If you haven’t already received pressure from your board, sponsor or CEO to invest in AI or GenAI, you will soon. Moreover, you’re probably drowning in pitches from vendors promising you pie-in-the-sky AI-driven transformation. How can you cut through the noise to understand the best real-world applications that will drive meaningful value?
We’ve outlined five facts CFOs need to know about opportunities for using AI and GenAI to disrupt outdated finance workstreams.
1. It’s not only about AI; it’s about finding the right tech to fix your pain point or seize an opportunity.
AI and GenAI hold transformative potential. Even in their current form, they can impressively enhance and accelerate specific business workflows. Despite the hype surrounding its promise, AI does not represent the be-all and end-all solution to all that ails (or slows) finance.
Different finance workstreams require different technology solutions ranging from robotic process automation to data platforms to analytics to machine learning, and, of course, GenAI. The key to selecting the best technology to address the finance team’s needs is to lead with the chief concern (i.e., the workstream you are trying to fix, improve or otherwise disrupt). Then you can identify the right technology—or, more commonly, the right combination of technologies—best suited to address that concern.
Bottom line: Pinpoint the problem or opportunity, then pick the proper tech.
2. Make no mistake: AI is coming for you.
It’s already embedded in commonly used software solutions and throughout customer experience journeys. To help simplify what can be a confusing landscape, you should think about AI and GenAI in three broad categories:
LLMs. Large technology companies invest billions in large language models and related infrastructure, releasing new, more powerful models every 6 to 12 months. These models are accessible to coders with APIs and are increasingly integrated into other software solutions. In addition, GenAI solutions such as ChatGPT and Microsoft Copilot are being rapidly adopted by the broader business population. However, beyond applications to improve employee productivity (e.g., generating meeting notes or first drafts of documents), very few businesses have leveraged solutions at scale without fundamentally redesigning the underlying workflows or combining GenAI with multiple technologies (e.g., RPA, ML, dashboards) to drive value and efficiency gains.
Core systems. Core systems (SAP, Oracle, Anaplan, for example) and virtually all general software systems are progressively embedding GenAI (e.g., Salesforce Einstein). To that end, it’s fair to expect that we will have GenAI in almost all software systems via chatbots, data analysis and automated reporting. Whether you want them or not, you already have GenAI tools. However, since the onus will be on individual executives to learn and leverage these new capabilities, only a tiny percentage of professionals will likely use them absent significant guidance from employers.
Point solutions. We are observing a proliferation of point solutions aimed at improving workflows within finance. These point solutions show significant promise (more on that later). The challenge finance leaders must meet to take advantage of these innovations is two-fold. First, they will need to identify the workflows they want to prioritize based on each workflow’s relative size and value. Second, they must assess which software solutions are mature enough to disrupt the current operating model and deliver substantial value (considering the organization’s specific data and infrastructure environment).
Bottom line: You already have AI and GenAI in your core systems. You need a perspective on how to leverage existing capabilities and a point of view on the merits of investing in additional solutions to address pain points or opportunities.
3. Only specific finance workflows are ready to be disrupted.
The question for CFOs pressured to invest in AI is: Where do I start? Here’s the answer: The right starting place is where three critical variables meet:
- A workstream with enough maturity (in terms of tech and data, people, and processes) that it’s ripe for disruption.
- Available solutions that are capable of disrupting those workstreams. (AI/GenAI but also RPA, data platforms, data analytics, automation, machine learning or others.)
- The workstream prioritized has enough value at stake that it’s worth the disruption investment and effort (i.e., costs in hours or money spent or the opportunity to improve top-line performance).
With that in mind, many workflows across FP&A, financial operations, accounting/close and treasury are good candidates for disruption. An easy place to start would be with one of the following:
- Automated close
- Cash-flow forecasting
- Contract intelligence
- Invoice-to-cash automation
- Sell-side readiness using data cube automation
Bottom line: CFOs should start by prioritizing those workstreams where the tech and processes are mature, and meaningful value can be gained.
4. Fixing the organization’s data is fundamental but don’t let it be a barrier.
While some AI solutions may be market-ready, not all companies are optimized for AI—at least in terms of maximizing its value. Why? Data. The stronger the data, the more value AI can create.
A “strong data environment” looks like a flexible data warehouse encompassing structured and unstructured data sources. It also includes the requisite systems to quickly, repeatedly and reliably clean internal and external data sources that serve as critical inputs for GenAI models. And finally, it leverages cloud technology or services to support larger volumes of data.
We’re not saying your data environment needs to be perfect to invest in AI—quite the opposite. Imperfect data can become an unfortunate psychological and organizational barrier to starting the disruption journey. It shouldn’t be. Instead, CFOs should take a dual approach to dealing with data issues:
First, identify those areas where the data, infrastructure and processes are good enough to inject AI or GenAI. This toe-in, discrete approach allows the organization to start creating AI-related value and muscle memory for more extensive efforts.
Second, simultaneously launch a structured initiative to improve data infrastructure and management. Work with the CIO to establish a single source of truth across the organization through master data management, data strategy (augmenting internal data with external sources), data infrastructure, KPI refinement and reports or dashboard development. Doing this will provide the organization with the integrated data and business intelligence infrastructure needed to improve decision-making and multiply the impact of AI.
Bottom line: You can start the AI journey with imperfect data if you concurrently invest in fixing your data infrastructure.
5. Address your technology, people and processes.
Real productivity gains only happen when companies rethink how they do business across multiple dimensions:
Tech/Data. Technology stacks will need to combine core finance systems of record with embedded AI and point solutions for finance + AI. (Best-of-breed solutions will layer on top of systems of record.) As noted above, data must be in order, as it will be the dominant element enabling innovation.
Process. To harness the full power of AI/GenAI, redesign any broken processes so your technology can do its work and your talent can focus on the value-add.
Talent. AI and Gen AI will never fully replace people in the office of the CFO. No matter the extent of innovation or automation, you will always need “a human in the loop.” But AI will begin to gradually penetrate select finance workflows so that your talent needs will evolve. Specific to hiring, AI/GenAI will create jobs in the finance function that did not exist prior (e.g., data engineering). Moreover, most finance executives will need to have some working knowledge of AI/GenAI. In terms of recruitment, as more tasks become automated, CFOs will focus on hiring those professionals capable of generating meaningful insights.
Bottom line. To take full advantage of AI, engage holistically by investing in the right tech, redesigning broken processes and hiring talent that adds value.
For those CFOs who are new to AI’s landscape or skeptical of emerging tech, we hope the points above convince you that there are genuine, discrete, practical use cases of AI and GenAI that are improving core finance functions now.
To all CFOs: Think about your pressing pain points. Look for any net new opportunities. Then, ask whether AI (or another applicable technology) can help you solve or seize them today.
