Most AI conversations in professional services start with analytics. "How can we use AI to predict which clients will churn?" "How can we analyze trends in our business?" "How can we forecast demand?"
I'm telling my clients to ignore that for now. Start with operations. Here's why.
The Operations-First Philosophy
Operations are the 30% of your team's day that's repetitive, clearly defined, and measurable. Email drafting. Document summarization. Client intake. Meeting notes. Research summaries.
Analytics is harder. It requires data, context, judgment, and integration with existing systems. It's more complex to implement and harder to measure impact.
But here's the key insight: Operations improvements compound. When you improve your client intake process by 20%, that's 20% more time your junior people have for other work. That compounds in productivity.
Analytics improvements are often one-time decisions. "We now know this about our market." And then what?
The Implementation Reality
Operations improvements work right now with technology that exists today. ChatGPT. GPT-4. Basic integrations.
Analytics improvements usually require:
- Clean, consistent data (most firms don't have this)
- Integration with your systems (takes time, money, IT resources)
- Training people to understand and act on the results
- Ongoing maintenance (your data will drift)
Analytics is a long play. Operations is quick wins.
The Adoption Curve
When you improve your team's daily workflow, they feel it immediately. They go from spending 2 hours on something to spending 1 hour. They notice. They like it. They use it more.
When you build an analytics dashboard, people look at it when they need to answer a specific question. Then they forget it exists.
Natural adoption matters. Operations improvements drive natural adoption.
The Proof of Concept
Executives and boards love proof of concept. "We're using AI in our operations and it's saving us 10 hours per person per month."
That's easy to measure, easy to explain, and easy to act on. "Let's do more of this."
"We've built an analytics model that predicts X with 85% accuracy" is harder to sell and harder to turn into action.
The Learning Curve
When your team improves their operations with AI, they learn how to think about this technology. They start seeing other uses. They become your internal evangelists.
You build organizational competence. That's more valuable than any single analytics project.
The Path Forward
Here's what I'm recommending to clients:
Year 1: Operations — Identify 3-5 operational bottlenecks. Use AI to improve each one. Measure impact. Build expertise within your team.
Year 2: Analytics — With operations stable and team skills up, invest in analytics. You'll know what questions matter. You'll have people who understand AI. You'll execute better.
Year 3 and beyond: Integration — AI becomes part of how you deliver service and run the business.
This path works. Not because analytics doesn't matter, but because operations get you there faster and more sustainably.
The Real Value
The real value of AI in professional services isn't in predicting client churn. It's in your team being more productive, less frustrated, and more engaged.
That's what operations improvements deliver.
Start there. Analytics will still be there when you're ready.