If you run a 20–100 person organization, you've probably heard the advice: "Hire a data scientist" or "Build an AI team."
Here's the problem: you don't have the headcount budget. And even if you did, most AI wins don't require PhDs in machine learning.
This post covers 3 high-impact areas where small teams can use AI without hiring specialists—and the mistakes to avoid in each.
1. Status Reporting & Meeting Prep
The problem: Your team spends 2–5 hours per week writing status updates, preparing meeting agendas, and consolidating scattered project information.
The AI opportunity: Use LLMs to draft meeting agendas, summarize task updates, and generate first-draft status reports from existing project data.
What this looks like:
- Pull task updates from your project management tool (Linear, Asana, Jira)
- Feed them to an LLM with a simple prompt: "Draft a 3-bullet executive summary of this week's progress"
- Review and send—cutting reporting time by 50–60%
Common mistake: Trying to automate the entire reporting workflow on day one. Start with "AI drafts, human reviews."
2. First-Pass Document Review
The problem: Contracts, proposals, and RFPs require multiple review rounds. The first pass is often just catching obvious issues—formatting, completeness, consistency.
The AI opportunity: Let AI handle the first-pass review so humans can focus on strategic edits and risk assessment.
What this looks like:
- Run contracts through an LLM with a checklist: "Are all sections present? Any formatting inconsistencies? Any unclear terms?"
- Get a structured review in 30 seconds instead of 30 minutes
- Use human time for substantive legal or business review
Common mistake: Treating AI review as final. It's a triage tool, not a replacement for subject-matter expertise.
3. Internal Knowledge Base Q&A
The problem: Your team has documentation scattered across Google Docs, Notion, wikis, and Slack threads. New hires (and veterans) waste time hunting for answers.
The AI opportunity: Build a simple RAG (retrieval-augmented generation) system that lets people ask questions and get answers pulled from your internal docs.
What this looks like:
- Index your internal documentation (no code required with tools like Glean, Hebbia, or simple Pinecone + OpenAI)
- Let your team ask: "What's our refund policy?" or "How do I submit expenses?"
- Get answers in seconds instead of Slack-searching for 20 minutes
Common mistake: Over-engineering this. Start with a small, well-defined knowledge base (e.g., onboarding docs only) and expand once you prove the value.
The Pattern: Triage, Draft, Summarize
Notice the theme? These aren't moonshot AI projects. They're triage and drafting workflows that save 30–60% of low-value work without replacing judgment.
You don't need a data scientist to implement these. You need:
- Clear constraints (budget, tools, timeline)
- A small pilot (one workflow, one team)
- Measurement (hours saved, errors reduced)
What's Next?
If you're ready to identify your team's top 2–3 AI opportunities and run a focused pilot, we built a guide for exactly that.
Download the AI PMO Starter Kit — a practical checklist for your first 90 days of AI implementation, including templates for scoping, piloting, and measuring outcomes.
Or book a working session to map your AI priorities with zero jargon and a clear path to production.