Measuring ROI in AI
Everyone wants to know the ROI of AI, but you can’t measure the return if you haven’t captured the before.
Whether or not you end up launching an AI assistant, tracking support metrics like volume, resolution time, and staff workload is just smart operational practice. It helps you understand how your team is spending time, what members are asking for, and where there’s room to improve. When you are rolling out AI, these same metrics become the foundation of your before-and-after story.
If your goal is to reduce support volume, speed up response times, or lighten the staff load, start by setting yourself up to prove it worked. Here’s how to do that, along with some best practices used by support teams that are already seeing real results.
1. Track Your Baseline Support Load
Before AI rolls out, document what your team is handling manually.
How many member questions come in each week?
What channels do they use (calls, email, chat)?
What’s your average response time?
How long do common questions take to resolve?
Even a rough snapshot from your help desk, inbox, or a basic spreadsheet is better than nothing. You're creating a “before” picture you can later compare against.
Best practice: Tag or categorize a sample week of support questions. If you can identify the top 20 repetitive topics, you’ll be able to track how many of those are deflected once AI is live.
2. Time = Money (So Let’s Do the Math)
Estimate the real cost of support.
What’s the hourly rate of the people answering member questions?
How many hours a week go to repetitive support?
What percentage of those questions could AI realistically answer?
For example, if your staff spends 20 hours a week on repeat questions and their blended rate is $40 per hour, that’s $800 a week that could be redirected to higher-impact work.
Best practice: Run a time audit for one week. Log how long each type of question takes and who handles it. This gives you a clear, dollar-based benchmark.
3. Identify What “Success” Looks Like
Support is more than ticket counts. It’s about experience, reputation, and team capacity. Ask yourself:
Are you trying to reduce overall volume?
Free up staff to focus on projects?
Improve response time or after-hours availability?
All of the above?
Knowing what you’re aiming for helps you track the right indicators and tell the right story later.
Best practice: Tie AI support goals to broader organizational KPIs like member satisfaction, staff efficiency, or retention. This is the kind of alignment leadership wants to see.
4. Start a Simple Support Log
If you don’t have a help desk platform, start a spreadsheet. Track:
Question type
Channel (phone, email, etc.)
Who handled it
How long it took
Whether it could have been handled by AI
It doesn’t have to be complex. You just need a consistent way to document your starting point.
Best practice: Assign a person or team to manage the log. Good data doesn’t happen by accident.
5. Prepare to Compare
Once your AI assistant is live, keep tracking for at least 90 days. Look for signs like:
A drop in repetitive questions
Fewer after-hours inquiries hitting your staff inbox
Time savings
Improved member feedback or staff satisfaction
If your AI assistant is doing its job, you’ll feel the difference. These metrics help you show it.
Best practice: Schedule a 30, 60, and 90-day check-in. Review what's working and retrain your assistant if needed. ROI improves when you treat AI like a living system.
Bottom Line:
The biggest mistake you can make is launching AI and then saying, “I think it helped.”
The second biggest is not being able to show it when someone asks.
If you prepare before launch, you’ll be able to tell a confident, data-backed story after. And that’s what turns AI from a nice-to-have into a clear, strategic win.