How to Build an AI Adoption System That Actually Scales

AI isn’t just about automating one task—it’s about what happens when you can do it again, and again, and again. That’s where the real value lies: in repeatability.

Last week, we focused on helping you get your first AI win. This week, we’re looking at what comes next.

How do you take a single experiment and turn it into a system your whole team can use? How do you document, refine, and share what works, without needing engineers or overhauling your workflows?

That’s what this issue is all about: building a lightweight, scalable AI adoption system inside your finance function.

📢 Upcoming Events for Finance Leaders – Save the Dates!

🔹FP&ACON2025 – May 27-28 (Free Online Event)

Insights and bold conversations about the future of finance and FP&A. Hear from top finance leaders, AI experts, and FP&A innovators as they tackle this year’s most pressing topics.

You can see me on the Session 3 Data analytics and AI for FP&A teams moderated by Glenn Hopper.

The Balanced View: How to Scale AI Across Your Finance Workflows

Last week, we introduced the 30-minute validation framework—a simple method to identify your first AI use case. This week, let’s build on that momentum.

You’ve blocked time, listed tasks, tested a few ideas, and validated one promising use case. Now what?

In this issue, we’ll walk through how to turn that single experiment into a lightweight, repeatable system for AI adoption across your finance team.

Step 1: Track Every Experiment 

Your first task is identified—great. But the real value starts with documentation.

Log every AI test, whether it worked or not. What tool did you use? What prompt? What result? This creates a learning log that compounds over time.

Why? Because what fails today may succeed next month as tools evolve.

Use a simple format like this:

Step 2: Build Repeatable Workflows from Wins

 Once an AI workflow shows promise, turn it into a repeatable system:

• Refine the prompt using real input

• Save the input and output templates

• Document the full workflow step by step

Add it to a dedicated "Automated Workflows" tab in your tracker. This becomes the starting point for building your team’s AI playbook.

A great way to automate this repeatable process is to use a custom GPT in ChatGPT, a Project in Claude, or similar tools.

Step 3: Share What You Built 

AI adoption accelerates when you involve others.

Share your successful workflow with team members doing similar tasks. Provide them with your prompt, your tracker, and your lessons learned.

Encourage feedback and iterations. Ask them to try the workflow and report back on how they adapted or improved it.

This moves AI from being a personal tool to a shared capability.

If you are a solo practitioner and don't have a team, consider joining a community of like-minded professionals, such as AI Finance Club, where a team of experts, including me, helps finance professionals with AI adoption through webinars, training, courses, and articles. Having peers who try to address similar challenges and sharing your failures and successes with them accelerates AI adoption significantly.

Step 4: Create Time and Space for Teamwide Experimentation 

To build momentum across your team:

• Encourage others to block 30 minutes weekly for AI experimentation

• Let them test their own tasks using the same validation approach

• Set clear guardrails—non-sensitive data only, documented outputs, etc.

Support this effort by:

• Hosting weekly AI office hours

• Adding AI use case reviews to team meetings

• Sharing results in Slack, Notion, or internal email threads

This fosters a safe, collaborative culture around AI.

Step 5: Track Outcomes and Revisit Failures 

To scale what works, measure what matters:

• Time saved ("3 hours reduced to 30 minutes")

• Accuracy improvement ("80 percent drop in reporting errors")

• Usage ("3 team members adopted the same prompt last week")

Don’t discard the early failures—schedule monthly reviews of what didn’t work. As tools improve and prompts evolve, old blockers may turn into easy wins.

If your 30-minute session helped you find one viable use case, you’re already ahead. Now it’s time to turn that test into a system:

You don’t need to be an AI expert to lead AI adoption—you just need a process. With one validated use case and a consistent framework for testing, documenting, and sharing, you’ve already laid the foundation.

The next step is to build your internal AI playbook. This doesn’t have to be complicated. A shared spreadsheet, Notion doc, or Google Drive folder works. What matters is making wins visible and repeatable.

Real-World AI Workflow Example: Weekly Sales Performance Updates

To make this framework more tangible, let’s look at a real-world example.

In this case, AI adoption was led by a controller who also served as the team’s informal AI champion.

What started as a 30-minute experiment evolved into a repeatable workflow that improved efficiency and internal communication, while following the same five-step process outlined above.

Step 1: Track What You Try

The controller began by listing a few high-frequency, repetitive tasks:

• Weekly sales performance tracking

• Monthly expense reconciliation

• Drafting team meeting agendas

• Flagging overdue accounts receivable

He experimented with automating each of these using ChatGPT. After testing, the weekly sales performance report was selected as the first use case. It was repetitive, time-consuming, and based on well-structured ERP export data—ideal for AI support.

Here’s how the process originally worked:

Each week, the controller downloaded the revenue report from the accounting system and updated a shared Excel file. Salespeople could view their numbers in the file, but the controller was also responsible for sending personalized performance emails to each salesperson, along with a high-level summary email to the leadership team.

Step 2: Build Repeatable Templates

Using ChatGPT, the controller developed and refined prompts that could:

• Generate individual performance summaries for salespeople

• Create a company-wide revenue summary for leadership

• Flag standout performers and underperformance based on targets

After several rounds of testing, the controller finalized the best-performing prompts and input formats. He ran the AI-generated outputs in parallel with the manual process for a few weeks to ensure consistency and accuracy.

Once confident in the results, he compiled the prompt instructions into a custom ChatGPT Project to make the process repeatable.

Step 3: Share With the Team

The controller then introduced the new workflow to both the finance and sales teams. Everyone was informed about the switch to AI-generated summaries and invited to flag any issues they noticed. This transparency helped build trust and early buy-in.

He also shared access to the ChatGPT Project, enabling team members to ask their own revenue-related questions using the same structured data and logic behind the automation.

Step 4: Encourage Weekly Testing

With strong initial results, the controller continued using the process weekly. While he still manually copied and pasted the AI-generated emails, he plans to automate delivery in the next phase using a tool like Zapier or n8n, eliminating the last manual step.

Step 5: Review and Measure

After several weeks, no inaccuracies had been reported. The emails were consistently accurate and faster to produce. The controller saved more than two hours per week, and the error rate dropped to zero, compared to frequent mistakes in the previous manual workflow.

The Result:

A single 30-minute experiment evolved into a trusted, scalable communication system, improving speed, visibility, and accuracy across finance and sales.

Even though saving two hours per week might seem like a small win, this was a perfect starting project. It didn’t involve sensitive data, it was highly visible across departments, and it delivered immediate value. Most importantly, it demonstrated how a lightweight AI solution can quietly reshape a recurring process and build momentum for broader adoption.

Closing Thoughts

Early AI adoption isn’t about perfection. It’s about consistency.

The teams that succeed are not the ones with the biggest budgets or most advanced tools—they’re the ones with a simple system: track what you try, repeat what works, and share your wins.

Whether you're leading a team or experimenting solo, this framework helps you build momentum without complexity. Start small. Build visibly. Involve others.

You’re not just testing AI—you’re building the habits that make it stick.

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO