near-term outlook on OCFO AI uptake

The near-term outlook for AI adoption within the OCFO is optimistic. While just 33% of respondents have used off-the-shelf GenAI solutions within the past 12 months, an additional roughly 20% plan to do so in the next three to five years. Even more striking is that while only about 25% of respondents currently use AI-powered features within third-party software, an additional approximately 44% plan to do so in the next three to five years, suggesting a preference for low-friction, integrated OCFO solutions over stand-alone tools. Investments and efforts are also expected to grow across AI governance and employee AI upskilling. Taken together, these shifts reflect a maturing mindset of moving from experimentation to more-structured, long-term investment in AI across multiple layers of the finance function (see Figure 5).

“... I think it’s going to be more important than ever to have technologies that give us the ability to make decisions, good decisions, quickly. We are data-rich and information poor, and that’s a real problem …”
— CFO, regional health system

AI’s impact on head count: Reallocation, not just reduction

One of the most-debated impacts of AI within the OCFO is its effect on the finance team head count. While relatively few CFOs report job losses to date, the majority anticipate this will shift meaningfully over the next few years. By 2028, most finance leaders expect a clear evolution of how teams are structured and in the nature of the roles required (see Figure 6).

In March 2025, EY launched its EY.ai Agentic Platform in partnership with NVIDIA, aiming to transform core tax, risk and finance operations. The firm has already deployed 150 AI agents across 80,000 tax professionals to automate data collection, document review and compliance workflows. While positioned as an augmentation strategy rather than a replacement, the scale of automation points to a potential near-term reality where future finance teams will be leaner, more tech-enabled and focused on oversight and analysis rather than execution.

Transactional functions such as invoice processing, journal entry posting, reconciliations and other repeatable, rules-based tasks are seen as the most vulnerable. AI-powered tools are already proving capable of executing these activities faster, more accurately and at a lower cost than traditional human-led processes. As adoption scales, many CFOs anticipate downsizing or redeploying staff who are currently focused on these operational areas.

“... I think in three to five years, finance teams will be smaller and smarter. Repetitive work will be owned by AI, while people focus on complexity and strategy ...”
— CFO, multinational packaging manufacturer

At the same time, demand is expected to grow for more strategic, analytical and decision-support roles — in particular, talent that can help interpret AI outputs, apply financial judgment and collaborate cross-functionally with business partners in other departments and organizations. These roles will be less about processing information and more about extracting insights and shaping forward-looking decisions in line with the continued evolution of the OCFO to more strategic functions.

“... Anything that’s considered manual or time-consuming, where you’re having to input data in a traditional data entry format, AI is going to replace and speed up the process ...”
— CFO, real estate firm

For CFOs, this creates both an opportunity and a challenge: To redesign team structures, invest in new skills and ensure that technology and talent evolve in tandem to meet the needs of the modern finance function.

Embedded AI > stand-alone AI

The overwhelming majority of CFOs express a strong preference for AI capabilities that are embedded within broader finance or ERP platforms rather than delivered as stand-alone tools. This preference is rooted in practical realities that finance leaders face today:

• Ease of adoption: Embedded AI features typically require less effort to implement, configure and integrate with existing workflows. Because they’re native to systems already in use, embedded capabilities deliver faster time to value with fewer IT dependencies. For many CFOs, this approach feels less like adopting a new tool and more like a version upgrade of systems they’ve already invested significant time and effort into building. It preserves familiar workflows and interfaces, which is especially important for a group that tends to be risk-averse and highly attuned to operational disruption. In contrast, stand-alone AI tools often come with steep onboarding curves, data integration challenges and greater change management overhead, making them often feel like a wholesale restart rather than an evolution.
• Data consistency and integrity: Platforms with integrated AI features provide a single source of truth across functions, enabling AI to operate on consistent, up-to-date data. This is critical for CFOs aiming to maintain accuracy and trust in their reporting, forecasting and analysis. Siloed tools can create gaps, redundancies or inconsistencies that undermine the reliability and trust in AI-driven insights.
• Vendor rationalization: Most CFOs are actively looking to reduce, not expand, the number of vendors they manage. Platform consolidation helps simplify vendor relationships, reduce licensing complexity and improve accountability for performance and support.

This strong preference for embedded AI reflects a broader trend toward platform consolidation across the OCFO. In fact, roughly 56% of survey respondents report that they prefer platform solutions, while over ~31% prefer best-in-class point solutions (about 13% were neutral).

“... We’re already seeing convergence in tools offered; some vendors are adding capabilities to become more of a platform because they know CFOs want fewer systems and tighter integrations ...”
— CFO, energy software and analytics company
 

Strategic implications for solutions providers and investors

The implications are clear: While AI adoption is still in its early stages in the OCFO, demand is growing and expectations are rising. For vendors and investors alike, this means:

• Focusing on real use cases, not futuristic promises: CFOs need solutions that drive ROI today, particularly for AR/AP, budgeting and forecasting, and cash flow management.
• Prioritizing embedded AI within platforms: Tools that require separate implementation or learning curves will face resistance. Winning solutions will “just work” inside the stack.
• Supporting education and changing management: Vendors must take the lead in demystifying AI, show real benchmarks and ease finance teams into adoption.
• Being ready to scale: The next wave of AI adoption will likely happen fast, especially once leaders start seeing results from peers.

Strategic implications for CFOs

For CFOs, the question is no longer whether AI will reshape the function — it’s how quickly and where to start. Across early adopters, one clear theme is emerging: The path forward is grounded in practical, operational wins, not sweeping transformation. AP and AR are proving to be fertile ground for early experimentation and are areas where CFOs can quickly realize time savings, improve accuracy and reallocate capacity to higher-value tasks.

CFOs who lean in will need to play the roles of both architects and change agents. Architects, in the sense that AI adoption, whether embedded in ERP systems or enabled through third-party applications, will require thoughtful coordination across finance, IT and the broader organization. And change agents, because success will depend not only on selecting the right tools but also on guiding teams through new workflows, building trust in AI-generated outputs and upskilling talent accordingly.

Most importantly, AI will reshape, not replace, the finance workforce. Transaction-heavy roles are already being automated, with many CFOs reporting a shift toward leaner teams supported by more strategic and analytically focused talent. While head count impacts are expected, the greater challenge may be enabling remaining team members to move up the value chain, from data processors to insight generators.
To prepare, leading CFOs are:

• Investing in finance-specific AI literacy
  Equipping teams to evaluate and implement AI tools confidently, not just use them passively
• Prioritizing embedded AI
  Favoring tools already integrated into existing platforms, reducing the friction of adoption and maximizing data fidelity
• Rewiring the operating model
  Realigning finance organization structures, responsibilities and key performance indicators to reflect a world where AI augments human judgment

For most finance leaders, the real risk is no longer doing too much too soon; it’s standing still. As peer organizations scale successful AI pilots, expectations for productivity, decision-making speed and cost efficiency will shift quickly. CFOs must ensure their teams are not only ready to keep up but positioned to lead.

Conclusion: The AI future is coming, but it’s embedded and practical

AI is no longer a buzzword — it’s a buy signal. But for most CFOs, adoption will come through trusted platforms, practical workflows and measurable results, not just flashy pilots or disconnected tools.

Software providers, platforms, investors and CFOs who align with this pragmatic view by embedding intelligent capabilities into existing OCFO workflows are best positioned to lead this transformation.

For more information, please contact us.

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