Recommendation: adopt OpenAI GPT-4o via ChatGPT Team/Enterprise as the company default, with explicit exceptions for long-document legal/research work and vendor-specific workflows.
Reasoning, writing, analysis, multimodal input, reliability, context length, and benchmark/field reputation.
Enterprise data handling, SSO/admin, retention, workspace separation, auditability, and vendor maturity.
Consumer-quality UX, templates/GPTs, file upload, mobile, connectors, and low training burden.
Per-seat pricing, API token prices, expected usage, and hidden support/change-management cost.
Integrates with our office suite, avoids lock-in where it matters, and leaves room for future model swaps.
I cannot browse from this environment. Model facts and prices are based on public information known through mid‑2024; vendor plans change quickly, so procurement should re-check pricing, data terms, and features before signing.
| Vendor / model | Best at | Watch-outs | Typical business route |
|---|---|---|---|
| OpenAI GPT-4o | Best all-around: fast, strong reasoning/writing/coding, image/audio, broad ecosystem. | Enterprise terms needed for sensitive data; custom pricing above Team. | ChatGPT Team / Enterprise; API |
| Anthropic Claude 3.5 Sonnet | Excellent writing, analysis, coding; strong long-doc experience and safety posture. | Fewer native workplace integrations; model family/tooling less ubiquitous than OpenAI. | Claude Team / Enterprise; API |
| Google Gemini 1.5 Pro / Flash | Very long context; strong if already deep in Google Workspace; low-cost Flash. | Quality can feel less consistent for general business assistant use. | Gemini for Workspace; Vertex AI |
| Microsoft Copilot | Embedded in M365: Teams, Outlook, Word, Excel, security/admin alignment. | It is a product layer, not one model; expensive per seat; value depends on M365 hygiene. | Copilot for Microsoft 365 |
| Meta Llama 3 / Mistral / Cohere | Open/controllable or specialized deployments, lower unit economics at scale. | More technical ops burden; weaker non-technical UX unless wrapped in apps. | Cloud/API/self-host; custom apps |
Model names to track during final procurement: GPT-4o, Claude 3.5 Sonnet/Opus, Gemini 1.5 Pro/Flash, Llama 3 70B/405B if available, Mistral Large, Command R+.
Illustrative score out of 100 using the stated weights. It combines public model performance, enterprise features, usability, and expected operating burden; it is not a lab benchmark.
| Product | Public list signal | 60 seats / month |
|---|---|---|
| ChatGPT Team | ~$25–30/user/mo | $1.5k–1.8k |
| Claude Team | ~$25–30/user/mo | $1.5k–1.8k |
| Gemini Workspace | ~$20–30/user/mo add-on | $1.2k–1.8k |
| M365 Copilot | ~$30/user/mo add-on | $1.8k |
| Enterprise plans | Custom | Procure quote |
API prices vary by model and token volume; for internal assistants, seat licenses are usually easier to govern than reimbursing many consumer accounts.
Recommendation: budget $30–35k year one: seats for all staff, plus training, prompt library, governance time, and a small API/automation reserve.
Often excellent at nuanced prose, summarization, contract-style reasoning, and maintaining tone across long context. Consider 5–10 specialist licenses for Legal, People, or Strategy.
If we operate mainly in Gmail/Docs/Sheets/Drive, Gemini’s embedded experience and long context may be compelling despite more variable output quality.
Useful when Teams/Outlook/SharePoint are clean and permissioned. Less attractive if our files/meeting notes are scattered or poorly governed.
Llama/Mistral/Cohere can be right for embedded products, regulated data, or high-volume tasks — but require engineering ownership.
Good for web research and source discovery, but should complement — not replace — a general work assistant.
GitHub Copilot, Cursor, Fireflies, Intercom/HubSpot AI, and design tools may be better in-context than a chat model.
Select Team vs Enterprise, enable SSO if available, disable training on business data where contractually applicable, publish use policy.
20 users across Sales, Ops, Finance, People, Support. Measure time saved, top use cases, bad outputs, and support questions.
Give everyone seats, run role-based trainings, create prompt library and approved GPTs for company tone, meeting prep, analysis, and SOPs.
Usage reporting, exception process, quarterly model review, data-loss reminders, and first automation backlog using API where ROI is clear.
70% monthly active use by month 3; at least 10 documented workflows saving 30+ minutes each.
Ops owns vendor/governance; IT owns identity/security; department champions own workflow adoption.
Two 45-minute trainings: “safe basics” and “role workflows.” Keep office hours weekly for first month.
| Risk | What can go wrong | Mitigation | Owner |
|---|---|---|---|
| Hallucinations | Confident but false output enters customer docs or decisions. | Source-check external claims; require review for legal/finance/HR; train users on verification. | Dept leads |
| Data leakage | Staff paste confidential data into unapproved consumer tools. | Single approved tool, clear data classes, SSO/admin, browser reminders, vendor DPA. | Ops + IT |
| Shadow AI spend | Multiple overlapping subscriptions and no visibility. | Central procurement; expense-policy block for unapproved AI tools; exception register. | Finance + Ops |
| Overreliance | Quality drops because outputs are not challenged. | “AI as junior analyst” training; mandatory human accountability; examples of bad outputs. | People + managers |
| Vendor lock-in | Prompts/workflows become hard to move. | Store key prompts/SOPs outside the vendor; quarterly model bake-off; avoid proprietary-only automations. | Ops |