Most teams approach AI with one of two extremes:
- "Let's try AI everywhere" — 15 pilots, zero production deployments
- "Let's wait until we're sure" — paralysis by analysis, competitive disadvantage
The right approach is somewhere in the middle: pick 2–3 bets you can execute in 90 days, measure the results, then decide your next move.
Here's how to choose those first use cases without over-committing or wasting time.
The 3-Filter Framework
When evaluating AI opportunities, run each through these three filters:
Filter 1: Can You Measure Success?
Good signal: "Cut reporting time from 4 hours to 1 hour per week" Bad signal: "Make our team more productive"
If you can't define what "better" looks like in numbers, you won't know if the pilot worked—and you'll struggle to justify expanding it.
Examples of measurable outcomes:
- Hours saved per week
- Error rate reduction (e.g., from 8% to 2%)
- Faster turnaround time (e.g., proposals in 3 days instead of 7)
Filter 2: Is the Data Already There?
Good signal: You have structured data in existing tools (Salesforce, project management, support tickets) Bad signal: The data is scattered, unstructured, or doesn't exist yet
Starting with data you already have dramatically reduces setup time and risk. Avoid use cases that require building new data pipelines on day one.
Questions to ask:
- Do we already track this information somewhere?
- Is it in a consistent format?
- Can we export or access it via API?
Filter 3: What Happens If It Fails?
Good signal: Low-stakes workflows where errors are easy to catch (e.g., first-draft emails, meeting summaries) Bad signal: High-stakes decisions with compliance or safety implications (e.g., loan approvals, medical diagnoses)
Your first AI pilots should be in low-risk, high-frequency workflows where you can learn fast without catastrophic downside.
The 2x2 Prioritization Grid
Once you've filtered your use cases, plot them on two axes:
- X-axis: Time to value (weeks to see results)
- Y-axis: Impact (hours saved, revenue gained, or errors reduced)
Prioritize the top-right quadrant: High impact, fast time to value.
Example:
| Use Case | Impact | Time to Value | Priority |
|---|---|---|---|
| AI-assisted status reports | 3 hrs/week saved | 2 weeks | High |
| Customer support triage | 10 hrs/week saved | 6 weeks | Medium |
| Contract review automation | 20 hrs/week saved | 12 weeks | Low (for v1) |
Start with the high-priority items. Resist the temptation to tackle everything at once.
Common Traps to Avoid
Trap 1: Choosing Use Cases Based on Hype
Just because "AI-powered sales forecasting" is trending on LinkedIn doesn't mean it's the right fit for your team.
Better approach: Start with your team's actual pain points. Where are they spending time on repetitive, low-judgment work?
Trap 2: Skipping the Pilot
Going straight from idea to full rollout is a recipe for expensive failures.
Better approach: Run a 4–6 week pilot with one team or workflow. Measure the results. Then decide whether to scale, pivot, or stop.
Trap 3: Picking Use Cases You Can't Control
If the success of your AI pilot depends on another team, a vendor, or a data source you don't own, you're adding unnecessary risk.
Better approach: Choose workflows your team controls end-to-end—at least for the first pilot.
A Real Example: Mid-Market SaaS Company
A 60-person SaaS company wanted to "do more with AI." Here's how they narrowed it down:
Initial list of ideas:
- AI chatbot for customer support
- Automated sprint planning
- AI-assisted sales emails
- Predictive churn modeling
After applying the 3-Filter Framework:
- Chatbot: High impact, but data wasn't structured yet (failed Filter 2)
- Sprint planning: Measurable, data exists, low risk ✅
- Sales emails: Fast to test, measurable, low risk ✅
- Churn modeling: High stakes, requires clean historical data (failed Filter 3)
They chose sprint planning and sales emails for their first pilots.
Results after 8 weeks:
- Sprint planning: saved 2 hours/week per PM
- Sales emails: increased response rates by 15%
They expanded sprint planning to all teams and are now piloting the chatbot (after cleaning up support ticket data).
Your Next Step
If you're ready to apply this framework to your team's AI opportunities, download our AI PMO Starter Kit — it includes a use case prioritization worksheet and a pilot scoping template.
Or book a working session to map your top 2–3 AI bets with clear success criteria and a realistic timeline.