Step 1: Understand your use cases
You don’t have to implement all the AI, all at once. In fact, 99% of sponsors believe that AI adoption should start with practical, discrete finance applications related to finance workstreams that can show value now, then expand into more strategic predictive analytics as solutions mature.
That means you should start by targeting specific use cases that could be transformed or enhanced with AI. Think about use 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, there are many use cases across FP&A, financial operations, accounting/close, and treasury that are prime candidates for AI/GenAI-led transformation:
- Invoice-to-cash automation
- Sell-side readiness using data cube automation
Step 2: Reveal the value
Once you’ve identified your use cases, the next step is to define and measure their potential value. It’s no surprise that sponsors cited value creation as their top reason for AI implementation; 99% of sponsors say that adoption will be essential for capturing EBITDA accretion, accelerating processes, and enabling value capture. The question is: Which use case do you prioritize first?
If multiple use cases emerge as important (a likely scenario), prioritize them by assigning each one a level (high, medium, low) based on anticipated value and feasibility of implementation.
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 be effective?
- What is the level of effort required to implement the use case?
Step 3: Start straightaway – but simultaneously improve your data story
Data is the foundational element ensuring disruption can be successful – but too many CFOs say that they haven’t deployed AI because their data environment isn’t optimized enough to see returns. The good news? It doesn’t have to be.
Sponsors acknowledge the concern that many finance teams’ data environments aren’t fully optimized for AI – and still encourage a dual approach where the data is “good enough” while launching a structured program to strengthen data infrastructure. This means:
- 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. And the truth is: 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.
But the biggest barrier to adoption isn’t technology. More than two-thirds of CFOs say they’ve been slow to adopt AI simply because they do not know where to begin or who to turn to for help.
As such, 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 to 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; sponsors want best-in-class solutions layered on systems of record to improve foundational workflows, and as capabilities mature, they expect AI to unlock more strategic predictive analytics, too.
- 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 AI 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:
Close automation:
- 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.
Cash flow forecasting:
- 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.
Invoice-to-cash processes:
- 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.
Contract intelligence:
- 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.
Sell-side readiness leveraging data cube automation:
- 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.