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Supercharge your Finance workforce with GenAI

Create a roadmap for success

Risk preparedness

Across nearly all risk challenges, risk executives rate themselves as “well prepared” to respond, according to our survey of 390 Chief Risk Officers (CROs).

Let’s look deeper at this high level of confidence in risk preparedness, especially given the risks across today’s business landscape, which range—from rapidly changing markets to mounting compliance requirements to social and political volatility—.

Read more

Knowledge workers in finance—and across the enterprise—have significant opportunities to capture value and reshape the workforce with the use of generative artificial intelligence (GenAI). Realizing these opportunities would require identifying and augmenting previously hard-to-automate tasks. 

Beyond the hype: What's in store for finance?

GenAI goes a long way toward solving one of the biggest pain points in finance: manual processes. Labor-intensive systems increase the risk of human errors, consume valuable resources, and limit real-time insights. While GenAI is not a replacement for human judgment—at least not in the foreseeable future—teams can work smarter with GenAI as a starting point.

Given the broad role for finance in strategy and risk management, finance professionals are uniquely positioned to spearhead GenAI. But they first need to determine the potential value of GenAI across their enterprise through the lens of workforce capacity and productivity.

As complex analytical creativity and decision-support activities are automated, finance can scale and do a lot more with less. The traditional pyramid structure—with inputs from a large base of people being reviewed and updated at every layer of a smaller team—will transform into a diamond when first-pass insights are processed by GenAI.

This transformation leads to increased efficiency, both within finance and across commercial and operational functions; however, it raises the need to care for compliance. 

Connecting GenAI to enterprise strategy, business value, financial goals and the workforce with great clarity is critical to launching and scaling a successful GenAI program.

Per Edin

KPMG Principal

GenAI has the potential to increase the speed and quality of intelligence and drive better business outcomes.

Generative AI For Finance: Lead adoption for powerful outcomes
CFOs and finance leaders are best positioned to lead generative AI, offering a balanced approach
Read the whitepaper

For example, cycle times to develop financial commentary and analysis, which are significant for most organizations, can improve exponentially through GenAI.

From the perspective of enterprise performance management, GenAI can use internal data sets to develop preliminary insights, recommend actions to mitigate risks, or capture opportunities. It can also integrate external data, such as competitive intelligence, in a faster, more effective way to improve decision making.

With growing possibilities for GenAI tools, new roles and skill sets are emerging. Prompt engineering, creative design thinking, and a drive for continual learning are increasingly important. Strong foundational competencies in data and analytics are essential to interpreting and working with insights generated by AI tools. 

Yet many finance professionals are still figuring out how to implement the technology on a day-to-day basis. In a KMPG webcast , Reshaping your Finance Workforce with Generative AI, 66 percent of finance professionals said they are in “learning mode” when it comes to their level of awareness and experience with GenAI.

Watch the webcast here

GenAI alone will not solve all problems

GenAI is another component that needs to be integrated into the digital ecosystem. Organizations still need to drive standardization and adoption of other technologies, such as cloud solutions, machine learning, and robotics for data sets that get value from GenAI.

  • Accelerate growth: Enabling faster, more effective innovation and investment decisions
  • Improve profitability: Integrating GenAI across systems to enhance forecasting and enable optimal resource allocation
  • Increase profitability and efficiency: Focusing on knowledge workers and capabilities
  • Enable dynamic risk management and compliance: Proactively addressing governance, regulatory requirements, and ethical dilemmas

“Connecting GenAI to enterprise strategy, business value, financial goals and the workforce with great clarity is critical to launching and scaling a successful GenAI program," says Per Edin, a KPMG principal.

Per Edin

GenAI has the potential to increase the speed and quality of intelligence and drive better business outcomes.

For example, cycle times to develop financial commentary and analysis, which are significant for most organizations, can improve exponentially through GenAI.

From the perspective of enterprise performance management, GenAI can use internal data sets to develop preliminary insights, recommend actions to mitigate risks, or capture opportunities. It can also integrate external data, such as competitive intelligence, in a faster, more effective way to improve decision-making.

With growing possibilities for GenAI tools, new roles and skill sets are emerging. Prompt engineering, creative design thinking, and a drive for continual learning are increasingly important. Strong foundational competencies in data and analytics are essential to interpreting and working with insights generated by AI tools. 

Yet many finance professionals are still figuring out how to implement the technology on a day-to-day basis. In a recent KPMG webcast, Reshaping your Finance Workforce with Generative AI, 66 percent of finance professionals said they are in “learning mode” when it comes to their level of awareness and experience with GenAI.

Watch webcast

GenAI alone will not solve all problems

It is another component that needs to be integrated into the digital ecosystem. Organizations still need to drive standardization and adoption of other technologies, such cloud solutions, machine learning, and robotics for data sets that get value from GenAI.

But these other technologies mainly benefit the lower levels of organizations. GenAI is different in that it helps knowledge workers, the high-skilled roles from directors all the way through the C-level.

This has the potential to transform decision-making and reinvent business in various ways: 

  • Accelerate growth: Enabling faster, more effective innovation and investment decisions

  • Improve profitability: Integrating GenAI across systems to enhance forecasting and enable optimal resource allocation

  • Increase productivity and efficiency: Focusing on knowledge workers and capabilities

  • Enable dynamic risk management and compliance: Proactively addressing governance, regulatory requirements, and ethical dilemmas

GenAI alone will not solve all problems

It is another component that needs to be integrated into the digital ecosystem. Organizations still need to drive standardization and adoption of other technologies, such cloud solutions, machine learning, and robotics for data sets that get value from GenAI.

But these other technologies mainly benefit the lower levels of organizations. GenAI is different in that it helps knowledge workers, the high-skilled roles from directors all the way through the C-level.

This has the potential to transform decision-making and reinvent business in various ways: 

Accelerate growth

Enabling faster, more effective innovation and investment decisions

Improve profitability

Integrating GenAI across systems to enhance forecasting and enable optimal resource allocation

Increase productivity and efficiency

Focusing on knowledge workers and capabilities

Enable dynamic risk management and compliance

Proactively addressing governance, regulatory requirements, and ethical dilemmas

In contrast, risk leaders are more confident about their ability to respond to their other leading risk challenges: regulatory/compliance risks and cybersecurity threats/date breaches.

The two risks CROs are least prepared for have something in common: They are hard to plan for or control, and they often exacerbate or create other interrelated risks. This “compound volatility” creates potentially high exposure and impact. 

28%

Less than 3 in 10 CROs feel unprepared to manage to regulatory/compliance risks, while just 21% feel unprepared to manage cybersecurity/threats/data breaches.

While 21%

feel unprepared to manage cybersecurity/threats/data breaches.

1

Identify the roles most affected by GenAI and quantify the impact on the work they perform. 

2

Develop proof-of-concept pilots and outside-in analyses, focusing on capacity gains, job deconstruction, and role augmentation. Then test, deploy, and scale.

3

Reshape the workforce structure by rethinking your operating model and talent agenda in alignment with GenAI. This will inform the overall workforce strategy. 

Drawing a roadmap for talent

Another major pain point for finance is attracting knowledge workers with GenAI skills. The skillset is not as specialized as coding or regulatory compliance. It’s more about adopting GenAI and learning how to apply the solutions. Yet this talent pool is in high demand and costly, so organizations would be well served to map out a training program.

Key roles, both unique to a business and common across front-, middle-, and back-office functions, represent significant opportunities for efficiency. An enterprise value roadmap is the place to start.

Finance can navigate by identifying discrete use cases that have a direct impact on growth, gross margin, cost takeout, and people. The goal should be to achieve tangible quick wins that will naturally build momentum.  

Adopting a unified approach for technology and the workforce is critical. We recommend a methodical three-step method to drive workforce transformation with GenAI: 

1: Identify the roles most affected by GenAI and quantify the impact on the work they perform. 

2: Develop proof-of-concept pilots and outside-in analyses, focusing on capacity gains, job deconstruction, and role augmentation. Then test, deploy, and scale.

3: Reshape the workforce structure by rethinking your operating model and talent agenda in alignment with GenAI. This will inform the overall workforce strategy. 

A data-driven view can be the basis for an integrated plan that quantifies estimated value and investment.

Return on investment depends on your strategy, sector, and business model. The ROI can come from growth and increased revenue, both organic and inorganic. It may come from profitability based on better pricing, dynamic pricing, or operational efficiency. The success criteria must be defined and tie back to your strategy so you can prioritize. Where are the best tactical opportunities for the near-term versus strategic long-term goals? Finally, consider the competitive environment and threat that AI may have on your business model. New products or services may be needed. Strategic alliances with vendors or partnerships could be necessary.

Closing thoughts

As companies begin building their AI capabilities, creating strategic partnerships with the right vendors will help support short- and long-term transformation goals. Now is the time for chief financial officers to consider the commercial and operational applications of GenAI that will have the most impact. 

Dive deeper
Want to learn more about how your peers and competitors are navigating pressing risk challenges—and how your approaches and capabilities match up? Read our 2023 CRO Survey for timely insights, perspectives and recommendations for risk leaders.
Read full report
Dive deeper
Want to learn more about how your peers and competitors are navigating pressing risk challenges—and how your approaches and capabilities match up? Read our 2023 CRO Survey for timely insights, perspectives and recommendations for risk leaders.
Read more

Least ready for the most complicated risks

A closer analysis reveals that risk readiness is not uniform, nor as rosy as it looks at first glance. Confidence lags in dealing with reducing risk exposure to especially fast-moving, unpredictable, and far-reaching disruptions.

Of the challenges risk executives identified as the biggest their organizations’ will face in the next 2-5 years, they feel least prepared to address:

  1. Economic downturn/recession – 40% feel unprepared to manage
  2. Geopolitical risks – 37% feel unprepared to manage

In contrast, risk leaders are more confident about their ability to respond to their other leading risk challenges: regulatory/compliance risks and cybersecurity threats/date breaches. Less than 3 in 10 CROs (28%) feel unprepared to manage to regulatory/compliance risks, while just 21% feel unprepared to manage cybersecurity/threats/data breaches.

The two risks CROs are least prepared for have something in common: They are hard to plan for or control, and they often exacerbate or create other interrelated risks. This “compound volatility” creates potentially high exposure and impact.

How to prepare for uncertainty

How can companies prepare for and respond to these broad and volatile market risks?

One key is to enhance risk intelligence and data-driven decision-making?

Here are four starting tips to help your organization on the path to improving risk preparedness in this era of compound volatility:

  • Centralize the risk technology environment: Integrate and connect risk tools and technologies to embed risk intelligence and management closer to the point of risk origin.
  • Look at data more holistically: Collecting and analyzing data across the organization will help provide clarity on the compounding effects of risk events across business functions.
  • Identify risk signals quicker with dashboards: Use advanced dashboarding capabilities to enable real-time and continuous monitoring of risks and adopt a more proactive risk posture.
  • Improve risk awareness and involvement: Expand transparency and access to risk insights to allow more stakeholders to quickly act to respond to risks.
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Thank you for your interest in the KPMG 2023 Chief Risk Officer Survey.

Check out the interactive version of the report here.

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Explore the findings on our webcast November 30, 2023

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Meet our team

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Michael Kokotajlo
Partner, Advisory, Finance Transformation, KPMG US
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John Doel
Principal, Human Capital Advisory, KPMG US
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Danny Seto
Managing Director, Human Capital Advisory, KPMG US
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Colleen Mohnkern
Director, Advisory, Finance Transformation, KPMG US
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Michael Kokotajlo
Partner, Advisory, Finance Transformation, KPMG US
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John Doel
Principal, Human Capital Advisory, KPMG US
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Danny Seto
Managing Director, Human Capital Advisory, KPMG US
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Colleen Mohnkern
Director, Advisory, Finance Transformation, KPMG US
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