AI/GenAI presents a real opportunity for CFOs to transform Finance and accelerate value creation.
This isn’t some off-in-the-distance dream. With escalating investment into the billions, (one of every four dollars invested in US startups is going toward AI-related solutions), AI/GenAI is here now. AI innovation is also accelerating rapidly. Offerings are evolving from Large Language Models (LLMs) to horizontal and vertical products, including a proliferation of point solutions to address Finance-specific workflows. As AI’s specialization capabilities increase, so too can its impact.
And yet, despite all this innovation, not all of AI’s promises are prime-time ready. Why? Hype doesn’t always translate into practical use cases. Solution functionality is not consistent across the board. Human involvement is still necessary for critical workstreams and activities subject to regulation. Organizational data environments are not structured to optimize AI’s transformative value. A disproportionate focus on point solutions distracts from enterprise-wide tools capable of driving tangible value. And finally, off-the-shelf solutions need significant customization, integration, and calibration to prove useful.
As a PE-backed CFO, you’re likely stuck at the intersection between AI’s potential and its pitfalls. If you’re feeling sponsor/board pressure to tap into the former (and avoid the latter), you must be wondering where to begin. We can help.
Enter Accordion’s how-to guide, which simplifies the process by highlighting the 5 steps you can take to get started on your AI/GenAI journey.
Step 1: Understand your use cases
The launchpad for any disruption journey must be the identification of the financial workstreams and use cases you are trying to fix, improve or otherwise disrupt by leveraging AI/GenAI. The key is to find cases that:
- Have the upside to warrant re-engineering. (In other words, the areas in your organization that, if improved, could measurably reduce costs or improve top-line performance.)
- Can feasibly be disrupted. (Because the areas are mature enough and the technology is ready/available.)
Given those considerations, we believe there are many use cases across FP&A, financial operations, accounting/close, and treasury that are prime candidates for AI/GenAI-led transformation. If you’re looking for a good place to start, we suggest considering one of the following:
- Automated close
- Cash flow forecasting
- Contract intelligence
- Invoice-to-cash automation
- Sell-side readiness using data cube automation
Step 2: Reveal the value
When leaders are intentional about defining and assessing value, there’s a greater likelihood that value will be captured in practice. As such, once the use case is identified, the next step must be to measure the expected value from transforming that workstream.
This step will not only provide a clearer picture of ROI, but it will also help you pinpoint your AI/GenAI starting point. Let’s say you have identified multiple potential use cases. By assigning each a priority level (high, medium, low) based on anticipated value and feasibility of implementation, you can identify exactly where to begin.
As for how to estimate the value and ease of implementation, consider the following factors:
Estimating value
- What is the revenue upside?
- Is there revenue downside, perhaps in the form of potential cannibalization of an existing product or service?
- Will the implementation reduce costs, contribute to margin, or provide some other value to the customer?
- Will the implementation enhance innovation or customer lifetime value?
Calculating ease of implementation
- What are the proof points of a successful implementation?
- Is the data environment strong enough for AI/GenAI models to effective?
- What is the level of effort required to implement the use case?
The idea of standing at the starting line of an AI marathon can be daunting. By identifying discrete use cases to prioritize you can start, instead, with productive and profitable AI/GenAI sprints.
Step 3: Start straightaway – but simultaneously improve your data story
Data is the foundational element ensuring disruption can be successful. Now, we’re not saying that your data house needs to be perfect in order to invest in AI/GenAI. In fact, quite the opposite. We know that imperfect data can become an unfortunate psychological and organizational barrier to starting the disruption journey. It shouldn’t be. CFOs should, instead, take a dual approach to dealing with data by:
- First, identifying those areas where the data, infrastructure and processes are good enough to start injecting AI/GenAI: This toe-in, discrete approach allows the organization to start creating AI-related value and muscle memory for larger efforts.
- Simultaneously, launching a structured initiative to improve your data infrastructure and management: Work with your CIOs 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/dashboard development. Doing this will provide you with the integrated data and business intelligence infrastructure you need to improve decision making and multiply the impact of AI/GenAI.
Step 4: Select your solution (and pick your partners) wisely
You’re getting a lot of pressure from your sponsor and board to invest in AI/GenAI, but the truth is it’s not the only/best technology solution to disrupt all workflows.
Now, GenAI is impressive. That said, there’s no magic tech bullet to solve all of your pain points or capture all potential value – which is why business problems are most commonly (and appropriately) solved by leveraging multiple technologies including robotic process automation (RPA), digital/software solutions, data platforms, data analytics, machine learning, and of course AI/GenAI.
You’ll need to rely on trustworthy partners to help find the most appropriate combination of solutions to address the workstream you’ve prioritized. There are probably dozens of tech vendors or consultants knocking on your door and flooding your inbox with AI/GenAI promises. In selecting the best partner to help you navigate your journey you should look for experts who:
- Focus on PE-backed CFOs: You need a partner who understands where AI/GenAI does and does not add value specific to the unique and nuanced requirements of PE-backed CFOs. You also need experts who “get” your sponsor’s expectations and goals.
- Pass the practicality test: This means a partner who can make AI/GenAI “real” by assembling a cross-functional team of financial and technical experts who will curate and implement solutions that drive measurable value…now.
- Can fix the data: You know that it’s essential to have solid data in order maximize the value of AI/tech. So, you need a partner who can actually get your data environment ready for tech-enablement.
- Have repeatable processes to solve your problems: You want a partner who serves as an expert general contractor of sorts, bringing a ready-to-go ecosystem to optimize the benefits of AI/GenAI. These partners have proven processes – they have done this again and again in a PE-backed environment.
- Bring a holistic approach: By which we mean a partner who looks beyond the tech to help fix the people and processes that underlie workstreams and contribute to their efficacy.
Step 5: Consider your implementation comprehensively
Let’s double-click on that last point. Technology alone is not, and will never be, the answer to effective workflow disruption. You can only achieve real productivity gains if you rethink how you do business across multiple dimensions:
- Tech: 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.)
- Process: In order to harness the full power of AI/GenAI, you will need to redesign any broken processes so that your technology can do its work, and your talent can focus on the value-add.
- Talent: To that point, AI/Gen AI will never fully replace people in the office of the CFO. But AI/GenAI will begin to gradually penetrate select Finance workflows such 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, the majority of 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.
Following the steps above will set you up for success in your disruption journey. In terms of what you can expect out of that journey, the use cases we outlined, if executed with the right partner and a holistic approach, can provide significant tangible value:
- Solutions to expedite, streamline, and improve visibility into close processes through AI-powered close checklists, automated reconciliations, and flux analyses.
- Advantages include 20-30%+ reduction in days-to-close, redeployment of accounting resources to higher-value initiatives, automated controls, reduced error rates, and improved audit and compliance outcomes.
- 13-week cash flow models leveraging machine learning tools to analyze historical baseline, recommend forecasting methodologies, populate standard outputs, and analyze variances and cash management opportunities.
- The benefits of workflow disruption include more accurate cash flow predictions, improved cash flow visibility and forecasting, and modular deployment of AI-powered forecasting, where appropriate.
- The introduction of automation and GenAI to expedite full invoice-to-cash processes, including credit checks, invoice matching, deductions, and collections correspondence with customers.
- Your team can realize a 5-15% reduction in DSO, and a 10% reduction in bad debt.
- GenAI is leveraged to automate extraction and analysis of supplier contractual terms. This enables the comparison of pricing, invoicing, and payment terms to actual operations, and the identification of cost and working capital improvements.
- Disruption benefits here can lead to a 25-30% reduction in manual contract review activities and better identification of supplier contract non-compliance risks and mitigants.
- These are solutions that extract, load, and transform data from disparate sources and rapidly synthesize them into a sell-side data cube, with industry specific KPIs and benchmarks.
- Your team can use these AI-generated analytics to inform actionable insights on business performance drivers. Tech-enabled disruption accelerates cube creation (from 5+ weeks down to 1-2).
The tech is here. The sponsor mandate is clear. The benefits are real and quantifiable. The time to start your AI/GenAI journey is now. By identifying mature workstreams and matching them to market-ready solutions, prioritizing based on ROI and practicality, enhancing your data environment to better capture value, redesigning your processes (where appropriate), and evolving your talent, you’ll not only start on that journey, but you’ll also quickly reap its rewards.