There is growing pressure on finance leaders to define an AI strategy. Since generative AI entered the mainstream, many organizations feel the need to act quickly to avoid falling behind.
In practice, that urgency often leads to investment before the underlying data and processes are ready.
When leadership teams invest in a new reporting dashboard or an advanced AI platform, they expect an instant transformation. In practice, adoption often stalls. Within a few months, usage declines and teams revert to familiar processes like manual reconciliation in spreadsheets.
Why does this happen? The technology didn’t fail. The organization’s readiness did.
The Bottleneck Isn't the Tech: It's the Architecture
Right now, the market is severely underestimating what it actually takes to deploy advanced analytics. Data is the fundamental currency for any of these tools. If your data-gathering mechanisms are disjointed, if your source documents are messy, and your internal processes are broken, an AI tool isn’t going to save you. It’s just going to process your bad data faster. Garbage in, garbage out.
We saw this firsthand within Scrubbed. A few years ago, key data points such as lead tracking and client contract information were not maintained in a centralized system. Instead, they were spread across multiple sources, which limited visibility and consistency. When we began evaluating AI capabilities, it became clear that our data environment was not yet structured to support it effectively.
This wasn’t something we could address quickly. It took over two years to clean, structure, and map our data before we could build reliable KPI dashboards, let alone apply AI effectively. Data doesn’t organize itself; it accumulates. If you don’t intentionally design how you gather it from day one, it snowballs into a massive roadblock.
The Human Element of Adoption
When a new dashboard goes unused, the instinct is to blame the team for resisting change. But usually, the failure is a design and leadership problem.
Human nature defaults to the familiar. If a new system isn’t intuitive, if it wasn’t tailor-fit to the actual workflows of the people using it, or if the learning curve is just a hassle, your team won’t use it. You cannot force adoption. It is the responsibility of leadership to manage that change, to ensure there is proper training and a bridge between the shiny new tool and the humans expected to use it every day.
Start With the Pain, Not the Product
For CFOs or COOs who want to become more data-driven, my practical, day-one advice is to ignore the market hype for a minute.
Don’t start by shopping for software or looking for a one-size-fits-all solution, because it doesn’t exist. Start by identifying your pain points. Where is your team spending the most manual hours? What is actually causing the bottleneck in your decision-making?
Before you authorize a massive AI overhaul, look for the low-hanging fruit. Sometimes the root cause is a broken manual process that just needs a simple, cost-effective automation to fix an immediate bottleneck. Sometimes you just need a clean, basic dashboard, not a complex AI model.
Sustainable innovation is built one step at a time. Don’t let the pressure of the AI buzz push you into buying solutions your organization isn’t ready to absorb. Address the foundational pain points first, and clean your data. When your underlying data and processes are consistent, advanced reporting tools can deliver the results you expect.
