A pragmatic recommendation for a 60-person company — based on capability, cost, privacy, and what a non-technical team can actually adopt.
Today, ~25 of our 60 people use AI tools — mostly ChatGPT free, some Claude, some Gemini, a few on paid personal plans. That's fragmented, insecure, and leaves value on the table.
Mostly non-technical: sales, ops, marketing, finance, HR, plus a small engineering team.
Drafts, summaries, customer replies, internal docs, light data work, occasional code.
Some teams handle customer PII and financial data. We can't send that to consumer-grade tools.
A non-technical team can't evaluate models on benchmarks alone. We weight what actually matters for daily use.
Not leaderboard scores. Can it write a clean customer email? Summarize a 40-page report without hallucinating? Follow a 5-step instruction reliably? Reason about a spreadsheet?
Can a non-technical hire be productive in 30 minutes? Is there a real UI (not just an API)? Good docs? Responsive support? Works in our existing browser/Slack/Google Workspace?
There are dozens of models. For a 60-person non-technical company, only these five have the product maturity, support, and business plans to consider.
Pricing and capability assessments reflect publicly available information as of early 2026 and should be re-verified before contract signing.
Scores are qualitative assessments based on public benchmarks, hands-on testing, and vendor reports. Re-run an internal bake-off before signing.
Heavy users can hit message caps on Team plans. Enterprise plans lift these but cost more and require annual commits.
If we ever automate workflows (Zapier, internal tools), API usage is metered separately and can dwarf seat costs.
At our scale, GPU inference + an MLE to maintain it lands at $15–25/user/mo equivalent — before opportunity cost.
The real privacy risk isn't the vendor — it's us. Shadow use of personal ChatGPT accounts, copy-pasting customer data into free tools, and uploading confidential docs to consumer tiers are happening today. Standardization fixes most of this.
For a 60-person company whose work is mostly writing, analysis, and judgment-heavy tasks, Claude's writing quality and reasoning edge outweigh ChatGPT's familiarity advantage. Cost is essentially identical.
Excludes API usage for automation. If we automate >5 internal workflows, add ~$3–8k/yr in API costs.
| Risk | What it looks like | Mitigation |
|---|---|---|
| Vendor lock-in | Prompts, custom GPTs, and workflows built into one vendor's UI become hard to migrate. | Keep all prompts in a plain-text internal library. Use vendor UI as a thin layer over standardized prompts. |
| Hallucinations | Non-technical users trust confident wrong answers — especially on numbers and citations. | Training covers "verify before you share." No AI output goes to customers without human review. |
| Data leakage | Employees paste customer PII or financials into AI tools outside our control. | Block consumer AI tiers at the network level. Clear acceptable-use policy. SSO-only access to approved tools. |
| Cost overruns | Heavy users burn through rate limits; future API automation bills spike. | Start on Team plan (predictable per-seat). Move to Enterprise only if caps become a real problem. |
If pilot results match expectations, we proceed to full rollout in week 3. If Claude underperforms in our bake-off, we fall back to ChatGPT Team — same plan, same timeline. Either way, we standardize by end of quarter.