Operations · Leadership Decision Brief

Which AI model should we standardize on?

A recommendation for our 60-person company — what to adopt, why, what it costs, and what could go wrong.

Prepared byOperations Lead

ForLeadership Team

DateJune 2026

The decision & the lens

One question, judged on five things that matter to us

01

Capability

Quality on the work we actually do: writing, analysis, summarizing, coding help.

02

Cost

Per-seat licenses for staff + API usage for anything we build. Predictable, not surprise bills.

03

Privacy

Our data is not used to train models; clear retention, admin controls, and compliance.

04

Ease for non-tech

A teammate in finance or sales can be productive day one — no prompt engineering degree.

05

Fit to our stack

Works with the tools we already pay for (Google Workspace / Microsoft 365).

We are choosing a default org-wide assistant for ~60 mostly non-technical staff — not the single highest benchmark score. The "best model" and the "best choice for us" are different questions.

The current landscape — mid-2026

Four serious contenders, plus open-weight as a wildcard

Claude

Anthropic. Opus 4.8 / Sonnet 4.6. Strongest writing & coding ecosystem; privacy-first defaults.

ChatGPT

OpenAI. GPT-5.5. Most familiar to staff; broadest feature set & integrations.

Gemini

Google. 3.1 Pro. 1M-token context; baked into Workspace; strong value.

Copilot

Microsoft. GPT-based, embedded in Microsoft 365 apps. Zero new tool to learn.

Open-weight

Llama 4 / Mistral. Self-hostable, lowest marginal cost — but ops burden falls on us.

Sources: IntuitionLabs enterprise comparison; LM Council benchmarks (Jun 2026); DevTk & AIonX pricing tables. Models move fast — figures reflect late-June 2026 public data.

Side by side · honest trade-offs

How they compare on what we care about

Option	Capability	API cost / 1M tok	Privacy (business tier)	Ease for non-tech
ClaudeOpus 4.8 / Sonnet 4.6	Top tierBest prose; strong reasoning & code	$5 / $25 (Opus)$3 / $15 Sonnet	No training by defaultClear retention, admin console	HighClean UI, "Projects", little fuss
ChatGPTGPT-5.5	Top tierMost versatile; rich features	$5 / $30Premium output pricing	No training (Enterprise)Strong on Enterprise tier only	HighMost familiar to staff already
Gemini3.1 Pro	Top tierLeads reasoning; 1M context	$2 / $12Best value of the frontier set	Good in WorkspaceNo training on paid Workspace	High if on GoogleIn Docs/Gmail you already use
CopilotMicrosoft 365	StrongGPT under the hood, app-scoped	~$30 / seatLicensed, not metered	Stays in M365 tenant	Highest if on M365No new app to open
Open-weightLlama 4 / Mistral	GoodNear-frontier, not leading	LowestCompute only; self-hosted	Full controlData never leaves us	LowNeeds us to build the UI/ops

Cost · two ways we pay

API output price — where the real bills land

Most staff use a $20–30 / seat / month licensed plan — flat & predictable.
API pricing only bites on things we build (automations, bulk processing).

~$18k

/ yr · 60 seats at a $25 blended business plan

Use a cheaper "workhorse" model (Sonnet / Gemini Flash) for high-volume API jobs; reserve the flagship for hard tasks. That alone cuts build-cost 60–80%.

Fit for a non-technical team

Capability is now table-stakes — fit is the differentiator

All four frontier assistants now clear the bar on quality for everyday work.

The deciding factors become how easily a non-technical teammate succeeds and whether it lives inside tools they already use.

Open-weight models are capable but land far left: they need us to build and run the experience.

Positions are directional judgments for our context, not benchmark coordinates.

RECOMMENDATION

Standardize on Claude (Team) as our default assistant.

Pair it with a low-cost workhorse model for anything we build via API, and keep one fallback account open. Revisit in 6 months.

The one caveat: if we are already deep in Microsoft 365, run a 30-day Copilot pilot in parallel — integration may outweigh raw quality for our staff.

The reasoning

Why Claude clears the bar for us

Best at our actual work. Most of our 60 people write, summarize, and analyze. Claude produces the most usable prose and handles long documents cleanly — the daily 80%.
Privacy posture is the default, not an upsell. On business plans, our inputs aren't used for training, with clear retention and an admin console — the safest answer for client and HR data.
Low learning curve. A clean interface and "Projects" let a non-technical teammate be useful on day one without prompt tricks.
Cost is mid-pack and controllable. Seats are ~$20–30; API jobs can run on cheaper Sonnet. No lock-in to a single expensive tier.
Not betting the company. It's a flat per-seat tool. If a competitor pulls ahead, switching cost is low — staff skills and prompts transfer.

Rollout & cost

A phased 90-day rollout we can afford

Rollout plan

1

Weeks 1–2 · Pilot10 power users across teams. Set the usage policy & data rules.

2

Weeks 3–6 · ExpandRoll to ~30 staff. Run two 45-min trainings + a shared prompt library.

3

Weeks 7–12 · StandardizeAll 60 on the business plan. Name an internal champion per team.

4

Ongoing · ReviewTrack usage & wins; reassess the market every 6 months.

Annual cost (est.)

~$18k

60 seats × ~$25/mo business plan

~$3–6k

API usage for automations (workhorse model)

~$22–24k

All-in / year ≈ $370–400 per person

Benchmark: even a 2% productivity gain per knowledge worker dwarfs this spend.

Risks & mitigations · eyes open

What could go wrong — and how we cover it

Data leakage

Staff paste sensitive info into the wrong place. Mitigation: business tier (no training), written policy, approved-tool-only rule.

Over-reliance / wrong answers

Confident but incorrect output. Mitigation: "verify before you send" norm; humans own final decisions.

The market moves

A rival leaps ahead next quarter. Mitigation: flat per-seat tool, low switching cost, 6-month review built in.

Shadow AI

People use unapproved free tools anyway. Mitigation: give a good sanctioned option early so there's no reason to.

Stack mismatch

We're more Microsoft 365 than we assumed. Mitigation: the parallel Copilot pilot decides this on evidence.

Price / terms change

Vendor raises prices or alters terms. Mitigation: annual budget cap; keep one fallback vendor account warm.

The ask

Approve a 90-day rollout of Claude as our standard.

~$22–24k/year, all-in. Parallel 30-day Copilot pilot if we're Microsoft-heavy. Full review at month six.

Decision rule going forward: pick the assistant that makes our existing tools and existing people better — not the one with the highest benchmark that week.