While word of mouth, digital marketing and professional networks are still the main ways accounting firms promote themselves, an entirely new path has emerged over the past few years: AI. With more people using AI every day, CPA firms across the country are reporting an uptick in clients who discovered them through conversations with ChatGPT, Gemini, Claude and other models.
AI-sourced client referrals are a relatively recent phenomenon, with most firms only starting to get them this year or last. This makes sense, as many early AI models lacked the ability to browse the internet, and their overall user base was smaller. But now there are hundreds of millions of people using AI every day, some of whom need accountants.
In general, these clients are not asking their AI models to find them a CPA, any CPA, and picking from a list of suggestions. Rather, they tend to query their models on highly specific accounting or finance issues, and then find the firms through the supporting links provided. For example, Patrick Camuso, head of crypto-specialist firm Camuso CPA, reported that most people who contact him through AI have specific needs related to digital assets.
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“It will be ‘I got a tax notice and haven’t done my crypto accounting for five years’ or ‘I need crypto tax planning for my portfolio’ or ‘I need to set up a Web3 accounting system.’ It can vary with what they type in. Sometimes it’s broad-based crypto [topics], sometimes it is very specific with what they’re looking for. But it is all basically crypto related,” he said.
Sasha Tchulkova, marketing director of advisory services for Top 25 firm Withum, says her firm has had similar experiences. One recent example was a venture capital firm with what she said was a “very unique scenario” that, at a certain point in a deal, required “very specific accounting support with the necessary compliance and accounting needs.” The client used ChatGPT to evaluate Withum’s ability to address the issue versus other firms.
“They went into ChatGPT and put exactly their scenario in,” said Tchulkova. “They already had Withum on the radar, so they [then] asked to compare Withum with three or four other CPAs based on their needs and what would meet them. After reviewing that, they knew Withum was it, so when we had a sales call they had already decided who they were going with.”
The hyper-specificity of these clients was noted by other firms as well. For instance, while her practice offers many different services, from tax advisory to bookkeeping, Katherine Bunschoten, head of North Carolina-based Certum Solutions, noted that pretty much all the clients referred to them by AI wanted the same thing: accounting software training.
“It’s not so much straight bookkeeping they’re really looking for. … A lot of them are trying to find how to do things within their software and then they’re running into some of our content,” she said.
Similarly, while Withum also offers a wide variety of services, Tanina Khanuja, the firm’s digital marketing director, said that every AI-referred client that contacts them needs tax services.
“Right now, the clients we acquired through AI are all tax services… When we do attribution tracking, right now it is tax services all the time,” she said.
Sometimes this specificity is less about type of service and more about sector expertise. Suzanne Reed, chief marketing officer for top 50 firm LBMC, agreed that people aren’t asking for a general CPA and instead are making targeted inquiries on specific issues, some of which her firm happens to specialize in.
“Health care continues to be the top driver, especially around services like health care valuations, physician compensation, MSO structuring and broader health care consulting. We’ve also seen interest in cybersecurity audits and related compliance work. These prospects typically know what they need, but they’re not sure who to call — so they’re turning to AI to point them in the right direction,” she said.
Another point mentioned by multiple firms is that leads from AI tend to be of high quality, perhaps because of this specificity. Camuso, for example, noted that clients referred to him by AI tend to already be familiar with blockchain and cryptocurrency concepts, which significantly eases onboarding, as he does not need to spend time educating them and developing trust. They usually come in eager to work with him.
“[They’re] very motivated to work with me already. It’s very contextual. They’re like ‘Hey, I’m looking for X, Y or Z. Can you confirm if you can do this?’ It is very calibrated. None of these were wild random leads, which is very interesting,” he said, adding that they also tend to do their own research.
Kathleen O’Toole, chief marketing officer for Top 25 firm PKF O’Connor Davies, reported similarly, noting that the ratio of qualified leads over total leads for such referrals is actually higher than the firm-wide ratio. Further, she said, the conversion rate from AI traffic is three times that of the site average (with the top referrer being, by far, ChatGPT), which is likely fueled by the specific content on the firm website.
“We can see in our reporting on web leads that they came to our site and in most cases landed on a specific service or industry page that had content that was apparently relevant to their conversation,” she said.
Another point of commonality mentioned over and over is that these AI-referred clients tend to be younger and more tech savvy, which aligns with the overall demographics of AI users.
“[They’re] definitely more tech focused, for sure. These are people who are embracing new technology, much like ourselves, and are learning how much artificial intelligence can help in the right context,” said Bunschoten.
Despite these strong commonalities, there are wild inconsistencies from firm to firm. For one, the number of AI-referred leads varies greatly. Camuso said AI has already become a serious referral channel for his firm, as at this point such clients make up 15-20% of his overall leads. Similarly, Bunschoten said AI referrals have become “pretty common” at her firm, now making up between 10-20% of her overall leads.
Conversely, Reed from LBMC said that while the firm has noticed a clear increase in AI-attributed leads over the last several months ( AI traffic has increased 200% in the past year), they remain a small slice of LBMC’s overall leads, an estimated 5-7% (though Reed noted it was 0 last year). Similarly, Khanuja, from Withum, said 1% of the total traffic comes from AI and, of that, 0.5% of conversions are from AI.
There’s also little consistency where these leads come from. Camuso, Bunschoten and Reed all said their referrals are from all over the world. However Khanuja and Tchulkova, from Withum, said the referrals tend to come from Withum’s primary markets. And O’Toole from PKFOD said about 40% of its AI referrals came from either New York or New Jersey, “which makes sense because we have more offices there and we are headquartered in NY.”
Regardless of where AI referrals lead, there was broad agreement that firms will likely be seeing more of them as time goes on, whether it’s a steady climb or a dramatic spike. This means that understanding AI and how it chooses what information to convey will become increasingly important for accounting firms looking to get noticed.
“I do believe that more is going to get funneled into AI … It’s probably going to get more competitive, so it’s hard to say exactly [how it will work for everyone] but I think [the number of] people using AI is going to go up and the people that go and find service providers and CPAs are going to be doing it with these AI systems,” said Camuso.
But wait! How did the AI models find these firms to begin with? And what can firms do now to encourage AI-guided recommendations? Read on in part 2 of this story!
Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.
Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.
In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.
Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.
Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.
Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.
Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.
Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.
The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.
In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.
AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.
When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.
Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.
This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.
Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.
Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.
Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.
Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.
Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.
This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.
Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.
By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.
Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.