If you haven't already received pressure from your board, sponsor, or CEO to invest in AI or GenAI, you will soon. Additionally, you're probably drowning in sales pitches from vendors promising AI-driven transformation. How do you cut through the noise to understand the best real-world applications that create meaningful value?
We've outlined five facts that CFOs need to know about the opportunity to use AI and GenAI to disrupt outdated financial workstreams.
1. This isn't just about AI. Finding the right technology to solve a problem or seize an opportunity is key.
AI and GenAI have transformative potential. Even in its current form, it can significantly enhance and accelerate certain business workflows. Despite all the hype around AI's promise, it's not the ultimate solution to everything that ails (or slows down) your finances.
Different financial workstreams require different technology solutions, from robotic process automation to data platforms, analytics, machine learning, and, of course, GenAI. The key to choosing the best technology to address your finance team's needs is to lead with your primary concern (i.e., the workstream you're trying to fix, improve, or disrupt). You can then identify the right technology, or more generally the right combination of technologies, that best addresses that concern.
Conclusion: Pinpoint the problem or opportunity and choose the right technology.
2. Make no mistake, AI is coming for you.
This is already built into commonly used software solutions and the entire customer experience journey. To simplify a potentially confusing situation, we need to think of AI and GenAI in three broad categories:
LLM. Big technology companies invest billions of dollars in large-scale language models and related infrastructure, releasing new and more powerful models every 6 to 12 months. These models are accessible to coders using APIs and are increasingly integrated into other software solutions. Additionally, GenAI solutions such as ChatGPT and Microsoft Copilot are rapidly being adopted by a wide range of businesses. But beyond applications that improve employee productivity (e.g., writing meeting notes or first drafts of documents), we're also looking at fundamentally redesigning the underlying workflows and integrating GenAI with multiple technologies (e.g., RPA, Few companies leverage solutions at scale without combining them (ML, dashboards). Drive value and efficiency.
core system. Core systems (e.g., SAP, Oracle, Anaplan) and virtually all popular software systems are gradually incorporating GenAI (e.g., Salesforce Einstein). To that end, it's natural to expect to see GenAI implemented into nearly every software system via chatbots, data analytics, and automated reporting. Whether we want it or not, GenAI tools already exist. However, the responsibility to learn and take advantage of these new capabilities lies with individual executives, so it is likely that only a small percentage of professionals will use these capabilities without sufficient guidance from their employers. Masu.
point solution. We are seeing a proliferation of point solutions aimed at improving workflows within finance. These point solutions show great promise (more on that later). There are two challenges that financial leaders must address to take advantage of these innovations. First, you need to identify which workflows you want to prioritize based on the relative size and value of each workflow. Second, you need to evaluate which software solutions are mature enough to disrupt your current operating model and deliver substantial value (taking into account your organization's unique data and infrastructure environment).
Conclusion: YThe core system already has AI and GenAI built into it. You need a perspective on how to leverage existing capabilities and the benefits of investing in additional solutions to address pain points and opportunities.
3. Ready to interrupt only specific financial workflows.
The question for CFOs facing pressure to invest in AI is: Where do I start? The answer is: A good starting point is where three important variables match:
- Well-mature workstreams (in terms of technology, data, people, and processes) are ripe for disruption.
- Available solutions that allow you to interrupt these workstreams. (In addition to AI/GenAI, it also includes RPA, data platforms, data analytics, automation, machine learning, etc.)
- Prioritized workstreams are well worth the disruptive investment and effort (i.e., time and money spent, or opportunity to improve bottom-line performance).
With this in mind, many workflows across FP&A, treasury operations, accounting/closing, and finance can be subject to disruption. You can easily start with one of the following:
- Invoice-to-cash automation
- Sell-side preparation using data cube automation
Conclusion: CFOs should start by prioritizing workstreams where technology and processes are mature and deliver meaningful value.
4. Correcting your organization's data is fundamental, but don't let it become a barrier.
While some AI solutions may be able to bring to market, not all companies are optimized for AI, at least when it comes to maximizing their value. why? data. The more powerful your data, the more value AI can create.
A “powerful data environment” looks like a flexible data warehouse that includes structured and unstructured data sources. It also includes the systems needed to quickly, repeatedly, and reliably clean up internal and external data sources that serve as critical inputs to GenAI models. And finally, leverage cloud technologies and services to support larger amounts of data.
This is not to say that your data environment needs to be perfect to invest in AI. In fact, the opposite is true. Incomplete data can unfortunately create psychological and organizational barriers to launching disruptive initiatives. That can't be true. Instead, CFOs should take a two-pronged approach to addressing data issues.
First, identify areas where your data, infrastructure, and processes are sufficient to deploy AI or GenAI. This comprehensive and personalized approach enables organizations to begin creating AI-related value and muscle memory for broader initiatives.
Second, simultaneously launch a structured effort 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, and KPI improvements and reporting or dashboard development. I will. This provides organizations with the unified data and business intelligence infrastructure they need to improve decision-making and increase the impact of AI.
Conclusion: By simultaneously investing in fixing your data infrastructure, you can start your AI journey with incomplete data.
5. Address technology, people, and processes.
Real productivity gains will only occur if companies rethink the way they do business across many dimensions.
Technology/Data. The technology stack should combine core financial systems of record with embedded AI and point solutions for finance + AI. (The best solutions are layered on top of systems of record.) As mentioned above, data is a key enabler of innovation, so it needs to be organized.
process. To fully leverage the power of AI/GenAI, redesign broken processes to enable the technology to work and free up talent to focus on adding value.
talent. AI and Gen AI will never fully replace people in the CFO office. Regardless of the degree of innovation or automation, “humans involved in information transmission'' are always required. However, as AI begins to trickle into some financial workflows, talent needs will evolve. Specializing in employment, AI/GenAI will create jobs in the finance sector (such as data engineering) that did not exist before. Additionally, most finance executives should have a working knowledge of AI/GenAI. When it comes to hiring, as more tasks become automated, CFOs will focus on hiring experts who can generate meaningful insights.
Conclusion. Getting the most out of AI requires a holistic approach by investing in the right technology, redesigning broken processes, and hiring value-adding talent.
For CFOs new to the world of AI or skeptical of emerging technologies, the points above should convince them that there are genuine, discrete, and practical use cases for AI and GenAI to improve core finance functions. I would appreciate it if you could.
For CFOs: Think about the issues at hand. Look for new opportunities. Then ask whether AI (or other applicable technologies) can help solve or capture them today.
