Last month’s AI for Finance Forum in New York City (co-sponsored by AWS and CFO Leadership) was winding down when the closing keynote delivered an uncomfortable metaphor: Your finance function is like a duck. Above water—calm, composed, delivering on time. Below the surface—thrashing frantically through manual reconciliations, spreadsheet gymnastics and low-value work that consumes the majority of your team’s time.
After a full day of AI partners and use cases, the room full of finance leaders knew exactly what came next—AI offers a chance to stop paddling and start swimming. But the metaphor stuck because it reframed everything we’d heard that day around the only question that actually matters: How do we move from AI pilots to real business value?
Six Lessons That Will Shape Finance’s Future
1. Technical Literacy Is No Longer Optional
You don’t need to become a data scientist, but you do need to understand that AI isn’t magic, it’s probability. AI models make predictions based on patterns in data. They can be wrong. They reflect the quality of the data and context you provide.
This understanding changes how you evaluate AI solutions and set expectations with your teams. It helps you ask better questions of vendors and partners. Most importantly, it helps you lead your organization through change with credibility. For finance leaders, literacy isn’t just knowing that models are probabilistic. It’s stepping into co-ownership of the AI stack—partnering with your CIO and CDO on which use cases to prioritize, which data is “authoritative” and what level of risk is acceptable for which decisions.
2. AI Will Only Get Better from Here
AI today is the worst it will ever be. The pace of advancement means models will become more capable, more accurate and more accessible over the next 12 to 24 months.
This has two implications. First, don’t wait for perfection. Start learning now, even if the tools aren’t ideal. Second, build flexible systems and processes. The way you implement AI today may need to evolve as capabilities improve. Don’t build a single “AI project.” Build a living AI roadmap. Assume your models, tooling and even your agents’ responsibilities will change every six–12 months. The finance teams that win will be the ones that can adapt their workflows as quickly as the tech evolves.
3. Start with Pain Points, Not Technology
Too many organizations are asking, “Where can we use AI?” The better question is, “What problems are costing us the most time, money or risk?” One forum attendee described how her team spent hours each month reconciling intercompany transactions across many subsidiaries—work that was error-prone and mind-numbing.
Where are your teams spending hours on repetitive work? Where do errors create downstream problems? Where are you unable to scale because processes don’t allow it? Map those pain points first. Then evaluate whether AI can address them.
4. Measure Value, Not Activity
Your AI strategy needs to answer one question: What business outcome will improve?
Define success before you start. Will this reduce cycle time? Free up hours for higher-value work? Reduce errors that create financial risk? Improve the quality of insights you deliver to business partners?
The most successful AI implementations have clear metrics tied to business value. The teams that struggle can’t articulate what success looks like beyond “we’re using AI.”
5. Create Space for Experimentation
Innovation requires capacity. Your team can’t experiment with new approaches if they’re buried in spreadsheets and manual processes.
One example from the forum: A finance leader asked team members to put Excel away for certain analyses and use a new AI-powered tool instead. This forced experimentation and created space for the team to learn, make mistakes and discover better ways to work.
AI doesn’t just make existing work faster, it unlocks contributions that weren’t possible before. Team members who had ideas but lacked technical expertise can now bring those ideas to life. The analyst who never learned Python can now build custom tools. The FP&A manager with a hypothesis can now test it without waiting for data science support.
If your team is at 110 percent capacity, AI will just become another thing they don’t have time for. You need to create room, whether that means carving out dedicated learning time, pausing non-critical initiatives or reducing meeting load to free up capacity.
6. Business Context Is Your Competitive Advantage
Every company has access to the same AI models. What differentiates outcomes is the business context you provide.
Document processes, capture institutional knowledge and make tacit expertise explicit. When an AI agent understands your chart of accounts structure, your revenue recognition policies and your business unit nuances, it delivers better results than a generic implementation.
Business context turns AI from a commodity into an asset.
Moving Forward
The finance leaders who will thrive in the next five years won’t be the ones with the most AI projects. They’ll be the ones who solve real problems, deliver measurable value and build technical literacy across their teams using AI to do this more efficiently.
Start with one high-impact pain point. Build a small team with protected capacity. Define clear success metrics. Document your business context. Experiment, measure, learn and iterate.
The question isn’t whether AI will transform Finance—it’s whether your team will still be drowning in spreadsheets while your competitors are already swimming laps.
