The Rise of Vertical AI in Accounting

By Marc Andrusko and Seema Amble

Following the publication of Death, Taxes and AI, our look at how generative AI is permeating the accounting market, and our video discussion on the same topic, we heard from countless accounting professionals who are excited for what gen AI can unlock for their profession. From our conversations, we learned that while AI pilot programs are prevalent industrywide and firms are enthusiastic to try them, fully deployed vendor relationships are still relatively scarce. This is probably because AI models are not yet as proficient with numbers as they are with text, and because accountants (unsurprisingly) are fairly risk averse. As such, we wanted to offer a quick update on one big trend we’re observing within accounting firms, and the implications for builders looking to serve them.

Nearly every accounting firm, large or small, to whom we spoke brought up one specific growth vector: Client Advisory Services, or CAS. CAS comprises a mix of outsourced CFO and controller services, making it a particularly strategic department for three reasons.?

1. CAS creates sticky, recurring relationships with clients that can serve as the basis for cross-selling engagements into other parts of the firm (namely tax and audit).
2. CAS revenue growth is outpacing broader accounting-firm revenue growth. Firms with a CAS practice are reporting 30% median revenue growth year over year, while the industry at large reports ~9% annual growth in net-client fees.?
3. Qualitatively, CAS broadens the surface area for advisory revenue, which can often be more predictable and less seasonal than traditional accounting and tax engagements.

But with scaled CAS, comes great labor needs! Many of the activities that constitute outsourced controller services (e.g., helping with month-end closes, transaction reconciliation, expense management, etc.) require a person to repetitively perform rote tasks; we’ve previously referred to these “jobs to be done” as data collection and ingestion. Additionally, this manual extraction of data (from invoices, contracts, emails, general ledgers, and the like) is work that’s usually completed by a junior CPA or by an offshore laborer. The promise made by AI for accounting is to cut time spent on these activities down from hours to minutes, delivering immediate ROI in the form of freed-up labor hours.?

While this sounds ideal, it’s much harder to pull off than one might think, even with the powerful new technologies that underpin our current wave of AI-native accounting applications. For starters, accounting is a field in which accuracy is of paramount importance. For software to actually free up firms to either repurpose the skilled labor currently performing this work or end relationships with third-party BPOs, it has to actually work. Not only that, it needs to work “horizontally” across industries, many of which are riddled with unique data sources (industry-specific ERPs) and processes (how jobs get billed, how money flows, etc.).?

Consider construction versus healthcare. In construction, most financial transactions must be reviewed and coded proactively, well before they appear in a bank statement. Subcontractor invoices need to be checked for work completion, accurate job costing, and compliance before payments are made. Same thing with client invoices: construction isn’t an industry with “set it and forget it” invoices. These are detailed invoices built off of a construction budget based on either percentage of work completed, cost plus, or other forms of billing. Healthcare faces similarly complex workflows when it comes to month-end reconciliations. For example, reconciling patient billing and insurance reimbursements requires matching detailed claim data and medical codes (e.g., ICD-10) with actual payments, a process prone to errors even with today’s non-AI, industry-specific tools.?

It should come as no surprise, then, that CAS teams are already typically organized by vertical. Much like investment banks have coverage groups across specialties — healthcare, financial institutions, consumer retail, and so on — CAS has dedicated staff that understand the nuances of the customers they serve.

So what does this all mean for startups? While we are optimistic that as models improve and their associated costs continue to come down, the most prudent approach for technology companies hoping to serve CAS practices might be to really lean into specific verticals, around which they can build a brand and subject matter expertise. If they can demonstrate an immediate and clear ROI to one vertical in CAS with highly accurate results, they will deepen customer trust in their product. As a result, they will be able to dramatically shorten their sales cycles for firmwide expansion.?

We’re starting to see this with our portfolio company Adaptive, which has built an accounting automation platform specifically for construction and has drawn significant interest from the construction teams at large CPA firms. Early stage startups that don’t organize by vertical risk playing permanent whack-a-mole with all that’s needed to operate fully horizontally in the space. Many clients will require net-new systems integrations (primarily ERPs and banks, which each have very long tails) and bespoke logic with respect to how certain revenue and cost items should be treated within the context of their specific industry.

We’re still in the very early days of this market, and we’re excited to watch both approaches go head to head. The more horizontal players will be making a bet that the current infrastructure in the market is enough to make sense of a lot of industry specific nuance and noise. The vertical players will initially be vying for a smaller prize, but with laser focus and the potential for unmatched trust. If you’re building towards either future, please reach out!

Regulation Becomes Code

We asked 50 a16z partners to preview one big idea that will spur innovation in 2025. Here, General Partner Angela Strange shares why she thinks 2025 will be the year that Regulation Becomes Code.

Companies in the banking, insurance, and healthcare industries spend countless hours and millions of dollars staying in compliance. Today, banking and insurance regulations span tens of thousands of pages; SBA lending documentation alone exceeds 1,000 pages. For businesses, keeping on top of these codes requires byzantine workflows and many hours spent hiring and training staff. Imagine, instead, that those lengthy documents — including text, images, and case precedents — could be used to train regulation-specific LLMs. Suddenly, compliance would become as simple as a Google query: “Is [X] compliant? What modifications need to be made?”

The onerous process of staying up to speed on regulation also poses a less obvious cost to consumers. To give just one example, an estimated 1.5 million consumers fall behind on their mortgages every year. What if those people could talk to someone steeped in Fannie Mae’s 1,000+ page servicing guide to get quick, accurate answers on how to modify their loan and get some relief? AI agents can be quickly trained and are infinitely patient. LLMs can streamline this traditionally fraught process.

The labor-intensive business of compliance is ripe for new software. AI can make our systems safer, more straightforward, and more efficient for consumers and companies.?

Read other Big Ideas from a16z?

More From the a16z Fintech Team

Most financial institutions still bank on software built before the internet, but Jack Henry’s CTO, Ben Metz, is leading a quiet revolution to change that. In this episode of “In The Vault”, with a16z General Partner Angela Strange, Metz shares how banks’ historical tech infrastructure and regulatory obstacles have stymied innovation — and how Jack Henry is helping them overcome these challenges. Metz outlines actionable ways startup founders can help smaller community banks leverage artificial intelligence and cloud computing, reshaping the industry at large.

a16z portfolio company Clutch raised $65 million in Series B funding to continue revolutionizing how credit unions deliver products and services to their members and communities.

a16z fintech infrastructure company Method raised $42 million in Series B funding. Method’s platform powers debt and debt repayment features in fintech applications.

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