- Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
- To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data security to tech investment strategy to customer experience.
- This is part 13: Finance.
Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data security to tech investment strategy to customer experience.
This is part 13: Finance.
Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data security to tech investment strategy to customer experience.
This is part 13: Finance.
AI adoption has already helped reduced sales forecast errors by 57%
In today’s dynamic business environment, your organization faces a perfect storm: inflation, geopolitical uncertainty, rapidly changing regulation, shifting regulatory and reporting standards, tariff and trading uncertainty, and volatile cost of capital. And yet, despite these economic headwinds, growth and profit expansion have never been more essential.
To address these challenges, CFOs—and the finance functions they lead—must adopt a new approach to financial management that leverages the power of generative AI. This advanced technology can help CFOs and their teams understand which bets let the organization move faster—and deliver the right insights at the right time.
As technology reshapes the business landscape, CEOs need finance leaders to advise where finite budgets should be spent. CFOs play a crucial role in this moment. They must know how tech investments are performing and where the enterprise is getting the biggest bang for its buck. By connecting the dots between tech spend and business value, finance can provide insights CEOs need to dramatically accelerate transformation and growth.
Success depends on how quickly finance can turn data into actionable insights. And this is where generative AI delivers the most value. It unlocks a previously untapped world of unstructured data to help finance create better, more strategic narratives.
While traditional AI can only recognize patterns in financial data, generative AI can identify key themes and trends, highlight an organization’s strengths and weaknesses, and uncover hidden market opportunities. It can help CFOs navigate a complex risk landscape, make investment decisions more confidently, improve productivity, drive cost take-out, increase forecasting accuracy, and create new business value.
But, at least for now, AI adoption in finance remains low. A recent IBM IBV survey of CFOs found that just one in 10 finance organizations have optimized traditional AI technologies. And only 2% are optimizing generative AI.
If CFOs are to be the strategic transformation partners CEOs require, this needs to change fast.
The IBM Institute for Business Value has identified three things every leader needs to know:
1. You need generative AI for FinOps—and FinOps for generative AI.
2. Generative AI gives your finance team superpowers.
3. Generative AI doesn’t just cut costs—it creates new business value.
And three things every leader needs to do right now:
1. Fine-tune FinOps with generative AI—and apply FinOps across the enterprise—to make technology even more valuable.
2. Attack labor-intensive tasks that are ripe for generative AI automation.
3. Employ generative AI assistants to execute strategies more effectively.
Additional content
Meet the authors
Cindy Anderson, Global Executive for Engagement and Eminence, IBM Institute for Business ValueChristian Bieck, Europe Leader & Global Research Leader, Insurance, IBM Institute for Business Value
Spencer Lin, Global CFO Research Lead, IBM Institute for Business Value
Anthony Marshall, Senior Research Director, Thought Leadership, IBM Institute for Business Value
Download report translations
Originally published 02 April 2024
This chapter is available in the latest edition of the IBM Institute for Business Value's book, The CEO's Guide to Generative AI.
1. FinOps
+ Generative AI
What you need to know
You need generative AI for FinOps—and FinOps for generative AI
As organizations upgrade critical infrastructure—and business functions find new opportunities to innovate with generative AI—finance must be able to effectively measure the value of each tech investment in turn. But beyond this, finance must be able to understand and communicate a complete picture of the business benefits technology yields—in other words, the true technology ROI.
Executives across industries agree that generative AI can give companies a competitive edge. For their part, CFOs tell us embedding generative AI throughout the enterprise will be a top enabler of competitive advantage over the next three years.
But not every use case is created equal. While experimentation is essential to identify where generative AI delivers the most business value, the finance function can help CEOs and other business leaders assess where generative AI shows the greatest potential, and manage and account for the new revenue streams created.
FinOps, or financial management for cloud-based investments, should play a big part in generative AI investment decisions. It increases visibility into cloud-related financial data, finding operational efficiencies and measuring returns. It formalizes and structures what might otherwise be a largely random process—and reduces the potential for unexplained cloud expenses.
As most organizations shift to hybrid cloud environments, cloud infrastructure costs could explode—and enterprise-scale generative AI could compound these costs exponentially. That’s why integrating FinOps early is essential: it models future scenarios to help you see where spend will be needed—and where it can be avoided. This financial management framework—and the new cultural and governance practices established as a consequence—are essential to maximizing the business value of cloud, the business value of generative AI, and everything between.
But it’s not just FinOps increasing the value of generative AI adoption. Generative AI can also make FinOps more valuable by quickly analyzing and summarizing information from multiple sources, such as vendor bills and IT architectural guidelines, and identifying areas for improvement. It can also let individuals chat with an AI assistant that has deep organizational knowledge, helping teams respond to risks and opportunities faster.
Research shows that most of the business value derived from using FinOps in cloud transformation comes from innovation (38%), improved resiliency (28%), and cost efficiency (19%). And most organizations that have activated a FinOps model using AI-powered software report cost savings greater than 20%. Peers not using AI save less than 10%.
But despite clear potential benefits, only 31% of organizations currently have FinOps capabilities in place. And only 26% of CFOs are collaborating with technology leaders to scale generative AI across the enterprise.
By providing a systematic approach to managing the cloud-related financial aspects of generative AI development, deployment, and maintenance, FinOps can help enterprises scale adoption efficiently and effectively. For example, FinOps for generative AI can help enterprises more accurately estimate both the costs and potential benefits of specific use cases, helping leaders rank them by potential business impact. And analysis of smaller fit-for-purpose generative AI models can drive better price performance than large language models, resulting in millions in savings as the models are scaled across the enterprise. Additionally, FinOps can help finance teams assess the risks associated with specific uses and develop contingency plans to address issues that may arise.
What you need to do
Fine-tune FinOps with generative AI—and apply FinOps across the enterprise—to make technology even more valuable
Using generative AI to improve FinOps can make it more accurate and efficient. Applying FinOps to generative AI improves cost-benefit analyses and helps teams prioritize use cases.
- If your organization doesn’t have FinOps capabilities, take this opportunity to set up an organizational home for generative AI FinOps. Establish governance through a responsibility assignment matrix with resources from finance, IT, and the business.
- If your organization is already embracing FinOps, immediately apply FinOps practices to your generative AI investments. Implement a cost estimation and tracking framework that can help your team understand the costs associated with generative AI projects.
- Use generative AI to enhance FinOps capabilities. Simulate financial data and scenarios that can help increase the accuracy of financial models, improve risk management, and support strategic decision-making.
2. Transformation
+ Generative AI
What you need to know
Generative AI gives your finance team superpowers
Generative AI makes finance an even bigger force to be reckoned with. It frees finance teams from tedious tasks, unleashing a torrent of productivity and more strategic contributions. On average, AI adopters attribute 40% of finance function FTE redeployment to AI, which lets teams focus on higher-value, more strategic activities, such as readjusting planning decisions quickly in real time.
What might these automatable tasks be? From accounting and forecasting to contract and compliance management, potential use cases abound. Our research shows that the finance areas poised to realize the greatest value from generative AI include predicting anomalies (47%), explaining variances (41%), generating scenarios (40%), creating reports (39%), and managing accounts—both payables (38%) and receivables (38%).
Generative AI can capture and leverage financial institutional knowledge. For example, finance staff can ask for advice about a policy and be provided with recommendations on accounting treatment and actions. Generative AI can also streamline compliance audits by analyzing financial data to identify discrepancies and anomalies, reducing the amount of time human employees must spend on monitoring and manual testing.
Generative AI can make the process of reviewing contracts and negotiating terms more efficient, as well. It can analyze legal language to identify potential issues, risky clauses, or opportunities for improvement, informing more strategic negotiations. It gives finance teams more time to identify, assess, and mitigate risks that make it harder for the organization to adhere to regulatory compliance and ethical standards—and more time to focus on relationship management.
While use cases differ, one common theme remains the same. With the right governance in place, human employees have more capacity to embrace real innovation, improve financial performance, and drive growth.
For the one in 10 organizations that have already optimized traditional AI in finance, generative AI can accelerate and expand the benefits. AI adoption has already helped reduce sales forecast errors by 57%, reduce uncollectable balances by 43%, and cut monthly close cycle time by 33%. For top performers, AI has helped them respond to changes in strategy/business models—and with generative AI, benefits can be even greater.
And the farther along organizations are in their AI journey, the more value is delivered. Organizations that are still in the implementation phase of AI already report an 18% ROI from their AI investments. Those that have operationalized AI see a 24% ROI. But organizations that have advanced to the optimization phase see ROI that’s twice as high: 51%.
What you need to do
Attack labor-intensive tasks that are ripe for generative AI automation
Incent your CFO to introduce generative AI tools that automate manual and mundane tasks. Focus on use cases that identify and mitigate risks before making investments that might impact core finance functions—but scale quickly once you have a strong technical foundation in place.
- Acknowledge generative AI’s short-term limitations while planning to capitalize on its full potential. While generative AI isn’t proficient at numeric analysis yet, it will be soon. Develop value-adding use cases now to prepare for this future generative AI capability.
- Build governance structures across the finance organization to steer generative AI use cases. Bridge governance gaps and develop ethical guidelines that will support the ethical adoption of generative AI. Extend a culture of personal human responsibility and accountability into the technology.
- Ask your CFO to drive targeted generative AI adoption in day-to-day activities. Get individuals to use generative AI in specific daily tasks. Aligning generative AI adoption to real-world responsibilities will help overcome employee resistance and prove the value of this transformative technology.
3. Decision-making
+ Generative AI
What you need to know
Generative AI doesn’t just cut costs—it creates new business value
It’s abundantly clear that generative AI can drive unprecedented productivity gains. But that’s just the beginning. Adopting generative AI in finance is about more than controlling costs and increasing efficiency. It’s a capability CFOs can tap to enhance decision-making and execute enterprise strategy.
To be the strategic advisors CEOs require, CFOs must focus on business model innovation, not just back-office finance processes. While many CFOs aren’t tech experts, they need to be able to measure the business value created by digital transformation—and recommend investments that will do the most to move the needle.
Generative AI helps finance teams cut to the chase. By creating commentary for financial dashboards, analyst communications, and sales analysis, it can increase finance staff time focused on decision support by 90%. Generative AI can also cut revenue loss by 60% to 70% by automating the validation of customer claims and deductions to reduce unnecessary payouts. These performance improvements can provide a major lift to a company’s bottom line. But generative AI can only be as effective as the people using it. This means businesses must train their finance teams to tap this technology responsibly in their daily work.
Many leaders understand this need and have invested accordingly. For example, nearly two-thirds of organizations have already invested in the machine learning and algorithmic skills needed to train cognitive systems for finance. And at least half have invested in data science and modeling and simulation skills for finance teams. However, fewer than 40% of finance organizations have implemented a center of excellence for AI—and only 18% have trained staff on generative AI.
Enterprise data is another piece of the puzzle. In many organizations, unstructured data remains stuck in silos. But uniform data, as well as an integrated data architecture, is necessary for generative AI to inform strategic decision-making at the CEO-level. We know that 71% of the very top finance organizations have implemented a standardized data architecture, compared to just 39% of their peers. And with this data foundation in place, CFOs can do more than deliver financial data—they can define the underlying business KPIs that drive financial performance.
What you need to do
Employ generative AI assistants to execute strategies more effectively
By quickly analyzing large stores of data, AI assistants can identify trends and provide insights that inform strategic decision-making and help organizations stay ahead of the competition. Using generative AI in mainstream finance functions helps CEOs respond to changing market conditions faster and more effectively.
- Train generative AI models to accurately explain forecast and budget variances to support business decisions. Expand scenario planning and use of generative AI-enabled predictive models that incorporate historical data and external information to accelerate innovation.
- Introduce digital assistants to increase financial accuracy, scalability, and speed. Automate manual processes to reduce the likelihood of human error and streamline the financial analysis that informs business decision-making. For example, in financial planning and analysis, create a generative AI assistant for questions and answers on profit and loss. In procure to pay, utilize generative AI assistants to reduce vendor inquiries on accounts payables or reduce employee inquiries on travel and expenses.
- Embrace at least one finance-specific end-to-end use case. For example, in record to report, use generative AI to craft and narrate impactful internal management and operational performance reports. In order to cash, use generative AI to recover cash by validating customer claims and deductions.
The statistics informing the insights on this page are sourced from proprietary IBM Institute for Business Value data, IBM Consulting case studies, and several external sources. IBM Institute for Business Value data includes a global survey of 2,500 executives regarding generative AI fielded between December 2023 and February 2024, focusing on the responses of 125 CFOs; a performance management and benchmarking survey of 601 global finance leaders on their AI adoption in finance and its impact on metrics fielded in 2023; the 2022 Chief Financial Officer Study; and the global CFO study 2024, which surveyed 2,000 CFOs from November 2023 to February 2024. External sources include Google Cloud and Tangoe.
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Additional content
Meet the authors
Cindy Anderson, Global Executive for Engagement and Eminence, IBM Institute for Business ValueChristian Bieck, Europe Leader & Global Research Leader, Insurance, IBM Institute for Business Value
Spencer Lin, Global CFO Research Lead, IBM Institute for Business Value
Anthony Marshall, Senior Research Director, Thought Leadership, IBM Institute for Business Value
Download report translations
Originally published 02 April 2024
This chapter is available in the latest edition of the IBM Institute for Business Value's book, The CEO's Guide to Generative AI.
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