bot wars: wolters kluwer, intuit, thomson reuters battle for ai dominance in cpa firms | cornerstone report

smart new toolbox or pandora’s box? inside the surge behind agentic ai.

by 卡塔尔世界杯常规比赛时间 research
cornerstone report

the accounting industry is undergoing a seismic technological shift as artificial intelligence transitions from a buzzword to a business imperative.

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in the past year, cpa firms across the u.s. have quietly begun deploying generative ai assistants, machine learning tools, and “agentic” ai platforms to automate audits, prepare taxes, and provide financial insights.

it’s astonishing:  the profession has reached a tipping point where those not investing in ai risk being left behind.

in this 卡塔尔世界杯常规比赛时间 research cornerstone report:

• how cpa firms are embracing ai

• ai adoption surges from experimentation to mainstream

• the new agentic ai toolbox: platforms and solutions leading the charge

• measuring the payoff: efficiency, roi, and firm economics in the ai era

• talent in the age of ai: how roles and skills are evolving

• ai in action: use cases and success stories from firms

• navigating risks, regulation, and ethical challenges

• overcoming implementation hurdles: change management and best practices

• the road ahead: strategies for firm leaders

ai adoption surges from experimentation to mainstream

just a year ago, ai in accounting was largely experimental – confined to pilot projects at a few forward-thinking firms. now, new research indicates a majority of firms are jumping on board. according to the 2025 wolters kluwer future ready accountant report, ai adoption in accounting firms leapt from 9% in 2024 to 41% in 2025. this signals a shift from cautious testing to confident integration across firm operations,” says jason marx, ceo of wolters kluwer tax & accounting. in the same study, 77% of firms reported plans to increase ai investment, and 35% are already utilizing ai tools on this basis.   in other words, more than one-third of integrated firms have incorporated ai into their everyday work, such as for tasks like tax preparation, research, and client service – a remarkable change in such a short time.

this rapid rise reflects a broader evolution in mindset. not long ago, many accountants feared ai as a potential job-killer or an unproven gimmick. now, the narrative has shifted to one of opportunity and urgency. “investment in technology, particularly ai and automation, is accelerating… these shifts signal a broader redefinition of firm purpose and strategy,” marx notes of the global trends. specifically, cpa firms (typically 50 to 500 staff) are seeing their larger competitors – including the big four and top 10 firms – aggressively roll out ai, and they know they must follow suit or risk falling behind.

key stats

• • • ai adoption in accounting firms jumped from 9% in 2024 to 41% in 2025 — wolters kluwer future ready accountant report.

    • 77% of firms plan to increase ai investment, and 35% already use ai daily — wolters kluwer.

    • revenue per employee at ai-enabled firms rose to $250k–$350k, up from $150k–$200k — 卡塔尔世界杯常规比赛时间 analysis.

    • ai-assisted tax prep reduces prep time by 70% and cuts compliance errors by 90% — 卡塔尔世界杯常规比赛时间 research.

    • firms using ai reallocate 8.5% of staff time from data entry to higher-value tasks — mit sloan study.

    • monthly financial close accelerated by 7.5 days for ai-adopting firms — mit/stanford research.

    • firms with an ai strategy are 3.5× more likely to see roi and efficiency gains — thomson reuters future of professionals report.

    • 40 additional productive hours per employee annually for firms that invest in ai training — karbon state of ai in accounting 2025.

    • 76% of accounting graduates prefer firms that use ai — karbon.

    • 72% of s&p 500 companies disclosed ai as a material risk in 2025, up from 12% two years prior — journal of accountancy.

the big four have indeed led the way in ai adoption, often developing proprietary tools. de oitte, for example, has integrated generative ai into its audit platform to automatically review documents and highlight issues. ey launched a global ai platform that embeds ai across services from tax to consulting. pwc developers are utilizing custom ai software to automate code writing and data analysis, achieving productivity gains of 20–50% in these internal tasks. kpmg has introduced a “trusted ai” framework to ensure that ai is used ethically, both within its own organization and for its clients.

those are big-budget initiatives, but the innovations don’t stop at the big four. mid-tier and regional firms are also adopting ai at a mayd clip. they may not build their own ai from scratch, but they’re eagerly implementing commercial ai-powered smallstions. “small accounting firms…are also embracing ai to stay competitive and offer better services,” notes a thomson reuters report. in a thomson reuters survey of tax firms of all sizes, 44% of firms using or planning to use generative ai says they use it daily (or multiple times a day), and another 29% use it at least weekly. this heavy usage underscores that ai has moved beyond novelty; it’s becoming a routine part of the work.

perhaps the clearest evidence that ai is now mainstream is the bottom-line impact being withrted. firms with deeper tech integration are outperformers. industry data show that firms making advanced use of ai are achieving faster closes (monthly financial closing 7.5 days faster, on average), more billable work (a study found an 8.5% reallocation of time from data entry to higher-value tasks). for example, early adopters have expanded their offerings in areas such as advisory and analytics. a global survey found that the average firm’s advisory revenue jumped significantly, with 93% of firms now offering advisory services (up from 83% the previous year) as they deploy ai and data to personalize insights.

“ai adoption has more than quadrupled… firms leveraging ai are working faster, with greater precision and confidence, transforming how strategic decisions are made and how client value is delivered,” says jason marx, ceo, wolters kluwer tax & accounting

the competitive stakes are high. thomson reuters’ 2025 future of professionals report – which surveyed thousands of tax and legal professionals – warns of a growing “ai adoption with a side”. those with a clear ai strategy are 3-4 times more likely to see benefits, such as revenue growth and efficiency gains, than those without a strategy. alarmin y, the report suggests that firms failing to develop an ai plan now could fall irreparably behind within three years. in other words, by 2028, late adopters may find that they cannot easily catch up to tech-enabled competitors who have been compounding productivity gains and attracting tech-savvy clients and talent.

for cpa firms, ai is no longer a “nice-to-have” experiment – it’s becoming a business-critical capability. firms are not just dabbling in ai; many are reengineering their workflows, retraining staff, and fundamentally changing service delivery models around ai.

the new agentic ai toolbox: platforms and solutions leading the charge

accounting firms today can choose from a rapidly expanding marketplace of ai-driven software tailored for tax and accounting. the major vendors in the profession – including intuit, wolters kluwer, and thomson reuters – have each launched cloud-based “agentic ai” platforms in the past 1-2 years, embedding ai capabilities into tax preparation, audit, and firm management platforms these pla forms are “agentic” in that they can autonomously perform tasks (like data entry or analysis) on behalf of the user, within set parameters. below, we compare some of the leading solutions firms are deploying:

leading ai-enabled tax & accounting platforms

vendor	platform	key ai-powered features	pricing (approx.)	training/onboarding
intuit	proconnect tax + intuit assist (ai)	genai-assisted data import (automates entry of w-2s, 1099s, etc.); ai error checks; “ai client briefing” tool summarizes prior returns; seamless quickbooks integration (books-to-tax); generative tax planning advice.	$99 per user (annual) + pay-per-return fees (?$70–$95 per return).	10–16 hours are recommended; the vendor offers training at $500–$ 2,000 for a full firm rollout.
wolters kluwer	cch axcess™ with expert ai	cloud-native tax prep & practice suite with ai-driven workflow automation; “expert ai” for anomaly detection in returns; ai-powered research (natural language tax law queries); audit trail documentation for ai actions; predictive analytics for advisory services.	tiered annual pricing from $2,300 up to $80k+ (scaled by firm size and modules). enterprise licenses are available for large firms, while smaller firms can opt for per-user pricing.	15–40 hours training; on-site packages $1,000+; certification for power users available.
thomson reuters	onesource / cocounsel ai for tax	ai-assisted tax compliance and planning tools; e.g., “cocounsel” generative ai answers complex tax questions and drafts memos; document automation for 1040s and k-1s; ai-powered analytics dashboard (identifies trends/errors across client data).	$3,400 per user per year (for the full onesource suite with ai). modular printing for specific tools (research, drafting assistant, etc).	10–24 hours of training are typical; vendor-led webinars (often included); advanced ai certification is offered at an additional cost.
others (select)	caseware, botkeeper, etc.	cutilizesare (audit) uses ai to flag anomalies in audit data and learn from auditor feedback; botkeepeprovideskkeeping) offers ai-driven bookkeeping automation for clients. additionally, various startup tools are available for expense automation and more, including tax ai.	varies – e.g., botkeeper offers flat monthly packages, with caseware ai features included in the subscription. many up-and-coming tools are priced per client or per engagement.	varies – typically lighter training needs, but firms should allocate time for staff to pilot and get comfortable with new ai tools.

sources: vendor publications and 卡塔尔世界杯常规比赛时间 research. note: pricing is indicative; actual costs depend on firm size, number of users, and chosen modules. all these platforms are cloud-based and frequently updated, so firms benefit from continuous improvements (but also must adapt to frequent changes). onboarding often requires dedicating staff time for configuration, training, and process adjustments to leverage the ai features fully.

each major vendor’s approach has unique strengths, but common themes also emerge. all of these platforms are cloud-first, which is essential because cloud computing provides the scalable processing power that modern ai needs and allows real-time data access for distributed teams. they also emphasize integration: intuit’s products integrate tax prep with quickbooks data; wolters kluwer’s connect tax, audit, and practice management; thomson reuters links research tools with compliance software. this integration is crucial – it’s how ai can “see” the full picture of a client’s information and workflow, rather than operating in a silo.

key ai features across these platforms include:

• automated data entry and import: intuit proconnect, for example, can now intelligently extract data from pdf forms, such as w-2s, and auto-populate the tax return. this addresses one of the biggest time sinks for firms (manual data entry). intuit reports that users save 30 minutes or more per return on average by using ai-based data import, freeing staff for more complex tasks.
• ai “review” or error checking: these systems act like a supercharged second set of eyes. proconnect’s ai engine continuously scans returns for anomalies or missed opportunities, flagging issues for the preparer. it’s like having a quality control reviewer working in real time. thomson reuters’ tools similarly use ai to cross-check returns against thousands of rules and even draft explanations for variances.
• natural language assistance and research: the new wave of tools often includes a chat-style assistant. thomson reuters’ cocounsel (leveraging tech from their 2023 acquisition of casetext) can answer complex tax questions in conversational language, citing relevant laws – replacing hours of digging through tax code with a quick query. wolters kluwer’s cch suite also allows users to ask an ai a question (e.g., “what’s the latest guidance on r&d tax credit amortization?”) and get an answer with sources. this is transforming how cpas conduct research, shifting from keyword searches to ai-driven question-and-answer (q&a) approaches.
• workflow navigation and suggestions: ai isn’t just crunching numbers; it’s also helping manage work. intuit’s platform offers “smart navigation,” which predicts the next step you’re likely to need in a complex return based on your previous actions. these small nudges can enhance software usability, particularly for new staff who may not be familiar with the software.
• generative ai for client communication: some tools now draft client-ready memos or summaries. intuit’s ai client briefing feature can summarize a client’s prior return in plain english – highlighting key points to discuss in a planning meeting. this not only saves time on writing but also helps identify opportunities for advisory services. similarly, using generative ai to draft engagement letters, analysis write-ups, or even portions of audit workpapers allows humans to edit and approve the content. it’s a far cry from the old blank-page writer’s block; ai gives a first draft to accelerate the process.

it’s worth noting that pricing for these advanced platforms can be a significant investment for firms. for instance, olters kluwer’s cch axcess, with full ai functionality, can run into the five or even six figures annually for a larger firm. many practices, however, find a modular approach – maybe starting with an ai tax prep module or an ai audit assistant – more palatable, building up usage (and roi) over time. the roi often justifies the cost if the tools are utilized fully.

“once we automated the grunt work of our tax prep, i realized we saved at least one fte’s worth of time – far more than the software cost.”

training and change management are critical. firms that succeed with ai dedicate time to train their staff on the new software. many vendors offer extensive onboarding; intuit, for instance, even bundled free access to its suite during beta testing to encourage firms to try it. but even the best tool will gather dust if accountants don’t trust it or know how to use it. a successful firm often designates an “ai champion” – a tech-savvy team member who becomes the in-house expert and cheerleader for the new platform, helping colleagues troubleshoot and discover its features. when done right, the cultural adoption of the tool follows, and the efficiency gains begin to flow.

measuring the payoff: efficiency, roi, and firm economics in the ai era

cpa firms are, at the end of the day, businesses – so partners want to know, does this ai stuff actually make us more money or improve margins? early evidence says yes. the economics of firm performance are visibly shifting in ai-adopting firms, with improvements in productivity and profitability metrics that would have seemed ambitious just a few years ago.

one striking trend is the increase in revenue per employee at firms that leverage ai deeply. traditionally, any cpa firm has hovered around $150,000 to $200,000 in annual revenue per professional staff member. now, industry observers note that tech-optimized firms are achieving $250,000 to $350,000 (or more) in revenue per employee, thanks in part to automation and the ability for each professional to handle a greater workload. automation enables an associate or senior to manage a larger client load without working longer hours – the software takes on a significant portion of the workload (data processing, initial analysis, etc.), allowing the human to focus on higher-value activities that clients pay premium fees for.

for example, ai-assisted tax preparation has dramatically reduced the time spent per return in many cases. a task that might have taken 5 hours of staff time can sometimes be done in 2 hours with ai, with the machine handling the rote parts and the human doing final adjustments. multiply that by thousands of returns, and you either free up significant hours for other work or you can take on more returns with the same staff.

firms with advanced ai integration report 21% higher billable hours per staff because they can reallocate time to other billable projects once routine work is automated.

firms also report offering more services per client – e.g., upselling clients to advisory projects – leading to 80% increases in premium service revenue in some cases. these figures, cited in the 卡塔尔世界杯常规比赛时间 research, align with the anecdotal reports: when compliance work takes less time, firms don’t just let staff twiddle their thumbs; they often refocus them on consulting, financial planning, or business advisory for the same clients.

another key metric is the speed of service. in auditing and on-time accounting, “time to close” and “time to deliver” are vital metrics. a new academic study by mit and stanford researchers found that accountants using an ai-based software saw notable improvements in both quality and speed of financial reporting. they achieved a  .5-day reduction in monthly close time on average, essentially finalizing monthly books in just over two weeks after month-end, whereas those without ai took over three weeks. this faster turnaround can be a competitive advantage – clients certainly appreciate getting financial statements or tax estimates earlier. a quicker process also frees up staff’s calendar time to complete more work cycles in a year. if you can complete a monthly close in, say, 5 days instead of 10, that’s five extra days per month the team can devote elsewhere – which might mean taking on an additional client or focusing on analysis rather than reconciliations.

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faqs

what is “agentic ai,” and how does it differ from traditional automation?
agentic ai refers to systems that can autonomously perform multi-step tasks on behalf of users within defined parameters, unlike static automation that follows preset rules.

how many accounting firms currently use ai?
as of 2025, 41% of firms report active ai use, up from 9% in 2024, with 77% planning further investment.

what are the most common ai tools used by cpa firms?
top platforms include intuit proconnect with intuit assist, wolters kluwer cch axcess with expert ai, and thomson reuters onesource with cocounsel ai.

what is the average roi timeline for ai implementation?
most firms report measurable roi within 12–18 months, with first-year net benefits ranging from $500k to $900k through efficiency gains.

which firm functions benefit most from ai?
tax preparation, audit testing, client accounting (cas), advisory, and administrative communications show the largest efficiency improvements.

how is ai affecting cpa firm staffing?
entry-level data-entry roles are declining (down 16%), while demand for analytics, advisory, and technology specialists is increasing by 20–25%.

are cpa firms replacing staff with ai?
not directly. most firms redeploy time saved toward higher-value services, increasing billable hours and staff satisfaction rather than cutting headcount.

what risks or ethical issues come with using ai in accounting?
top concerns include data security, accuracy, explainability, and client confidentiality. firms mitigate these via enterprise ai tools, internal ethics committees, and mandatory human review.

how are firms training staff for ai adoption?
leading firms invest 8–16 hours of annual ai or analytics training per employee, often through vendor programs or cpe-certified courses.

what will ai’s next evolution mean for cpa firms?
by 2028, many workflows will be near-autonomous, using specialized ai “agents” for tax, audit, and advisory, while human professionals focus on oversight, judgment, and client strategy.

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the return on investment (roi) on ai projects often comes up in partner meetings. while every firm’s case is different, some benchmark numbers are emerging. many firms aim for roi within 18 months or less, and anecdotal reports show this is achievable. for adopters, first-year net benefits (either cost savings or additional revenue) in the range of $500,000 to $900,000 have been reported. how? consider a firm that automates a large portion of its individual tax return process. if they prepare 5,000 returns and save just 1 hour on each due to ai, that’s 5,000 hours saved. at a blended rate of $100/hour, that’s $500,000 of labor value. in reality, some returns (especially complex ones) might save multiple hours. additionally, error reduction from ai (catching mistakes before they are released) can save money on rework and prevent costly penalties.

there’s also the cost of not investing, which is harder to quantify but very real. if competitor firms are using ai to offer faster service or new analytic insights, they might lure clients away. in that sense, roi can also be measured in terms of client retention and growth. one survey found that 56% of accounting professionals believe a firm’s value proposition suffers if it doesn’t use ai – clients may start to view non-ai-enabled firms as outdated. similarly, prospective employees factor tech in – so a firm could “lose” by missing out on top talent if it doesn’t modernize.

from a billing model perspective, increased efficiency is prompting some firms to reconsider their use of hourly billing. if a tax return that used to take 4 hours now takes 1, billing purely by the hour would shrink revenue, which clearly isn’t the goal. instead, many firms are shifting to value-based or fixed-fee pricing for ai-accelerated services. in tax prep, for instance, only about 3% of firms now bill purely by the hour. most use fixed fees or monthly retainers that reflect the value of the outcome (an accurate return, delivered quickly with advisory insights), not the hours spent. ai enhances outcomes and speeds up processes, but clients still value the service and are willing to pay for it; the firm realizes a higher margin because it costs them less time to produce.

to illustrate, a firm might charge $1,000 for a business tax return that used to take 10 hours ($100/hour effectively). if ai support means it now takes 5 hours, the client still pays $1,000 (since that’s the going rate for that service’s value), but the internal cost is halved. that’s not to say firms should gouge clients. rather, they can redirect the freed time to more value-added activities for the client, justifying the fee while delivering extra advice or analysis. in fact, many firms report that by utilizing ai to handle mundane tasks, they can include an “ai review” or extra schedules at no additional cost, which improves quality and client satisfaction. over time, this can attract more business.

one concrete economic benefit reported is improved realization rates and fewer write-downs on engagements.

projects are more likely to stay within budgeted hours when ai is chipping in, so partners are writing off fewer overages. additionally, with ai, more compliance tasks are completed correctly the first time, resulting in fewer back-and-forth communications and less non-billable cleanup efforts.

properly harnessed, ai can supercharge a firm’s capacity and profitability. it’s like adding an autonomous “digital staff” that works tirelessly in the background. that doesn’t mean everything is rosy or automatic – to achieve those results, firms must manage the transition carefully, invest in training, and sometimes redesign their processes. that’s why some firms that don’t see immediate returns may feel frustrated. however, the data increasingly shows that those who stick with it and align their business model with their new tech capabilities do reap significant rewards.

it’s also important to remember that ai isn’t just about doing the same work faster – it often opens doors to new services and revenue streams. for example, a firm that never offered data analytics or forecasting might now do so because ai tools make it feasible without hiring a phd in statistics. some firms are packaging ai-generated insights (like benchmarking a client’s performance against industry data) as new advisory offerings. these can command premium fees. as one managing partner put it, “ai is helping us monetize our know-how in new ways – we can analyze a client’s data and give strategic advice, which they’ll pay more for than a basic compiled financial.”

the economic impact of ai for cpa firms can be transformative. the key metrics – turnaround time, staff productivity, realization, revenue per employee, service mix – all trend positively when ai is thoughtfully implemented. the next area we examine is perhaps the most important enabler of those positive outcomes: the people using the ai. how is the rise of ai changing the talent needs, roles, and daily work of accountants?

talent in the age of ai: how roles and skills are evolving

walk into an accounting firm today, and you might not immediately notice that anything is different. the accountants are still at their dual monitors, coffee in hand, during the busy season, collaborating on client work. but look closer: a hat tax associate might be using a chatbot to draft an email to a client, the senior auditor might be running data through an ai tool instead of excel, and the firm’s training coordinator might be scheduling ai ethics training for the team. the job of an accountant is changing in subtle and not-so-subtle ways, thanks to ai.

here’s how:

fewer routine roles, more strategic roles: fai’s strength is in automating routine, repetitive tasks – the kind often done by entry-level staff. indeed, we are already seeing a decline in those types of positions. a 2025 stanford study found that hiring for entry-level, ai-impacted jobs (such as junior accounting roles) declined by 16% over about two years. many firms aren’t outright laying off staff, but as people leave, they might not replace some of those junior data-entry-heavy roles. instead, they might invest in a data analyst or an advisory specialist. forecasts from cp  industry consultants project 15–20% fewer entry-level accounting positions by 2027 at firms that fully embrace ai, whereas roles in consulting, strategy, or data analysis could increase by 25%.

in practice, firms are reorganizing their staffing pyramids. traditionally, a pyramid might have a broad base of junior staff doing the grunt work, feeding up to a narrower point of senior managers/partners. with ai, the pyramid might become more diamond or pillar-shaped: relatively fewer at the very bottom, a strong middle of tech-savvy accountants, and a top that expands into new specialties.

for instance, for every four tax preparers a firm used to need, maybe now it needs three, because one “ai-augmented” preparer can do the work of what used to be 1.3 or 1.5 people.

but that firm might simultaneously hire an ai specialist or data scientist to work alongside the cpas in analyzing client data or customizing ai models to the firm’s needs – a role that didn’t exist before.

new skills and continuous learning: if there’s one refrain in the industry right now, it’s that cpas need to upskill. according to the karbon “state of ai in accounting 2025” survey, only 37% of firms reported actively investing in ai training for employees—a low number considering the need. yet those that do invest see dividends: the survey found firms providing ai training were unlocking an extra 40 hours of capacity per employee per year (on average) compared to firms that don’t. training can range from formal courses on data analytics to lunch-and-learns on how to use the firm’s new ai-based tax software.

the aicpa has also been pushing members to attain “data analytics” and “ai literacy.” in fact, a new cpa specialty credential around technology is being discussed. many states now offer ai and analytics courses as part of cpas’ mandatory continuing education requirements. forward-thinking firms mandate at least 8-16 hours of ai or tech-focused cpe for their staff annually. some larger firms have internal ai certifications – e.g., a bronze/silver/gold level indicating what tools you’re proficient in.

what specific skills are in demand? for starters, data analysis and interpretation. with ai tools spitting out results, someone needs to make sense of them. accountants with stronger quantitative and analytical skills are highly valued. advisory and communication skills have also become more crucial. if ai handles the heavy lifting of number-crunching, the accountant’s value is in interpreting those numbers and communicating the impact to clients. so, being able to translate an ai-identified trend into actionable business advice is gold. on the audit side, t’s audit and understanding of ai algorithms is emerging. for example, auditors now need to consider how to audit a client’s ai system or incorporate ai outputs as audit evidence, which requires knowledge of how ai models work, at least at a conceptual level, to assess their reliability.

recruitment and retention: the war for talent in accounting has been intense in new years (even pre-ai). now, tech adoption is a differentiator for potential hires. young accountants coming out of college are often digital natives who’ve used ai (like chatgpt) in school or at home. they expect employees to have modern tools. a striking 76% of counting graduates say they are more likely to join firms that actively use ai and advanced technologies. this aligns with what firm leaders are observing in interviews – candidates ask about what technology the firm uses, whether they’ll get to learn new software, etc.

firms can actually use this to their advantage against larger competitors: a regional firm that’s heavily invested in tech can attract a star candidate by offering a forward-thinking environment, whereas a big four firm that might be perceived (rightly or wrongly) as using armies of staff to grind through work could seem less appealing. we spoke to one hr director at a 100-person cpa firm who says they revamped their recruitment pitch to emphasize “you’ll be working with cutting-edge ai tools and not just ticking boxes in excel all day.” it has helped sway some candidates their way.

on retention, the story is similar. burnout and overwork have been significant issues in the accounting profession. ai has the potential to alleviate some of that by eliminating drudgery and overtime caused by manual tasks. indeed, intuit’s marketing manager, langley barth, noted that ai tools in proconnect are about “reducing burnout from tedious tasks” and allowing staff to focus on more meaningful work. if firms can show that ai makes their jobs saner and more interesting, they have a better shot at retaining them in this competitive labor market.

however, there’s nuance: some employees, especially older ones or those in routine roles, fear ai or feel threatened by it. change management is needed to reassure them. the firms that succeed create a culture where human workers understand they are “ai managers” or “ai augmenters” – not cogs to be replaced. accountants are encouraged to see ai as a tool that can make them more effective and free them from tasks they never liked anyway (like endless data entry or cross-footing spreadsheets).

human-ai collaboration – the new norm: we are entering an era of blended teams. a gartner prediction, shared at an aicpa conference, was that by 2029, one-third of finance staff will be in roles where they work interdependently with ai – essentially sharing their job with an ai counterpart. that idea is no longer far-fetched. already, we see roles ike “ai-augmented auditor” or “tax reviewer & ai specialist” popping up in job descriptions. these aren’t separate roles, but rather expectations within roles: e.g., a tax senior who knows how to utilize ai tools and will manage a portion of the workflow through them.

the day-to-day is changing accordingly. for example, an auditor might use an ai tool to examine 100% of journal entries for anomalies (something that would have been impossible manually). the ai flags, say, 17 transactions as high-risk. the auditor then uses their professional judgment to investigate those. she might even ask the ai to follow up with questions (if it has that capability), such as “explain why these were flagged,” and get some reasoning (perhaps they were posted at odd hours or just under approval thresholds). the auditor then determined whether it was an issue or a false positive. in essence, the ai did the heavy lifting of combing through data, and the human applied the reasoning and decision-making – a partnership.

another example is in tax planning: a cpa might ask a generative ai tool to draft a memo on the tax implications of a client’s potential move from new york to florida. the ai drafts a comprehensive memo in seconds, citing relevant statutes about domicile and residency rules. the cpa then reviews it, corrects a nuance about new york city tax, and adds a personalized recommendation for the client. the final product is a team effort between ai and humans. the human’s roles shift to editor, fact-checker, and relationship manager – ensuring the output is correct and tailored to the client.

in terms of attitude, a quote by mark koziel, aicpa ceo, encapsulates the current sentiment among forward-looking cpas: “ai is not going to disrupt the accountant – it’ll change what the accountant does, but it will not replace the accountant. as long as we keep up with the skills we need, we’ll continue to be a profession that prospers.”

it captures the optimism that accountants, armed with ai, can actually become more valuable. they’ll spend more time on advisory conversations, strategy, and judgment calls—the things clients truly value—and less on rote processing. however, it also comes with a warning: “as long as we keep up with the skills we need.” that’s the onus on every professional now, to learn and adapt continuously.

the talent landscape in accounting is tilting toward a more technologically fluent, advisory-focused workforce. firms that manage this transition – by reskilling their employees and perhaps hiring new types of expertise – are finding that ai can enhance their human capital rather than diminish it.

ai in action: use cases and success stories from firms

it’s easy to talk about ai in abstract terms, but what does it actually look like in an accounting firm’s daily work? consider several real-world use cases – drawn from common practice and specific firm case studies – that illustrate how ai is being applied, and the tangible outcomes.

tax compliance & preparation

use case: automating tax return preparation and review.

how ai helps: ai can auto-extract data from client documents (like w-2s, 1099s, and trial balance reports) and populate tax returns. it also runs algorithm c checks on returns to catch errors or missing info.

benefits: huge time savings – many firms report 50-70% reduction in preparation time for individual returns when using ai-driven import and validation. for example, instead of a staffer spending three hours entering data and another hour proofreading, the ai might complete the entry in minutes and flag a few items for review, with the staffer spending a total of one hour on review. some firms have been able to have one preparer handle what used to be the workload of two during peak season. also, accuracy improves when ai doesn’t get tired at 11 pm in march and transpose digits. a firm that adopted an  i tax prep tool saw a dramatic drop in review notes from partners because returns were cleaner to begin with – they even boasted “90% fewer compliance errors” year-over-year after implementation.

case in point: a firm integrated an ai module into its tax software last tax season. they use it primarily for their hundreds of individual 1040 clients. the ai auto-maps client quickbooks data to tax form line items and pulls in scanned documents. the firm calculates that it prepared 55% more returns per preparer than in the prior year, with similar staffing. instead of laying people off, they can take on more clients and reassign some staff in april to advisory projects (like tax planning scenarios) because compliance was under control.

tax research & advisory

use case: rapid tax law research and proactive client advisory.

how ai helps: natural language processing (nlp) allows accountants to query vast tax law databases by asking plain-english questions. ai can also analyze a client’s financial scenario and suggest tax strategies or flag potential issues (e.g., detecting a large income increase that may warrant estimated tax adjustments or a review of reasonable compensation for an s-corp).

benefits: speed and depth of insight. traditionally, researching a nuanced tax question (say, on multi-state income sourcing or a new provision in a regulation) could take many hours of reading textbooks or searching tax libraries. now, ai-powered research tools like thomson reuters checkpoint edge, which utilizes generative ai, or bloomberg’s similar ai assistant, can produce an answer in seconds, often with citations to the relevant code and regulations. accountants still must verify and interpret, but it significantly compresses the time. also, ai can surface planning opportunities: for instance, if a client’s data is in the system, an ai can scan it and say, “client’s charitable contributions went up 50% – consider advising on a donor-advised fund.” firms report that thanks to ai analytics, they have increased the volume of value-added client touches. one global survey found that 9% of firms now reach out to clients at least monthly with proactive insights, often aided by ai tools that scan client data.

case in point: at rsm us, they developed an internal generative ai called guidepath to assist with writing complex tax position memos. as rsm’s digital leader, ergio de la fe says, when dealing with thorny client issues (such as interpreting new tax law changes for a specific scenario), their ai can search through the firm’s internal knowledge (including prior memos, court rulings, and irs releases) and draft a detailed position paper. “we’ve leveraged language models to help write those memos… accessing all our databases,” de la fe says. this doesn’t eliminate tax experts – on the contrary, it augments them, allowing senior tax experts to produce high-quality analyses more quickly and involve juniors in higher-level work by letting them explore what the ai has found and whether they agree. rsm also uses ai as a “resource of resources,” meaning staff can ask the ai, “what tool can i use to do x for this audit?” and it will suggest the appropriate software or approach – essentially internal consulting at one’s fingertips.

audit & assurance

use case: automating audit data analysis and routine testing.

how ai helps: ai tools can scan entire ledgers for anomalies, perform invoice matching, read contracts for specific clauses, and even handle confirmations via bots. ai can also prepare audit workpapers by summarizing client documents (like contracts or bank statements) into key points.

benefits: the audit process becomes faster and more thorough. instead of sampling 30 transactions out of thousands, ai can check all the thousands for certain criteria. this increases assurance (a better chance of detecting fraud or errors) and can reduce the time auditors spend on manual verification and reconciliation. deloitte’s ai, for instance, can do an initial read of all lease agreements to spot unusual terms, which a human might miss until late in the audit, if at all. firms have reported audit fieldwork time reductions of 20–30% with ai, meaning less disruption for clients and lower cost of service (or ability to perform more audits with the same staff). one published statistic from ase studies is that ai enables a 69% faster audit document review. and because ai can maintain an immutable log of what it checked, documentation is often improved – the ai-generated workpapers can detail what was done, which auditors can then annotate with their conclusions.

case in point: pkf o’connor davies, a large regional cpa firm, applied ai in its audit data prep. their tech advisory lead, suma chandar, said they look for cross-functional use cases where “doing one or two things can bring in 30-40% efficiency in their day, whether tax, audit or esg”. one success was using ai-driven rpa (robotic process automation) to handle routine soc 2 compliance testing and investment account reconciliations for audits. they built a “digital work r” (rpa bot with ai) that logs into 100+ investment accounts, pulls data, consolidates it, reconciles to the general ledger, flags anomalies, and produces an audit trail report. according to carmel wynkoo, the partner in charge of ai at the firm, this saved approximately 500 hours of manual work each year – time that their audit staff can now spend on risk assessment and investigating issues rather than gathering data. another firm, garbelman winslow (gwcpa), used an ai tool to analyze client financial statements: they upload a pdf of the statements (after scrubbing client-identifying info) and ask, “what key factors should we alert the client to?” the ai’s quick analysis often surfaces insights or inconsistencies that the firm then discusses with the client.

samantha bowling, gwcpa’s managing partner, says that within seconds, the ai provides a helpful rundown, effectively acting as a junior analyst in the process.

client accounting & bookkeeping

use case: automating write-up work and bookkeeping tasks.

how ai helps: ai and machine learning algorithms categorize transactions, reconcile accounts, and even generate draft financial statements with minimal human input. for firms offering client a counting services (cas), ai tools can handle bill pay, invoice coding, and basic financial analysis alerts.

benefits: many cpa firms have been expanding into outsourced bookkeeping services for clients (cas practices). ai is turbocharging those operations. for instance, ai-driven bank feed rules can learn a client’s transaction patterns and post items accurately 90% or more of the time. in contrast, a human bookkeeper might achieve similar accuracy but at a slower pace. ai also provides continuous accounting – books can be kept up-to-date in near real-time, rather than waiting for the end of the month. this enables the firm to give clients more timely advice. also, because these tasks are highly automatable, firms can scale cas services without linear increases in headcount. there’s a claim that current automation tech (including ai) can handle up to 70-80% of basic accounting transactions automatically, which allows existing staff to manage more clients.

case in point: a notable case study is botkeeper, an ai-powered bookkeeping platform utilized by numerous cpa firms. one firm implementing botkeeper for their small-business clients found they could reassign three staff who were formerly doing manual data entry into more analytical roles (like preparing kpi dashboards for clients) because botkeeper’s ai handled the routine entries. another example is an internal tool at pwc (a big four, but instructive): they developed ai to automatically draft portions of the financial statements and footnotes by pulling data from the trial balance and prior reports. while firms might not build that themselves, they benefit from vendor software that performs similar tasks on a smaller scale (such as xero and quickbooks online, which increasingly offer ai categorization and even draft reporting features). the result is that the account team’s role shifts to reviewing what the ai has done, correcting it if necessary (and in doing so, further training the ai), and then focusing on advising the client on the meaning of the numbers.

administrative and other uses

beyond core technical work, firms are deploying ai in supporting roles as well:

• email and document drafting: as mentioned, tools like openai’s chatgpt or microsoft’s copilot are being used (carefully) to draft engagement letters, proposal text, marketing content, or routine client emails. for example, mbs accountancy, a california-based firm, utilizes ai to standardize the tone of communications and generate polished follow-up emails to leads. their marketing manager notes it helps those whose “grammar might not be their strongest point” and ensures a consistent, professional voice in client communications.
• internal q&a and training: some firms have established ai chatbots that function as internal knowledge bases. ask it “how do i handle revenue recognition for a software client under asc 606?” and if it’s been fed the firm’s methodologies, it can produce a quick answer or point to the right template. cliftonlarsonallen (cla) implemented an ai to answer staff questions by pulling from past workpapers and manuals. this saved a huge amount of manager time that was previously spent answering repetitive queries. cla reported that one of their internal processes – extracting information from lengthy client pdfs for audits – was automated with ai, saving hundreds of hours that were previously allocated to a staff member who spent half their day on copy-paste tasks.
• seo and web content: accounting firms even use ai for generating website content or optimizing their blog posts for search engines. a marketing specialist at mbs accounting mentioned they feed blog drafts into ai to refine seo keywords and even spin up quick landing pages for campaigns.

common ai use cases in cpa firms and reported benefits

use case	ai functionality	reported benefit/roi
tax prep & compliance	auto-extract data into returns; ai cross-checks for errors & omissions.	70% time reduction in prep; 90% fewer errors on filings. can handle 50%+ more returns per staff.
tax research & planning	nlp search of tax law databases; scenario analysis and strategy suggestions.	complex queries answered in seconds (vs hours); enables more frequent proactive planning (many firms now contact clients monthly with insights ).
audit testing	transaction anomaly detection, document ai review, and confirmation bots.	up to 58% faster close of audit cycles; greater coverage (100% transaction analysis vs sampling); 69% faster doc review. staff hours reallocated to analysis from data entry and processing.
client accounting (cas)	ai categorization of transactions, auto-reconciliation, and draft financials.	55% faster monthly close; staff can handle 2x clients. real-time books allow advisory on current data (not last month’s) – adding value to cas offerings.
advisory & forecasting	ai-driven financial modeling; “what-if” scenario planners (e.g., for cash flow, tax impact).	things that took weeks of modeling are now done in minutes. one firm reported that it had engaged 80% of its clients in premium advisory services following the implementation of ai. clients see immediate value, boosting satisfaction and retention.
administrative/other	ai drafting (emails, reports); internal chatbots; hiring/screening resumes.	proposal writing time cut by 50%. internal help desk ai saves partners hours. hr uses ai to quickly screen candidate resumes. overall firm efficiency gains, though harder to quantify, improve with these incremental aids.

 

ai excels at tasks that are data-heavy, rules-based, and repetitive, and by taking on these tasks, it boosts efficiency and often improves quality (by reducing human errors). meanwhile, human professionals are refocusing on tasks that require judgment, context, and interpersonal skills – such as consulting with clients on the implications of that data, making final decisions on uncertain transactions, or simply spending more time talking to clients rather than poring over paperwork.

a firm in the midwest implemented an ai-based audit confirmation service (which uses ai and rpa to send confirmations to banks, lawyers, etc., and chase them up). the managing partner reported that their audit seniors – who used to dread managing confirmations and often had to put in extra hours to get responses – were “absolutely thrilled” that the ai bot now handled 90% of the chasing. morale went up, and seniors had more time to focus on analyzing the confirmation responses. little improvements like that in multiple areas added up to them cutting one week off their average audit timeline and giving staff a less chaotic experience.

it’s worth noting that not every ai use case yields dramatic results; some might even fail or need iteration. some firms have attempted to use ai for complex analytical reviews and found that it wasn’t accurate enough without significant adjustments. the smart approach is usually to start with “low-hanging fruit” use cases (like data import, which is relatively straightforward) and then progress to more sophisticated ones (like predictive analytics for clients) as confidence and competency grow.

with the enticing success stories, however, also come concerns and new risks. for every digital “worker” who saves 500 hours, a partner is likely to ask: are we sure it’s being done correctly and not missing something? are we exposing ourselves or our clients to any risk by using ai? those are valid questions, and addressing them is crucial for success.

navigating risks, regulation, and ethical challenges

every new technology presents a set of risks and challenges, and ai is no exception – in fact, ai might pose more risks than most, given its black-box nature and potential for misuse.cpa firms, like all others, must grapple with ensuring that ai is used responsibly, securely, and in compliance with both professional standards and laws. let’s break down the key risks and how firms are handling them:

data security & privacy: accounting firms are custodians of highly sensitive client data, including financial information, social security numbers, and earnings. plugging ai into the mix raises questions: where is the data going? who can see it? many ai tools, especially those developed early on, were cloud-based, with data potentially passing through third-party servers. firms have to ensure that any client data sent to an ai (for example, to a cloud ai service for analysis) is protected. a major concern is that staff may unintentionally input confidential information into public ai services (such as chatgpt’s free version), which could result in data leaks. alarming stats from a 2025 kpmg s rvey show 46% of u.s. workers admitted to uploading sensitive company info to public ai platforms, and 53% even presented ai-generated output as their own work without disclosure – essentially bypassing controls. for cpa firms, that’s a huge no-no if it’s client data.

mitigation: firms are instituting strict ai usage policies. many have outright bans on using public ai with client data. instead, if they want those capabilities, they either use a paid enterprise version with privacy guarantees (openai offers a business tier where data isn’t used to train the model, for example) or leverage ai solutions that run in secure clouds specifically for their data (some larger firms even host ai models on-premises). also critical is training staff, ensuring that everyone understands that entering client details into a random chatbot is akin to shouting them in public. some firms go further and use dlp (data loss prevention) software that flags or blocks attempts to use certain web ai tools at work.

accuracy and “hallucinations”: ai tools, especially generative ones, can produce inaccurate or completely made-up outputs. the term “hallucination” is used to describe an ai confidently generating an entirely false answer. in accounting, even a small error can have big consequences (e.g., a wrong number on a tax form or an incorrect interpretation of a law). according to a karbon survey, 70% of accountants expressed concern about data security in ai, and nearly half (47%) worried that ai might diminish human touch or judgment. accuracy is a top concern: one study noted 62% of accountants worry about errors in ai-generated reports.

mitigation: human review remains paramount. the golden rule many firms adopt is: ai can draft or analyze, but a human must validate any final output that goes to clients or official filings. in a thomson reuters survey, professionals stated that ai outputs would need to be “100% accurate” before they’d be comfortable using them without human review – an arguably impossible standard, hence human review remains in the loop. firms are creating checklists for reviewing ai work (e.g., “if ai drafted a memo, verify each factual assertion and citation”). additionally, some ai tools themselves provide a confidence score, and accountants are trained to interpret those. the mit/stanford research found that experienced accountants were good at using ai confidence scores to decide when to trust or double-check an output. that kind of discernment needs to be taught.

another mitigation is starting ai usage in low-risk areas first. for instance, use ai to draft an internal report or conduct a preliminary analysis, but not to finalize an sec filing footnote without extensive verification and review. over time, as the firm builds trust in a tool (and possibly customizes it to their needs), they can expand its autonomy.

explainability and auditability: clients, regulators, even the firm’s own partners will ask, “how did the ai get this result?” if an ai flags a strange journal entry as fraud risk, you need to be able to explain why (maybe it spotted an outlier pattern). black-box models that produce answers without a clear rationale can be problematic. and if regulators come knocking (say, the pcaob examining an audit), they will want documentation of what the auditors did – that now includes what the ai did.

mitigation: many ai software providers in this space are adding explainability features. for example, an ai tax research tool might show the sources it used (cite irc sections or rulings) to derive an answer, which the cpa can document in their workpapers. in audits, if ai is used to select samples or flag entries, auditors include those ai parameters and outputs in their audit documentation. some firms say they treat the ai like a junior staff member – everything it does is reviewed and documented as if a person did it. additionally, there’s the concept of an ai audit trail, where some systems maintain a log (including date/time, action, and result) for all ai-driven processes. for instance, if an ai auto-posts an entry, it logs the reason (e.g., rule applied, etc.).

from a governance perspective, boards and firm leadership are taking note. the wolters kluwer report highlighted that best-in-class firms are doing quarterly ai oversight meetings, and annual disclosures to clients about ai use are becoming more common (e.g., telling audit committees that “we used ai tool x in conducting the audit of accounts receivable”). this transparency helps build trust and also forces the firm to reflect on how ai is affecting its risk profile.

ethical and professional responsibilities: cpas are bound by ethical standards that include confidentiality, integrity, and due care. the use of ai touches these areas. for example, confidentiality is at risk if ai is not secure. due care is relevant if a cpa relies on ai without sufficient competence or skepticism – e.g., if they accept an ai’s tax answer without understanding it, are they exercising due professional care? additionally, if ai is used to assist in making judgments (such as valuation or going concern), how are biases identified and mitigated?

there’s also an ethical dimension in terms of disclosure: should clients be informed when ai did a significant portion of the work? some might argue it’s only fair that the client knows if a letter was ai-drafted (though a human edits it). most clients probably don’t mind as long as quality is high, but transparency is generally a good policy.

mitigation: the aicpa hasn’t yet (as of 2025) released formal ai ethics guidelines, but it is likely to do so. in the meantime, firms are leaning on existing rules. the aicpa code says a cpa can use a third-party service provider (which could include an ai service). still, it must ensure confidentiality and should disclose to the client if it’s a significant portion of the engagement (with some exceptions). many firms have begun updating their engagement letters to mention the potential use of ai or outsourced services in performing the work, while ensuring confidentiality and quality controls.

firms are also establishing internal “ai ethics committees” or task forces. these groups review new ai use cases for ethical implications, set boundaries (e.g., “we will not use ai to make final judgments on high-risk areas; we will use it only as input”), and monitor the landscape for new guidance.

regulatory uncertainty: while we have accounting-specific standards to consider, broader ai regulation is also looming. the eu’s ai act, for instance, would impose requirements on ai systems, especially for high-risk use cases. in the u.s., there is no federal ai law yet, but sectoral regulations (such as sec or ftc guidance) could emerge if ai causes issues. accounting firms also have to consider things like whether using ai might violate any privacy laws (say, feeding personal data into a model could engage laws like gdpr or california’s privacy law, if not handled properly).

mitigation: monitoring regulatory developments is crucial. larger firms typically have policy teams; smaller firms often rely on industry associations or legal counsel for updates. some firms opt to err on the side of caution – implementing higher standards voluntarily. for example, treating their ai systems as if they were already subject to the eu ai act (ensuring explainability and risk assessments) could be a strategic move to stay ahead of the curve. the journal of accountancy reported that one firm began aligning with european guidelines because u.s. guidelines were lagging. avani desai, ceo of schellman (a top 100 firm focusing on tech audits), advised, “work with regulators early. don’t wait for the mandate.” that might involve participating in industry groups that liaise with the sec or irs on ai issues, allowing the profession to shape sensible rules.

model bias and fairness: although not as prominent in tax and audit as it is in, say, lending decisions, bias is something to be aware of. if ai were used in hiring or evaluating staff, for example, firms would need to ensure it’s not discriminatory. or if ai summarizes client data, does it inadvertently reflect bias (e.g., prioritizing certain data over others)?

mitigation: use reputable models and data sets, and test them. the “trusted ai” frameworks (like kpmg’s) emphasize bias testing. in accounting use cases, bias might show up as systematic errors (like always misinterpreting certain legal provisions in one direction). continuous monitoring and feedback help identify these issues. in auditing, reliance on ai might inadvertently introduce bias if, for instance, the ai overlooks certain anomalies because they were not included in its training data; therefore, auditors still need to apply their skeptical mindset.

one emerging practice is “parallel testing” – running ai tools in parallel with traditional methods for a period to compare results. for instance, if ai selects audit samples, also have a human select samples the traditional way and see if ai missed anything. if it did, tune the system. over time, confidence builds that the ai’s choices are sound.

client perception and trust: finally, an often-overlooked risk: how will clients feel about the firm using ai on their work? if a client learns that a computer generated a report, will they think, “am i paying high hourly rates for a computer to do the job?” this is more of a reputational and communications issue.

mitigation: framing is important. many firms choose to be upfront about ai as a positive – “we use advanced tools, including ai, to provide you the best service possible.” they focus on outcomes: faster delivery, more insights, fewer errors. most clients are fine with that. some high-touch clients might say, “i want john to do this, not a machine,” in which case the firm can explain that john is still doing it, just with the help of smart software. also, firms can emphasize their review and quality control over the ai.

interestingly, a 2025 fortune magazine analysis noted that 72% of s&p 500 companies identified ai as a material risk in their own disclosures, often citing concerns related to reputation and compliance. so, business clients are aware of ai issues themselves. this can actually be a conversation point that elevates cpas to a consultative role: “we use ai responsibly in our work, and we can also advise you on how to govern ai risk in your company.” cpas and consultants are seizing that as a niche – helping clients with ai governance, which is a new advisory service some firms now offer, leveraging their own learning.

cpa firms must implement strong governance around ai use.

the formula often cited is people + process + technology controls:

• people: train everyone, raise awareness of risks, assign clear responsibility (e.g., an ai champion or risk officer overseeing it).
• process: update firm policies, engagement letters, and quality control procedures to incorporate ai. maintain documentation (logs of ai outputs, validation steps).
• technology: use secure, vetted ai tools; insist on enterprise-grade solutions that offer privacy and allow oversight. and consider technical controls (like turning off certain ai features that pose risk or controlling access rights to who can use ai for what).

leading firms view this as an ongoing effort, rather than a one-time initiative. they might start with a conservative approach, then gradually allow more ai autonomy as controls prove effective. for example, at first, maybe ai suggestions are never automatically enacted – always, a human clicks “approve.” later, for low-risk tasks, they might let ai auto-complete things, with periodic audits of its work.

the consensus thus far is that the benefits of ai can far outweigh the risks, provided that the risks are managed proactively. i

overcoming implementation hurdles: change management and best practices

adopting ai in an accounting firm is not like flipping a switch – it’s a journey that involves technology, but perhaps even more so, people and process changes. many firm leaders have shared that the tech was ready before their organization was. let’s look at the common challenges firms face when rolling out ai initiatives and how savvy firms are addressing them:

challenge: resistance to change – “we’ve always done it this way.”

partners and staff with decades of experience performing manual tasks may be skeptical or apprehensive about the use of ai. they trust their tried-and-true processes more than a new algorithm. some may worry that embracing ai reduces the perceived value of their own skills or even threatens their job security.

response: top-down vision, bottom-up engagement. successful firms often start by having leadership clearly communicate why ai adoption is happening – focusing on positive outcomes, such as “this will free us to do more value-added work” or “this will help us grow without burning out.” at the same time, they engage the rank-and-file early. one accounting firm formed an “ai working group” that included not only it professionals but also a senior partner, several junior accountants, and a tax manager. this group piloted the ai tool and worked out kinks, so when it rolled out firm-wide, these insiders could champion it and train others.

there’s also a cultural element: firms emphasize that ai is a tool, not a replacement.

“this is like when excel replaced the 10-key calculator. it doesn’t make you any less of an accountant; it makes you a faster, better accountant.” drawing parallels to past tech transitions can help ease minds.

challenge: training and skill gaps.

not everyone is tech-savvy, and ai tools can have a learning curve. if not trained properly, staff might misuse the tool or not use it at all (leading to shelfware). a survey by corporate compliance insights found that only 22% of organizations have a defined ai strategy, and many plunge in without proper training, which can lead to problems.

response: invest in comprehensive training and continuous learning. firms that budget time for training (even if it means non-billable hours) reap the rewards. some arrange vendor-led sessions followed by internal peer-to-peer training. many create quick reference guides or “ai tool faqs” tailored to their workflows. and training isn’t one-off; as ai too s update, they offer refresher courses.

a smart strategy is to use real client work in training. for example, during training on an ai tax software, have staff actually prepare a sample return using it, rather than relying on abstract demos. this builds muscle memory. additionally, pairing less tech-confident staff with a tech-savvy “buddy” can be beneficial during the initial phases. the buddy can be on call to assist until the person becomes comfortable.

challenge: data preparation and integration.

a saying in ai is “garbage in, garbage out.” if a firm’s data (like client records, prior working papers, etc.) is disorganized or locked in silos, ai tools won’t perform well. many firms find they first need to clean up data or migrate to cloud systems before layering ai on top. integration with legacy systems can also be a headache – for example, making an ai tool communicate with an outdated practice management system.

response: phase the project and build a data foundation. rather than tackling everything at once, firms often start with a subset of data or a particular service line. for instance, digitize and clean the tax workpapers for the last two years to feed the tax ai (ensuring consistency in how data is labeled), rather than trying to do tax and audit simultaneously. some firms create new data warehouses or “lakes” – essentially central repositories where data from different sources is aggregated in a clean, ai-ready format. this might involve hiring a data engineer or using a consultant, but it’s an investment that pays off beyond ai, too (it improves reporting, etc.).

in terms of integration, many modern ai solutions offer apis and can connect to common software; however, a firm may still need to build custom connectors. the workaround for smaller firms is to have a manual import/export in the interim (e.g., exporting a quickbooks file for the ai to analyze until a direct integration is established). the key is not to let integration challenges halt the entire project – find a way to pilot it, even if it’s a bit kludgy at first, and then refine.

challenge: mesuccess success (or lack thereof).

implementing ai isn’t cheap or easy, so management wants to see results. however, if they don’t establish clear metrics, they may not realize the value or might prematurely pull the plug. on the other hand, a poorly managed project might actually not deliver value if, say, people only use 10% of the tool’s features.

response: define key performance indicators (kpis) and track them. firms set targets like “reduce tax pre-hours by 30%” or “each auditor to automate 50 confirmations.” by tracking baseline stats before ai and then after, they can quantify gains. it’s essential to be realistic – perhaps in year 1, the goal is 15% time savings, which can be ramped up as everyone gains proficiency. if metrics show underperformance, the firm digs in to find out why. often it’s a usage issue – maybe people reverted to old methods under stress. additional training or refining the process can resolve that issue. it’s an iterative approach: measure, adjust, measure again.

some firms also celebrate wins to keep momentum. for example, if the first ai-prepared ax returns are released with zero review notes from partners, that’s announced firm-wide as a success. recognition (even small things like an email shoutout or a gift card to the “ai champion of the month”) can motivate staff to engage positively.

challenge: cost and roi pressure.

especially for firms with tight budgets, ai investments (such as software subscriptions and possibly new hires like data analysts) can cause partners to worry about the short-term impact on profits. if results aren’t immediate, there could be pressure to cut losses.

response: start small, prove value, then scale – aka “quick wins.” many firms begin with pilot projects, such as testing the ai tool on 10 clients or for one quarter. this limits the spend and risk. if the pilot demonstrates clear time s vings or quality improvements, it’s much easier to gain buy-in for a wider rollout. quick wins also provide internal case studies you can use to convince skeptics.

additionally, some firms negotiate with vendors for phased or performance-based pricing – perhaps a lower cost in year 1, with the understanding that it will expand later. vendors competing in this space often accommodate pilots because they know landing a whole firm is a big win.

roi analysis should also factor in intangible or long-term benefits, not just immediate labor hour reduction. for instance, if ai leads to winning a new client due to enhanced capabilities, that revenue might not be immediately attributed to the ai in a simple roi calculation, but it’s part of the picture. one approach: create a “benefits register” where all positive outcomes (time saved, new engagements, client compliments, reduced overtime costs, etc.) are logged to build the case.

challenge: workflow disruption and teething troubles.

implementing ai can temporarily disrupt workflows. perhaps the ai outputs require new review steps, or initial results contain errors that need to be corrected. staff can become frustrated if the new process appears more challenging initially.

response: refine processes and don’t abandon after the first hurdle. it’s common for the first few weeks of using a new ai tool to feel slower than the old way – a learning curve effect. firms need to message this expectation and encourage perseverance. many create new standard operating procedures (sops) that incorporate the ai: e.g., “step 1: import data via ai tool; step 2: run ai diagnostics; step 3: human review and finalize.” embedding it into checklists ensures it’s not seen as optional or extra, but part of the job.

during the initial period, frequent feedback loops are useful. daily or weekly quick meetings to discuss: what’s working, what’s not? perhaps the ai is misreading some documents – feed that back to the vendor or adjust the doc template. perhaps staff discovered a feature that saves more time – share it across the team. in one firm, juniors discovered that the ai tax software could also auto-generate a comprehensive client summary letter from the return – something the firm hadn’t been doing due to time constraints, but can now. they incorporated that into the process, adding value to the client.

challenge: keeping up with continuous change.

ai tools evolve rapidly. new features roll out, models get updated. the firm’s processes and controls may also require updates. it’s a moving target, not a one-time implementation like switching from one tax software to another, which then remains stable for a year.

response: stay agile and foster a culture of adaptability. firms sometimes establish an ongoing “i committee” (beyond initial rollout) to continuously monitor tech developments. they subscribe to industry newsletters, attend conferences, and network with peers about what’s new. if a vendor releases a great new feature (e.g., ai in auditing can now test entire populations for something that wasn’t possible before), these firms pounce and pilot it. essentially, they treat ai adoption as a continuous improvement program, rather than a project with a defined end date.

some adopt an experimental mindset, allocating, for example, 5-10% of staff time or the it budget to experiment with new ai ideas on a quarterly basis. not all will pan out, but this way they don’t stagnate.

“we’ll never be ‘done’ implementing ai – it’s like saying you’re done improving.”

they have embraced that notion.

challenge: vendor selection and reliability.

with so many ai solutions, picking the right one is daunting. relying on an external tool means that if it experiences downtime or issues, your work could be compromised.

response: exercise careful due diligence and have backup plans. firms are increasingly doing proofs of concept with multiple vendors before committing. they involve the actual end-users in trials to gauge which tool fits best. they also review vendor security (due to the earlier point on data protection) – many send vendors security questionnaires or even do on-site audits for bigger deals. once a tool is chosen, firms negotiate service-level agreements (slas) whenever possible to ensure support in the event of an issue.

having a backup process is wise, for example, “if the ai system is down, we revert to manual for that period.” or maintain the capability to revert to the old way in parallel for a while, until trust in the new way is absolute. some firms keep legacy processes documented just in case, but over time, as confidence grows, those become less needed.

having discussed these implementation aspects, it’s evident that adopting ai is as much about change management as it is about technology. firms that succeed often say the key is getting their people on board and iterating on processes, rather than any magical tech trick.

at this point, we’ve covered the past and present of ai in accounting firms – the surge in adoption, the tools, the benefits, the human element, and the risks/challenges. finally, let’s look ahead to the future: what can firm leaders expect in the next few years, and what should they be doing now to prepare?

the road ahead: strategies for firm leaders

as the profession spins forward into the late 2020s, ai will become even more deeply ingrained in accounting workflows, and the firms that leverage it effectively will set the pace for the industry.

here are some projections and recommendations as the profession enters this new era:

nearly autonomous processes: we can expect many routine accounting processes to become “near-autonomous.” that doesn’t mean no humans involved, but minimal intervention. for example, consider the tax compliance cycle – within a few years, it’s plausible that a client’s transactions flow continuously into an ai-driven tax engine, which updates an ongoing draft tax return in real time. by year-end, the return is 80-90% complete, with minimal human intervention required, aside from confirming any unusual items. at that point, the role of the cpa is to review and strategize on the final 10%, rather than manually entering data through january and february. workflows will approach a point where they “run themselves,” with professionals overseeing multiple automated streams of work rather than manually preparing each item. audit processes, too, could reach near real-time status – some firms talk about “continuous audit” aided by ai agents that monitor client systems year-round, flagging issues immediately.

proliferation of vertical ai agents: just as spreadsheets were a general tool that spawned specialized versions (for finance, stats, etc.), ai will likely spawn specialized agents for different domains of accounting. we might see an “ai audit assistant” that is distinct from a “tax planning genie” or a “cas bot.” in fact, thomson reuters already branded “cocounsel” as a tax-and-accounting-specific ai assistant, separate from generic ai. we’ll see more niche ai solutions for things like sales tax compliance (imagine an ai that stays on top of every sales tax rule change and automatically configures a client’s systems), or for forensic accounting (an ai that can sniff out fraud patterns across millions of transactions and maybe even generate a draft of the investigative report). these vertical agents will multiply the mark’s impact, because they’ll be tuned to understand accounting concepts at a deeper level than broad models. for firm leaders, staying abreast of the latest developments and being willing to adopt the right tool for the right task (instead of one-size-fits-all) will be key.

heightened emphasis on explainable and ethical ai: as ai is used for more critical decisions, regulators and clients will demand more explainability and accountability. we touched on how 72% of large companies now disclose ai risks; that trend will likely spur formal regulations or, at the very least, industry standards. we might see audit standards updated to address ai – e.g., the pcaob could issue guidance on using ai evidence in audits (perhaps requiring documentation of how ai tools were validated). the irs or state boards may set rules on ai-prepared returns (for instance, requiring disclosure if ai was used, or guidelines on taxpayer responsibility when ai largely prepared a return). smart firms will engage with bodies like the aicpa to help shape these standards. the aicpa, in fact, launched an “ai task force” (hypothetically, given the direction) to issue recommendations on ai governance for firms. we can anticipate frameworks akin to today’s cybersecurity frameworks – perhaps an “ai control framework” – becoming part of firm peer reviews or practice inspections. one could imagine future peer review checklists: do you have a policy for using ai? how do you validate outputs? how do you secure data in ai systems? being ahead on those governance fronts will become a competitive differentiator (clients might favor firms with an “ai seal of quality” if such a thing emerges).

new services and business models: ai will also open new business opportunities for cpa firms. many firms are already advising clients on implementing ai in finance or conducting risk assessments of their clients’ ai systems. this can bloom into a service line – aki to how firms developed it consulting or cybersecurity practices. given the trust in cpas, there’s talk of cpas serving as independent ai verifiers for clients, somewhat like how they lend credibility to financial statements. the journal of accountancy even floated the concept of “cpas as ai system evaluators.” for instance, a client might ask their c  to evaluate the controls and bias testing of an ai they want to use in finance, providing a level of assurance to stakeholders. therefore, firm leaders should consider investing in internal expertise not only to utilize ai, but also to consult on it. this might involve hiring a data scientist or training auditors in ai risk, thereby positioning the firm to undertake ai-related advisory projects.

talent strategy shift – from recruitment to reskilling: the talent crunch isn’t going away, but ai might change its nature. if fewer entry-level individuals are needed (or if they require a different skill set), firms could partner with universities to shape the accounting curriculum toward a more technology-focused approach. some firms are already hiring non-traditional talent – for example, economics or computer science graduates – into accounting roles and training them in accounting, as they believe it might be easier to teach accounting to a technologist than to teach technology to an accountant. the next generation of cpas will likely have some coding or ai familiarity – the pipeline will adjust (in fact, the cpa exam is changing to incorporate more technology knowledge).

for existing staff, expect a continuous upskilling mandate. those unwilling to learn might find their career prospects limited. but those who do may find accelerated paths: perhaps the traditional 10-15 year path to partner shortens if technology leverage means younger cpas can take on more responsibility sooner. firm leaders should create clear pathways for tech-savvy staff – maybe a role like “director of ai implementation” or making such skills a criterion for promotion to manager.

competitive landscape and consolidation: historically, technological shifts in accounting (such as the transition to pc software or the cloud) have initially been leveraged by larger firms, but they have eventually leveled the field or at least reconfigured it. ai could, in some ways, level the playing field (a 50-person firm with great ai tools can potentially outperform a 200-person firm that doesn’t utilize them effectively). we might see some consolidation as laggards decide it’s easier to merge with a tech-forward firm than to catch up. on the other hand, nimble firms that excel in ai may capture more market share, potentially expanding beyond their traditional regions or sectors, as technology removes some barriers. if your ai-enhanced team can handle twice the clients, you might expand services to smaller companies or not-for-profits that you couldn’t profitably serve before.

client expectations: clients – especially younger entrepreneurs or cfos – are coming to expect their cpa to have ai-powered capabilities. they’ll ask questions like, “do you use a  to ensure nothing is missed in my audit?” or “what analytics can you provide us from our data?” clients may not be familiar with the term “agentic ai,” but they’ll want the speed, insight, and proactive advice that ai enables. keeping client communication about ai pos tive is key: some firms, for example, highlight in proposals that “we use advanced ai tools that allow us to complete the engagement faster and with deeper analysis, at no extra cost to you.” that signals efficiency and innovation, which can help win business.

recommendations for firm leaders:

• develop or refine your ai strategy now: if you haven’t documented an ai game plan, do it. this means identifying where ai can have he most impact in your firm (tax, audit, advisory, internal admin?), setting priorities, and aligning with your overall firm strategy. according to thomson reuters data, firms with a clear strategy achieve significantly higher returns. it doesn’t have to be elaborate – even a page roadmap with short and long-term goals helps clarify direction.
• invest in infrastructure: this is the time to ensure your it infrastructure (cloud platforms, data warehouses, integration capabilities) can support advanced tools. utilizing modern cloud-based practice management and accounting systems will facilitate the integration of ai. also consider data infrastructure – clean client data archives, standardize data formats, etc. it’s not glamorous, but it’s the foundation for ai success.
• cultivate an innovation culture: make it acceptable to experiment and even fail fast. perhaps allocate a modest budget for a couple of small ai pilot projects each year. encourage staff to bring ideas – maybe a junior says, “why don’t we use ai to automate how we do xyz schedule?” listen and let them try on a small scale. some firms hold internal innovation competitions or hackathons to crowdsource innovative ideas.
• collaborate and learn: join industry groups focusing on ai (the aicpa, state cpa societies, and global accounting networks are all hosting ai roundtables these days). sharing experiences with peer firms can prevent reinventing the wheel and keep you up to date. additionally, involve stakeholders like clients – some firms have beta-tested ai-generated analysis with friendly clients to gather feedback on its usefulness.
• mind the human touch: as processes automate, double down on human relationships. ironically, the more we automate, the more critical the distinctly human aspects become – trust, empathy, judgment. train staff in “soft skills” to complement their technical expertise. ensure partners spend time with clients discussing the meaning of all the fancy new reports ai produces. in essence, utilize ai as an enabler to enhance client service, rather than as a substitute for it.

the tools are here, and adoption is accelerating.

cpa firms have a chance to leap forward by embracing these technologies thoughtfully. those who act with clarity and intent – training their people, managing risks, and focusing on delivering greater value to clients – are likely to thrive and set themselves apart. those who drag their feet may find the gap widening to a point where catching up is extremely difficult.

the accounting profession has undergone many evolutions – from paper ledgers to spreadsheets to cloud platforms – and each time, firms that adapted emerged stronger. ai is arguably a bigger leap than any of those, but it carries the same promise: freeing smart people from menial tasks so they can do more impactful, meaningful work.

for firms, this is a moment of opportunity to punch above their weight by leveraging ai.

with strategic adoption, continuous learning, and a commitment to delivering ethical, high-quality service, these firms can not only enhance their own operations but also increase the value they provide to clients in a rapidly changing business world.

the future of accounting won’t eliminate accountants – it will elevate them. and the journey to that future is happening now, one ai pilot and one success story at a time.

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glossary

advisory services – high-value consulting and strategic analysis services offered by cpa firms, increasingly powered by ai-driven insights and predictive analytics.

ai-augmented auditor – an accounting professional who uses ai tools to analyze data, identify anomalies, and focus human judgment on risk and interpretation rather than manual testing.

ai literacy – the foundational understanding of how artificial intelligence works, its capabilities, limitations, and ethical implications within professional practice.

artificial intelligence (ai) – a branch of computer science that enables machines to simulate human intelligence processes such as learning, reasoning, and problem-solving.

audit trail – a documented record of transactions and system activities used to verify accuracy, completeness, and integrity of data — now often generated automatically by ai systems.

bias mitigation – processes designed to detect and correct for unfair or inaccurate outcomes in ai-generated analyses or decision-making.

change management – the structured approach firms use to guide employees through transitions in technology and process, essential to ai adoption success.

continuous learning – ongoing professional development to maintain technical and analytical skills in a fast-changing ai environment.

data integration – the process of combining data from multiple systems (tax, audit, practice management) into a unified view that ai can process efficiently.

data privacy – legal and ethical safeguards ensuring that sensitive client or firm information is not disclosed or misused in ai workflows.

data security – protective measures to prevent unauthorized access or loss of firm and client data, often reinforced by enterprise ai and encryption standards.

digital transformation – the strategic integration of technology, including ai, across firm operations to improve efficiency, accuracy, and client service.

ethical ai – the responsible use of artificial intelligence that prioritizes transparency, fairness, privacy, and accountability in professional practice.

human-in-the-loop (hitl) – a model where humans oversee ai systems, reviewing and validating ai outputs before client or regulatory use.

knowledge graph – a structured database that maps relationships among data points, enhancing ai’s ability to provide contextual answers in tax and audit queries.

large language model (llm) – an advanced ai architecture trained on massive text datasets, capable of generating natural-language responses, analysis, and summaries.

natural language query (nlq) – the ability for users to type or speak questions in plain english to retrieve ai-generated results from databases or tax law repositories.

neural network – a computational system inspired by human brain architecture, forming the basis of deep learning and generative ai.

practice management system – software that coordinates firm operations, billing, workflow, and client data; increasingly integrated with ai modules.

professional skepticism – the critical mindset accountants apply when reviewing ai outputs, ensuring conclusions are evidence-based and not blindly accepted.

prompt engineering – the process of crafting effective queries or instructions for ai tools to produce accurate, context-specific results.

regulatory compliance – adherence to accounting, tax, and privacy laws when deploying ai systems; includes sec, irs, pcaob, and gdpr considerations.

reskilling – training existing staff to acquire new technical and analytical skills to work effectively with ai technologies.

smart workflow – automated task routing in practice software that uses ai to predict next steps and optimize process efficiency.

supervised learning – a machine learning approach in which ai models are trained on labeled datasets, allowing them to make accurate predictions or classifications.

technology stack – the combined software, platforms, and data systems that support a firm’s digital operations and ai capabilities.

trusted data environment (tde) – a secure infrastructure ensuring all firm data processed by ai tools is verified, compliant, and protected from leakage.

upskilling – expanding an employee’s current skill set to include digital literacy, analytics, and ai-related competencies.

value-based pricing – a billing model that charges clients based on the value of outcomes rather than time spent, made feasible by ai-driven efficiency gains.

workflow automation – the use of ai and process software to execute recurring accounting or administrative tasks with minimal human input.

zero-trust architecture – a cybersecurity framework for ai systems that assumes no implicit trust within or outside the network, enforcing strict verification at every access point.

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references:

1. bramwell, jason. “accounting firms are choosing transformation over tactics, wolters kluwer report says.” cpa practice advisor, oct. 9, 2025. (highlights ai adoption surge from 9% to 4, with 77% planning more investment and 35% daily use)

2. thomson reuters tax & accounting blog. “how do different accounting firms use ai?” nov. 4, 2025. (examples of big 4 and smaller firms’ ai use cases; 44% using ai daily among those using/planning to use genai)

3. karbon. “state of ai in accounting report 2025.” (survey data: 85% of accountants are positive on ai, but only 37% of firms invest in training; firms investing in ai training save 22% more time; 76% believe graduates prefer ai-using firms; 56% say firm value drops if not using ai )

4. mit sloan. “how generative ai can make accountants more productive,” aug. 5, 2025. (study by xie & choi: ai use led to 7.5-day  ster monthly close; 8.5% of time reallocated from data entry to higher tasks; improved reporting detail +12%; need human judgment for low-confidence outputs)

5. strickland, bryan. “reputation, security, compliance: why ai risk disclosures are surging.” journal of accountancy, oct. 29, 2025. (72% of s&p 500 companies disclosed ai risk in 2025, up from 12% two years prior, citing reputational, security, and compliance risks)

6. nysscpa. “six accounting firms report on how they’re using ai,” may 7, 2024. (real firm examples: pkf using ai for cross-functional efficiency, armanino’s digital audit worker saving 500 hours, gwcpa using ai to analyze financial statements in seconds, cla automating info extraction saving hundreds of hours, rsm using generative ai for tax memos )

7. hood, daniel. “ai in accounting: weighing the pros and cons.” accounting today, apr. 2, 2025. (quote from aicpa ceo mark koziel: “ai will change what accountants do but will not replace them”; gartner prediction: by 2029, one-third of finance staff will have ai-human shared roles )

8. kpmg. “the american trust in ai paradox: adoption outpaces governance,” apr. 29, 2025. (study findings: 70% of u.s. workers eager for a benefits but 75% wary of downsides; 44% use ai without authorization, 46% uploaded sensitive data to ai; quote emphasizing need for strong “trusted ai” governance )

9. thomson reuters (joan feldman via attorney at work). “ai adoption divide dominates the 2025 future of professionals report.” oct. 2025. (tr survey: firms with an ai strategy are 3.5x more likely to get benefits; 81% with a strategy see roi vs 23% with none; 91% pros say ai must be more accurate than humans, 41% say ai outputs need 100% accuracy before no human review; barriers include accuracy, security, lack of time/resources)

10. axios (ina fried). “ai is already taking jobs away from entry-level workers,” aug. 26, 2025. (stanford study using payroll data: 22–25 y/o employees in ai-impacted jobs down 13% since late 2022; brynjolfsson notes speed of change and disparate impact on entry-level; cautions that companies using ai to augment hire more, those using it to replace hire less.)

posted on november 1, 2025