The AI Strategy Playbook for Companies Under 100 Employees
Most AI strategy content is written for companies with 500+ employees, a dedicated data team, and a seven-figure budget. That's not helpful if you're running a 20-person company trying to figure out where AI fits.
Here's what actually works at smaller scale.
Start with the Pain, Not the Tech
The biggest mistake small companies make: starting with "we should use AI" instead of "here's a problem that's costing us money."
Before you touch any AI tool, answer three questions:
- What's eating your team's time? Look for tasks that are repetitive, rule-based, and high-volume. Support tickets, data entry, report generation, content drafts.
- What decisions are you making with bad or slow information? Pricing, inventory, lead scoring, hiring prioritization.
- Where do you lose customers? Slow response times, inconsistent quality, manual processes that create friction.
Pick one. Not three. One problem with clear before/after metrics.
The Small Company Advantage
You have something enterprise doesn't: speed.
No procurement process. No six-month vendor evaluation. No committee approvals. You can test an AI tool on Monday and know if it works by Friday.
Use that. The right approach for a small team is rapid experimentation:
- Week 1: Pick the problem, define what "good" looks like
- Week 2: Test 2-3 off-the-shelf tools against your actual workflow
- Week 3: If something works, integrate it. If nothing does, scope a custom build.
- Week 4: Measure results against your baseline
Four weeks to a real answer. Enterprise takes four months to write the RFP.
Off-the-Shelf First, Custom Second
You probably don't need a custom AI solution. At least not yet.
For most small company use cases, existing tools handle it:
| Problem | Tools to Try First |
|---|---|
| Customer support volume | Intercom Fin, Zendesk AI, Freshdesk |
| Content creation | Claude, ChatGPT, Jasper |
| Meeting notes and follow-ups | Otter, Fireflies, Granola |
| Lead qualification | Clay, Apollo AI |
| Document processing | Docsumo, Rossum |
| Internal knowledge base | Notion AI, Glean |
| Code assistance | GitHub Copilot, Cursor |
These tools cost $20-$500/month. Try them for 2-4 weeks before spending $20K+ on a custom build.
When you do need custom: When your workflow is unique enough that no off-the-shelf tool fits, when you need AI integrated into your existing product, or when data privacy requirements rule out third-party tools.
Budget Reality Check
Here's a realistic AI budget for a company under 100 people:
Phase 1: Exploration ($0-$500/month) Subscribe to 2-3 AI tools. Let your team experiment for a month. See what sticks.
Phase 2: Integration ($5K-$20K one-time) Take the tools that worked and properly integrate them into your workflows. Set up automations. Train your team.
Phase 3: Custom builds ($20K-$75K per project) Only if phases 1-2 reveal a gap that off-the-shelf can't fill. Start with an MVP. Prove it works before going bigger.
Most companies under 100 people should spend 6-12 months in phases 1 and 2 before considering phase 3. The ROI from properly using existing tools is often bigger than building custom.
Common Traps
Hiring an AI engineer too early
A full-time ML engineer costs $150K-$250K/year. For most small companies, that money is better spent on a focused consulting engagement ($20K-$50K) that delivers a working system your existing team can maintain.
Hire when you have enough AI workload to justify a full-time role. Not before.
Buying the platform play
Some vendors sell "AI platforms" that promise to do everything. They're expensive, complex, and designed for enterprise. You'll spend months configuring a tool that does less than a well-chosen set of simple tools.
Ignoring your data situation
AI tools work best with clean, structured, accessible data. If your customer data lives in spreadsheets, your sales info is in someone's head, and your docs are scattered across five tools, fix that first. A shared database and proper documentation will do more for your AI readiness than any tool purchase.
Trying to automate everything at once
Pick one workflow. Get it working. Learn from it. Then pick the next one. Companies that try to "AI transform" everything simultaneously end up with five half-finished projects and nothing in production.
What to Do This Week
- List your team's top 5 time sinks. Ask people where they feel like they're doing work a computer should handle.
- Pick the one with the clearest metrics. Hours spent per week, error rate, response time, whatever you can measure.
- Search for existing tools. Spend 30 minutes looking for tools that solve that specific problem.
- Run a 2-week trial. Give it a real shot with real work, not a toy test.
- Measure the result. Did it save time? Improve quality? If yes, roll it out. If no, move to the next problem.
That's it. No strategy deck required.
Need help figuring out where AI fits in your business? We do focused discovery sprints for small teams. Let's talk.