What if an accountant could support 100 businesses with the effort it currently takes to serve 10 – and still offer sharper insights, faster answers, and more precise financial guidance?
This isn’t hyperbole. It’s where we’re headed as embedded finance (EF) infrastructure meets the rise of autonomous software agents. If accountants are to remain not only relevant but central to business strategy in the years ahead, it’s time we reimagine their workflows, tools, and influence. The accountant definitely stays there in the mix, but mostly to supervise and take responsibility.
Our latest research found that 51% of UK SaaS product leaders are investing in AI, but the type of AI we’re talking about requires access to the right tools to do its magic. In the context of accounting software, that’s where embedded finance matters. Embedded finance gives AI agents the levers to trigger payments, apply rules, or initiate transactions securely and with oversight. This is no fringe trend since 92% of the research participants expect embedded payments to be live in their platforms by the end of 2025.
The automation narrative is old. The augmentation story is just beginning.
For years, the accounting profession has been adapting to automation. Bank feeds replaced data entry, OCR reduced invoice tedium, and cloud platforms gave shared visibility. These were necessary steps but they were incremental.
Now, artificial intelligence is shifting the curve. So far, much of what we’ve seen falls into the category of AI enhancement: summarising reports, flagging anomalies, accelerating workflows. However, what’s emerging next is more profound: agentic AI, which are software agents that can reason, plan, and act across systems to achieve business goals. While SaaS tools have long helped accountants see what’s going on, and to support workflows, agentic AI will begin to use the data and execute the workflow, replacing much of what accounting software users do today. For that, it needs more than intelligence. It needs tools and infrastructure, and embedded finance is the closest thing there is to a machine-accessible tool to perform financial activity.
Embedded finance: Connecting insight with execution
At its simplest, embedded finance integrates financial products and controls directly into business platforms. This removes the need for switching between accounting software and banking portals, financial actions – like paying a bill, funding a wallet, or setting spend limits – happen within the system of record.
For accountants, this isn’t just about convenience, it’s about tighter control, fewer errors and real-time data. For AI, on the other hand, it’s all of that, but crucially, it’s what unlocks execution.
If an AI agent identifies rising supplier costs, it can initiate a review or payment only if the underlying system allows it. If challenges are spotted in cash flow, an embedded infrastructure enables the response, securing working capital, freezing spend, applying new thresholds in real time.
Accountants as growth enablers
This convergence of AI and EF doesn’t replace the accountant. It enhances them.
Accountants already serve as financial translators interpreting what’s moving through the business and advising on what it means. They manage compliance, cash flow, risk, and investment. In smaller firms, they often handle payments directly, and in larger businesses, they guide the systems and teams that do.
But historically, that advisory function has been constrained by tools that are backward-looking, fragmented, and dependent on after-the-fact reconciliation.
Embedded finance closes those gaps and ensures that financial events are captured cleanly and contextualised instantly. When paired with AI – whether to forecast trends, identify risks, or triage workflows – the accountant becomes a strategic node in a system that can think and act together. Agentic AI doesn’t just have the potential to reduce workload,it expands the scope of impact. One accountant can advise more clients, internal finance teams can run leaner without sacrificing insight ,and the advice they give is grounded in real-time data and reliable execution.
Navigating the risks
Agentic AI raises new questions: who’s responsible when software initiates a payment? How do we ensure controls aren’t bypassed? What does “authentication” look like when the actor isn’t a person?
Today’s financial systems are designed around humans, not agents. They rely on passwords, approvals, and policies meant for cognitive actors with legal responsibility.
Embedded finance offers a way forward. When designed with APIs and policy logic, EF infrastructure supports:
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Rolebased permissions
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Delegation of authority with spend limits
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Real-time, human-in-the-loop oversight
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Comprehensive audit trails
In fact, it’s not hard to take an embedded finance solution and make it accessible to agents via protocols such as MCP.
However, the future will require more. We’ll need new protocols to authenticate software actors, and methods to assign liability and trace decision-making across agent ecosystems. Practical deployments would still need to navigate consent management, strong customer authentication, and existing anti-fraud systems designed to keep out non-human actors (bots).
Accountants, who are already fluent in compliance and control, will play a central role in shaping and applying these frameworks. Actions that have financial, and even legal, consequences need a responsible human in the loop; indeed, this assumption of responsibility, while scary, could become the best protection that accountants have against their job being replaced by AI.
Building the new foundations of trust
Just as embedded finance once reshaped compliance and risk management by bringing financial capability inside software, it now must evolve again to support agentic systems operating at machine speed.
The accountant’s role in this is not to resist change, but to shape it.
By designing controls for agent-mediated activity, advocating for systems that integrate execution with insight, and embracing the tools that turn data into decisions, accountants become more than guardians of the ledger. They become architects of trusted automation.
That’s why AI and embedded finance are not competing trends. They are co-dependent layers of a smarter, faster financial function.
The platforms that win in 2026 won’t simply report. They’ll respond. And the professionals who lead them won’t just interpret the numbers. They’ll design the systems that act on them.
