Originally published: https://www.linkedin.com/pulse/how-build-ai-adoption-system-actually-scales-anna-tiomina-mba-puzec/

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.

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.

Takeaway: Turn a Test into a System

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:

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.

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