AI adoption is often measured by the hours it saves. For accounting firms, that is only the beginning and potentially the least interesting part of the story.
An hour removed from data entry does not, by itself, improve a firm’s economics. Return appears when recovered capacity is used to serve more clients, deepen advisory relationships, or grow without an increased headcount.
That distinction is becoming more important as AI moves from experimentation into everyday work. Xero’s Modern Practice Playbook, based on survey responses from more than 500 independent senior accountants and bookkeepers in the U.S., points to three ways firms are translating AI use into financial results: embedding it in existing workflows, putting the recovered time toward higher-value services, and moving away from time-based pricing.
The real measure of adoption is capacity
Experimentation is no longer the main hurdle: 87% of surveyed practices have tried AI in some capacity. The more meaningful divide is between firms that occasionally test AI and those that build it into their regular workflows.
The numbers show why this distinction matters. Across combined staff, AI tools save surveyed practices an average of 7.6 hours per week. Among practices actively using AI in day-to-day workflows, that figure rises to 9.1 hours, while estimated annual savings are $387,000, compared with $231,000 across all surveyed practices.
Those savings are only part of the ROI calculation. A firm does not become more profitable simply because software removes process time. Firm owners still have to decide where that recovered time gets allocated.
47% of practices using AI say it has significantly helped them maintain or grow output without adding employees. Those hours can translate into servicing more clients during busy periods, more frequent client communication, additional review time, or room to build advisory services.
The Playbook found that 44% of practices plan to invest AI-created time in business development and client acquisition. Another 42% plan to redirect it toward advisory and other higher-value work. In other words, the business case for AI increasingly rests more on how firms utilize the time saved.
Adoption is an operating-model question
This is why the most important AI decision may not be which tool a firm buys, rather which bottleneck the firm is trying to remove.
For one practice, that bottleneck may be the hours employees spend entering receipt and invoice information. Automation can reduce rekeying and improve the quality of the data entering the ledger, giving staff more time to review unusual transactions or discuss cash flow trends with clients.
Another constraint may be bank reconciliation. When high-confidence transactions are automatically matched, staff can focus on exceptions that require judgment and use faster month-end work to identify client-worthy issues.
The common thread is not the application, it’s the workflow around it.
Process documentation appears to be one factor distinguishing experimentation from regular use. 97% of practices using AI daily describe their processes as well documented, compared to 36% of practices that have not adopted AI. Documented procedures make it easier to identify places of potential points of automation, set review points, and measure whether a change has actually improved the business.
Firms may not need to add another application to their tech stack either. 63% of respondents using AI rely on tools integrated into their accounting software. Keeping AI inside software that staff already use can reduce adoption friction and make review, security, and access controls easier to manage.
AI is not so much a strategy on its own, but a capability that becomes valuable when it is connected to a firm’s operating model.
Convert Capacity Into Higher-Value Services
For many firms, the clearest destination for recovered capacity is advisory work. Cash flow forecasting, budgeting, virtual CFO services, and KPI reporting allow firms to turn time previously spent on routine production into client-facing work.
The economics make that shift worth examining. Advisory services carry an average profit margin of 51.2%, among the highest of the services measured in the playbook. More than half of practices offering advisory expect that revenue to grow faster than compliance revenue in the next three to five years.
Yet capacity remains a barrier to offering advisory, with 26% of practices citing capacity as what is holding them back. Reducing the manual work behind reporting, data preparation, and routine analysis can give firms room to support more frequent client meetings, build a standardized forecasting offering, or flag changes in client data that warrant a conversation.
That is the difference between efficiency as a cost-saving exercise and efficiency as a growth strategy.
Value creation drives the pricing conversation
Greater efficiency also exposes weakness in hourly billing. If AI helps a firm complete the same work faster, the firm can deliver more value while recording fewer billable hours. The link between time spent and revenue earned becomes harder to defend.
Practices currently price an average of 78% of client engagements using non-hourly terms. Among practices using value-based pricing, 46% say it makes their practice more profitable, and 48% say it helps clients better understand their services.
A defined fee makes it easier to show clients what they are buying: the accountant’s judgment, analysis, and ongoing support, rather than a block of hours. That distinction matters particularly for advisory work, where the value might be helping a client anticipate a cash shortage, evaluate an investment, or plan for growth.
The business outcome as the new measure of success
None of this removes the need for discipline. Firms still need clear standards around AI use, including data handling, approved tools, accuracy, and professional review. Firm staff also need room to test approved tools against real workflows and determine where they save time or create additional work.
However, hours saved should not be the final measure of success. The firms that capture the most value from AI will be the ones that deliberately turn those hours into better client service, higher-value work, and profitable growth.
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Bjorn Monsson, Head of Partner Growth & Solutions at Xero, leads sales strategy and execution across the company’s Partner Consultants and Services, Partner Success, and Melio Payments organizations. His responsibilities span enterprise, pre- and post- sales, Gusto, Syft, and Xero Practice Manager, as well as partner cross-selling, upselling, and retention. He also oversees full-cycle sales for Melio Payments. Bjorn is a Certified Public Accountant (CPA) and holds a Bachelor of Business Administration in Accounting from San Francisco State University
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