How accounting firms use finance AI to reclaim time and trust

For CFOs and IT managers under pressure to modernize financial processes, automation, as we have seen in several generations of RPA (robotic process automation), is not enough. Clearly, transparency and interpretability are equally important.

Accounting firms and finance functions within organizations are now turning to AI systems that do the thinking, not just the computing. One of the most ambitious examples is Basis, a US-based startup founded just two years ago that is building AI agents designed to automate regulated accounting work, keeping human oversight in the loop.

Such systems indicate a shift in enterprise automation. Instead of replacing people, AI agents extend human expertise and combine the accuracy of AI models with the needs of financial oversight professionals for compliance and customer trust.

Business Impact: Efficiency with Accountability

Basis develops artificial intelligence agents that handle routine financial tasks such as reconciliations, journal entries, and financial summaries. The platform is built on OpenAI’s GPT-4.1 and GPT-5 models, which give operators the ability to independently examine every decision step made.

Accounting firms using the Basis system report savings of up to 30% time and a consequent higher capacity for consulting work. This is the type of value creation that traditional automation cannot deliver at the same speed or at a similar cost to a company.

Unlike many automation tools that act as black boxes, Basis focuses on reviewable reasoning. Each recommendation includes an account of the data used and the logic behind it. Visibility means that accountants can validate each result and remain accountable for the results, which is always an important feature in financial operations, especially in highly regulated industries.

Implementation and challenges: Building systems that learn

Agent AI can handle accounting as a network of workflows, not as isolated tasks. A supervisory AI agent, powered by GPT-5 in the case of the Basis platform, manages the entire operations. It can delegate sub-agents for specific tasks operating on different models, with the AI ​​model chosen depending on the complexity of the task and the type of data to be processed.

For example, for quick queries or clarifications, Basis uses GPT-4.1 for its speed, while for complex taxonomies or month-end closes, GPT-5 provides better reasoning and context processing.

The company benchmarks each of its models against real-world accounting workflows to determine when it’s safe to let agents handle more responsibility. Finance professionals can always see what the system did, why it made certain choices, and how confident it is in its recommendations.

This flexible architecture allows companies to scale AI and help ensure accuracy while increasing levels of automation. This process reflects the hybrid collaboration between human and artificial intelligence that is now the norm in sectors such as legal services and risk management.

Lessons for other sectors

What makes multi-agent fundamental and financial AI relevant beyond accounting is the model orchestration approach, routing tasks to the most appropriate AI model based on its performance and latency.

Coordination can benefit similar deployments in purchasing, human resources, or compliance; In fact, anywhere, where large amounts of structured decisions need transparency and accountability.

Basis’ collaboration with OpenAI shows how powerful AI logic engines can be in secure data environments.

The goal is not pure speed, but rather automation that increases confidence in the operator and in the models themselves. These are systems that evolve without humans losing control of the outcomes.

conclusion

AI in accounting is not just about automating entries, but is moving more towards building systems that think like accountants, not machines.

For enterprise leaders, the Basis model demonstrates a path toward automation that improves over time. Each improvement in the model makes teams faster and smarter without giving up control of the automation process.

(Image source: “Accounting Charts” from the World Bank Image Collection is licensed under CC BY-NC-ND 2.0.)

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