State of AI in Finance 2026: report findings and what they mean for CFOs

The question of whether AI would reshape the finance function is settled. The more useful question now, as the state of AI in finance moves firmly post-hype, is how quickly your team can move from isolated experimentation to genuine transformation. CFOs and finance leaders have tested the potential, explored use cases, and faced growing pressure from boards and CEOs to actually capture the promise. What separates the teams doing that from the majority still waiting is the focus of the State of AI in Finance 2026, produced by CFO Connect in partnership with Oracle NetSuite, Remote, and Spendesk.

This article summarises the report's core findings: where adoption stands, what is holding most teams back, which tools are gaining traction, and what six leading organisations are doing differently. It also covers what the AI era now demands from finance professionals themselves.

The state of AI in finance today: mainstream adoption, wildly uneven maturity

The headline number from the Top CFO Tools Report 2025 is striking: 56% of finance leaders now use AI-powered tools in their daily work. That is up from 31% in 2024 and just 17% in 2023. As the State of AI in Finance 2026 notes, AI use has essentially doubled year over year. The optimism is also broad: in a survey of more than 3,600 business leaders, Remote found that 75% of HR leaders expect AI to handle more than half of their routine admin tasks by the end of 2026.

But adoption statistics obscure as much as they reveal. Finance and accounting is the lowest-ranked function for AI deployment across all business departments, according to the June 2025 General Atlantic AI Survey:

Full workflow automation and agents are already mainstream in engineering, marketing, and customer success. Finance is not there yet. The State of AI in Finance 2026 identifies a clear divide: "tinkerers" (experimenting with ChatGPT or Copilot for basic tasks, relying on manual copy/paste, lacking governed data infrastructure) and "integrators" (a select few teams for whom AI almost entirely automates repetitive processes and delivers previously unseen insights).

AI in finance industry adoption today is moving fast, but most teams remain in tinkerer territory. Pauline Babel, CFO at Spendesk, frames the challenge: "Most adoption remains focused on administrative workflows. The opportunity now is to extend AI deeper into core finance functions: accounting, forecasting, compliance, and strategic decision-making."

Michiel Boere, CFO at Remote, explains what the integrators have figured out: "The true value of AI is in amplifying the productivity of every person in finance by automating routine tasks and analysing information with unprecedented speed. The CFOs seeing the most success are not waiting for flawless autonomous systems. They are empowering their teams to use AI for immediate, practical efficiency gains in their daily workflows."

What's holding teams back, and what early movers are already gaining

The four barriers slowing real progress

The State of AI in Finance 2026 identifies four structural barriers preventing finance teams from moving beyond experimentation.

Fragmented data. Finance leaders consistently raise the issue of data scattered across ERPs, billing systems, CRMs, contract repositories, spreadsheets, and legacy tools. Phil Sharp, Interim CEO & CMO at Subscript, describes what he hears repeatedly from CFOs: "Nine out of ten finance leaders think their data is an absolute hot mess. Everyone whispers it like they're the only one facing this problem, but I hear it five times a day. Messy data is the norm, not the exception. The real question becomes: how do you operate when data will always be somewhat messy?"

Cumbersome close cycles. It is ironic that the teams who need AI most often lack the time to set it up. Ido Peled, Head of Finance Data & Technology at Adyen, names it plainly: "The biggest challenge is time and focus. Finance professionals are busy with closing and reporting. To innovate, we must create protected time for experimentation." Monthly and quarterly close cycles absorb most spare capacity, leaving AI as a permanent side project for teams that never carve out structured learning time.

Security and confidentiality concerns. Finance handles some of the organisation's most sensitive data. Leaders worry about exposing confidential information, particularly with public models. Enterprise AI tools with dedicated data protection (ChatGPT Enterprise, Gemini Enterprise, Microsoft Copilot with EDP) have reduced but not eliminated this concern. Explainable AI in finance contexts, where teams can see how outputs are generated and trace decisions back to source data, is a meaningful part of what makes enterprise tools more trustworthy than consumer alternatives.

Fear of the unknown. The State of AI in Finance 2026 reports that 68% of CFOs say they have been slow to adopt AI because they do not know where to start. Sharp is candid about the mood: "There's widespread scepticism about AI. I rarely hear, 'AI is changing my life.' It's more like, 'This seems useful, but is it snake oil?'" Jessica Pillow, a senior HR leader, describes what the most honest teams are actually doing: "There's definitely no playbook. We're all sitting around wondering how we have this AI, and it's not going anywhere. What we're doing at our company is treating it like a product: constantly testing, iterating, and making small, incremental movements."

The benefits of AI in finance: what the early adopters report

Where AI is actively embedded, the gains are both operational and strategic. The State of AI in Finance 2026 identifies five categories of benefit.

Faster cycles and time savings. The most immediate win is the collapse of manual prep time. Babel: "Before, closing books in D+1 or D+2 was reserved for large corporations. AI is now unlocking so much automation that it becomes possible for companies that cannot afford heavy and expensive tooling. This is the case at Spendesk, where we can cut the month-end close down to a couple of days." Yubo She, Head of Technical Accounting at OpenAI, puts the shift in roles: "AI accelerates prep work, letting humans focus on strategy and oversight. And preparers become reviewers and exception managers."

Parallel processing and real-time decisions. Classic finance operates sequentially: close books, then hand off to FP&A, who spend a week figuring out what just happened. Axel Demazy, CEO of Spendesk, describes the waterfall problem: "Finance teams are drowning in operational work, chasing receipts, closing books, and reconciling transactions. Accounting closes, then hands it off to FP&A, who spend a week figuring out what just happened." AI reasoning is not sequential. Individual flows can run in parallel. Demazy again: "AI moves finance from backward-looking reporting to augmented decision-making. Realtime data, cloud ERPs, and AI compress monthend into a continuous close. Variance analysis goes live. Forecasts update as signals change."

Lower costs and a new resource mix. Intelligent automation in finance is beginning to change the cost structure of the function itself. Dan Zhang, CFO at ClickUp, states it directly: "Today, 80% of finance costs are payroll. Within three years, a growing share will come from AI tooling and model usage." The State of AI in Finance 2026 is clear that this does not mean shrinking teams. It means AI absorbs high-volume, low-value tasks, headcount becomes more specialised, and productivity rises faster than team size.

Better business partnering. AI enables finance teams to answer business questions at scale rather than one request at a time. Peled describes what Adyen built: "We built a knowledge hub where we put all the documentation, videos, and other sources of information. We allow them to ask the tool a question and get an immediate answer. So that not only frees up time for us, but it also lets them get instant answers no matter where they are in the world." Julien Lafouge, CFO at Photoroom, frames the CFO's role in this shift: "CFOs can collaborate with the rest of their teams in thinking about productivity: how much faster can we make great decisions as a company? If it takes three months to make a strategic decision on a new product, or to enter a new market, or to change a part of our infrastructure, can we do that in two months instead?"

Higher job satisfaction and new strategic capabilities. Rebeca Bichachi, Product Marketing Director at Oracle NetSuite, describes a customer who automated himself out of a reconciliation role and into a strategic FP&A position: "Top talent seeks meaningful work, not endless manual tasks. AI helps unlock that shift." And the scope of what AI enables is expanding. Gabriel Hubert, Co-founder and CEO of Dust, argues for a three-lens view: "AI increases productivity in three ways: doing things faster, doing them better, and doing things you couldn't before." Zhang gives a concrete example of the third lens: "One use case is the AI BDR, engaging the long tail of small customers we've never touched before because the unit economics doesn't make sense. That's a zero to one unlock; a whole new capability. That's where I would put resources."

The best AI tools used in financial services right now

Finance leaders looking for a definitive shortlist will find the picture still in flux. Phil Sharp puts it plainly: "Honestly, there's no killer app yet. Most CFOs use general AI tools like ChatGPT for spreadsheet analysis or policy drafting. But finance-specific AI still isn't ready." Olivia Man, CFO at Promise, offers the practical guidance: "AI shines in policy analysis, contract review, and month-end close for simpler orgs. But we're still early. Stay curious and keep experimenting. Be sceptical of AI hype, but stay informed. The payoff will come for those who keep learning."

According to the Top CFO Tools Report 2025, the tool landscape by adoption among finance teams breaks down as follows:

Finance leaders report using these tools for preparing financial presentations, meeting notes and workshop preparation, reporting assistance and data analysis, industry research, data sourcing and document retrieval, consistency checks for legal documents, writing and verifying contracts, and improving internal financial communication.

Generalist platforms leading the field

ChatGPT Enterprise (OpenAI) is the most widely adopted tool. OpenAI's own finance team demonstrates what is possible at the high end: Yubo She describes how they connected their contract repository to their enterprise ChatGPT instance: "We amalgamated all of our contracts into one source of truth and connected it to our own enterprise version of ChatGPT. As we sign a revenue or vendor contract, these contracts flow through our model. ChatGPT knows that these are the types of products and dollar amounts." The bot can extract contract terms, draft ASC 606/IFRS 15 memos, and generate journal entries directly in the ERP.

Microsoft 365 Copilot is integrated into Outlook, Excel, PowerPoint, Teams, and SharePoint, making it immediately accessible across the organisation without additional procurement. David Fortin, CPA and Microsoft MVP, puts the pace of the capability in concrete terms: "Copilot writes formulas, inserts columns, and even explains the logic line by line. Copilot generated a complete three-statement financial model, with linked formulas, in 96 seconds." The Analyst Agent can also compare budget and actuals files, highlight variances above any threshold, and generate visualisations. Tasks that previously took hours now take 5-10 minutes.

Dust is emerging as the platform of choice for teams that want secure, internal-only agentic AI in finance contexts. Finance teams use it to auto-generate visual dashboards, retrieve answers from financial models and contracts, automate reporting cycles, and trigger recurring workflows. Alban Dumouilla, Head of Customer Success at Dust, describes a finance-specific agent he built: "I've created a Financial Analyst agent whose objective is to create interactive visualisations from financial spreadsheets to help users understand trends. The agent automatically generates a summary of financial analysis, key insights, and budget breakdowns."

Finance-specific platforms worth knowing

For teams asking what the best FP&A software for an AI-first finance function looks like in 2026, the report focuses on three categories: close automation (Numeric), AI-native planning (Drivetrain), and embedded-AI ERPs (Oracle NetSuite). Each is grouped by the workflow it owns rather than ranked head-to-head.

Numeric is built specifically for the financial close. It syncs directly with the general ledger every 5 to 15 minutes and auto-drafts account-level insights. Nathan Tully, Manager of EMEA Sales at Numeric, describes it: "Numeric integrates directly with your GL, syncing every 5 to 15 minutes to ensure accurate data. It automatically drafts insights for each account, giving finance teams a real-time data mirror of their ERP for analysis and close management."

Drivetrain sits in the AI-native category of FP&A software, built specifically for finance planning teams rather than retrofitted onto spreadsheets like most legacy FP&A tools. It connects to over 800 systems including ERPs, CRMs, HRIS, and data warehouses. Kirk Kappelhoff, Director of Sales at Drivetrain, illustrates the generative AI in finance capability: "Drive AI can create a P&L dashboard with KPI charts, an income statement table, and bar charts for trends, automatically. You can query data conversationally in Slack: 'What happens to profit if operating expenses increase by 20%?' Drive AI calculates the scenario instantly."

Oracle NetSuite is the most popular ERP among CFOs, and its AI capabilities are expanding continuously. Bichachi summarises the platform's value: "NetSuite provides this foundation: unified data architecture, embedded AI capabilities, automated workflows and predictive insights." Its AI Connector Service links NetSuite data to tools like ChatGPT or Claude, while Text Enhance generates narratives for reports and contracts.

Workflow automation platforms (Zapier, Make, n8n) are the connective tissue that makes AI available to low-code users across finance. Ryan Roccon, CFO at Zapier, describes how his own team uses the platform: "We rely heavily on Zapier ourselves, connecting internal and AI tools for end-to-end automation. For example, automating virtual mailbox workflows using Zapier AI to classify, summarise, and share tax notices via Slack, saving hours weekly."

For more on the platforms surveyed CFOs use most, see the Top CFO Tools Report 2025 at /resources/reports/top-cfo-tools-report-2025-download-the-report/.

Six organisations showing what real AI adoption looks like

Spendesk: continuous reconciliation and live budgets

Spendesk addresses the stale-data problem at its root. Financial planning depends on spending actuals, but those actuals are typically locked inside a month-end close cycle. Spendesk uses automation to speed reconciliation and coding throughout the month rather than at month-end, keeping books current at all times. Live budgets let every team track spend against plan as it happens.

"This isn't just about efficiency," says Babel. "It's about repositioning finance from operational to strategic. Moving from reporting the past to shaping the future." Demazy sets the ambition for the operating model: "The new operating model for finance is forwardlooking by default, embedded where decisions happen, and measured by impact, not report volume."

CFO Connect's deeper write-ups on Spendesk's finance-data approach are linked from the Top CFO Tools Report 2025 above.

OpenAI: Contract Reader Bot and technical accounting automation

OpenAI's internal finance team operates at roughly 22% of the headcount of comparable tech firms while routinely delivering faster outcomes across the close cycle. Their Contract Reader Bot extracts terms, applies ASC 606/IFRS 15 logic, and auto-generates journal entries, eliminating most manual effort from the close.

She describes the data foundation work behind it: "Our first step was mapping all data sources and aggregating them where possible." Two approaches were used in parallel: full integration into a master system for control-heavy areas, and data aggregators for lower-risk processes. "We've done both: merging critical data for control-heavy areas and using aggregators for private-company-level risk processes." Once the data foundation was rebuilt, OpenAI expanded AI into reporting drafts, disclosure writing, controls testing, and deal-prep analysis.

Adyen: the Finance Data Core

Adyen's approach was to rebuild its data infrastructure before applying AI anywhere. A multi-year programme to centralise all financial data into the Finance Data Core now powers automated reconciliation, accounting memo generation, and faster reporting cycles. An internal knowledge hub lets employees query company information for instant answers.

Adyen treats adoption as a human challenge rather than a technical one. Controllers and FP&A analysts rotate directly into technical teams, co-designing use cases with engineers. Peled explains why: "Adoption is fundamentally a trust issue. If finance teams don't understand how AI outputs are generated, they won't use them."

Zapier: company-wide AI mandates

Zapier's approach is the most direct: make AI usage non-optional. Roccon: "Transformation requires mandates, not nudges. At Zapier, around 98% of employees use AI tools." The finance team measures ROI through a combination of objective metrics (cycle times, pull requests merged, issues resolved) and subjective surveys, creating a clear framework for evaluating what works.

ClickUp: consolidation as strategy

ClickUp's finance team arrived at a counter-intuitive insight: the problem was not finding AI tools, it was having too many. Zhang: "The main challenge isn't finding AI tools. It's having too many. We replaced five separate AI note-taking tools with a native one built in ClickUp." Beyond consolidation, ClickUp built internal agents for investor-relations workflows, scenario modelling, internal reporting, and companywide knowledge retrieval. The cultural expectation is made explicit: "AI is not optional; AI is required."

Microsoft: ready-built Copilot agents for finance

Microsoft 365 users have two ready-built AI assistants available without any additional procurement. The Researcher Agent acts as a personal chief of staff, compiling schedules, flagging emails, and pulling relevant documents ahead of meetings. The Analyst Agent handles complex but repetitive finance tasks: comparing files, running Python-based data analysis, and generating charts and visualisations automatically through natural language. Fortin's demonstration of the Analyst Agent's speed (a complete three-statement financial model in 96 seconds) illustrates the practical value for teams already inside the Microsoft ecosystem.

The new finance skill set AI requires

AI is not just reshaping what finance teams do. It is reshaping what finance professionals need to know. The State of AI in Finance 2026 identifies two tiers of capability: technical skills and the soft skills that AI amplifies rather than replaces.

A CFO Connect Summit 2025 poll found that 56% of finance professionals believe workflow automation (using tools like Copilot and ChatGPT) will be the most important AI-related skill for their work in the next two years. Data literacy ranked second at 21%, followed by cross-functional collaboration with AI at 14%, and AI governance and compliance at 4%.

The technical must-haves for AI in accounting and finance are: LLM literacy (knowing how to prompt effectively, validate outputs, and ground responses in source data); finance automation (identifying friction in processes and automating it through tools like Copilot, Zapier, and embedded ERP agents); data literacy and governance (reading, interpreting, and communicating data confidently across structured and unstructured sources); and systems integrations (understanding how data flows through ERPs and how to connect systems effectively).

On the human side, Bichachi identifies a shift in what CFOs value: "Finance leaders increasingly value problem-solving and storytelling skills. Only 23% of CFOs now rank deep accounting knowledge as the top hiring priority." As AI handles more of the analysis, the uniquely human value moves to interpreting results, framing decisions, and influencing stakeholders. The State of AI in Finance 2026 also identifies strategic prioritisation and change management as core skills: the ability to choose high-impact opportunities, guide colleagues through new workflows, and shape organisational culture around AI adoption.

And Zhang connects adoption to measurement discipline: "If we only measure AI by time saved, we miss its real value." Teams that excel will be those who classify AI use cases across all three value types: time savings from automation, new capabilities that were previously impossible, and better quality insights and decisions.

For teams who want structured upskilling, the report's authors note that the most useful AI in finance courses are workflow-led rather than tool-led: they start by identifying a high-friction task and then choose the right tool, rather than starting with the tool and looking for a use case.

The same principle applies to finance automation courses: the strongest curricula teach process-mapping and integration design first, then layer in the specific platforms (Zapier, Make, n8n, embedded ERP agents) once teams know what they are automating and why.

Frequently asked questions

How do AI tools integrate with existing finance systems?

The source material shows four main integration patterns. First, direct ERP connection: tools like Numeric sync with the general ledger every 5-15 minutes, while ChatGPT Enterprise can be connected to contract repositories and ERP systems so it processes data in near-real time. Second, AI Connector Services: Oracle NetSuite's AI Connector Service links NetSuite data directly to tools like ChatGPT or Claude, without requiring a separate data engineering project. Third, native embedding: Microsoft 365 Copilot works within Excel, Outlook, and SharePoint as an embedded assistant, requiring no new procurement for existing Microsoft users. Fourth, workflow orchestration: platforms like Zapier, Make, and n8n stitch AI agents to existing tools (ERP, CRM, Slack, Sheets) through event-triggered automations. The key insight from the State of AI in Finance 2026: before purchasing any new AI solution, audit your existing tech stack, because many organisations underuse embedded AI features they already pay for.

Note: Aveni AI does not appear in the State of AI in Finance 2026 report or any of its source chapters. Questions about Aveni specifically are outside the scope of this article. The integration frameworks above reflect what the report's research covers.

Where to start: your first 30 days

The State of AI in Finance 2026 recommends a deliberately narrow focus for the first 30 days: identify one high-friction workflow (spend categorisation, variance analysis, reconciliations, or management reporting), audit your existing tech stack for unused AI features, and begin tracking impact metrics beyond time saved. Speed of decisions, forecast accuracy, error reduction, and stakeholder satisfaction all matter more than hours saved alone.

The 90-day phase is about structure: an automate-upskill-govern plan that defines which processes will be automated, what skills the team needs to develop, and how AI use will be governed. Governance should enable safe experimentation, not close it down. The 6-12 month horizon is about scale: building a governed finance data core, redesigning roles around AI capabilities, and standardising pilots that have proved their value across teams or business units.

The companies that have moved furthest share a common pattern: a solid data foundation, clear targets, cross-functional collaboration between finance and engineering, and a cultural expectation that AI is not optional. Roccon distils the change management principle: "Start bottom-up to spark adoption, but pair it with a top-down edict to drive real transformation."

The findings and case studies in this article draw on the State of AI in Finance 2026 and the Top CFO Tools Report 2025, both produced by CFO Connect. For event-recap deep dives on how teams are operationalising AI in finance, see /resources/event-recaps/.