A lot has changed in finance over the past few years, and the role of Chief Financial Officer has transformed.

Economic volatility has become the norm. Fraud is becoming more sophisticated. Cash flow remains under scrutiny. Against that backdrop, finance teams are being asked to deliver more strategic value than ever before.

At the same time, artificial intelligence has become impossible to ignore.

Every vendor seems to have an AI story. Every conference has AI on the agenda. Every CFO is being asked what their AI strategy looks like. It becomes exhausting.

The challenge is that most discussions about AI are surface level and focus on technology rather than outcomes. Finance leaders don't wake up wondering how many AI models they're using. They care about improving cash flow, reducing risk, protecting the business from fraud, increasing efficiency, and helping their teams focus on higher-value work than rote manual approvals.

That's why the more useful conversation isn't about whether your organization is using AI. Every organization is clamoring to say they’re using AI. It's understanding where your organization sits on the AI maturity curve and how AI can create measurable business value.

 

The AI Maturity Model: A Simple Framework for Understanding Progress

Most organizations are on a journey that moves from manual processes to autonomous operations, and are often early in that journey.

The easiest way to understand that journey—and where an organization needs to go — is through an AI maturity model.

 

Level 0: Manual

This is where many finance organizations are operating today.

People drive the process. Information sits in spreadsheets, emails, ERP systems, bank portals, and shared drives, siloed away. Teams spend significant time gathering information, reconciling data, tracking approvals, and manually investigating exceptions.

For example, an AP manager might receive hundreds of invoices each week. Team members enter invoice data manually, route approvals by email, and spend hours fruitlessly following up on stalled invoices. If something goes wrong, someone must discover it and figure out what happened.

The process works, but it is heavily dependent on people and time, and thus difficult to scale.

 

Level 1: Assistive AI

This is where many companies are just beginning their AI journey.

Assistive AI helps individuals complete tasks more efficiently. It might summarize documents, draft communications, answer questions, or retrieve information. This could involve an AI assistant creating a supplier communication, summarizing a contract, or explaining a payment exception. The employee still does the work, but they’re able to do it faster.

The process itself hasn't changed, but it has sped up. Productivity improves, but business outcomes often remain largely the same.

 

Level 2: Adaptive AI

At this stage, AI moves beyond helping people with their workflows and starts helping workflows achieve greater efficiency.

Instead of reviewing every transaction one at a time, AI begins identifying patterns, highlighting anomalies, and recommending actions across tens of thousands of payments.

Let’s take a finance team processing 10,000 invoices each month. Rather than asking employees to review every invoice, AI identifies the 50 transactions that appear unusual based on supplier behavior, invoice values, payment timing, or account details. The finance team then focuses their attention on those 50 invoices.

This is often where organizations begin seeing meaningful efficiency gains and improved visibility into risk and performance.

 

Level 3: Supervised AI

This is where AI starts taking action directly and is the stage where good governance is the key to unlocking greater success.

Rather than simply making recommendations, AI can execute tasks within established guardrails while escalating exceptions to employees for review.

For example, an invoice arrives from an existing supplier. The system validates the supplier, classifies the invoice, routes approvals, and prepares payment processing automatically. If everything falls within the parameters that have been established, the workflow continues. If bank account details suddenly change or the payment amount appears unusual, a finance professional is alerted and the workflow pauses.

Again, the system handles routine work, and humans focus on exceptions. This stage just turbocharges it.

This is also where organizations typically see measurable improvements in processing costs, invoice cycle times, cash visibility, and operational efficiency.

 

Level 4: Autonomous AI

At the highest level of maturity, AI manages workflows from one end to another while operating within policies and controls defined by the finance team.

Consider a supplier submitting an invoice. In this ideal scenario, highly agentic AI validates the supplier, verifies account details, assesses payment risk, determines optimal timing based on cash position and supplier terms, executes the payment, delivers remittance information, and updates financial systems automatically.

Finance teams remain in control through governance, oversight, and auditability, but they are no longer involved in every operational task.

The focus shifts from managing processes to managing outcomes and using employee time on more valuable outcomes.

 

Why Finance Is Different

AI adoption will look different in finance for one simple reason: These teams are justifiably wary of handing responsibility over to AI.

A marketing mistake might lead to lower engagement from prospects, while a procurement mistake may result in oversupply. These are problems, but not fatal ones.

A financial mistake, however, can result in fraud losses, payment errors, supplier disruption, compliance concerns, or working capital challenges. In a worst-case scenario, it could mean all of the above.

That's why finance leaders need to have confidence alongside automation. AI in finance must operate within clearly defined controls. It must be auditable. It must provide transparency. Most importantly, it must support accountability in when things go wrong. This is particularly important because finance operations have become increasingly fragmented.

Accounts payable, accounts receivable, treasury, cash management, banking systems, and ERP environments often exist in separate systems with separate processes. Finance teams spend an enormous amount of time simply trying to follow the movement of cash throughout the organization.

AI creates value when it helps connect these workflows. If it’s a disconnected tool organizations can’t trust, it won’t be used.

 

The Biggest Opportunity: Following the Cash Lifecycle

AI must plug seamlessly into the end-to-end cash lifecycle. Happily, it can.

AI’s most exciting world of possibility involves improving decisions across the entire process of paying and getting paid. On the accounts receivable side, AI can identify customers who are forecast to pay late and recommend actions before invoices become overdue.

Within treasury, AI can help analyze cash positions, forecast liquidity requirements, and highlight potential risks earlier than traditional reporting methods.

For accounts payable, AI can identify duplicate invoices, unusual supplier activity, suspicious account changes, or payment requests that deserve closer attention.

In fraud prevention, AI can recognize those behavioral patterns and anomalies that are hard for even automated systems and nearly impossible for humans to detect manually across millions of transactions.

Each improvement has value, with a more intelligent and connected finance operation being the true goal.

 

How Paymode Thinks About AI

At Bottomline, AI is not a standalone feature. It’s an opportunity to embed an unprecedented level of intelligence into the payment lifecycle.

That means applying AI in the areas where finance teams already spend their time and where business outcomes can be improved with the greatest impact.

The gold standard example is supplier onboarding.

Historically, onboarding suppliers has involved substantial manual effort. Teams gather information, validate records, confirm payment details, and assess potential risks.

AI can help prioritize suppliers, identify risks earlier, surface inconsistencies, and accelerate the process without sacrificing the level of control CFOs expect. The same is true of fraud prevention, which often relies on rules that must be more dynamic to keep up with the way fraud is evolving.

AI is good at identifying unusual behaviors, suspicious account changes, abnormal payment patterns, and activity that doesn't align with historical norms. Rather than simply enforcing rules, AI helps organizations recognize emerging risks before money leaves the business, adding a powerful tool that plugs into existing efforts and turbocharges them.

Finally, think of payment optimization. Every payment decision has implications for cash flow, working capital, supplier relationships, rebates, and discounts. AI can evaluate those variables continuously, helping finance teams make more informed decisions about when and how payments should occur.

In all of these cases, finance teams do not cede control. Instead, their expertise goes toward the most valuable decisions in every workflow, and AI makes better outcomes possible.

 

The Future Is Outcome-Driven Finance

The most important shift happening in AI is that organizations are moving away from task-focused conversations.

The question is no longer "Can AI help me do this task faster?"

The questions now are:

"Can AI help me improve cash flow?"

"Can AI help me reduce fraud?"

"Can AI help me optimize working capital?"

"Can AI help my team spend less time processing transactions and more time driving business performance?"

 The organizations seeing the greatest value from AI are not necessarily using the most advanced technology but answering those questions by applying intelligence to areas that have an outsized impact on business outcomes.

 

The Road Ahead

AI maturity is a business transformation journey rather than a technology project.  Whether organizations are heavily manual, experimenting with assistive AI, or deploying supervised and autonomous workflows, the direction of business is clear.

Finance teams are moving from manual execution toward intelligent operations, from managing transactions toward managing outcomes, and from reacting to problems toward preventing them.

AI will never fully replace finance professionals. The future is destined to be defined instead by finance professionals using AI to make better decisions, reduce risk, optimize cash flow, and create stronger business performance.

Once organizations understand what drives the business forward, they will win not by being the earliest adopters or the power users of AI, but the ones who bend the technology to achieve the outcomes that matter most.