Almost all sponsors and board members are doing the same duties. CFOs need to prioritize AI:Now. Still, financial functions are slower adoption.
In the face of it, it may seem like an inexplicable cutting, but it is not. Deploying AI is especially difficult for CFOs who are not necessarily engineers.
And it could be even more difficult for CFOs who know a little more about technology. Rather, for CFOs who understand that effective AI requires a powerful data environment. It's not that you don't know where to start with AI. They don't think their data environment is ready.
We are here to tell you: you become a perfect enemy to a perfect enemy.
If you want to meet sponsor and market expectations, you don't have to wait for the perfect data environment to kickstart your AI. Instead, you can start with more individual steps. This is the way.
Pinpoint prioritys.
There are no easy AI buttons. You can't snap your fingers and make all the inefficient processes into AI. However, what CFOs can do is identify the areas where they will most benefit from automation and begin preparing them at digestible stages. This means focusing on specific, shocking use cases where AI can drive real value. Look for some selection areas. a) where the manual process controls b) if the business unit is already in data recognition or technical order, or c) if a quick victory shows AI reliability and can build internal momentum.
For example, the finance department manually processes hundreds of vendor invoices per week. It includes data entry, validation, approval routing, and other tasks. It takes time, is error-prone and delays the end of the month. Using automated processing to deploy visual AI features can be a huge win with business impact. Now it's not a one-size solution that addresses all manual bottlenecks, but it doesn't have to be.
Clean your data (fully).
Yes, AI is built on solid data foundations, but at present there is no need to solidify the entire company's data universe. We are looking at data that enhances identified high impact use cases (such as invoice processing). that Data: Can you access the correct data? How complete, consistent and reliable is it? Where is the gap?
The goal here is not perfect. Addressing these questions can help you create a dataset that is more than “starter” data, but with a reasonable size that is less than a multi-year investment. It often means mining basic data domains such as customers, projects/SKUs, pricing, transaction-level finance.
Also note that AI can be used to empower AI. Gen AI accelerates the process by cleaning, converting and enriching data.
Organize your people into AI.
AI Preparation Initiatives cannot be successful on their own. They are exposed to workflows that go beyond functionality, influence decision-making, and reconstruct how people work in their daily roles. This means your people need to be part of the process.
Get them on early. Aligning priorities to business leaders, understanding what is changing and why, and responding proactively by framing AI as job enablers rather than threats. Ensuring people's buy-in is key to making AI successful and sustainable.
Create part of the process.
Your data and people are safe and consistent. Next, it's time to integrate AI into the way businesses run. Too many AI projects stall at this stage. CFOs are often paralyzed by numerous business processes that require increased efficiency. But the good news is that we are already targeting key use cases for incorporating AI: those that have passed business relevance, feasibility and data preparation.
Now you need to operate it. For example, consider an identified invoice processing use case. Operation here means:
- Designing workflows that incorporate AI output in a reproducible way – here we automate invoice intake, extraction and routing.
- Build a feedback loop when users engage in AI (accept, modify, or override recommendations) to improve model logic and improve accuracy over time. and
- It requires not only buying in from the team, but training to understand and trust the system.
Don't let technology (or governance) get out of the way.
The issues of technology and governance. But they are there to support your business rather than piloting it. In other words, after solidifying your AI goals, there should be a high-tech and governance health check.
For Tech Stack, ensure that you have a core system (ERP, CRM, etc.) to support AI services that fit your use case. Additionally, it utilizes flexible cloud-based technology that enables seamless data flow across the system and scales as your organization evolves. As organizations (and AI goals) evolve, you should not invest in implementing huge custom technology platforms before you know where your AI journey is going.
When it comes to governance, the reality is that many boards and sponsors cannot move forward without AI-related moves. But there is a compromise. You don't need a full-fledged governance model to get the show on the road. Start with “light touch” governance. Have secure enterprise-grade tools with clear usage guidelines and a process to prioritize use cases based on business goals.
AI races are happening now. Here's what we know: Winning doesn't mean unfolding all of the AI at once, but losing it means that it's not beginning at all.
