Interview · Practice Technology · 12 min read
On the Record
Rebecca Kahn
Practice Technology Consultant · Former audit senior manager, Big Four (London)
Rebecca Kahn has spent twelve years moving between audit practice and technology consulting — the last four of them advising small and mid-sized firms on how to actually deploy the tools the Big Four are showcasing. AWSCPA Journal sat down with her to work through what the 2025 adoption data really means for firms that don’t have a hundred-million-dollar innovation budget.
— Opening —
The gap between the Big Four and everyone else, examined.
Thomson Reuters’ 2025 report put GenAI adoption among tax firms at about 21%, with another 53% planning or considering it. That’s a significant shift from 2024. What’s actually changed on the ground for firms of the size you work with?
The shift is real, but it’s not uniform. What I’ve observed working with UK and European mid-market firms over the past year is that the conversation has moved from “should we?” to “what and how?” — which is a different problem entirely. A year ago, partners were asking me whether AI was going to displace their juniors. Now they’re asking which workflow they should automate first, whether to buy a point solution or wait for their practice-management vendor to ship something, and how to explain the change to a team that’s already anxious about it. The question has become operational, which is a much healthier place to be than existential.
The Thomson Reuters data showed that 52% of the firms using GenAI are using open-source tools like ChatGPT rather than industry-specific software. That seems surprising.
It shouldn’t be, actually. It’s a sign of how far behind the industry-specific tooling is. When a tax senior needs to draft a client memo or summarise a regulatory change, they reach for the tool that works today — which for most firms is a generic LLM with a business subscription. The specialised tools exist, but they’re expensive, their training data is narrower, and their user experience often lags what consumers have been using at home for two years. That gap will close. But right now, open-source tooling is genuinely better at the long tail of accounting-knowledge-worker tasks than most of what’s being sold as “AI for accountants.”
Is there a risk problem with that? Client data running through consumer AI tools?
A significant one. I’ve done governance reviews for three firms in the last year where I discovered staff were pasting client-identifiable information into consumer ChatGPT accounts to get help drafting something. Not out of malice — just convenience. The firms had no written policy about it. This is one of the areas where smaller firms are actually at more risk than the Big Four, because the Big Four have enterprise licensing agreements with data protection terms that consumer tools don’t offer. The first question I ask any firm deploying GenAI isn’t “what’s your use case” — it’s “what’s your policy on client data entering a model, and how do you enforce it?”
— The numbers behind the conversation —
| Status | 2024 | 2025 | Direction |
|---|---|---|---|
| Actively using GenAI | Lower base | ~21% | Rising |
| Planning or considering | Roughly half | ~53% | Broadly stable |
| No plans to use | ~49% | ~25% | Falling fast |
| Using open-source tools (of adopters) | Not measured | ~52% | Dominant in 2025 |
| Using industry-specific tools | Not measured | ~17% | Early stage |
Source: 2025 Generative AI in Professional Services Report (Thomson Reuters Institute).
— The Big Four Playbook —
Proprietary platforms, enterprise agreements, and a five-year head start.
Deloitte, EY, PwC, KPMG — they’ve all been public about their AI investments. Deloitte’s audit platform has agentic capabilities. EY launched a unified AI platform in 2023 and has announced capabilities supporting 160,000+ global audit engagements. PwC claims 20–50% productivity gains in their internal development. How much of this is real, and how much is marketing?
It’s mostly real, but heavily caveated. The Big Four have spent the last four years investing billions in proprietary audit and tax platforms. The capabilities are genuine. But the productivity numbers they cite tend to be measured in narrow domains where the tooling has been optimised — not across the whole engagement. PwC’s 20–50% gain on development productivity is probably true for specific code-generation and code-review tasks; extrapolating that to “audit engagements run 30% faster” would be a mistake. The tooling is real. The uniform productivity transformation is more measured than the press releases suggest.
What about KPMG’s Trusted AI framework and the governance approach? Does that scale down to smaller firms?
The framework itself does, conceptually. The staffing around it doesn’t. KPMG’s framework works because they have dedicated ethics, risk, and technology teams reviewing model deployments and controls. A fifteen-partner firm can adopt the principles — transparency, human oversight, audit trails, bias monitoring — but they can’t replicate the apparatus. What I recommend to mid-market firms is to adopt a simplified, practical version: a one-page policy, a named AI risk owner, quarterly review of what’s being used, and mandatory training. It’s 5% of what the Big Four do but captures 60% of the value.
The Big Four are spending billions. Realistically, how far behind do their proprietary tools put everyone else?
On audit-specific tooling, probably three to five years. But that gap is narrowing fast, and the commercial equivalents — the CoCounsels and the Caseware AI modules and the Intuit Practice AI tools — are close enough that the directional capabilities are available to anyone willing to buy a subscription. What the Big Four actually retain is not the capability advantage. It’s the integration advantage — the ability to build a tightly-coupled platform where the audit tool talks to the tax tool talks to the advisory tool talks to the client portal. That integration is what’s genuinely hard to replicate, and that’s what keeps the moat around Big Four engagements for the largest clients.
The Big Four don’t really have a capability advantage any more. They have an integration advantage — and for most engagements, that’s a different kind of moat entirely.
Rebecca Kahn
— The Smaller Firm Advantage —
Five workflows where mid-market firms are already competitive.
Let’s flip the question. Where are smaller firms actually holding their own, or even winning?
Five places, consistently. Tax research is the first and strongest — a well-configured AI research tool puts a sole practitioner on surprisingly close footing with a Big Four tax senior for routine research questions. Tax return preparation is the second; the commercial tooling has closed most of the gap. Tax advisory work is the third — AI-assisted scenario modelling lets a small firm offer genuinely strategic conversations they couldn’t previously price. Bookkeeping automation is the fourth, and probably the most transformative for the smallest firms. And document summarisation is the fifth — contracts, invoices, receipts, source documents. It’s unglamorous work, but it’s where most of the labour savings actually sit.
Of those five, which has the clearest ROI?
Bookkeeping automation, without much hesitation. A firm doing monthly bookkeeping for thirty to fifty small-business clients can realistically cut labour on those engagements by 40 to 60 percent using a combination of OCR, ML-based transaction classification, and direct bank feed integration. The savings are immediate, measurable, and survive audit. The other four use cases deliver real value but the ROI is harder to quantify. Bookkeeping is the one where you can show the partners a time-savings number at the end of the quarter and point to it.
Is the ChatGPT-for-tax-research workflow really production-grade?
For first-draft work, yes. For final work, absolutely not — and every tax professional I work with understands this. The workflow that works is using GenAI to produce a first pass, then reviewing it against authoritative sources, then editing it for accuracy and client-specific context. Time savings on a typical research memo are probably 40 to 60 percent. The risk, and it’s a real one, is a junior analyst who skips the review step because the first draft reads convincingly. That’s where the governance policy matters.
The Thomson Reuters data showed 44% of firms using GenAI are using it daily or multiple times a day. That’s extraordinary penetration for a technology most firms had never heard of two years ago.
It is, and it tells you something important. These aren’t firms experimenting. These are firms that have integrated the tool into their daily workflow in a way that would be painful to remove. That’s the threshold at which technology genuinely changes a profession — not when it’s adopted, but when removing it would cause real operational disruption. We crossed that threshold for GenAI in tax and accounting somewhere in late 2024. Most of the profession hasn’t quite realised it yet.
— Where the time savings actually sit —
| Use Case | Typical Labour Saving | Maturity |
|---|---|---|
| Tax research | 40–60% on research memos | Production-grade |
| Tax return preparation | 30–50% on routine returns | Mature |
| Tax advisory & scenario modelling | Variable; widens engagement scope | Emerging |
| Bookkeeping automation | 40–60% on recurring engagements | Production-grade |
| Document summarisation | 50–70% on contract/invoice review | Mature |
Labour-saving estimates are practitioner-observed ranges from mid-market firm deployments. Actual results depend heavily on data quality, workflow design, and staff training.
— What Actually Stops Firms —
The three barriers that matter, and the three that don’t.
When a firm comes to you saying they’ve tried to deploy AI and it hasn’t worked, what’s usually the real reason?
One of three things, almost always. First: they skipped the data cleanup. They bought a tool, pointed it at a messy chart of accounts and a vendor master full of duplicates, and were disappointed when the outputs were unreliable. The tool wasn’t the problem. Second: they tried to roll it out firmwide at once, without a single well-run pilot to validate the workflow. Third: they didn’t train the team properly, so partners end up with junior staff producing AI-generated outputs they don’t know how to review. The barriers are almost never the technology itself. They’re operational.
And the barriers firms worry about that actually don’t matter much?
Cost is the big one. Firms agonise over whether they can afford a $15,000 annual subscription when the actual question is whether they can afford to be eighteen months behind their competitors on workflow. Licensing is also a common source of anxiety that evaporates on examination — most enterprise tools have reasonable data-protection clauses, even if reading them feels painful. And the “will it work with our stack?” question matters less than firms think, because almost every serious vendor has invested heavily in integrations.
What about the CPA shortage? The US is facing approximately 75,000 fewer accountants entering the profession than the industry needs. How does that interact with adoption?
It’s accelerating everything. Firms that dragged their feet on automation through the 2010s are now aggressively catching up because they cannot hire. Every new technology conversation I have with a mid-market firm starts, or ends, with staffing. Automation is no longer an optimisation question; it’s a survival question. That’s also changing the kind of firms that are investing. Five years ago, tech-forward firms were the exception. Today, firms that aren’t investing in automation are the exception, and they’re mostly ones that have decided to shrink rather than adapt.
Is this a specifically American dynamic, or are you seeing it elsewhere?
Everywhere, with local variations. UK practices are dealing with the same staffing pressure. Nordic markets — Sweden, Norway, Denmark — have been ahead of the curve on digital accounting for years because their tax authorities pushed early for digital filing and real-time reporting. That actually gave Nordic firms a head start on data hygiene, which is why Swedish practices like redovisningsbyrå Kungsholmen — working in a market where digital bokföring has been the default for a decade — often have cleaner data foundations for AI deployment than equivalent US or UK firms. The technical readiness is actually better in some of those markets than in the ones where the vendors are headquartered.
That’s counter-intuitive. I’d have guessed the opposite.
Most people do. The assumption is that the US leads because that’s where the platforms are built. But platform availability and practice-level readiness are different things. A Swedish firm that has been running fully digital bookkeeping since 2014 has ten years of clean, structured data. A UK firm that migrated from desktop Sage in 2022 is still cleaning up the legacy. When you deploy an AI transaction-classifier, the Swedish firm gets usable output in the first month. The UK firm spends six months on data remediation first. Geography matters less than people think. Digital maturity matters more.
— What firms report vs what actually blocks them —
| What Firms Say | What’s Usually the Real Issue |
|---|---|
| “The tools are too expensive” | Opportunity cost of not adopting is usually larger |
| “Our data isn’t ready” | True — but the cleanup is the project, not a blocker to it |
| “Our staff will resist” | Mostly a training and framing problem, not a will problem |
| “We need to wait for better tools” | Current tools are already production-grade for key workflows |
| “Integration will be painful” | Real concern; largely solved by modern API-first vendors |
| “We don’t have the expertise” | Valid; external implementation support closes this fast |
| “We’re too small” | Smaller firms often adopt faster due to lower decision friction |
— Looking Ahead —
Agents, end-to-end audit, and what breaks next.
PwC has publicly suggested an end-to-end AI-driven audit solution by 2026. How seriously should the rest of the profession take that?
Seriously, but not literally. “End-to-end AI-driven audit” is a marketing phrase. What PwC and the others will actually ship by 2026 is a tightly integrated suite where AI-assisted tools handle substantially more of the audit workflow than they do today — risk assessment, sampling decisions, document review, anomaly detection, first-draft workpaper preparation. Human judgement will still sign off on every significant conclusion, because professional standards require it. But the ratio of human-hours to audit-quality outputs will shift meaningfully. The signoff won’t go away. The work leading up to it will compress.
Agentic AI — systems that can execute multi-step workflows autonomously — is the newest hype cycle. Is it real for accounting?
In narrow workflows, yes. An agent that can receive an invoice email, extract the data, match it against a PO, route it for approval, and schedule payment — that works today in well-instrumented firms. Deloitte is shipping agentic capabilities in their audit platform; they’re not lying about it. What doesn’t work yet is general-purpose autonomy across diverse, ambiguous tasks. An agent can handle AP end-to-end. It can’t handle “draft our Q3 advisory letter and send it to the client” without human intervention at multiple steps. The envelope expands every six months. But the honest 2026 assessment is that agentic systems are production-ready for constrained workflows and demo-stage for unconstrained ones.
What breaks next? If we’re having this conversation in two years, what will we be saying has shifted?
Three things. First, the pricing model of accounting engagements will have moved decisively away from hourly billing. It’s already breaking; by 2027 it will be broken. Firms still running hourly-billed compliance work will be losing meaningful market share to firms offering fixed-fee, AI-enabled alternatives. Second, the regulatory frameworks will have caught up. The PCAOB and AICPA are actively writing guidance on AI use in audit; in two years, there will be explicit rules about documentation, human oversight, and liability. Third, the consolidation of mid-market firms will be happening noticeably faster. Firms that invested in automation will be acquiring firms that didn’t, at attractive valuations. That’s a pattern I’m already watching begin in the UK market.
Last question. A managing partner reads this interview tomorrow morning. What should they do on Monday?
Three things, in order. First, walk around the office and find out what generative AI tools your staff are actually using today — you will be surprised by the answer, and the answer matters for your data protection position. Second, pick one workflow where you already know you have a bottleneck — bookkeeping, document review, tax research, whichever — and commit to a structured sixty-day pilot on a commercial tool. Don’t try to do more than one at once. Third, write a one-page AI policy that covers client data, disclosure, and mandatory human review. You can refine it later. The worst governance posture is the one you don’t have at all.
— Editor’s Note —
The interview was recorded over two conversations in March and April 2026.
Rebecca Kahn consults independently with accounting and professional-services firms on practice technology strategy. She has no commercial relationship with any of the vendors mentioned in this interview, and the views expressed are her own. Statistics referenced come from the 2025 Generative AI in Professional Services Report published by the Thomson Reuters Institute, with supplementary practitioner estimates where noted.
AWSCPA Journal remains editorially independent of all vendors and consultancies referenced in our coverage. When we cite a specific platform, framework, or practice, it is because we believe the reference serves our readers — not because we were compensated for it. For coverage of how regulators are approaching the same questions, see our earlier brief on the Bank of England and FCA’s 2024 survey of AI in UK financial services.