Some firms, the ones that constantly grab headlines, are spending billions of dollars to create bespoke artificial intelligence systems to service their Fortune 500 clients who need complex compliance and advisory services to support their global footprint.
In contrast, the vast majority of firms are spending maybe a few thousand dollars to license commercially available models, and perhaps a few thousand more to train staff and integrate systems. Overall, if someone is not building billion-vector custom models housed on a massive server farm, AI is actually quite cheap, especially considering the capacity upgrades it can present.
Firms with about one to 200 people are mostly engaging with the subscription model products right now, according to TJ Lewis, innovation strategist with Rightworks, an accounting-specialized cloud provider. “They’re not building out their own models,” he added. “They’re not securing a bunch of server time or things like that to spin up their own things by and large.”
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Part of this is because smaller firms don’t have the resources to construct their own custom AI models, especially the oceans of data to feed them, as well as the technical experts to bring it all together, according to Donny Shimamoto, head of IntrapriseTechKnowlogies, a tech advisory practice specializing in CPA firms.
“It kind of makes sense,” he added. “In order for AI to work to a good extent, you need a high volume of data, and smaller firms just don’t have that volume. They need to teach the AI. And they also don’t have simply the teams to be able to build that out cost effectively.”
Another reason is they really don’t need to, he added. Huge sophisticated AI models are generally used for highly complex tasks for highly complex companies, which is why the international-scale firms tend to invest in them. Conversely, the tasks most local accountants are handling for their clients are simpler by many orders of magnitude. In the majority of cases, said Shimamoto, a commercially available model will work fine for their purposes.
“There’s personal AI or personal LLMs that, if a practitioner had a decent amount of content that they wanted to have readily searchable, they could use those LLMs like [Google’s] Notebook [LLM] or something. Those personal LLMs are designed to run off of a laptop, so you won’t see these huge incremental costs coming along,” he said. The cost of commercial AI solutions has also been going down over the years, he added, and many of the ongoing costs of these products are now at the vendor level.
Furey Financial Services, a 38-employee firm in Hoboken, New Jersey, that was also named one of this year’s Best Firms for Technology, can relate. A highly tech-focused firm, with IT taking up 29.5% of its total operating budget, Furey has been an enthusiastic adopter of AI, making sure to equip all its staff with the latest available tools. It invests in both its own proprietary AI solutions as well as AI-enabled commercial products. However Chip Waller, the firm’s chief operating officer, said Furey aims to be judicious in its AI spending. While Furey’s tech ops team is “focused on building for the future,” he conceded it’s “a balancing act of investment versus being too reactionary.”
“From our perspective we’re not trying to build our own LLMs or infrastructure,” he said. “It was really about how do we from a low-cost perspective leverage some of these models out there and plug them into our workflow, so you can differentiate AI into that platform component. … We’re going to really focus on the application layer and see where we can put these things to use while not trying to build the new AI model ourselves.”
This falls in line with the general advice Shimamoto had for smaller firms looking to invest in AI. It is the same as it is for any other major tech purchase: Firms must start with the use case, then find technology to fill it. Too many firms, he believes, do it the other way around, much to their detriment.
“It’s the same way we’ve prioritized IT spend for the last two decades at least,” he added. “It comes down to where is the business value? What is the business strategy? And how will AI contribute to that? We do have to be careful of AI being a solution looking for a problem, but I have been seeing that a bunch.”
Waller said that when Furey was first thinking through its approach to AI, it considered building its own proprietary model, or to train one using an open source model like Llama as a base. However the firm calculated that this would carry not just a significant one-time cost for development but ongoing expenses such as server space. “We decided not to go that route and really just say ‘Hey, let’s get it plugged into our workflow but let’s hold off on running our own model,'” said Waller. This has helped the firm gain efficiency and productivity bonuses from AI while keeping IT costs low.
However, Furey is more than just a consumer of AI products. While it’s not prepared to drop millions of dollars on custom systems, it has found great cost savings in the form of creating its own API access point for OpenAI’s models. During the development process, Furey estimated its expenses would be hundreds of dollars per month, but as time went on and OpenAI introduced new capacities, the cost began to drop. While the cost savings are nice, Waller said the real benefit is in better quality client services.
“That cost has gone to near zero,” he added. “Once we got our whole team up and running on it, all the clients, we’ve got thousands of [API] calls, [but] we’re in no more than 10 bucks a month. But the investment really is on our team knowing which way to go and connecting the API client to the API gateway in a secure way, doing all that dev work to plug that into our templates on a daily basis and go through that.”
Doug Schrock, managing AI principal for Top 25 Firm Crowe, said small firms should be actively experimenting with AI beyond just buying or licensing a commercial solution, which he called “the homeowner level of AI.” While the investment is much less, there is an upper limit on the value it can create because it does not enable more significant redesigns to processes and tasks that are offered by more complex solutions. Overall, he said, firms should be seeking to innovate and make strategic relationships with some of the larger AI players out there.
While it’s fine for now to stick mostly with what’s on the market, he warned that smaller firms will need to increase their AI capacities soon, or else be outcompeted by other firms. Smaller players who can’t or won’t make these investments, Schrock predicted, will start falling behind. They might need to do things like hire consultants to help get them to that next stage of AI development. “The market is moving and folks like us get a higher level of value allowing us to get more cost competitive and deliver value and speed they maybe can’t,” he added.
“In the next six to 12 months, get your people using the tools tied to your existing system,” Schrock said. “If everyone is running MS Suite, turn on Copilot. It’s $30 bucks a month per user. Have your people start using the AI features built into your core system, then maybe get some spot LLM tools like ChatGPT or some AI-based research tool. They need to get in the game now if they haven’t already.”
A proposal from the U.S. Securities and Exchange Commission to potentially shift some public companies away from quarterly financial reporting toward a semiannual model is drawing significant pushback from investors, even as it continues moving through the regulatory process. The debate has direct implications for corporate finance teams, auditors, and the broader transparency of U.S. capital markets.
What the SEC Proposed
According to a summary published by accounting advisory firm Cohen & Co., the SEC issued a proposed rule on May 19, 2026, aimed at simplifying financial reporting requirements for many U.S. public companies. The proposal would potentially reduce the frequency of certain mandatory disclosures from quarterly to semiannual, a structural change that has not been made to core U.S. reporting requirements in decades.
The proposal follows an extended debate within U.S. policy circles, with proponents arguing that reduced reporting frequency could lower compliance costs and free up management time for longer-term strategic planning rather than quarter-to-quarter results management.
Why Investors Are Pushing Back
Comment letters submitted in response to the proposal have been extensive, and according to Cohen & Co.’s review of the public record, investors “appear to be largely opposed” to the shift, viewing frequent interim reporting as a core benefit of U.S. capital markets relative to other jurisdictions.
Accounting and law firms have taken a more measured position, generally urging any changes to remain aligned with the Financial Accounting Standards Board (FASB), whose existing disclosure requirements and guidance are built around a quarterly reporting cadence. A shift to semiannual reporting without corresponding changes to FASB guidance could create friction between SEC filing requirements and GAAP-based disclosure expectations.
Lessons From the U.K. Experience
The debate is not without precedent. The United Kingdom moved away from mandatory quarterly reporting for listed companies in 2014, returning to a semiannual disclosure requirement. According to Cohen & Co.’s analysis, that experience offers a cautionary data point: there was no measurable increase in capital expenditure or R&D investment following the change, while analyst coverage of affected companies declined as reliable interim information became less available — a particular risk for smaller and newly public companies that rely on analyst coverage to maintain investor visibility.
Practical Implications for Finance Teams
Beyond the debate over disclosure philosophy, the proposal carries practical complications. Many companies have debt covenants and credit agreements structured around quarterly financial delivery; a shift to semiannual reporting could require renegotiating those terms. Reduced reporting frequency would also extend the “window of market silence” between disclosures, a factor that governance and investor-relations teams would need to manage carefully to avoid information asymmetry.
Separately, and unrelated to the reporting-frequency debate, the SEC and FASB have continued finalizing more routine updates this year. New Accounting Standards Updates are taking effect for December 31, 2026, fiscal year-ends covering income tax disclosures, credit loss measurement, induced debt conversions, and stock compensation, according to Eide Bailly’s review of 2026 ASU activity. Additional guidance on paid-in-kind dividends and environmental credits is also on the near-term horizon.
What to Watch Next
The semiannual reporting proposal remains in the comment and review phase, and no final rule has been adopted as of this writing. Finance leaders should monitor the SEC’s regulatory agenda for further movement, while treating the current quarterly reporting requirement as the operative standard until any final rule is issued and an effective date is set.
Given the extent of investor opposition documented in the comment file, a full shift to mandatory semiannual reporting appears more likely to result in either a scaled-back compromise or continued study rather than swift adoption — though the SEC’s ultimate direction remains uncertain.
The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.
The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.
Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.
Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.
Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.
Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.
Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.
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.