AI is no longer a futuristic concept — it’s here, reshaping financial operations, forecasting, and compliance. Is your team AI-ready?
Manual workflows, from data entry to financial reporting, have long dominated finance making it heavily dependent on extensive human oversight and predefined rules. AI’s potential to augment the field is opening up new and exciting possibilities that enhance accuracy, speed, and decision-making.
Why should you attend?
Finance teams are under immense pressure to move faster, reduce inefficiencies, and make smarter decisions — yet traditional methods have held them back. AI is the game-changer that transforms financial operations from reactive to proactive. Here’s your chance to catch up to the recent advancements and leverage them to your advantage.
- The state of AI in finance – Drivers of adoption and why top-tier finance teams are investing millions in it right now
- AI use cases in finance and accounting – From automation to forecasting to compliance.
- Avoiding the pitfalls of AI – How to navigate compliance, accuracy, security, and many other nightmares.
- Adopting AI – Preparing your team, implementation plan, tools, and best practices.
Who should attend?
Whether you're a CFO, controller, FP&A leader, or finance professional, this session will give you the knowledge and strategies to leverage AI effectively.
Speakers
Bill Dillmeier is a seasoned CFO, founder, and investor with 20+ years of experience in finance. With deep expertise in finance automation, forecasting, and AI-driven analytics, Bill has helped high-growth companies navigate revenue recognition, cash flow forecasting, and pricing transitions.
Webinar summary
1. How did you first begin using AI in finance?
At one of the Series A companies I worked with, I began by integrating Salesforce API data with AI tools to improve pipeline forecasting. I created a repeatable, prompt-driven system that assessed the probability of deals closing within 30, 60, and 90 days. Previously, this process took me an entire weekend of manual Excel work, and it was highly subjective. By using AI, I reduced human bias and increased the accuracy of forecasting. It gave me more precise and real-time insights into the sales pipeline, freeing up my weekends and enhancing overall operational efficiency.
2. Do you believe AI adoption is a "make-or-break" moment for finance leaders?
Yes, it absolutely is. Gone are the days when revenue growth was prioritized at any cost. In 2021 and 2022, capital was cheap, and businesses were willing to spend without strict oversight. Today, investors want sustainable growth and lean operations. AI is the key to building a lean, efficient finance function. Finance leaders who don’t adopt AI will be at a significant disadvantage—they’ll struggle with speed, accuracy, and cost-efficiency in comparison to AI finance tools enablers.
3. Can you share a real-world example of using AI in revenue recognition?
In one company, we sold annual contracts that included a set number of units, with revenue recognized based on actual usage billing. Previously, I spent hours manually curating usage data to ensure proper revenue recognition. With AI, I was able to automate the process using data from Metabase and regression models to forecast customer consumption rates. This allowed me to recognize revenue more accurately and proactively identify opportunities for upsell or renewals based on usage patterns. It saved time and improved the accuracy of financial reporting.
4. How did AI help in automating billing processes?
In a previous role, managing billing was a challenge due to complex pricing models, such as tiered usage-based contracts. AI was invaluable in automating our billing system. I built a system that automatically tracked consumption, matched it to the terms of the contract, and triggered invoices when customers reached specific thresholds. This eliminated manual errors, reduced the time spent on invoicing, and ensured customers were billed accurately and on time. It also improved cash flow by reducing delays in billing.
5. How do you use AI for churn prediction?
I started by integrating usage data with sentiment analysis from our customer success team. If a customer’s usage dropped, but the CSM flagged a sentiment like "unresponsive" or "delayed onboarding," the system would trigger an alert. This proactive churn detection allowed me to work closely with customer success teams to intervene early and prevent customer losses. AI made it possible to combine hard usage data with softer, qualitative feedback, providing a more accurate and early warning system for churn.
6. For finance leaders with no engineering background, how do you start integrating AI?
Start small with a metric that matters to your business, like accounts payable or days sales outstanding (DSO). Map out the manual steps and identify where AI can streamline the process—whether it’s automating invoice follow-ups, improving cash flow forecasting, or tracking spend patterns. Tools like Zenskar are great for automating invoicing or payment tracking without needing to build a custom solution. The key is to start with simple automations, build familiarity with the technology, and gradually scale up as you gain confidence.
7. How do you get your engineering or data science teams to prioritize AI for finance?
The most successful AI projects I’ve seen in finance happened when the CEO and CTO worked together to prioritize AI initiatives for the finance team. At one of my clients, the CEO framed AI adoption as a strategic priority. This motivated the data science team, who were eager to use their skills to solve real business problems. When data science teams feel like they’re making a direct impact on business outcomes, they’re more likely to invest time and resources into AI projects for finance.
8. What if data science or engineering says no due to bandwidth?
If engineering or data science teams are unavailable, then it’s time to buy an off-the-shelf AI tool. Products like Zenskar offers AI-driven automation for finance workflows, eliminating the need for custom-built models. If your organization insists on an internal solution, then consider leveraging open-source tools, which require minimal engineering effort to implement. The bottom line is that if a problem can be solved effectively by a ready-made product, it’s more efficient to buy rather than build.
9. How have you used AI for cost optimization, particularly in areas like AWS spend or marketing channels?
I used AI to perform real-time channel analysis in marketing. By analyzing the cost per click, lead conversion, and pipeline value for each channel, I was able to identify which marketing channels were most effective at driving sales-qualified leads at a lower cost. For AWS costs, AI helped me pinpoint which workloads were driving up costs, allowing us to optimize or eliminate unnecessary instances. AI analytics helped us make smarter decisions about where to cut costs without harming business performance.
10. How can finance leaders ensure data privacy when using AI tools?
The first step in securing data privacy is to categorize your data—ensure that personally identifiable information (PII) or sensitive financial data is not exposed to public models. In my experience, using private models hosted securely in Snowflake or similar platforms helped maintain control over sensitive data. Additionally, anonymizing sensitive fields in Excel or using private models to process data ensures that your data remains secure while still benefiting from AI insights.
11. Do smaller companies need to worry about building their own AI models?
Not necessarily. I’ve worked with companies with $5M to $8M in annual recurring revenue that have built their own lightweight private AI models. However, it’s important to assess your company’s growth stage. In the early stages, third-party tools are sufficient, but as you scale and your data volume increases, it may be necessary to build in-house models to protect data and ensure compliance. If you’re a small business, it’s okay to rely on existing AI tools until you reach the point where custom models provide more value.
12. Can you share a forecasting mistake and how AI helped you avoid it?
I once overestimated Q2 revenue because I failed to account for the impact of the Easter holiday shift in Europe. AI helped prevent this mistake by incorporating seasonal dips into future forecasts. By training the model to recognize these calendar events, our forecasting became more precise. AI didn’t just use revenue trends—it factored in external events like holidays, ensuring more accurate forecasts and preventing budget shortfalls.
13. How can AI support strategic decision-making and long-term planning in finance?
AI can help finance leaders create high/medium/low forecast models and update them dynamically based on real-time inputs. For instance, if there’s a slowdown in bookings, AI can adjust the forecast and recommend which costs should be deferred or what resources should be reallocated. It enables finance teams to make informed decisions quickly, without waiting for the next budgeting cycle. AI also helps simulate the impact of strategic decisions, allowing finance leaders to act with confidence based on data.
14. How do you use AI in capacity planning and resource allocation?
AI can automate and optimize capacity planning by analyzing historical data and predicting future needs. For example, if you know you need to hire customer success managers (CSMs), AI can assess the current customer base, predict growth, and calculate the optimal number of CSMs needed. It helps create a more accurate hiring roadmap and ensures that the business has the right resources in place to meet customer demands. This allows you to plan proactively and avoid hiring surpluses or shortages.
15. What are the most important steps finance leaders should take when adopting AI tools?
Start by identifying the most critical metrics and processes within your finance function. Choose one area—whether it’s billing, forecasting, or accounts payable—and look for ways AI can streamline that process. Invest in training and familiarize your team with your AI manifesto. Once you’ve seen the results in one area, you can scale AI across other functions. The key is to start small, measure success, and then expand gradually. Don’t hesitate to use off-the-shelf tools if you lack the resources to build custom solutions in-house.
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