For years, the promise of automation has dangled like a carrot in front of CFOs and CIOs. Modernising finance operations, reclaiming precious hours, driving efficiency – it all sounds fantastic on paper. But for many, the reality has been a bit more nuanced. Traditional robotic process automation (RPA), while certainly helpful, often feels like a powerful calculator operating behind a locked door. You get the answer, but how it got there? That’s often a black box.
And let’s be honest, in the world of finance and accounting, transparency isn’t just a nice-to-have; it’s non-negotiable. Explainability matters just as much as computation, especially when compliance, audits, and client trust are on the line. This is precisely why we’re witnessing a significant shift: accounting firms and finance functions are now turning to AI systems that don’t just compute data, but truly reason.
This isn’t about replacing human expertise, but extending it. Imagine a world where AI doesn’t just crunch numbers faster, but provides a detailed account of its thought process, allowing human professionals to validate every step. That world is already here, exemplified by innovative start-ups like Basis, which are pioneering AI agents designed to automate structured accounting work while keeping human oversight firmly in the loop. This signals a new era for enterprise automation – one built on efficiency, accountability, and ultimately, trust.
Beyond Simple Automation: The Rise of Reasoning AI
The journey towards full financial automation has been fascinating. We’ve moved from basic macros to sophisticated RPA bots handling repetitive tasks. Yet, a fundamental challenge persisted: the lack of clear insight into how automated decisions were made. In finance, where every decimal point counts and every decision can have significant repercussions, this opacity was a major hurdle.
Think about a typical reconciliation process or a complex journal entry. An RPA bot might execute it flawlessly, but if an auditor or a client asks, “Why was this specific adjustment made?” or “What data informed this classification?”, the traditional bot offers little in the way of an explanation. This creates a bottleneck, forcing human teams to double-check, re-verify, and essentially, distrust the automation they implemented to save time.
Enter AI agents. Unlike their RPA predecessors, these systems are built to reason. They leverage advanced AI models, like those from OpenAI, to not only process information but also to articulate the logic behind their actions. This fundamental difference is a game-changer. It transforms automation from a blind execution tool into a collaborative partner, extending the precision of AI models with the critical oversight finance professionals need for compliance and client confidence.
Reclaiming Time and Trust with Agentic AI
For accounting firms, time is money, and trust is currency. The new generation of AI agents is proving instrumental in reclaiming both. Firms using platforms like Basis are reporting impressive results: up to a 30 percent time savings. Imagine what a finance team could do with an extra 30% of their bandwidth – more strategic advisory work, deeper client relationships, or focusing on high-value analysis that truly drives business growth.
Basis, for instance, develops AI agents that proficiently handle routine yet crucial finance tasks such as reconciliations, journal entries, and financial summaries. The platform itself is built on powerful models like OpenAI’s GPT-4.1 and GPT-5, giving it the advanced reasoning capabilities required for complex financial operations. But here’s the crucial part: it’s not just about speed, it’s about visibility.
Unlike many automation tools that operate as black boxes, Basis champions what they call “reviewable reasoning.” Every single recommendation, every decision step taken autonomously by the AI, comes with an account of the data used and the precise logic behind it. This means accountants can examine each decision, validate every outcome, and maintain full responsibility for the results. In highly regulated industries, this level of transparency isn’t just an advantage; it’s an absolute necessity. It builds a foundation of trust not only in the technology but also in the entire financial operation.
An Evolving Partnership: Building Systems That Learn and Adapt
One of the most compelling aspects of agentic AI in accounting is its ability to treat financial operations as an interconnected network of workflows, rather than a series of isolated, disjointed tasks. Imagine a supervising AI agent, powered by a sophisticated model like GPT-5, overseeing the entirety of these processes. This master agent can then intelligently delegate specific tasks to sub-agents, each running on different AI models tailored to the job’s complexity and the type of data involved.
For instance, for quick queries or straightforward clarifications, a sub-agent running on GPT-4.1 might be deployed for its speed and efficiency. However, when it comes to complex classifications or the intricate details of a month-end close, a GPT-5 powered sub-agent, with its superior reasoning and context-handling capabilities, takes the reins. This intelligent model orchestration ensures that the right tool is always applied to the right task, optimising both accuracy and efficiency.
What truly sets these systems apart is their capacity to learn and evolve. Companies like Basis continuously benchmark their models against real-world accounting workflows, meticulously determining when it’s safe to grant agents greater responsibility. Finance professionals are never left in the dark; they can always see precisely what the system has done, understand the rationale behind its choices, and gauge its confidence level in each recommendation. This malleable architecture allows firms to scale their AI deployment incrementally, ensuring accuracy and building confidence as automation levels increase. It beautifully mirrors the hybrid human-AI collaboration that is rapidly becoming the norm in sectors from legal services to risk management.
Lessons for the Broader Enterprise Landscape
While we’ve focused on accounting, the implications of this model-orchestration approach extend far beyond. What Basis is doing in finance offers a blueprint for other sectors wrestling with large volumes of structured decisions that demand both efficiency and transparency. Think procurement, human resources, or compliance operations – any area where accountability is paramount and a black box approach simply won’t cut it.
The strategy of routing tasks to the most appropriate AI model based on its performance and latency is a powerful one. It demonstrates how advanced AI reasoning engines, when deployed within secure data environments, can be incredibly effective. The ultimate goal here isn’t merely speed, though that’s a welcome byproduct. It’s about cultivating automation that fosters increased trust in the operator, and crucially, in the AI models themselves. These are truly intelligent systems designed to evolve and improve over time, all without humans ever losing sight – or control – of the outcomes.
AI in accounting is no longer just about automating entries; it’s about building systems that begin to think like accountants, not just machines. For enterprise leaders across the board, the Basis model offers a clear pathway towards automation that not only improves over time but also empowers teams. Each advancement in the AI models makes teams faster, smarter, and more strategic, without ever surrendering control to the automation process. This is the true promise of intelligent automation – a future where technology amplifies human potential, fostering both innovation and unwavering trust.
