A New Era for CFO Teams

CFO teams have historically been among the hardest enterprise buyers to sell to, for several structural and cultural reasons. CFOs are trained to be risk-averse, detail-oriented, and deeply accountable for the financial integrity of their organizations. Unlike departments that can experiment with new tools in isolated pilots, finance operates at the core of a company’s control system — mistakes or data errors can have legal, tax, and compliance consequences. This makes CFOs cautious about adopting new technology, especially from unproven vendors. The burden of proof is higher: any new product must demonstrate not just ROI, but trustworthiness, auditability, and security across every transaction.

Another reason selling to CFOs has been difficult is that finance teams have historically been underserved by modern, user-friendly software. Much of their workflow depends on legacy ERP systems, manual spreadsheets, and highly customized internal processes. This creates inertia: even if a new platform promises efficiency, the perceived cost and disruption of switching can outweigh the benefits. Moreover, CFO buyers are skeptical of hype — they value precision, not promises. Vendors that lack a deep understanding of accounting standards, compliance requirements, or how financial data actually flows through an organization tend to struggle to gain credibility with this audience.

That said, the landscape is shifting quickly. The rise of AI-native finance tools—especially those that automate reporting, reconciliation, forecasting, and compliance—has opened new doors for innovation in the CFO suite. Finance leaders are under increasing pressure to move from backward-looking reporting to forward-looking intelligence, and they recognize that automation and AI are becoming essential to stay competitive.

The new generation of CFOs, particularly at high-growth and tech-forward companies, is more open to tools that integrate seamlessly with their existing systems and provide immediate, quantifiable insight. In short: while CFOs have historically been tough customers, the timing has never been better to win them over—if the product speaks their language of precision, reliability, and measurable financial impact.

An AI CFO for Everyone

The emergence of the AI CFO marks a new era in business finance—one where advanced financial intelligence, once reserved for large enterprises, is now accessible to companies of all sizes. Historically, smaller businesses couldn’t afford dedicated CFO talent or the complex financial systems used by major corporations. But with recent advances in AI, LLMs, and real-time financial data integrations, it’s now possible for SMBs to have a CFO in their pocket. These AI-driven tools can automate bookkeeping, generate forecasts, benchmark performance, and even provide strategic financial recommendations—all through natural language interfaces. SMBs can ask: "Can I afford to hire three more engineers next quarter?” or “What’s my burn rate if revenue dips by 10%?”. For small businesses, this is transformative: it’s like having a world-class finance team available 24/7 at a fraction of the cost. The result is a future where every business, no matter the size, can operate with CFO-level insight, foresight, and precision powered by AI.

For corporates, the AI CFO represents the next stage of automation and intelligence across financial operations. Instead of siloed accounting, FP&A, and treasury systems, AI-native finance stacks are integrating these functions into a unified intelligence layer that continuously monitors performance, liquidity, and risk. These systems can automatically close the books, reconcile transactions, and produce real-time forecasts without manual input. More importantly, they can synthesize massive volumes of data—internal and external—to recommend strategic actions such as capital allocation, hedging, or cost optimization. What used to take finance teams weeks to prepare and analyze can now happen in minutes, freeing human CFOs and controllers to focus on strategy, growth, and investor alignment.

At the heart of every business is cash flow. Treasury and finance teams have the critical task of managing current and future cash flows, ensuring enough working capital to meet financial obligations and investment of assets for future activities.

Treasury teams align under three pillars: cash management, risk management, and strategic finance. In smaller organizations, treasury is usually covered by the CFO or finance department, while larger enterprises have their own treasury departments with the Treasurer reporting to CFO. Below are three critical functions of a treasury:

1. Cash Management: Treasurers need to understand their cash and liquidity position in real-time to make decisions for working capital. This includes making payments such as accounts payable, reconciliation of income and outgoing payments, and investments of assets for working capital to mitigate any risks.
2. Risk Management: Depending on the size, location, and nature of the business, treasury and finance teams must accurately monitor and offset potential risks. Common areas include foreign exchange risk for companies that transact globally or interest rate risk affecting any borrowing and lending activities. Other areas might include: commodity risk (hedging any commodities or raw materials), credit risk (assessing creditworthiness of customers), liquidity risk (fulfilling short term obligations), and operational risk (transaction fraud or internal/external events).
3. Forecasting: Treasury teams will work with strategic finance teams to make predictions on the company’s future cash position to properly plan and allocate resources. While treasury teams have typically focused on short term cash management, and financial planning & analysis on longer-term models, both teams have strengths for collaboration. FP&A teams might drive scenario models for top-line revenue, headcount, operating expenses, with treasury teams providing valuable guidance on 12–18 month forecasts.

Opportunities to Build in the Market

At Montage, we’re excited about the emergence of mission-critical infrastructure to help the treasury and finance teams manage cash flow. Below are opportunity areas we are actively exploring and collaborating with founders:

Intelligent Cash Management and Banking

In the current economic environment of rising interest rates, there is a wedge for startups to provide better yield on idle cash for small to mid-market companies. A 4.50% yield on a $3M deposit can generate $90K interest yearly, a potentially meaningful impact on operations. Providing companies with cash returns that are protected via insurance, instant liquidity, and automated portfolio management is a great wedge for incumbents to gain trust before overseeing broader money movement.

A global AI business neobank could fundamentally reshape how companies interact with money. By integrating financial intelligence directly into accounts payable, receivable, and payroll workflows, it could eliminate the friction between banking and business operations. Businesses would no longer need to manage separate systems for accounting, payments, and forecasting—the bank itself would understand their cash flow patterns, revenue cycles, and financial goals. Moreover, with advancements in stablecoins and instant settlement rails, such a neobank could enable seamless cross-border transactions without the delays or fees of traditional intermediaries. The result is a borderless financial platform that not only stores money but actively manages and multiplies it—empowering businesses everywhere to operate with the speed, precision, and intelligence of tomorrow’s global economy.

Examples: Arc, Rho, Venn, Mercury, Airwallex

Accounting Automation

At least once a month, accounting and treasury teams must reconcile payments, a process that involves matching internal records to transactions on bank statements, cards, and other financial institutions. This process can lead to more accurate views of cash on hand and uncover any issues like unpaid invoices or fraudulent activity. Unfortunately, many teams rely on manual reconciliation through Excel, leaving whitespace for automated solutions. For small businesses with few bank accounts and low volume of transactions, standard accounting software like Quickbooks can check ties to bank statements, although users often face broken bank feeds. For larger companies, reconciling global payments can be complex, involving cross-checking against multiple bank accounts, payment methods, and currencies.

The future of AI in accounting automation is being built upon the modernization of one of finance’s oldest foundations—the ledger. Intelligent systems are transforming ledgers from static, after-the-fact records into dynamic, continuously updating sources of truth. Core accounting functions—such as journal entries, reconciliations, and reporting—are now being handled by AI-driven engines that operate in real time, ensuring accuracy and consistency across every transaction. LLMs bring contextual understanding to these systems, enabling them to interpret financial narratives, categorize transactions, and produce compliant documentation. AI-native companies are leading this evolution by embedding automation directly into accounting, ERP, and ledger systems. These intelligent platforms are accelerating the month-end close process by autonomously reconciling accounts, matching inter-company transactions, and identifying anomalies before they become issues.

At the same time, AI and LLMs are revolutionizing tax, compliance, and global reporting by layering reasoning capabilities on top of these intelligent ledgers. LLMs can parse complex tax codes, detect jurisdictional differences, and generate compliant filings across multiple markets. They can surface strategic insights—such as optimal entity structures, cross-border tax efficiencies, and audit risk factors—allowing companies to navigate increasingly intricate global accounting environments with confidence. The accounting function will evolve from a transactional service center into a proactive strategic partner—powered by intelligent agents that not only record the past but also anticipate the financial future.

Examples: Modern Treasury, Ledge, Nilus, Quanta, Numeral, Numeric, Digits ERPs: Rillet, Campfire

FinOps Agents: Payments, A/P, &A/R

B2B payments are a $25T market in the US, with SMBs representing about $10T. For majority of small and mid-market businesses, making payments to other businesses is still cumbersome, expensive, and slow. An estimated 40% of small businesses still choose to get paid by check. Given the rise of autonomous payments, instant settlement railslike RTP and stablecoins, we are heading closer towards instant transactions and visibility.

The future of agentic payments in accounts payable (AP) and accounts receivable (AR) is rapidly being shaped by autonomous AI agents capable of executing financial workflows end-to-end. Instead of static rules or manual processing, these agents will proactively manage invoices, verify payment terms, reconcile transactions, and trigger payments based on predefined policies and real-time data. This evolution moves finance teams away from reactive bookkeeping toward dynamic, self-managing systems that reduce errors, accelerate settlement cycles, and ensure continuous compliance. In this world, payments become intelligent, context-aware actions — not just transactions.

In accounts payable, agentic systems will autonomously process vendor invoices, verify contract terms, and initiate payments once conditions are met — all without human intervention. They will communicate directly with suppliers, detect discrepancies, and even negotiate adjustments or discounts based on historical data and current cash flow positions. These AI-driven workflows ensure that payments are not only timely but strategically optimized. For example, an agent might delay a non-urgent payment to preserve liquidity or accelerate one to capture an early-payment discount, all while maintaining full transparency and auditability.

On the accounts receivable side, autonomous agents will be able to follow up on outstanding invoices, send reminders, and dynamically adjust tone or frequency based on a customer’s payment history or relationship value. These systems can manage collections with empathy and precision, reducing days sales outstanding (DSO) while improving customer experience. Over time, agents may even coordinate directly between counterparties’ systems — initiating, confirming, and completing payments seamlessly across networks. The result is a fully connected financial ecosystem where AP and AR become synchronized, intelligent, and largely self-operating — enabling finance teams to focus on strategy and growth rather than routine execution.

Examples: Tabs, Melio, Settle, Balance, Paystand, PayOS, Payman

AI Native Billing: Pay per Use and Microtransactions

AI-driven usage-based billing is transforming how companies price, charge, and collect for their services—ushering in a new era of pay-per-use commerce. Traditional billing platforms like Stripe were designed around static subscriptions and periodic invoicing, not the dynamic, real-time environment required by modern digital products. These systems struggle to support “just-in-time” billing or continuous settlement because they batch transactions and rely on delayed clearing processes. As a result, they’re poorly equipped for industries that demand real-time usage tracking and automated billing logic—such as API services, cloud computing, and on-demand platforms—where every millisecond of usage can translate into revenue.

AI-native billing infrastructure, on the other hand, enables autonomous metering and real-time pay-per-use pricing. Intelligent agents can monitor consumption continuously, calculate charges instantly, and initiate payment the moment value is delivered. This turns billing from a back-office function into an active, adaptive system that responds to customer behavior as it unfolds. AI agents can even predict future usage patterns to forecast revenue, dynamically adjust prices, or apply custom discounts—all while maintaining complete transparency and auditability. Businesses benefit from improved cash flow, reduced operational overhead, and tighter alignment between pricing and actual value delivered.

A key enabler of this shift is the convergence of micropayments and blockchain-based settlement, particularly with stablecoins. For small, high-frequency transactions, traditional card networks impose excessive fees that make pay-per-use models economically unviable. Stablecoins allow these microtransactions to settle instantly and at a fraction of the cost, removing friction and unlocking new business models—from per-API-call billing to streaming payments for data or compute. When AI agents orchestrate this ecosystem—measuring, billing, and settling in real time—financial interactions become fluid, efficient, and global, powering the next generation of usage-based digital economies.

Examples: Metronome, Sequence, Stage, Octane, m3tr, Lava Payments, Paid

Disrupting FP&A

Traditional FP&A processes—manual data pulls, static spreadsheets, and monthly reporting cycles—are giving way to AI-native platforms that operate continuously, integrating data across accounting, ERP, and operational systems in real time. New AI-first financial platforms are bundling automated reporting, variance analysis, and forecasting into cohesive, self-updating dashboards. Instead of analysts spending hours compiling reports, these systems autonomously generate insights, flag anomalies, and recommend actions, enabling finance teams to focus on strategic analysis and scenario planning rather than data wrangling.

A new generation of AI-native ERP systems and accounting platforms is at the heart of this transformation. By embedding intelligent agents directly into financial data flows, these platforms can consolidate reporting, budgeting, and compliance processes within a single, self-managing environment. They automate repetitive workflows such as reconciliations, close management, and cash flow projections—while also contextualizing results across departments or business units. This bundling of capabilities transforms what were once fragmented, manual finance stacks into unified, autonomous ecosystems. As a result, CFOs gain a live view of performance metrics, operational health, and financial forecasts—updated dynamically as the business evolves.

At the same time, advancements from OpenAI and large language models (LLMs) are making financial modeling more intuitive, powerful, and collaborative. Tasks that once required hours of manual spreadsheet work—like building P&L forecasts, running benchmarks, or performing sensitivity analyses—can now be generated or iterated through natural language prompts. LLMs act as “financial co-pilots,” helping teams create complex formulas, model scenarios, and interpret outcomes without needing deep technical expertise. This democratizes access to sophisticated financial analysis and dramatically speeds up decision cycles. As AI continues to evolve, FP&A will move from being a backward-looking reporting function to a forward-looking, continuously optimizing intelligence layer that sits at the core of every business.

Examples: Equals, Causal, Mosaic, Abacum, Endex, Tracelight

Always On Global Risk Analyst

AI is rapidly becoming an essential tool for CFO teams looking to identify, manage, and offset global risk across audit, compliance, and financial operations. As businesses become increasingly complex and interconnected, traditional risk management frameworks—dependent on manual analysis and static reporting—are proving insufficient. AI systems can continuously monitor global financial, regulatory, and macroeconomic data, surfacing emerging risks before they materialize. For instance, intelligent agents can analyze thousands of data points—from geopolitical shifts to currency fluctuations—to provide CFOs with predictive insights into potential exposure. This transforms risk management from a reactive discipline into a proactive, data-driven capability that safeguards enterprise stability in real time.

In audit and compliance, AI is creating a new paradigm of continuous assurance. Instead of waiting for quarterly or annual reviews, AI systems can autonomously monitor transactions, detect anomalies, and flag potential compliance violations as they occur. Large Language Models (LLMs) can interpret evolving regulatory frameworks, map them to a company’s internal policies, and even generate compliant documentation or audit trails automatically. These capabilities dramatically reduce the risk of human oversight while improving transparency and accountability across financial systems. For global CFOs, this means audits that are faster, more accurate, and continuous—creating a living, verified record of financial integrity that meets regulatory standards across jurisdictions.

When it comes to financial instruments and exposure management, AI will fundamentally change how companies hedge and optimize risk. Intelligent models can simulate multiple macroeconomic scenarios to assess interest rate risk, credit exposure, or foreign exchange volatility. By integrating real-time market data and predictive analytics, AI systems can autonomously recommend or execute hedging strategies—such as adjusting derivative positions or reallocating liquidity—to maintain optimal financial stability. These capabilities extend CFO decision-making beyond static spreadsheets into continuous, AI-driven portfolio optimization. In essence, AI empowers CFO teams to transform risk management from an afterthought into a strategic advantage—anticipating volatility, enhancing resilience, and ensuring the enterprise remains agile in a dynamically shifting global economy.

Examples: Inscope, Variance, Bead, Bound

Ways to Win and How We’ll Help

Across these opportunity areas, we see three core pillars towards building a defensible platform play:

1. Deep connectivity across a sprawling landscape of vendors;
2. Real-time visibility in a world of instant payments;
3. An initial wedge targeting a core use case, with expansion opportunities through new product features or embedded fintech opportunities.

At Montage, we are deeply committed to collaborating as an entire team to help founders we back to succeed. We work for founders, bringing new introductions to customers and partners, advising on product and GTM strategy, closing critical hires, and coaching through future financings.

Founders building in these areas — we would love to chat with you. Please send a note to Connie connie@montageventures.com and Matt matt@montageventures.com