KPMG

KPMG global AI in finance report

Transforming into a new era with the AI-empowered finance function

KPMG. Make the Difference.

KPMG International | kpmg.com/AIinFinance

03 Foreword
04 Research background
05 AI maturity framework
07 Transforming finance through AI

10 How AI leaders drive ROI
15 Overcoming barriers to AI usage
18 Shifts in financial reporting
21 Conclusion and key recommendations

Foreword

Artificial intelligence (AI) has already begun to transform business processes and capabilities — and finance is in the vanguard of this change.

In April 2024, KPMG International conducted a study among 1,800 companies spread across 10 major economies and found that almost three-quarters were already using AI to some degree in their financial reporting processes, with virtually 100 percent expecting to do so within the next three years.

Ordinarily, one would wait a year or more before conducting follow-up research — but AI is no ordinary topic. Such is the pace of its development — and the speed of its adoption — that we decided to carry out further research in September 2024 to see what had changed. We significantly expanded the research sample, widening the number of countries and territories from 10 major markets in North America, Europe, and Asia to 23 developed and emerging markets across all world regions.

The result? Sure enough, AI has already moved on significantly in only half a year. More companies are rolling out AI, and not only within their financial reporting processes but across wider areas of finance, including treasury management, risk management, and tax management. More companies are moving onto the “hot ticket” of generative AI.

And although adoption in emerging economies is behind that of major markets, the gap is not huge. AI is truly a global phenomenon, and it is being adopted by finance teams everywhere.

The reasons aren’t hard to uncover. Embedding of AI can offer a range significant and tangible benefits including faster, more efficient processes, more granular data analysis and accuracy, and better predictive power. This supports finance staff to get more done and faster and spend more time on value-adding tasks.

As a result, AI is yielding significant ROI. Among the cohort of leading adopters we identified in our research, 57 percent say that ROI is not just meeting expectations, it is beating them. Even among businesses in the earlier stages, nearly 30 percent say the same. That’s a remarkable achievement.

While there are barriers to overcome and risks to avoid, the effort is worth it because the benefits are real – and because the benefits will accelerate over the coming years. As ‘AI Finance Agents’ enter the organization, providing diverse experiences and insights at the speed of light, collaborating across geographies and siloes at the speed of light, we can only begin to imagine the power of an AI enabled Finance function. CFOs and their teams should be preparing the way right now.

In all of this, there is a key part for auditors to play. The role of the auditor is evolving as companies look to them for support around reviews of their AI controls, governance maturity assessments, and attestation of the technology being used. Companies also expect their auditors to use AI within their own auditing processes, bringing them a smarter, more real-time, and insight-laden audit experience.

We hope this report brings you fresh and illuminating insights that can help you in your own AI journey. To provide further assistance, we have developed a KPMG AI maturity benchmarking tool to help you assess your progress and identify further key actions to take.

Without doubt, AI is a game-changer. And KPMG professionals are here to help you navigate what is already one of the biggest business transformations since the internet itself was born.

David Rowlands Global Head of AI KPMG International

Research background

In April 2024, KPMG surveyed 1,800 companies headquartered in 10 major markets across North America, Europe, and ASPAC about their progress in using AI for financial reporting.

Given the fast pace of technological change, in September 2024 we conducted a second survey of financial executives on their use of AI. To provide deeper insights, we expanded the scope o our AI research beyond financial reporting to encompass the entire finance function, including accounting, risk, tax operations, and treasury management.

To better understand global trends, we also widened the country coverage from the original 10 major markets to 23 industrialized and emerging economies in North and Latin America, Europe, Asia-Pacific, the Middle East, and Africa.

The expanded study surveyed a wider group of executives across finance at companies in the same industries and revenue sizes
sampled in our April 2024 study. To conduct this broader analysis, we increased the number of
respondents from
1,800 to 2,900.

The AI maturity framework

To assess how AI is advancing for financial reporting and across finance, we created an AI maturity framework based on three survey questions.

• Over the past six months, how much progress has your company made in the use of AI, specifically in financial reporting? How much progress does your company plan to make in three years?

• Over the past six months, how much progress has your company made in the use of generative AI, specifically in financial reporting? How much progress does your company plan to make in three years?

• How much progress has your organization made in leveraging AI across the following financial areas? (Risk management, treasury management, accounting, financial planning, and tax operations, reporting, and planning).

Based on responses to these questions, respondents were grouped into three categories: 18 percent were beginners in AI usage, 58 percent were implementers, and 24 percent were leaders.

Maturity breakdown

24%

Leaders

58% /0

Implementers

Beginners

KPMG’s AI Maturity Assessment Tool: How does your organization measure up?

Is your organization a leader, implementer, or beginner? KPMG has developed a benchmarking tool designed to help organizations assess their progress in the AI transformation journey. Take our quick assessment to see where your organization stands.

This will identify strengths and weaknesses based on your answers — and highlight areas for prioritized action based on your industry.

KPMG global AI in finance report

The maturity framework was used to look at companies maturities by revenue, industry and region.

Figure 1: Maturity by revenue

Figure 2: Maturity by industry

Figure 3: Maturity by region

The study surveyed financial executives and board members at 2900 companies across six industries and 23 markets in industrialized and emerging economies in North and Latin America, Europe, Asia-Pacific, the Middle East, and Africa.

Transformin finance throughAI

In companies around the world, AI-empowered finance teams are emerging — a development that is generating a rich range of benefits to organizations. These include increased efficiency and accuracy, reduced human error, faster and better data-based decision-making, lower costs, and improved regulatory compliance.

AI is a game-changer for finance

Our research shows that the use of AI is rapidly expanding across finance: 71 percent of companies are using AI in finance, 41 percent of them to a moderate or large degree. Organizations in North America, ASPAC, and Europe are furthest ahead, while those in the Middle East, Africa, and Latin America, comprising mostly emerging markets, are the furthest behind.

Nonetheless, there are wide variations among companies in industrialized and emerging markets. For example, while companies in the US, Germany, and Japan are well ahead in AI usage, other major economies, such as Italy and Spain, are behind. The same dichotomy is evident in emerging markets, with China and India ahead in AI usage, and Saudi Arabia and the African countries we surveyed further behind.

Embracing AI in finance is not just about leveraging advanced technology; it’s about cultivating a foundation of trust. By prioritizing transparency and ethical practices, organizations can harness the power of AI to enhance decision-making, optimize financial strategies, and foster a culture of accountability, ultimately driving sustainable growth and innovation.

Sebastian Stöckle

Global Head of Innovation and AI, Audit KPMG International

AI usage is spreading across all finance areas

Companies are turning to AI in every area of finance. According to our findings, the accounting and financial planning groups are furthest ahead in using AI because of the potential benefits it brings to many of their activities, from improved data processing and financial reporting to real-time insights and predictive analysis. Currently, nearly two-thirds of companies are piloting or using AI for accounting and financial planning.

Other areas of finance are following suit: nearly half of companies are now piloting or using AI for treasury and risk management. This can generate better debt management, cash-flow forecasting, fraud detection, credit risk assessment, and scenario analysis in the treasury and risk management functions.

Figure 4: Progress made in the use of AI in finance areas

Tax management trails behind other finance areas

Less than one-third of companies are now piloting or using AI for tax management, with about half still in the planning stage. Progress in AI usage has been delayed for many reasons, including complexity of tax regulations, lack of up-to-date data, onerous legacy systems, and the reliance on human judgment for many tax-related decisions.

Tax is not an area where forms of traditional AI like machine learning have been utilized up to now. As a result, they have fallen a little behind — but that is changing with the arrival of Gen AI. With Gen AI, so much more is possible from a tax perspective.

Christian Stender

Global Head of AI Tax & Legal KPMG International

Finance unlocks value from AI investments

Finance teams are investing in a mix of AI technologies, from chatbots and natural language processing to anomaly detection and computer vision. But they find the most value in sophisticated technologies, such as machine learning, deep learning, and Gen AI.

Overall, most companies report that the ROI on using these technologies is meeting or exceeding expectations — an outcome that is likely to propel AI usage across industries in the future. “After careful evaluation of AI performance, the ROI of deployed projects was highly satisfactory. The plan is to deploy it for every department, making necessary adjustments to improve results,” said an Audit Committee Director at a Japanese hardware company.

The focus on AI in finance is part of a bigger AI trend happening across industries. Companies surveyed on average are spending about 8.5 percent of their IT budgets on AI technologies and solutions. The percentage will jump to 13.5 percent over the next three years.

“The lack of sufficient data can hinder AI model development and accuracy,” explained the CTO of a UK consumer packaged goods company that has been struggling to use AI for tax compliance. The CFO of a large Australian bank said that their use of AI for tax compliance had been hindered by difficulty in “the integration of AI into our existing financial system”.

Figure 5: ROI on AI investments in finance

HowAI 000 leaders drive ROI

Based on KPMG’s maturity framework, 24 percent of respondents were identified as leaders. Leader firms tend to be in the mid-implementation phase across most areas of AI maturity. However, there are one or two areas where each firm is more advanced, in the completed phase.

Leaders embrace AI across finance

More than three times as many AI leaders as others use AI in finance. This wide gap between leaders and others is present in every finance area — in accounting, for example, 88 percent of leaders have selectively or widely adopted AI in accounting vs. just 19 percent of others. Even in areas where leaders have not made as much progress — such as risk management and tax reporting, where just over half of leaders are selectively or widely using AI — the gulf with others is stark.

Innovative uses of AI

Leaders are using AI in a variety of innovative ways. A Canadian bank surveyed is merging AI with blockchain to ensure secure and transparent financial transactions. A French logistics company is using it to create adaptive pricing algorithms that optimize prices based on current market trends. And a major US insurance company is drawing on AI for performance evaluation and training of finance department employees.

Leaders bet on Gen AI

AI leaders also are embracing Gen AI. They are using it for dynamic report and narrative generation, forecasting models and scenario generation, document management, compliance monitoring and reporting, and automated tax preparation, among other applications.

For example, the SVP of finance in an Irish manufacturing company is using the technology “to generate various financial scenarios and their potential impact on our business to assist us in making strategic decisions.”

More than as many AI leaders use AI in finance, three times compared to others.

AI is a versatile tool for leaders

Leaders are moving fast to develop different uses for AI that will free up financial staff to focus on high-level tasks. These use cases show the art of the possible for other companies exploring AI options.

On average, leaders have six use cases for AI, almost double the number that others have developed. Leaders are well out front in the use of Gen AI for composing documents and summaries. Leaders are also ahead of others in more mundane uses of AI, such as for administrative tasks, performance evaluation and training, and data entry — use cases for about half or more of leaders.

“We are using AI across our finance function, and our team has been adopting it with ease,” said the Chief Audit Executive of a French consumer markets company.

The SVP of accounting for a Dutch aerospace spoke glowingly about Gen AI: “Our company has implemented Gen AI as part of a company-wide initiative to use it for tasks such as contract analytics, recruitment tools, cyber threat analysis, and procurement. This initiative has yielded satisfactory results, and we plan to continue using this technology to achieve even more significant results.”

Figure 6: Top AI use cases piloted or implemented in finance

	Leader	Other
Research and data analysis	85%	46%
Fraud detection and prevention	81%	46%
Predictive analysis and planning	78%	45%
Generative Al for composing documents,and other content	75%	33%
Risk management and cybersecurity	62%	27%
Administrative tasks, such as automating repetitive processes	52%	27%
Performance evaluation and/or training	50%	28%
Custom virtual assistants	48%	25%
Data entry and document processing	43%	27%
Monitoring and complying with changing regulations and tax laws	39%	19%
Tracking expenses and tax deductions	33%	21%
Average number of use cases	6	3.6

Figure 7: Portion of company's overall IT budget spent on AI-related activities

Leaders lay the groundwork for AI success

Becoming a leader in AI requires the proper financial and human resources. That is why AI leaders invest nearly twice as much as others in enterprise-wide AI activities as a proportion of IT budgets.

Over the next three years, that percentage of AI spend on company-wide activities will grow to more than 16 percent of the IT budget for leaders. As others play catch up, the gap between them and leaders will narrow.

Building AI skills

AI leaders ensure they have the talent and skills to drive AI innovation in finance. To do this, they build up their own internal AI resources — either a central team within finance or separate groups within each department in their group.

They also draw more on resources from outside of finance. For more than two-thirds, this includes the company’s central AI team. Nearly half also make greater use of external AI resources, such as technology outsourcing companies or consultants.

Taking it to the next level

Leaders go far to lay the groundwork for AI success. One German chemicals company in our study worked with AI startups to drive fast results. A Spanish bank creates a culture of innovation that encourages the financial team to suggest new ideas and AI solutions.

Leaders prioritize AI governance and assurance

To ensure the responsible use of AI, leaders have taken more actions to improve AI governance. This includes putting AI risks and controls within the scope of financial reporting, which more than twice as many leaders do compared to others.

It also includes adopting and/or publishing an AI framework to guide implementation and usage. “Our company is creating frameworks to manage privacy, security, and compliance to ensure the ethical use of AI,” said the Head of Financial Reporting at a German retailer.

Assurance

Obtaining assurance is critical when using AI to maintain the integrity of the output and trust of stakeholders. That is why more than half of leaders gain third-party controls assurance over AI processes and controls, more than twice as many as others.

Leaders are also more apt to include AI controls assurance in the scope for reports for vendors or third-party processes. More than twice as many leaders as others request certification or attestation reports that cover AI processes and control objectives.

At the same time, AI leaders use AI as a control mechanism. For example, a Chinese aerospace company uses AI for “verifying compliance and detecting potential risks by analyzing critical terms within financial contracts.”

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It is clear that leaders pull all levers to accelerate on AI. Six months ago, most companies weren’t sure how best to organize themselves for the transformation. Now, leading practices have become clearer — with teams creating Centers of Excellence highlighting new best practices. It is no accident that leaders have stronger governance processes and gain greater degrees of external assurance — this creates the trust that links it all together.

Nikii McAllen
Global Head of Finance Advisory
KPMG International

“

Amongst clients who haven’t started on the AI journey or are in the very early stages, one of the most common blockers is that they are worried about return on investment. However, our research shows that the ROI is there and significant. Companies can take confidence from this and get started.

Christian Stender Global Head of AI Tax & Legal KPMG International

Leaders see greater AI benefits

As the use of AI in finance grows, the dividends multiply. When starting out, finance teams report two to three benefits. By the time they are leaders, the number is seven.

Leaders report five main clusters of benefits from the use of AI in finance. The top one revolves around AI’s capacity to bring data to life: improving its accuracy by automatically finding and correcting errors, as well as generating deeper data insights and predictive analysis for better decision-making.

Together with other widespread benefits including greater speed to insight, lower costs, increased efficiency and productivity, and more highly skilled staff (increasing talent attraction and retention) — the upsides are compelling.

Figure 8: Average number of benefits seen

Leaders drive greater ROI from AI

Just as the benefits from AI rise with its usage, so does the return on investment. This can be seen in the reported results: one-quarter of beginners report higher-than-expected ROI on AI, compared with 57 percent of leaders. Here are some of the leading ways that AI benefits are driving ROI, according to surveyed executives:

Figure 9: Level of AI-ROI expectations being met

Overcoming barriers to AI usage

Barriers to using AI in finance

AI adoption in finance does not come easily. Because AI systems contain vast amounts of sensitive data, they are more susceptible to data breaches. In addition, integrating AI systems with other components, such as cloud services and APIs, can increase the number of entry points that hackers might exploit.

Figure 10: Biggest barriers to adoption of AI

Lack of AI skills and talent, cited by over half of executives, is another challenge. “Lack of clear knowledge of deploying AI is the main reason why AI efforts are failing,” says a top finance executive at a German industrial products company.

Alongside more technical aspects, such as consistency of data and transparency of AI solutions, costs continue to be a pain point for many. As the Controller of a Singaporean industrial manufacturer said: “We are low on budget and lack a modernized IT framework to support AI platforms.”

Hurdles along the AI journey

Barriers morph as companies mature in use of AI. In the early stages, data security, skills and costs dominate. But as companies broaden their use of AI their concerns shift. Finding consistent data in large datasets becomes harder. The potential for bias and misinformation rises, particularly as finance teams tap into Gen AI. Integrating AI solutions and tools into existing systems presents challenges. And staff resistance rises as jobs are affected.

Resourcing is key to overcoming AI barriers. As AI capabilities grow, companies simultaneously need to invest in their people capabilities. General upskilling of staff raises the knowledge level across the company as a whole so that they are comfortable using AI tools and learn new skills.

Nikii McAllen

Global Head of Finance Advisory KPMG International

AI introduces new risks and concerns

Financial executives are not naïve about AI. They know it comes with a set of limitations and risks. And Gen AI is heightening their apprehensions due to its ability to independently generate content and analysis from massive data sets.

In fact, concerns about Gen AI are greater than those around traditional AI across most spheres. Cybersecurity and data privacy are the most prominent concerns given Gen AI’s potential to draw on sensitive and proprietary data. By introducing new tools and systems, it also expands the attack surface for bad actors to exploit.

Legal concerns around Gen AI

Data sovereignty and intellectual property rights are also larger worries for financial executives because of Gen AI’s ability to trawl huge databases, which may include information that is copyrighted or protected by laws in other countries. And because Gen AI generates new content from patterns in data without an understanding of the underlying facts, it is more likely than traditional AI to present wrong or even biased conclusions.

However, not all concerns grow with the use of Gen AI. Accuracy and transparency are equal concerns when using both traditional and Gen AI. In contrast, data management is a bigger hurdle for traditional AI, which generally requires clean, preprocessed, and well-structured data.

Leaders do more to overcome AI challenges

Financial executives see many of the same barriers and concerns when drawing on AI — but leaders take more measures, and in greater numbers, to overcome them.

AI leaders take a value-based investment approach, piloting AI use cases first to validate ROI before making additional investments. They also conduct change management and education programs to provide their teams with the AI skills and innovation mindsets that they will need to succeed. Crucially, they continue to increase their AI budgets to fund future AI initiatives.

Our research reveals critical blind spots

When implementing AI solutions across finance operations, executives naturally want to focus their attention on the chief areas of concern.

Accordingly, our research shows that most pay higher attention to the attributes of AI adoption that they consider most important — privacy, data integrity, and security. At the same time, they pay the least attention to the issues they consider to be less vital, such as explainability and accountability.

Blind spots to avoid

However, our correlation analysis reveals critical blind spots — areas of importance that may require further attention. One of the biggest is around the transparency of AI initiatives. Because of AI’s “black box” nature, stakeholders are unsure whether to trust its results. For example, the chief audit executive of a Brazilian industrial manufacturer blamed AI’s lack of transparency for insipid ROI results. “There are no sources or links for data validation,” according to the executive.

AI and sustainability

Sustainability is another glaring blind spot. The high energy consumption that AI requires means that sustainability concerns should also be addressed head on — particularly as the journey toward net zero continues. But AI is a double-edged sword: it not only can undermine sustainability, it can also be used to improve it. For instance, a large German chemical manufacturer applies AI for chemical process optimization and sustainable agriculture. According to the company’s Chief Digital Officer, “AI models have been effectively used [for sustainability], demonstrating satisfactory results.”

Figure 11: Most important AI attributes vs. biggest blind spots

Shifts in financial reporting

AI in reporting advances in just six months

While the use of AI is spreading across finance, it is in financial reporting where some of the most significant progress has been made. Over the last six months, the use of AI in reporting has expanded in most of the 10 major industrialized markets surveyed in April, particularly in Canada, Australia, and Japan.

However, progress in key countries in the European Union, such as France and Netherlands, has slowed. One possible reason is that companies in the region are waiting for clarity on the proposed EU Artificial Intelligence Act and other AI regulations.

For both traditional AI and Gen AI, the proportion of companies in the 10 major markets actively using solutions has grown. While the percentage increases are not enormous, they are significant over such a short period of time. A striking feature is the drop in the number of companies that have no plans for Gen AI — which is becoming almost a mandatory feature in any organization’s AI toolkit.

Looking across the countries and territories in our September survey, the trajectory of change is clear: while 28 percent are now selectively or widely using AI in financial reporting, the percentage is likely to rise to a stunning 83 percent over the next three years.

Companies in the 13 additional industrialized and emerging markets surveyed in September are making progress, but at a slower pace than the original 10 major economies. While companies in some of these economies, such as China and South Korea, have made considerable headway, others, such as those in Italy and Africa (Kenya, Nigeria, South Africa), are trailing behind. However, over the next three years, the share of companies in these additional countries selectively or widely using AI in reporting should be expected to jump to 78 percent — showing that AI in financial reporting will become a global phenomenon.

The use of Gen AI in reporting is growing

Gen AI has become the ‘hot ticket’ in the AI arena, generating huge interest and discussion. However, Gen AI presents unique challenges and is more complex than some forms of traditional AI to embed into processes.

But while it is tracking further behind traditional AI, Gen AI is firmly on the agenda for financial reporting.

Figure 12: Selective or wide use of AI in 10 top industrialized markets

Where progress has accelerated

Where progress has slowed

Over four out of 10 organizations are piloting or actively using Gen AI already, and 56 percent are intending to use it in the future. In three years, nearly every company will be piloting or actively using Gen AI for reporting.

Leaders are far ahead — almost four out of 10 are already selectively or widely adopting Gen AI in financial reporting, compared to only a tiny minority (3 percent) of others. Considerably more leaders are prioritizing Gen AI for financial reporting over the next year compared to traditional AI. As a result, 95 percent leaders expect to be selectively or widely using Gen AI in financial reporting in three years’ time — compared to 39 percent of others.

As with AI overall, progress is somewhat slower in the 13 additional markets surveyed. Nevertheless, the fact that half of organizations in these countries expect to be selectively or widely using Gen AI in the next three years is a striking statistic that demonstrates the scale of the shifts to come.

Figure 13: Progress in Gen AI use for financial reporting by maturity (selective and wide adoption)

Companies want auditor support on AI

As companies make progress in harnessing AI for financial reporting, they often require more support from external auditors, particularly around governance and controls.

Our research finds that most organizations expect their auditors to conduct a detailed review of their control environment to ensure the responsible use of AI for reporting. Many others would also

like their auditors to conduct assessments of their AI governance maturity, third-party attestation over the use of AI technology, and readiness/gap assessments.

Many companies expect auditors to get on board

Many organizations, particularly leaders, also expect their auditors to utilize AI tools for their own auditing activities. The most common activities are data analysis, risk mitigation, anomaly identification, fraud detection, and predictive analysis. There is growing desire for auditors to speed up the auditing process and move to real-time auditing that can help companies to manage their risks more proactively throughout the year. Generally companies see both traditional and Gen AI as tools to help with these activities except for predictive analysis and speeding up the auditing process, where traditional AI is more useful.

Figure 14: Top 10 activities that companies want auditors to conduct with AI

Trad	Gen
Data analysis 66%	54%
Risk mitigation 57%	53%
Risk identification	55% 51%
Fraud detection	53% 45%
Predictive analysis	50% 32%
Speed up auditing process	45% 29%
Real-time auditing	39% 33%
Document/data gathering	37% 37%
Analyze trends	34% 30%
Improve responsiveness	32% 35%

Crucially, finance executives want more AI communication from their external auditors. This is not surprising, given both parties need to work closely together to ensure an effective and responsible use of throughout the reporting process.

Leaders especially want their auditors to step up. Right now, only 15 percent of leaders say their auditor communicates frequently with them about AI — but 51 percent would like them to. Amongst other companies too, most would like more communication than they currently receive.

Auditors should be speaking to companies about AI — our research finds that almost all businesses would welcome more communication. Businesses expect their auditors to be using AI in their audits — it’s table stakes. Through their own knowledge and experience of AI, and through their natural assurance and attestation skills and mindset, auditors will be critical in the independent testing and evaluation of an organization’s controls that use AI in the accounting and financial reporting functions.

Thomas Mackenzie

Global Audit Chief Technology Officer KPMG International

Conclusionand recommendations

Integrating AI across finance operations and processes is a journey that requires commitment, stamina and planning — but as KPMG’s research indicates, the potential rewards are high.

Here are seven key recommendations that may help you focus your efforts and make tangible progress, faster:

01.

Give top priority to the use of AI in finance.

Companies should follow the example of AI leaders in our study by implementing a wide range of use cases. These should include not just basic use cases around data entry and administrative processes, but also higher-order tasks around research, risk management, cybersecurity, fraud detection, and predictive analysis.

02.

Have a clear strategy and implementation plan for deploying Gen AI in finance.

These plans should include actively testing and refining use cases that leverage the power Gen AI, such as composing financial reports and summaries. But companies should also stay mindful of Gen AI’s limitations around data security, sovereignty, accuracy, and copyright and intellectual property.

03.

CFOs should make sure their teams think beyond accounting and financial reporting when applying AI.

While AI is currently most commonly used in accounting and financial reporting, its use is spreading across finance. Most AI leaders are already using the technology to optimize financial planning, treasury management, tax operations, and risk management, as well as to drive ROI across their departments.

04.

Staying ahead in AI is not just about technology — it is also about people.

To fully embed AI into their financial activities, financial management teams should go beyond drawing on AI support from outside their department. That means staffing up with AI specialists within finance, while providing training on the use of AI to the general financial staff. Using AI to improve the productivity, engagement, and retention of staff should be top of mind.

05.

06.

Tackle the barriers. A lack of AI skills, inconsistent data, high costs, and data security and privacy concerns can often hold companies back from fully leveraging AI i n finance.

Stay aware of potential blind spots that will require management attention.

To overcome these barriers, act early to establish AI guidelines and governance mechanisms, create digital processes to meet regulatory requirements, and shift to modern IT platforms that facilitate AI. Crucially, financial teams should pilot AI initiatives to validate ROI and ensure effectiveness before scaling these solutions across the department.

Because of the complexity of AI algorithms and the black box nature of AI solutions, transparency is a common blind spot that, if left unattended, could lead to a loss of trust and accountability. Sustainability is another area often overlooked: an increase in AI-driven data consumption can push up carbon footprints.

07.

Expect — and demand support on AI from auditors.

Companies should look to their auditors to help ensure an effective control environment, including assessing AI governance maturity and providing third-party attestation over the use of AI. Auditors should be advanced in using AI themselves, such as for data analysis and risk detection in the audit — so organizations should expect an auditor to communicate with them about AI and engage in a constructive, two-way dialog that can help them further build their approach.

AtKPMG, we “speak AI”

At KPMG, we have made major investments in AI technology through our alliances with Microsoft and other major technology providers that help to place KPMG firms as leaders in AI understanding and deployment.

Our audit workflow, KPMG Clara, has AI tools and techniques integrally embedded and is used by KPMG audit professionals around the world. Our research finds that companies want auditors that communicate with them about AI, use AI themselves to smarten their audit processes and deliver more value, and that provide sound AI governance, assurance and attestation services and advice.

Meanwhile, KPMG Advisory professionals have extensive experience in working with organizations on the mapping and deployment of AI tools and techniques across the enterprise — from finance and reporting to HR, procurement, sales & marketing, and operations.

Our Tax Digital Gateway Gen AI platform enables clients to tap directly into flexible, secure, actionable AI solutions that are ready to be applied across numerous taxrelated use cases. We help clients understand what the potential of AI is for them, through design workshops and discussion of the art of the possible.

Alongside and supporting this, KPMG’s Trusted AI framework is used to help design, build, deploy, and use AI tech solutions in a responsible and ethical manner while also accelerating value and making the difference for clients, people and communities.

Working with numerous leaders on their AI solutions for finance operations and other business areas has brought AI into the center of our thinking. AI is in our mindset — it has become integral to how KPMG professionals tackle challenges and create technology solutions that empower and enable clients.

Artificial Intelligence is more than just an innovation. It’s fundamentally transforming finance, making it more efficient, accurate, insightful and strategic. Yes, AI can automate routine tasks, reduce errors, and speed up the mundane, but more than this, agentic AI will generate insights that were previously unimaginable. AI Finance Agents with deep expertise will collaborate to solve some of our most intractable problems, pushing the boundaries of what is possible – and it’s coming soon!

David Rowlands Global Head of AI KPMG International

KPMG firms and Microsoft are collaboratively assisting clients in adopting new technologies and transforming rapidly to ensure they remain competitive. It is crucial for organizations to recognize the impending industry changes and shift from a reactive mindset to a proactive approach. By digitizing their data estates and leveraging AI technology, clients can gain insights more swiftly, ultimately enhancing their success in an increasingly fast-paced environment.

Seth Eisner
CVP, Commercial Products
Microsoft

Contacts

David Rowlands Global Head of AI KPMG International david.rowlands@kmg.co.uk +44 780 144 3602

Nikki McAllen
Global Head of Finance Advisory
KPMG International
nmcallen@kpmg.com.au
+61 3 9288 5330
Christian Stender
Global Head of AI Tax & Legal
KPMG International
cstender@kpmg.com
+49 174 307 8130
Sebastian Stöckle
Global Head of Innovation and AI, Audit
KPMG International
sstoeckle@kpmg.com
+49 302 068 2716

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Publication name: KPMG global AI in finance report | Publication number: 139711-G | Publication date: November 2024

KPMG

KPMG global AI in finance report

Contents

Foreword

Research background

The AI maturity framework

KPMG’s AI Maturity Assessment Tool: How does your organization measure up?

Transformin finance throughAI

AI is a game-changer for finance

Sebastian Stöckle

AI usage is spreading across all finance areas

Tax management trails behind other finance areas

Christian Stender

Finance unlocks value from AI investments

HowAI 000 leaders drive ROI

Leaders embrace AI across finance

Innovative uses of AI

Leaders bet on Gen AI

AI is a versatile tool for leaders

Leaders lay the groundwork for AI success

Building AI skills

Taking it to the next level

Leaders prioritize AI governance and assurance

Assurance

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Leaders see greater AI benefits

Leaders drive greater ROI from AI

Overcoming barriers to AI usage

Barriers to using AI in finance

Hurdles along the AI journey

Nikii McAllen

AI introduces new risks and concerns

Legal concerns around Gen AI

Leaders do more to overcome AI challenges

Our research reveals critical blind spots

Blind spots to avoid

AI and sustainability

Shifts in financial reporting

AI in reporting advances in just six months

The use of Gen AI in reporting is growing

Companies want auditor support on AI

Many companies expect auditors to get on board

Thomas Mackenzie

Conclusionand recommendations

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AtKPMG, we “speak AI”

Contacts