AI’s evolution in finance: rules → copilots → agents
AI is often discussed as though it means one thing, leading many software vendors to describe themselves as “AI-powered” when in practice, their tools rely on rule-based automation. AI, however, extends beyond automation. Understanding it means recognising the differences between rule-based workflows, AI copilots, and AI agents. In finance, these distinctions matter because each approach supports very different outcomes, from automating stable, repeatable tasks to managing high-volume, exception-heavy processes that span multiple systems.
1.1 Rules-based automation
Rules-based automation is exactly what it sounds like: systems that follow defined rules to move data and trigger actions i.e if specific conditions are met, a specific action will be executed. This category includes technologies such as RPA, workflow engines, and no-code automation tools. It is important to note that Rules-based automation is not AI, but it remains the most effective and practical solution for certain use cases. Rules-based automation is most commonly implemented through Robotics Process Automation (RPA) and no-code workflow tools, and they work exceptionally well when:
The process is stable and predictable
Data is structured
Exceptions are rare
The correct answer is purely deterministic
In these environments, Rule-based automation is cheap to operate, easy to audit, highly robust and very fast. The problem is that finance work is often not like that. Real finance involves messy emails from customers and vendors, documents in hundreds of formats, missing or ambiguous information, many edge cases, and choices that require judgment. In these situations, rules-based systems tend to break down.
1.2 GenAI copilots
The second wave of AI adoption brought about large language models and copilots. These tools can understand text, answer questions, and generate narratives. They are genuinely useful — especially for explanation and analysis.
Copilots are good at:
Summarising large amounts of text
Helping you “chat with” financial data
Drafting commentary and answers
Explaining variances or policy details
However, copilots are still assistants in practice. They don’t run finance processes. They don’t close loops. They don’t own outcomes, and we’re yet to see high ROI implementations of AI co-pilots. They mainly help people think, write and analyse, which is valuable but often difficult to track in ROI terms. Teams feel more productive, but invoices are still chased manually and AP still gets coded by people. Copilots are helpful, but in many finance teams they haven’t fundamentally moved the revenue, cash or cost levers.
1.3 AI agents - systems that actually do the work
Agentic AI is not “a smarter chatbot.” It represents a shift from AI that assists humans to AI that acts on behalf of humans. An AI agent is given an objective, tools to reach that objective, and context.It works out how to achieve that goal, executes the steps required, and adapts along the way. Instead of generating text and waiting for a human to act, agents take actions inside your systems.
Agents can:
Plan and execute multi-step workflows
Log into or connect to tools like ERP, CRM, billing and email
Send messages, collect documents and update records
Track progress over time, not just within a single prompt
Escalate only when true human judgment is required
This is fundamentally different from copilots. A copilot will draft a collections email, an agent runs the entire collections process.
For example, in Accounts receivable, an agent can:
Read a customer’s email about an invoice
Open the ERP and locate the invoice
Identify that the PO field is missing
Request and collect the PO from the customer
Resend the corrected invoice
Confirm goods delivery if needed
Reply to the customer with updated details
Schedule and send follow-ups automatically
The agent isn’t “advising” the collector - it is doing the work of a collector.
Agents can also reason, not just follow rules. They evaluate context: prior payment history, tone of customer responses, dispute patterns, risk levels, policy rules. They can handle edge-cases such as partial payments, duplicate invoices, disputed line items or missing backup documentation, situations where rigid automation fails.
This unlocks automation of finance processes that were previously impossible to automate, including:
Billing support/finance inbox triage and replies
Credit control and collections follow-ups
Chasing missing POs or payment confirmations
Resolving invoice queries conversationally
Prioritising accounts based on risk and value
In short:
RPA follows rules
Copilots answer questions
Agents do the work
This is why agents are well-suited for finance operations. Finance is made up of high-volume, judgment-heavy, multi-system tasks, which is exactly the domain agents excel at.
What makes a process suitable for AI agents?
Not every process is right for agents. Some finance work is political, strategic, relationship-driven and inherently human. Nevertheless, a very large share is extremely well suited.
Strong candidate processes usually:
Repeat frequently
Still requires judgment
Involve messy, unstructured inputs
Jump across multiple systems
Have a measurable “done” outcome
Think of workflows such as:
Replying to billing queries
Chasing overdue invoices
Coding incoming invoices
Identifying duplicate payments
Onboarding a new vendor
Resolving disputes
A useful rule of thumb: if your team already recognises the “shape” of the task because they do it dozens or hundreds of times a week, there is likely an opportunity for an AI agent to take on much of that routine.
What are the finance use cases for AI agents with High ROI today?
Today, the highest-ROI uses of AI agents in finance are concentrated in operational finance, where work is high-volume, repetitive, exception-heavy, and outcomes are clearly measurable. While there is genuine hype in the market, there are also practical areas where agentic AI is already delivering real, measurable returns.
The two clearest are Accounts Receivable and Accounts Payable.
Accounts receivable - Tangible ROI for both cost and cash flow
Accounts receivable is almost purpose-built for agents: AR processes involve lots of communication and exceptions, and have clear outcomes.
Billing support & finance inbox automation
Agents can automate replies to large volumes of queries in the finance -inbox
“please resend the invoice”
“we need a PO added”
“what is this charge?”
“can you send a statement?”
They read emails, retrieve information from relevant systems such as ERPs and CRMs to get context, take required actions, and reply directly to customers. As a result, inbox backlogs shrink, resolution times fall, and billing issues are cleared faster which often translates directly into faster payment and improved cash flow.
Rather than blasting reminders, agents:
Personalise tone and cadence
Handle objections and excuses
Record promise-to-pay dates
Follow up automatically
Escalate where risk warrants it
Most importantly, they finally make the long tail economically viable — those hundreds or thousands of customers who together represent a lot of cash but cannot all get manual attention.
Accounts payable: High volume and structure with judgment
Accounts Payable remains one of the most labor-intensive finance activities.
Agents can:
Capture invoice data
Infer correct coding from history
Match to PO and GRN
Catch duplicates
Flag suspicious patterns
Because they understand content instead of just templates, supplier format changes don’t break the system.
The effect:
Lower processing cost
Fewer manual touches
Better fraud and error control
Faster cycle times
3.1.3 Financial close
AI can help in the financial close process, but it is often more co-pilots that assist humans than AI agents that perform the work:
Explain variances in plain language
Perform reconciliations and flag anomalies
Monitor completeness of tasks and checklists
3.1.4 FP&A
AI is helpful, but the ROI is less tangible. Agents can help draft management reports, prepare variance commentary, answer “what happened last month?", and empower non-finance user to answer questions in data.
Agentic-native vs “AI-powered”: how to choose vendors
We are in the era of AI-washing. Almost every vendor now claims to be AI-powered.
Just as every on-prem vendor in 2012 suddenly became “cloud,” today every legacy finance tool markets itself as “AI-powered.” In most cases, what this really means is: the same old workflow engine with the same form-based UI and human-driven processes, with a chatbot and some auto summaries bolted on top. These are not truly agentic systems. Nothing fundamental has changed in how work gets done. Agentic-native systems are different. They assume from the beginning that:
agents will act autonomously across systems
Workflows are multi-step and involve reasoning
Humans supervise, monitor and approve work; not perform the actual work
Guardrails and auditability are essential to deploy autonomous agents
When evaluating, ask:
Can it actually take actions — or only draft text?
Does it write back into ERP/CRM?
How is agent accuracy monitored?
Where are controls enforced?
If it cannot act, it’s not an AI agent.
How to get started
You don’t need a massive transformation programme. Start small, targeted and measured.
Lots of invoices? → start with AR
Lots of suppliers? → start with AP
Then:
Choose one workflow
Keep human approvals in the loop at the beginning
Measure concrete metrics like DSO, processing time, backlog and error rates
Increase autonomy gradually as confidence grows
This lets you build capability without risking operations.
Conclusion: Finance will be transformed by AI agents
Finance is behind other functions in AI adoption, yet finance teams are buried in manual repetitive work. AI agents finally match what finance actually does every day: high-volume, judgment-heavy, multi-step work across multiple systems. Finance is now ripe for agentic adoption.
The first and clearest use cases are in Accounts Receivable and Accounts Payable. Agents can already automate billing support, collections, invoice capture, coding, matching and duplicate detection. These are not experiments; they are live, operational workloads that deliver measurable ROI in cash and processing efficiency.
The most important strategic decision for finance leaders is no longer whether to adopt AI, but which kind. Legacy tools rebranded as “AI-powered,” with chatbots layered onto existing workflows, rarely change how work gets done. In contrast, agentic-native platforms like Paraglide built for autonomous execution, enable agents to take actions across finance systems, saving finance teams time and delivering measurable ROI.
The operating model of finance is now shifting from: humans doing the work, with software assisting, to: AI agents doing the work, with humans supervising, monitoring and approving work.
Finance will be transformed first where volume and repetition are highest.
Start with AR if you have many invoices
Start with AP if you have many suppliers.
Teams that move now and choose truly agentic, AI-native solutions — will define how finance is run in the next decade.