If you manage IT at a mid-size company in 2026, some version of this message lands in your queue every week: someone wants to use a new AI tool, or a new model, for work. Sometimes it is a single analyst asking for access to a coding assistant. Sometimes it is a whole team that has already started expensing a tool on personal cards and now wants it made official. Either way, the request rarely comes with a clean answer, because the real work isn't approving or denying it. It's figuring out how to say yes without adding one more subscription, one more set of credentials, and one more blind spot to the pile you already manage.
Provisioning access without creating a key to babysit
Every new AI provider means a new API key or admin console, and every key is something that has to be stored somewhere safe, rotated on a schedule, and revoked the day someone leaves. Multiply that by however many providers your teams have asked for over the past year and you get a quiet sprawl of credentials living in shared documents, personal password managers, and scripts nobody remembers writing. None of that is anyone's fault exactly; it's what happens by default when there is no single place keys are supposed to live.
Every request is a mini security review, whether you frame it that way or not
When someone asks to use a new model, the real question underneath 'can I use this' is 'what happens to the data I put into it.' Does the provider retain prompts. Does it train on them. Where is it processed, and does that matter for the kind of data this person actually plans to send. Answering that properly for every one-off request, on a case-by-case basis, does not scale past a handful of tools before it starts eating a meaningful chunk of your week, and skipping the review is exactly how sensitive data ends up somewhere nobody approved.
Spend control is now part of the job description
AI spend does not look like a normal software line item. A seat license is predictable; a metered model bill moves with usage, and usage moves with whatever a team decided to try this month. IT ends up holding a bill that swings for reasons nobody flagged in advance: a new workflow that calls a model in a loop, a team that switched to a pricier model without telling anyone, a single heavy user on a shared account. Catching that after the invoice arrives is always more painful than catching it the week it started, which means the job now includes watching spend the way you'd watch any other production metric, not reconciling it once a month.
The one-off subscription problem
Left alone, AI adoption inside a company tends toward exactly the pattern you don't want: a dozen small subscriptions, each individually reasonable, that add up to duplicated spend, inconsistent access, and no single person who could tell you, today, everything the company is currently paying for. Every one of those subscriptions is also a separate login, a separate place data might leave your governance perimeter, and a separate invoice that finance has to reconcile against a headcount that explains none of it.
A short checklist before you approve a new model or tool
- Where is the data processed, and does that satisfy the requirements for the data this team actually handles
- Is prompt content retained or used for training, and for how long
- What certifications, if any, back up the provider's claims
- Who owns the credential once it's provisioned, and how does it get rotated or revoked
- Does this duplicate a model or tool you already have access to under a different name
- What is the expected volume, and what happens to the budget if that estimate is wrong
How Switchboard helps
Switchboard turns each of those repeated questions into a one-time setup instead of a weekly fire drill. Every major model sits behind one login, so a new request is usually an allowlist change rather than a new procurement cycle. Provider keys are held centrally, rotated in one place, and never scattered across personal accounts or someone's laptop on their way out the door. Each model surfaces its own data residency, retention, and training practices up front, so the security review is already answered before anyone asks. Per-team budgets catch a spend spike the week it starts, not the month it gets reconciled, and native Excel, Word, and PowerPoint add-ins mean people get AI where they already work instead of signing up for one more standalone tool you'll be asked to approve next quarter.