Practice Technology Archives - AWSCPA Journal

Stanford Study: AI Is Making Accountants Faster and More Detailed, Not Obsolete

AWSCPA Journal — Fri, 17 Apr 2026 11:59:55 +0000

Research Brief · Academic · 5 min read

On the paper

Human + AI in Accounting: Early Evidence from the Field

Jung Ho Choi (Stanford GSB) · Chloe Xie (MIT Sloan)

One of the clearest empirical studies to date on what happens when working accountants actually use generative AI in production. The findings complicate the replacement narrative considerably.

For most of the past decade, accounting has occupied an unflattering position at the top of every list of professions most exposed to automation. The reasoning has usually been simple to the point of being lazy: accounting involves a lot of repetitive, rules-based work, therefore computers will do it, therefore accountants are in trouble. The narrative has been widespread enough to affect enrolment in accounting programmes and to raise genuine anxiety across the profession.

A recent working paper by Jung Ho Choi, an assistant professor of accounting at Stanford Graduate School of Business, and Chloe Xie of MIT Sloan, provides some of the first serious empirical evidence on whether that narrative holds up. It does not, at least in the form it has most often been told. What the data actually shows is more interesting — and more useful — than the simple replacement story.

The study combined survey responses from 277 accountants with task-level operational data from 79 small and mid-sized firms actively using AI-powered accounting tools. The analysis examined how the introduction of AI affected both the volume of work an individual accountant could handle and the quality of the work they produced. What emerges is a picture of genuine augmentation rather than substitution — with important caveats about who benefits, and by how much.

— The Four Headline Findings —

7.5 days

Faster monthly close for accountants using AI tools vs. traditional methods

8.5%

Less time spent on routine back-office processing

+12%

Increase in reporting granularity at firms using generative AI

62%

Of surveyed accountants are concerned about AI-generated errors

FINDING 01

AI-using accountants serve more clients, not fewer.

The strongest single result in the paper is a capacity expansion rather than a labour displacement. Accountants equipped with generative AI tools were able to support more clients per week than their peers using traditional methods, while simultaneously completing monthly statement preparation approximately 7.5 days faster. Routine back-office processing time fell by 8.5%. The time recovered by those efficiency gains did not disappear; it was redirected.

The redirection itself is what matters. Accountants did not use the recovered hours to process more transactions faster. They used them for the parts of the job that had previously been squeezed: business communication, quality assurance, client-facing advisory conversations. In the authors’ framing, AI helped with the “setup” — pulling information, connecting bank transactions, tracking vendor data — and that setup work was precisely the bottleneck preventing accountants from operating at higher client volumes.

FINDING 02

Quality did not fall. It rose.

The most counter-intuitive result in the study is that the throughput gains did not come at the usual cost of quality. If anything, the opposite. Firms using generative AI tools posted a 12% increase in reporting granularity — the level of detail captured in their bookkeeping records. Rather than grouping expenses into broad catch-all buckets such as “payroll,” AI-assisted firms broke payroll down into specific sub-categories: bonuses, benefits, meals, and similar. The resulting financial reports are more informative, easier to audit, and more useful for the underlying business to act on.

This matters more than it first appears. Most adoption studies of new technology in knowledge work show a short-term quantity-for-quality trade-off as practitioners climb the learning curve. The Choi and Xie findings suggest that, in accounting bookkeeping specifically, the technology is well-matched enough to the work that the trade-off either does not appear or is smaller than expected. The authors attribute this to the augmentation model — the AI is handling the tedious categorisation work that accountants were rushing through, freeing the human to apply judgement at a higher resolution.

The technology is not here to replace the human being — it’s here to augment the experts who are already in place.

Chloe Xie · MIT Sloan School of Management

FINDING 03

Seniority determines how much you gain.

One of the study’s more consequential findings for how firms structure AI deployment is that gains are not evenly distributed across experience levels. Senior accountants extracted substantially more value from AI tools than junior staff did — despite being, on many metrics, the group less in need of efficiency help.

The mechanism is straightforward. Senior accountants treated the AI as a collaborator whose outputs required verification. They noticed when the model’s confidence dropped on a particular suggestion and stepped in to apply human judgement where it was most needed. Junior staff, lacking the pattern recognition that comes with years of experience, were more likely to accept AI-generated outputs at face value — including outputs that the system itself had flagged as uncertain. The net result: senior accountants extracted both the efficiency and the quality gains; junior accountants captured efficiency but with a higher residual error rate. The research offers an early empirical look at how AI-generated errors can propagate through human-in-the-loop accounting systems, and where they are most likely to slip through.

FINDING 04

Accountants themselves are cautiously positive.

The survey side of the study captured what working accountants actually think about the tools they are now using. The answer is not uniformly enthusiastic, but it is more constructive than the popular narrative would suggest. Sixty-two percent expressed concern about AI-generated errors. Forty-three percent worried about data security implications. Thirty-seven percent reported anxiety specifically about the effect on job stability.

Against those concerns, though, nearly half of surveyed accountants reported that generative AI tools helped them meet deadlines more reliably and improved the accuracy of their work. Almost two-thirds identified the automation of routine tasks as the single biggest benefit of adopting AI. The picture is of a profession that is neither naively enthusiastic nor reflexively defensive — accountants see real benefits from the technology and also see real risks, and they are engaging with both.

— What the Findings Do Not Yet Tell Us —

The next frontier is audit, tax, and valuation.

The study’s sample is concentrated on bookkeeping activities — the foundational recording and organisation of day-to-day financial transactions. That is where the tools are currently mature and where the operational data exists to study. More complex and higher-stakes domains of accounting work — external audit, tax strategy, business valuation, complex advisory — remain largely untouched by the research, for the simple reason that they remain largely untouched by the technology.

That gap will close over the next survey cycle. Audit platforms are already beginning to incorporate AI for document review and anomaly detection. Tax tools are integrating LLM-based research assistants. Valuation work, with its heavy reliance on contextual judgement, is furthest behind. Whether the augmentation finding generalises to those higher-stakes domains — where the cost of an undetected error is much larger — is the empirical question the profession needs answered next.

— Reader Questions —

Eight questions on the study.

What is the main finding of the Choi and Xie study?

That generative AI acts as an augmentation tool rather than a replacement for accountants. AI-using accountants supported more clients, closed the books faster, and produced more granular reports — without a measurable trade-off in quality.

How large was the sample?

Survey responses from 277 accountants combined with task-level operational data from 79 small and mid-sized accounting firms that use AI-powered tools.

How much faster do AI-using accountants close the books?

Approximately 7.5 days faster than accountants using traditional methods, with an 8.5% reduction in time spent on routine back-office processing.

Does report quality suffer when AI is used?

No. Firms using generative AI tools posted a 12% increase in reporting granularity — breaking expenses into more specific categories rather than grouping them into broad buckets. Quality rose rather than fell.

Who benefits most from AI tools in accounting?

Senior accountants. They treat the AI as a collaborator, verify its outputs, and step in where model confidence is low. Junior staff see smaller gains because they are more likely to accept AI outputs at face value, including uncertain ones.

What are accountants most worried about?

Error propagation was the top concern at 62%, followed by data security at 43% and job stability at 37%. These concerns coexist with positive views of the efficiency and accuracy benefits.

Which accounting tasks is AI actually being used for today?

Primarily bookkeeping — transaction classification, reconciliation, and routine record-keeping. More complex domains such as external audit, tax strategy, and valuation remain largely untouched by the current generation of tools.

What should firm leaders take from this?

Three things. Deploy AI to expand capacity rather than to cut headcount. Pair AI tools with senior reviewers, not junior ones, where output quality matters. And treat the current bookkeeping-focused rollout as the first frontier, not the last — the higher-value domains are coming next.

The post Stanford Study: AI Is Making Accountants Faster and More Detailed, Not Obsolete appeared first on AWSCPA Journal.

The AI Leaders Gap: What the Top Quartile of Finance Functions Do Differently

AWSCPA Journal — Tue, 20 May 2025 11:50:12 +0000

Analysis · Finance Leadership · 15 min read

Seventy-one percent of finance functions globally are now using artificial intelligence in some form. But the gap between the firms genuinely winning with it and the ones merely deploying it has widened sharply — and the interesting question is no longer whether to adopt, but what separates the leaders from everyone else.

I. The Gap

Seventy-one percent, and widening.

The most recent round of global survey data on artificial intelligence in finance tells a clearer story than any vendor deck ever will. Seventy-one percent of finance functions across twenty-three industrialised and emerging markets are now using AI in some form. Within the next three years, that figure will approach universality. The technology is no longer a signal of innovation; it is becoming the baseline expectation of how finance teams operate.

What changes beneath that headline number, however, is far more interesting. The firms surveyed were grouped into three maturity tiers: beginners, implementers, and leaders. Eighteen percent of firms are beginners, still piloting and evaluating. Fifty-eight percent are implementers, deploying AI in one or two functions but not yet achieving integrated firm-wide adoption. Twenty-four percent are leaders — firms that have embedded AI across finance and are generating compounding returns from that investment.

The gap between the leaders and everyone else is not, as one might assume, primarily a technology gap. It is an investment gap, a governance gap, a talent gap, and above all a strategic-intent gap. Leaders spend more, deploy across more functions, institutionalise governance earlier, and pull assurance from their auditors into the AI lifecycle itself. They are pulling ahead fast enough that the gap will likely continue to widen through the end of the decade.

This article is a close reading of what, specifically, distinguishes the leaders from the rest of the market — drawn from global survey data spanning 2,900 finance functions across six industries and twenty-three markets, and intended as a practical reference for CFOs, managing partners, and controllers who want to know which of their current practices matter and which are theatre.

II. The Maturity Framework

Three tiers, three very different trajectories.

Table I — The Three Maturity Tiers, At a Glance
Tier	Share of Firms	Defining Characteristic
Leaders	~24%	Integrated AI across multiple finance functions; measurable ROI; institutional governance
Implementers	~58%	Active deployments in one or two functions; scaling not yet achieved
Beginners	~18%	Piloting, evaluating, or planning; no production-scale deployment

The distribution matters because the journey from each tier to the next is qualitatively different. The step from beginner to implementer is almost always a single successful production deployment in a high-volume, rules-based workflow. The step from implementer to leader is harder: it requires cross-functional integration, a serious governance model, and a willingness to reconsider how finance work is organised and priced. Most finance functions, looking at themselves honestly, sit in the large implementer middle.

III. The Geography

Geography is narrowing faster than most expected.

A year ago, a sensible working model for AI adoption in finance was that North America, Western Europe, and developed Asia-Pacific would dominate, with emerging markets following some years behind. That model is already outdated. Adoption in emerging economies lags the developed world, but not by much — and in several categories, it is closing quickly.

The variation within regions is larger than the variation between them. United States, German, and Japanese firms sit at or near the global frontier on AI deployment in finance. Italian and Spanish firms, despite sitting in the same regional economic block, trail visibly. Within the emerging-market cohort, Chinese and Indian firms are ahead of the global average on several adoption measures, while Saudi Arabian and several African markets remain in the early stages.

The implication for firms with international operations is straightforward: AI maturity cannot be inherited from a head office. Each market has its own trajectory, shaped by regulatory posture, local talent availability, and legacy technology stack. A successful global rollout depends on treating each country as its own project rather than as a copy-paste exercise from the most advanced operation.

Table II — Share of Leaders by Region
Region	Leader Share	Notable Markets
ASPAC	~37%	Japan, China, India at the frontier; regional variance is substantial
North America	~27%	US finance functions among the most advanced globally
Europe	~22%	Germany ahead; Italy and Spain behind regional average
Middle East & Africa	~24%	Considerable market-level variation
Latin America	~20%	Smaller base but accelerating

Two details stand out. First, ASPAC leads on raw leader share — driven substantially by Japanese and Chinese enterprise adoption. Second, the spread between regions is far smaller than the spread between individual markets within each region. Treating “Europe” or “Asia” as homogeneous blocks masks more than it reveals.

“

Leaders have six use cases in production on average. Everyone else has three or four. The compounding returns come from the breadth of deployment, not the depth of any single project.

— The Leader’s Advantage

IV. What Leaders Do Differently

Five behaviours that separate the top quartile.

When the survey data is sliced finely, five behaviours emerge that consistently distinguish the leaders from everyone else. None of them is exotic. All of them require deliberate effort from the CFO and, in most cases, from the managing partner or the board.

i. They spend nearly twice as much as everyone else.

Leader firms currently allocate approximately 12.5% of their IT budget to enterprise-wide AI activities, compared to roughly 7.4% for the rest of the market. Over the next three years, leader AI budget share is expected to rise to 16.5%. The rest of the market will narrow the gap but not close it. Spend discipline — not raw spend — is the pattern; leaders tend to move faster from pilot to production and allocate the savings back into the next wave of deployment.

ii. They deploy across more functions, earlier.

In accounting, 88% of leaders have selectively or widely adopted AI, compared to only 19% of everyone else. The pattern holds across every area of finance: financial planning, treasury management, risk management, tax operations. Leaders do not choose between use cases; they deploy across the portfolio. On average, a leader firm operates six AI use cases in production. The rest of the market operates three or four.

iii. They commit to generative AI early.

Approximately 38% of leaders are already selectively or widely using generative AI in financial reporting. Among non-leaders, that figure is approximately 3%. Within three years, 95% of leaders expect to be using generative AI in reporting — compared to 39% of the rest. The generative AI gap is larger than the traditional AI gap, and it is widening faster.

iv. They build internal AI capability rather than relying exclusively on vendors.

Leaders are far more likely to have either a dedicated AI team within finance or distributed AI specialists embedded in each function. Roughly two-thirds of leaders also draw on a centralised enterprise AI team outside of finance. External consultants and technology outsourcers are used by nearly half of leaders, but as accelerators rather than substitutes for internal capability.

v. They take governance seriously from day one.

More than twice as many leaders as non-leaders have placed AI risks and controls within the scope of their financial reporting frameworks. More than half of leaders obtain third-party assurance over AI processes and controls — more than double the rate of non-leaders. Published AI frameworks, defined review checkpoints, and documented audit trails are not corner-case concerns for the top quartile; they are table stakes.

Table III — Top AI Use Cases: Leader vs. Everyone Else
Use Case	Leaders	Everyone Else
Research & data analysis	85%	46%
Predictive analysis & planning	81%	46%
Risk management & cybersecurity	78%	45%
Performance evaluation & training	75%	33%
Data entry & document processing	62%	27%
Expense tracking & tax deductions	52%	27%
Fraud detection & prevention	50%	28%
Gen AI for document composition	48%	25%
Administrative automation	43%	27%
Custom virtual assistants	39%	19%
Regulatory monitoring	33%	21%

The pattern is striking and consistent. Across every category measured, leader deployment rates run between 1.6x and 2.4x higher than the rest of the market. There is no single use case that separates leaders from the pack; the separation comes from the aggregate breadth of deployment.

V. The Return

ROI is real, and it is asymmetric.

The most frequently voiced objection to AI investment in finance — voiced particularly by firms that have not yet started — is uncertainty about return on investment. The survey data substantially resolves that question. Among firms currently using AI in finance, 47% report that ROI is meeting expectations and a further 19% report that it is ahead of or well ahead of expectations. Only 15% report outcomes below expectations.

The asymmetry by maturity tier is the more important finding. Among beginners, 25% report higher-than-expected ROI. Among implementers, 30%. Among leaders, 57%. The relationship is not coincidental. Leaders report between two and three times as many distinct benefits from their AI programmes as beginners do — an average of seven benefits per firm, compared to two or three for early-stage adopters. The compounding nature of those benefits is what drives the ROI gap.

This matters for firms currently sitting on the fence. The evidence suggests that the “wait and see” posture has become a costly one. Firms that start later will face a larger adoption curve, compressed timelines, and a smaller pool of skilled staff to draw from. The returns are available now, they are proportional to deployment maturity, and the window to catch up is narrowing.

Table IV — ROI Outcomes by Maturity Tier
Maturity Tier	Ahead of Expectations	Meeting Expectations	Below Expectations
Leaders	57%	39%	5%
Implementers	30%	50%	19%
Beginners	25%	52%	23%

The dispersion in outcomes is the clearest single argument against a cautious posture. Leaders are not only more likely to hit their ROI targets; they are dramatically more likely to beat them. The firms that moved earliest are now the firms seeing disproportionate returns, and the structural reasons for that advantage — accumulated data, trained staff, institutionalised governance — do not diminish with time.

VI. The Barriers

What actually stops firms from getting further.

The most common self-reported barriers to AI adoption in finance cluster around a small set of familiar themes. Data security vulnerabilities lead at 57%. Limited AI skills and knowledge sit at 53%. Inconsistent data across systems is cited by 48%. High implementation costs, lack of transparency, compliance pressures, and concerns about bias all feature prominently.

What is more interesting is how the barriers shift as a firm matures. Early-stage adopters are most concerned about cost, security, and skills — the classic “can we afford this and can we run it?” questions. Firms further along the curve report different problems: finding consistent data inside larger datasets, managing bias and hallucination in generative AI outputs, integrating AI across increasingly complex legacy environments, and handling staff resistance as the scope of automation expands.

The pattern is worth internalising. The barriers do not vanish as a firm matures; they evolve. Solving the first set of problems simply unlocks the next set. Firms that expect a clean, linear path from beginner to leader are likely to be disappointed. The path is real but iterative, and each stage requires a different set of management attention.

VII. The Blind Spots

The issues nobody is paying enough attention to.

Finance executives consistently rate privacy, data integrity, and security as their most important AI concerns. They pay proportional attention to each. What emerges from the survey data, however, is a more interesting finding: the attributes that matter most to AI outcomes are not the ones currently receiving the most attention. Three specific blind spots deserve naming.

Transparency and explainability.

Because most modern AI systems behave as black boxes, stakeholders often have little basis to trust or challenge their outputs. A finance leader cannot explain why a particular model flagged a particular transaction — which becomes a serious problem the moment an auditor, regulator, or litigator asks for the explanation. Transparency is not a nice-to-have. It is the basis on which AI outputs become defensible.

Accountability for automated outputs.

When AI drafts an accounting memo, reconciles an account, or flags a fraud risk, who is professionally responsible for the output? The answer cannot be “the system.” Firms that have not defined clear accountability points — which human approves which AI output, under what review standard — are exposing themselves to liability that will eventually crystallise.

Sustainability.

AI inference and training consume significant amounts of energy. For firms with sustainability commitments, the energy footprint of an expanding AI deployment sits in tension with the carbon targets. Very few finance functions have begun measuring this, much less reporting on it. Expect the conversation to catch up over the next eighteen months as sustainability disclosure frameworks mature.

VIII. The Auditor’s Evolving Role

What companies now expect from their auditors.

One of the quieter but more consequential shifts captured in the research is how companies now expect their external auditors to engage with AI. The expectations have moved well beyond traditional control testing. Most surveyed organisations now expect auditors to conduct detailed reviews of their AI control environments. A large proportion want auditors to assess the maturity of their AI governance, provide third-party attestation over the use of specific AI technologies, and perform readiness and gap assessments ahead of scaled deployment.

The expectations flow in both directions. Companies also expect their auditors to be using AI themselves — in data analysis, anomaly detection, fraud identification, predictive analysis, and the general acceleration of the audit process. The survey data shows meaningful demand for real-time or near-real-time auditing, which represents a substantial departure from the annual cycle that has defined the profession for decades. The largest audit firms — Deloitte, EY, PwC, KPMG, and the next tier — have been investing heavily in AI-assisted audit platforms, with firms such as EY publicly foregrounding their AI-driven audit transformation in recent strategic communications.

Most notably, finance teams want more communication from their auditors on AI — and they are not currently getting it. Among leader firms, only 15% say their auditor communicates frequently with them about AI; 51% say they would like that frequency. The gap between current and desired communication is a genuine market opportunity for audit firms that can close it, and a liability for firms that continue to approach AI as a tangential client concern rather than a core audit subject.

Table V — Audit Activities Companies Now Expect AI to Support
Activity	Traditional AI Demand	Generative AI Demand
Data analysis	66%	54%
Risk mitigation	57%	53%
Risk identification	55%	51%
Fraud detection	53%	45%
Predictive analysis	50%	32%
Speed up audit process	45%	29%
Real-time auditing	39%	33%
Document / data gathering	37%	37%
Trend analysis	34%	30%
Improve responsiveness	32%	35%

“

Finance teams want more communication from their auditors on AI. Only fifteen percent of leader firms say they are getting it. That gap is the single clearest market opportunity in professional services right now.

— The Communication Gap

IX. Recommendations

Seven steps, in sequence.

Prioritise AI in finance explicitly

Move beyond the base level of reconciliation and data entry. Build a portfolio of use cases that explicitly includes research, risk, cybersecurity, fraud detection, and predictive analysis. Breadth beats depth when you are trying to move from implementer to leader.

Develop a specific Gen AI plan

Generative AI is where the largest capability gap between leaders and everyone else currently sits. A plan that addresses use cases, data-sovereignty controls, accuracy standards, and intellectual property boundaries is worth more than a generic “AI strategy” memo.

Look beyond reporting

Most finance functions have concentrated their first AI deployments in accounting and reporting. The next frontier sits in treasury, risk management, and tax operations. Leaders have already expanded beyond reporting; the rest of the market should follow.

Build internal capability

Staff up within finance. External vendors and consultants are accelerators, not substitutes. A dedicated internal AI capability — whether a central team or specialists embedded by function — is a consistent leader characteristic and a durable competitive moat.

Tackle the barriers head on

Establish governance, invest in data cleanup, run ROI-validating pilots before scaling. The barriers do not go away on their own; they require deliberate programme management. Firms that treat them as execution problems rather than strategic obstacles move faster.

Watch the blind spots

Transparency, accountability, and sustainability receive disproportionately little attention relative to their actual importance. The firms that address them early will find themselves ahead when regulatory and stakeholder attention turns that direction — which it will.

Expect more from auditors

A 2026 audit relationship should involve active dialogue on AI governance, assurance over AI controls, and demonstrable use of AI within the audit itself. If that conversation is not currently happening, raise it. Audit firms that do not have a credible answer are signalling a durable gap in their own capability.

X. What Comes Next

The next three years, in outline.

Global adoption of AI in finance is on a trajectory toward near-universal penetration. Within three years, the share of firms selectively or widely using AI in financial reporting is expected to rise from roughly 28% to approximately 83% across developed markets, and to 78% across the emerging markets included in the global sample. These numbers are striking because they describe a shift from “mainstream adoption” to “operating assumption” over an unusually compressed timeframe.

The generative AI component of that shift is more dramatic still. Across the global sample, nearly every surveyed firm expects to be piloting or actively using generative AI in financial reporting within three years. Among leaders, 95% expect to be selectively or widely deploying it. The maturity distribution captured in today’s survey will look very different twenty-four months from now, and the firms that emerge at the top of that new distribution will be the ones that treated the current window as an opportunity rather than a decision to defer.

Three trends are worth specific attention. First, agentic AI workflows — systems capable of planning and executing multi-step financial tasks autonomously — are moving from proof-of-concept to production in narrow domains. Second, the regulatory conversation on AI in finance is crystallising, particularly in the European Union, and will reshape vendor selection criteria over the next two years. Third, the professional standards bodies governing audit and assurance are actively developing guidance that will define what constitutes adequate human oversight of AI-generated financial outputs. Firms that build their practices now with these frameworks in mind will have substantially less rework to do when the rules arrive.

XI. Reader Questions

Twenty-five questions, answered plainly.

How many finance functions globally are now using AI?

Approximately 71% of finance functions across twenty-three industrialised and emerging markets are using AI to some degree, with 41% using it moderately or widely. Within three years, that figure is expected to approach universality.

What defines a “leader” firm on AI adoption?

Leaders have integrated AI across multiple finance functions, achieved measurable ROI, institutionalised governance, and established internal capability. They comprise roughly 24% of the surveyed population.

What is the biggest gap between leaders and everyone else?

Breadth of deployment. Leaders operate approximately six AI use cases in production; the rest of the market operates three or four. The ROI advantage comes from compounding returns across the portfolio rather than from any single exceptional project.

Is ROI on AI in finance actually being realised?

Yes. Among firms currently deploying AI, 47% report that ROI is meeting expectations and a further 19% report that it is ahead of or well ahead of expectations. Only 15% report outcomes below expectations.

How much more does a leader firm spend on AI than a non-leader?

Approximately twice as much as a share of IT budget. Leaders currently allocate about 12.5% of IT budget to enterprise-wide AI activities, versus 7.4% for the rest of the market. Over three years, the leader share rises to approximately 16.5%.

Which finance functions are furthest along?

Accounting and financial planning are the most mature, with nearly two-thirds of firms piloting or deploying AI. Treasury and risk management follow. Tax management lags, primarily because of data quality issues and regulatory complexity.

Why is tax the slowest to adopt AI?

Tax workflows are complex, regulation-heavy, and often dependent on data scattered across legacy systems. Traditional machine learning approaches struggle in this environment. Generative AI is changing that — but production-scale adoption in tax is further behind than in other finance functions.

What is the generative AI gap between leaders and others?

Approximately 38% of leaders are already selectively or widely using generative AI in financial reporting, compared to roughly 3% of non-leaders. Within three years, 95% of leaders expect to be using it, versus 39% of others.

Which regions are ahead globally?

ASPAC leads on raw leader share, driven by Japanese and Chinese enterprise adoption. North America follows. Europe trails both, with significant internal variation — Germany ahead, Italy and Spain behind. Emerging markets as a block are closing the gap faster than many observers expected.

What are the most commonly cited barriers?

Data security (57%), limited AI skills (53%), inconsistent data (48%), high implementation costs (45%), lack of transparency (40%), compliance concerns (39%), and potential for bias (37%). The ordering shifts as firms mature — early-stage concerns are cost and skills; later-stage concerns are data consistency and integration.

What are the major blind spots?

Transparency and explainability, accountability for automated outputs, and sustainability of AI energy consumption. All three receive less management attention than their actual importance warrants.

How many use cases does a leader typically run?

On average, six. For non-leaders, the average is closer to 3.6. The breadth is the strongest single predictor of ROI outcomes.

Do leaders build internal AI teams or outsource?

Both, but the pattern is deliberate. Leaders build internal capability — either a dedicated team within finance or distributed AI specialists embedded in each function — and use external vendors and consultants as accelerators rather than substitutes.

What governance practices distinguish leaders?

Published AI frameworks, AI risks and controls included in financial reporting scope, third-party assurance over AI processes and controls, documented human-review checkpoints for high-risk outputs. More than twice as many leaders as non-leaders have formalised these practices.

Do leaders get third-party assurance over AI?

More than half of leaders obtain third-party assurance over AI processes and controls — more than double the rate of non-leaders. They also routinely include AI controls assurance in the scope for vendor and third-party processes.

What do companies now expect from their external auditors on AI?

Detailed reviews of AI control environments, AI governance maturity assessments, third-party attestation over AI technology, and active use of AI within the audit process itself. Communication from auditors on AI is the single most common unmet expectation.

How are auditors using AI in their own work?

Data analysis, risk identification and mitigation, fraud detection, anomaly identification, predictive analysis, and accelerating the audit process. Real-time or near-real-time auditing is an emerging expectation that will likely reshape the audit cycle over the coming years.

Why do data quality problems delay AI projects so often?

Because AI deployment exposes underlying data hygiene issues rather than solving them. Inconsistent chart of accounts, vendor master duplicates, and fragmented ledgers all become visible — and operationally problematic — when a model is asked to operate on them at scale.

Is staff resistance a real problem?

It rises with deployment maturity. Early pilots rarely trigger resistance because the scope is limited. As automation expands and affects visible job content, resistance becomes material. Firms that frame AI as augmentation rather than replacement — and back that framing with visible investment in staff reskilling — see much higher adoption.

Should we start with a single pilot or multiple?

One pilot, measured carefully, expanded after validation. A sequence of disciplined pilots is how most successful firm-wide rollouts are actually built. Multiple simultaneous pilots dilute management attention and make attribution of results harder.

What is the typical payback period?

In narrow, well-chosen pilots — reconciliation, document processing, expense categorisation — under twelve months is achievable. For broader platform migrations, two to three years is more realistic once data cleanup, training, and process redesign are included.

Why do emerging markets lag the developed ones?

Smaller technology budgets, less established digital infrastructure, and earlier-stage data governance practices. The gap is narrower than many observers expected, however, and is closing quickly — particularly in China, India, and several ASPAC markets.

How much of the IT budget will AI command over the next three years?

Across the surveyed population, average AI spend is expected to rise from approximately 8.5% of IT budget to 13.5% over three years. For leader firms specifically, the figure rises from 12.5% to approximately 16.5%.

What should a CFO do this quarter?

Three things. Commission an honest maturity assessment. Pick one use case outside accounting and financial reporting — treasury, risk, or tax — and run a structured pilot. Open a formal conversation with the firm’s external auditor about AI governance, assurance, and the auditor’s own AI capability.

What is the single biggest risk of waiting?

The compounding nature of the leader advantage. Firms that move later face a larger adoption curve, compressed timelines, and a smaller pool of skilled staff. The evidence suggests that the window for catching up on favourable terms is narrowing rather than widening.

The post The AI Leaders Gap: What the Top Quartile of Finance Functions Do Differently appeared first on AWSCPA Journal.

75% of UK Financial Services Firms Now Use AI — What the 2024 Regulators’ Survey Actually Says

AWSCPA Journal — Thu, 18 Apr 2024 11:52:00 +0000

Regulatory Brief · UK · 5 min read

The UK regulators’ third survey of AI in financial services landed quietly in November 2024. Buried inside it are the numbers every CFO, compliance officer, and audit partner should have on hand.

— At a Glance —

75% of surveyed UK financial services firms are already using AI. A further 10% plan to within three years.
Insurance leads sector adoption at 95%. Financial market infrastructure sits at 57%.
Third-party implementations now account for a third of all AI use cases, nearly double the 2022 share.
55% of use cases involve some degree of automated decision-making. Only 2% are fully autonomous.
46% of firms admit to only “partial understanding” of the AI technologies they operate.
Cybersecurity is the top-rated systemic risk both now and projected three years out.

The adoption curve has bent.

The Bank of England and the Financial Conduct Authority have now run three surveys on artificial intelligence use in UK financial services — in 2019, 2022, and again in 2024. The trajectory between surveys is the clearest single summary of how the sector has changed. In 2022, 58% of surveyed firms reported using AI. By late 2024, that figure had climbed to 75%, with a further 10% planning to deploy within three years. If projected forward on the current slope, close to universal adoption arrives within the next survey cycle.

What is more revealing is the compression of the deployment timeline within firms that already use AI. The median respondent operates nine use cases today and expects to operate twenty-one within three years. Large UK banks already run a median of thirty-nine; international banks operating in the UK, forty-nine. The gap between AI-heavy firms and the rest of the market is already sizeable and appears to be widening.

— Sector Dynamics —

Insurance is quietly ahead; FMIs lag.

The sector-level numbers will surprise anyone who still thinks of banking as the frontier of AI in finance. Insurance led adoption at 95% of surveyed firms, with international banks close behind at 94%. UK retail banking — the sector that attracts most of the media attention — sat well inside the pack. At the other end of the distribution, only 57% of financial market infrastructure firms reported using AI, making them the single largest adoption gap across the surveyed population.

Operations and IT accounted for 22% of all reported use cases — twice the next-largest category, retail banking at 11%, with general insurance third at 10%. Optimisation of internal processes was the single most common application (41% of respondents), followed by cybersecurity (37%) and fraud detection (33%). Customer support, regulatory compliance, and further fraud-detection deployments are expected to be the largest incremental growth areas over the next three years.

— The Third-Party Problem —

One-third of AI now runs on someone else’s rails.

The single most consequential finding for systemic risk watchers is the sharp rise in third-party dependency. One in three AI use cases is now a third-party implementation, nearly double the 17% figure reported in 2022. Human resources, risk and compliance, and operations and IT show particularly high third-party rates — 65%, 64%, and 56% respectively.

The concentration within that third-party layer is where the systemic risk sits. The top three cloud providers account for 73% of all named providers. The top three model providers now account for 44%, up sharply from 18% in 2022. The top three data providers account for 33%, also up meaningfully. The conclusion is that the AI supply chain for UK financial services is not just increasingly outsourced; it is increasingly concentrated in a small number of non-UK vendors. Regulators flagged this directly as the systemic risk with the largest expected three-year increase.

— Automation & Oversight —

Automated, but not autonomous.

Despite the hype around agentic AI, the survey paints a more measured picture of actual autonomy. Of all reported use cases, 55% involve some degree of automated decision-making. Within that, 24% are semi-autonomous — systems that can make routine decisions independently but escalate ambiguous or high-impact ones to human review. Only 2% of deployed use cases are fully autonomous. For firms considering procurement of AI with meaningful decision-making authority, the prevailing design pattern is clearly still human-in-the-loop.

Forty-six percent of firms admit they do not fully understand the AI systems they operate. The biggest reason is third-party models, which they deploy but cannot inspect.

— The Understanding Gap

— Risk & Constraints —

The risks are mostly about data. The constraints are mostly about regulation.

Four of the top five risks reported by surveyed firms relate to data — privacy and protection, quality, security, and bias or representativeness. The fifth is model transparency. The risks projected to increase most over three years are third-party dependencies, model complexity, and embedded or “hidden” models inside vendor products. In plain terms: firms are worried about losing visibility and control as their AI stack becomes more outsourced and more complex.

On the regulatory side, data protection and privacy were cited as the single largest constraint on AI adoption — 23% of firms rated it a “large” constraint. Resilience, cybersecurity, and third-party rules followed, with the FCA’s Consumer Duty close behind. The complaint, notably, was not primarily about the stringency of the rules but about the burden of compliance and, in some cases, lack of clarity — 18% cited unclear treatment of intellectual property rights, 13% unclear application of the Consumer Duty to AI-driven decisions.

Non-regulatory constraints paint a familiar picture. Safety, security, and robustness of models ranked first. Insufficient talent and access to skills ranked second — 25% of firms rated it a “large” constraint. Appropriate transparency and explainability ranked third. Professional-services firms advising clients on AI deployment in financial services, including EY, have noted similar bottlenecks in their own client engagements across the sector.

UK Financial Services AI, 2022 → 2024 — Selected Shifts
Metric	2022	2024
Firms using AI	58%	75%
Firms planning to use AI	14%	10%
Use cases that are third-party	17%	33%
Top 3 model providers’ share	18%	44%
Top 3 data providers’ share	25%	33%
Foundation model use cases	Not measured	17%

— What to Watch —

The next survey will look very different.

Three trends in the 2024 data are likely to define the 2026 or 2027 survey. First, foundation models already account for 17% of all use cases and are growing fast; the next survey will likely show them as the majority category. Second, the third-party concentration risk in cloud, models, and data is on a trajectory that will, absent intervention, become a named systemic concern for the FPC. Third, the gap between firms with “partial” and “complete” understanding of their own AI is a governance problem that will only compound as complexity rises.

For CFOs and audit partners advising UK financial services clients, the practical implication of the 2024 survey is unambiguous. The adoption question is settled. The questions that replace it — governance adequacy, third-party concentration risk, model transparency, talent availability — are harder, more expensive, and will occupy regulatory attention through the rest of the decade.

— Reader Questions —

Eight questions, answered briefly.

What did the 2024 UK survey actually measure?

It measured AI adoption, use-case distribution, third-party exposure, automated decision-making, materiality, perceived risks and benefits, and governance practices across 118 regulated UK financial services firms. It is the third such survey conducted jointly by the Bank of England and the FCA.

How much has adoption grown since 2022?

Firms reporting active AI use rose from 58% in 2022 to 75% in 2024. Factoring in firms planning to deploy within three years, the combined figure reaches 85%.

Which sector is furthest ahead?

Insurance, at 95% of surveyed firms. International banks follow at 94%. Financial market infrastructure firms trail at 57%.

What does the third-party concentration risk actually look like?

The top three cloud providers account for 73% of named providers. The top three model providers account for 44%, more than double the 2022 share. The top three data providers account for 33%. Concentration is accelerating across all three layers.

Are fully autonomous AI systems common in UK finance?

No. Only 2% of reported use cases involve fully autonomous decision-making. The prevailing design pattern remains human-in-the-loop, with 24% of use cases classified as semi-autonomous.

What are the biggest perceived risks?

Four of the top five relate to data: privacy, quality, security, and bias. Model transparency rounds out the top five. Looking three years out, respondents expect third-party dependencies, model complexity, and hidden vendor models to grow fastest as risk drivers.

What is the biggest regulatory constraint?

Data protection and privacy, with 23% of firms rating it a “large” constraint. Resilience and cybersecurity rules follow, with the FCA’s Consumer Duty in third place.

What should finance leaders do with these findings?

Assess third-party AI concentration risk in their own stack; pressure-test governance adequacy against the 16-framework benchmark the survey used; ensure that leadership understanding of deployed AI systems matches the material risk they carry; and plan for a regulatory environment in which the adoption question is settled and the governance question becomes central.

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