Originally published on LinkedIn. Follow me, Harold Hare, for insights on disruptive industries shaping startups and enterprise.
AI accounting startup Basis reached a $1.15 billion valuation after raising $100 million in a funding round led by Accel with participation from GV, Lloyd Blankfein, and Khosla Ventures. Founded in 2023 by Matt Harpe and Mitchell Troyanovsky, the company focuses on deploying autonomous AI agents that perform core financial workflows. The system targets the operational tasks that dominate accounting practices, including financial statement preparation, tax filings, and expense tracking. Rather than positioning AI as an advisory tool, the platform executes accounting work directly within firm workflows.
The founders built the company around a model of “long-horizon agents,” software systems designed to complete complex professional tasks over extended periods of time. These agents are designed to work across workflows that often require coordination between tax preparation, financial reporting, and compliance activities. The platform is already deployed among a significant portion of major accounting firms in the United States. Bloomberg reported that the system is used by about 30% of the top 25 accounting firms and 20% of the top 150 firms.
From copilots to agents
Early enterprise AI tools were built to assist professionals within workflows while leaving operational tasks to human staff. Systems introduced in recent years often functioned as copilots, generating suggestions or providing research assistance. The accounting industry adopted many of these tools cautiously because financial work involves regulated processes and structured documentation. Advisory tools improved efficiency but did not fundamentally alter how accounting firms completed operational tasks.
Basis approaches the problem differently by treating accounting work as a sequence of operational processes rather than a collection of individual tasks. The platform deploys AI agents capable of operating within those workflows for extended periods. These systems can coordinate multiple accounting processes simultaneously and return completed deliverables for human review. This design moves the role of AI from assistance toward execution.
Autonomous agents are beginning to replace assistive AI tools across enterprise software. Similar approaches have emerged in software development platforms where coding systems complete complex engineering tasks without continuous human prompts. The founders of Basis have described accounting agents as analogous to those coding systems. In both cases the objective is to move from AI that answers questions toward AI that performs structured work.
Automating accounting workflows
Accounting firms rely heavily on repeatable workflows that involve multiple forms of documentation and verification. Preparing tax returns, financial statements, and regulatory filings requires assembling information from several sources before completing standardized reporting processes. These workflows often involve multiple team members working sequentially across different stages of a project. Automating the full sequence of steps requires systems capable of coordinating data inputs and procedural requirements simultaneously.
The Basis platform is designed to execute full accounting workflows across firm operations. Its AI agents operate across accounting practices including client advisory services, tax preparation, and audit support. These systems run in the background while coordinating multiple accounting processes and assembling completed deliverables for review by professionals. According to CPA Practice Advisor, the agents can perform end-to-end accounting workflows that traditionally required significant staff time.
One example involves the preparation of partnership tax returns, one of the more complicated filings within the U.S. tax system. A partnership return requires accountants to produce individualized tax documents for each partner, manage profit-sharing arrangements, and sometimes file across multiple states. The platform recently demonstrated an AI agent capable of completing this process autonomously. Tasks that normally require days of coordination can be executed by a system operating continuously over extended periods.
Inside major firms
The accounting profession faces a structural labor imbalance that has intensified over the past decade. Demand for accounting services continues to grow while fewer students pursue accounting careers. At the same time a large cohort of experienced accountants is retiring or leaving the profession. The Bureau of Labor Statistics projects continued demand for accountants despite the shrinking pipeline of new professionals entering the field.
Large accounting firms have responded by searching for ways to redistribute workloads across technology systems. Many firms historically relied on large teams of junior staff to perform labor-intensive tasks during tax and audit cycles. These responsibilities often involved repetitive documentation work that created long working hours during peak periods. The model became difficult to sustain as recruitment pipelines weakened.
Automation offers an alternative to expanding headcount indefinitely. By shifting the most repetitive accounting tasks to software systems, firms can redirect human professionals toward advisory services and strategic planning. Harpe described the transition as elevating the work accountants perform while preserving the role of professional accountants. Routine filings and financial documentation become automated while accountants focus on higher-value analysis such as tax strategy and capital allocation decisions.
A competitive accounting AI market
Basis entered a market where several technology companies are attempting to automate financial services workflows. Investors have directed significant capital toward startups building AI tools for accounting and financial analysis. Recent funding rounds for companies such as Accrual and Pennylane indicate growing interest in software that performs core accounting functions. Venture capital firms view the sector as a large operational market with substantial potential for automation.
The expansion of AI into financial services has also affected financial markets themselves. Shares of wealth management companies declined after AI-driven tax planning systems entered the market. Financial data providers experienced similar reactions after new AI models capable of conducting complex financial research were released. AI systems are beginning to change how financial services are delivered.
Accounting represents one of the most structured and data-intensive areas of financial services. The industry relies heavily on standardized documentation, regulatory frameworks, and repeatable workflows. Those characteristics make it a natural environment for automation systems designed to handle defined procedural tasks. Companies building AI agents for accounting therefore prioritize operational reliability and procedural accuracy.
What execution will determine
The long-term viability of autonomous accounting systems depends heavily on operational accuracy. Financial filings and regulatory documents must meet strict compliance requirements and tolerate little margin for error. AI agents performing these tasks must demonstrate reliability comparable to professional accounting teams. Consistent accuracy across multiple jurisdictions and tax frameworks will determine whether firms rely on the technology at scale.
Adoption also depends on whether firms integrate autonomous systems into existing operational structures. Accounting firms maintain complex internal processes that combine client management, regulatory review, and financial reporting. AI agents must operate within those structures without disrupting compliance workflows or internal controls. Systems that integrate smoothly into those processes are more likely to see sustained use.
Scalability across the broader financial services industry remains another critical factor. Accounting represents one of many professional services that rely on structured operational tasks. If long-horizon agents prove reliable in accounting environments, similar models may expand into areas such as financial analysis, legal documentation, and regulatory reporting. The central question raised by this model remains whether autonomous digital workers can perform regulated professional workflows with the consistency required to operate inside the world’s financial institutions.
