Finance teams should run all relevant data teams within an organization. We’ve talked about this at length, but simply put, Finance is best placed to run data teams, RevOps teams, and FP&A teams (of course) well. This becomes even more of a priority when you factor in the benefit that AI gains from centralized data.

Why? Because Finance has the right incentives and expertise to pull it off. Our job is to deliver actionable insights, drive accountability, and support decision-making across the business. And now that AI is becoming a meaningful layer on top of your data stack, the value of centralization has only grown.

Let’s take a story from a couple of months ago when I started leading finance at a tech company and found that employees were relentlessly confused: RevOps was buried in the CRM, Finance was stuck in the ERP, and the data team tried to reconcile everything in a data warehouse. The result? Everyone was confused, and any decision ‘based’ on data was spent resolving which information was right.

This will recur with any AI implementation if you haven’t centralized your data team under Finance. With AI, it will just result in more of the same:

• RevOps siloed on ‘responding to tickets’ or pipeline projections without looking at how customers perform after they’ve joined

• Data teams siloed on rolling out infrastructure reports disconnected from business value.

• Finance teams generating countless new reports that don’t integrate key non-financial metrics like usage, pipeline, or campaigns

But even worse, imagine letting the black box of AI work on all this data and then providing various “answers.” A decentralized approach will fail. It just adds three more sources for possible answers without solving the root problems. 

Instead, you should centralize the effort into one one team, that owns the source of truth, that uses AI automations to look across all of your data, so you can make smarter decisions by acting on what AI does best: summarizing lots of data. Finance is this team for the same reason it should already run the data function. In terms of how to structure the team, I’d check this out.

Let’s get to work. 

1. Finance owns data

It’s the core mission of the team: use data, in whatever form, to provide clear advice that leads to better decisions. In practice, this means combining operational data with financial data, the sweet spot of FP&A.

• ERP, CRM, HRIS, and billing systems all converge in Finance. No other team has this broad overview, nor the incentive to ensure it’s always reconciled. (Example: reconciling headcount in HRIS with Sales CAC)

• With Finance in control, there’s one authoritative source. The last thing you want is five different figures for ARR or conflicting views on pipelines based on unreconciled numbers. This will confuse everyone.

• Ownership ensures consistent definitions (revenue, customer, churn). And yes, this is more art than science, but if no one is overseeing the data that aligns from boardroom to front-line teams, you’re effectively working from different books.

Finance often spends a significant amount of time just making sure all the data ties together. So lean into centralizing the team to make this work.

2. Finance is cross between the technical and business sides

This has always been Finance’s job: converting numbers into insights. With AI, that role becomes even more critical by knowing how to check AI outputs (what’s right or wrong), how to ask the right questions, and how to standardize those questions across the organization. This makes sense because the team is:

• Already power users of data. Finance has more power users of data than most teams. It’s the people who write performance reviews in Excel and who see every table as a potential SUMIF.

• Natural business partners. Finance already supports other departments through budgets, tool evaluations, hiring plans, and more. The connections are already in place.

• A viewer of the full picture. The team understands how all parts of the company operate together across the customer journey in sales, marketing, product, support, and beyond.

• Accountable to profitability, cash, and compliance. These are the metrics that ultimately matter, and Finance is directly measured against them, making it even more crucial that these tools actually add value.

If you want a team to implement AI tools and use data well, that’s literally the sweet spot of Finance: translating numbers into actionable advice people can actually use.

Tip: AI will get things wrong, just like analysts. But Finance already has the muscle memory to quickly sanity-check numbers and know when something feels off.

3. Has a lot to gain from implementing internally before rolling out to others

We’ve hinted at this in previous projects, but it’s worth stating outright: Finance has a lot to gain from implementing AI within its own team, so that they can be both the implementers and the users, creating a tight feedback loop. Examples: Data cleanups, variance analysis, report building, scenarios, etc. This means:

• Finance has motivation to roll out to save themselves time. Finance is motivated to roll this out quickly so they can spend more time themselves on strategic insights instead of data reconciliation, which is the bane of the team’s existence.

• The team can validate the results. If the team implements it *and has to use it* they gain tremendous insights into how much AI is worth it.

• Able to share value across teams. Quick wins in Finance (e.g., setting up a persistent data reconciliation system) can be taught and case-studied when implemented for other teams.

• Offloading work efficiently. Many Finance questions are about helping others understand how the numbers connect. If Finance implements it on their own numbers, they can invite others to ask questions which can be their first experience in AI to build trust.

By implementing AI within their own team, Finance can introduce the work to others and evaluate if it *actually* delivers ROI. Win-Win.

Tip: Yes, Engineering teams are using AI to speed up coding, but that’s a highly technical use case which isn't as applicable to other teams that aren’t coding.

4. Has practice implementing tools for teams

In today’s modern tech stack, the key is integration to make sure the data actually connects across systems. Finance is already in the thick of this, often acting as the glue between tools and teams. This makes Finance a natural fit for leading AI implementation across departments because of its:

• Experience in ERP rollouts: Finance often leads (or shoulders) ERP implementations. They’re used to complicated rollouts and know how to push through the pain points.

• Role in tech stack integration: Finance lives and dies by its tech stack. When integrations break, reports don’t run. That makes them vigilant people who care about getting the plumbing right.

• Service of other departments: Finance is used to supporting other teams. That same mindset applies when implementing AI that answers knowledge-base questions or automates repetitive queries.

• Skepticism of tools: Let’s be honest, Finance leaders have seen every flavor of over-promised software, which makes them ideal to cut through the hype and buy tools that actually deliver.

Implementing AI across a centralized tech stack isn’t easy. But if you want your company set up for success, Finance is the team that knows how to do the heavy lifting and see it through.

In conclusion

Implementing AI across a centralized tech stack isn’t easy. That’s why you want the rollout led by the team best incentivized for success. Finance brings the right mix of experience, cross-functional perspective, and incentive alignment. They will be focused on delivering tools the business will actually use to make smarter, faster decisions.