Digital transformation has been the topic-du-jour for the better part of the past two decades. Even still, the latest survey findings from McKinsey show that finance teams in particular are only in the early stages of deploying digital technology—specifically, artificial intelligence (AI) and machine learning (ML) principals—within their own operations.
While 98 percent of the CFOs polled by McKinsey have brought some form of automation or digitization into their workflow, the vast majority—79 percent— have only automated or digitized 25 percent or less of their current processes.
Why are CFOs and finance teams slow to deploy AI or ML across their operations, despite almost universal buy-in that there’s massive potential value in digitization and automation?
Put simply: For most finance teams, there’s too much work, and still not enough resources.
This may seem counterintuitive—after all, much of the hype and forecasted value around AI and ML is about helping teams do more with less and increase day-to-day efficiencies.
But the reasons cited by CFOs in McKinsey’s survey are predominantly related to “already-demanding workloads” rather than a lack of tech infrastructure, data, or even aversion to new systems.
“Despite all the talk around GenAI, the data doesn’t show that finance is adopting it in a big way,” Ankur Agrawal, a partner in McKinsey’s corporate finance practice, said in an interview with CFO Dive. “I expect that pace to accelerate in the next couple of years.”
AI expectations outpacing adoption—for now
In fact, expectations around the promise of AI and ML have never been higher, with more than 80 percent of CFOs confident that AI will help reduce manual analysis long-term and improve employee productivity in finance and beyond, McKinsey reports.
Among CFOs in particular, 49 percent look forward to using generative AI for “strategy and leadership support,” including helping generate competitive intelligence and monitoring markets for insights. To that end, one-third of CFOs even expect to eventually leverage AI and ML for general accounting and controlling use cases.
But today, only about 20 percent of CFOs are actually using generative AI tools. To that end, half of those respondents who actually leverage these solutions are still in the “pilot or experimentation” stages of their deployment.
In many ways, these findings are all the more frustrating when you look at the outcomes that the smaller cohort of CFOs who use generative AI and other digitized solutions are enjoying:
- 71 percent report improved productivity of finance employees
- 54 percent cite better use of data in business decisions
- 48 percent are generating insights that allow employees to focus on higher-order tasks.
With finance teams that have already embraced automation and digitization broadly seeing the outcomes they desire play out in real-time, how can the CFOs who are still just onboarding new AI solutions accelerate the value-delivery of new tools and processes?
Breaking down organizational barriers to AI adoption in finance
As the McKinsey survey outlined, of the teams that are failing to capture tech-related value from their digital transformation efforts in 2024, the largest barriers are organizational, including:
- 70 percent cite “already-demanding workload of finance teams”
- 67 percent cite “lack of relevant capabilities within finance teams,” and
- 62 percent acknowledge “insufficient resources to invest in digital finance tools.”
The numbers may be large, but they aren’t altogether that surprising, as these challenges have been prevalent (and growing) among finance teams for years.
Since at least 2023, accountant and finance talent shortages have been a major issue for teams across industries. This trend has persisted, even as additional research confirms that generative AI is being deployed across “at least one business function” among 65 percent of the more than 1,300 companies surveyed worldwide in McKinsey research from earlier this year.
This is where it becomes critical for CFOs to start distinguishing from much-hyped generative AI tools, and more tactical, Analytical AI solutions that zero in on the challenges that are most hindering broader digital transformation efforts in finance.
Effectively deploying Analytical AI where it drives the most value
“Generative AI is no longer a novelty […] The leading companies are the ones that are focusing on reimagining entire workflows with gen AI and analytical AI rather than simply seeking to embed these tools into their current ways of working,” McKinsey Senior Partner Alex Singla said in a statement back in June.
Analytical AI tools are ones that leverage business intelligence to automate insights and inform processes. So while you may leverage ChatGPT or generative AI tools to speed up production, analytical AI solutions are ones that help teams actually work smarter.
These include tools that simplify data collection, data cleansing, and bring together data sets that may have been previously siloed to help human practitioners better understand their work at hand.
For CFOs in particular, it’s Analytical AI that holds the greatest promise, allowing them to keep pace with the rapid rate of change across their workforce and the larger economy.
Take, for example, applying for R&D tax credits. While this can unlock critical funding that can help teams extend their operational runway and drive even greater innovation, navigating tax code and compiling a claim that passes the increasingly-high scrutiny of regulators can itself be a full time job for finance professionals.
Leaving that money on the table is simply not an option—especially for younger, R&D-focused businesses that rely on innovation capital for product development, as well as more mature companies that need to stretch their investments as far as possible to achieve growth.
But when teams partner with experts in the field that leverage analytical AI to optimize their processes and practices—while maximizing claim totals and even handling audit defense—CFOs and finance leaders can focus on other priorities, including driving more AI ROI.
Boast has helped CFOs across North America streamline the R&D tax credit claim process while maximizing their access to innovation capital to extend their runway. Talk to one of our experts today to see how we can help you.
AI adoption among CFOs FAQ
- What is the current state of digital transformation and AI adoption among CFOs? According to McKinsey’s survey, while 98% of CFOs have introduced some form of automation or digitization, 79% have only automated or digitized 25% or less of their current processes. Only about 20% of CFOs are actually using generative AI tools, with half of those still in the pilot or experimentation stages.
- Why are finance teams slow to adopt AI and machine learning? The main reasons for slow AI adoption in finance teams are:
- Already-demanding workloads (cited by 70% of CFOs)
- Lack of relevant capabilities within finance teams (67%)
- Insufficient resources to invest in digital finance tools (62%) These organizational barriers are hindering broader digital transformation efforts.
- What benefits are CFOs who have adopted AI and digitization seeing? CFOs who have embraced AI and digitization report:
- 71% see improved productivity of finance employees
- 54% cite better use of data in business decisions
- 48% are generating insights that allow employees to focus on higher-order tasks
- What are CFOs’ expectations for AI in finance? Over 80% of CFOs are confident that AI will help reduce manual analysis and improve employee productivity long-term. 49% expect to use generative AI for strategy and leadership support, while one-third anticipate using AI and ML for general accounting and controlling use cases.
- How can CFOs effectively deploy AI to drive value in finance? CFOs should focus on:
- Distinguishing between generative AI tools and more tactical, Analytical AI solutions
- Implementing Analytical AI tools that leverage business intelligence to automate insights and inform processes
- Partnering with experts who use analytical AI to optimize processes, such as for R&D tax credit claims
- Focusing on reimagining entire workflows with AI rather than simply embedding tools into current ways of working
