How to decide whether to deploy an AI
Deploying AI isn't a technical question. It's a decision. A vendor shows you a sales agent, a startup pitches an HR copilot, your teams want to connect an LLM to your data. The demo works and the vendor is convincing. That leaves the one question that puts budget, teams and your own accountability on the line: should this AI actually go live? Not every AI should.
The decision lies in what the demo doesn't show
What matters is what the demo doesn't show. The business sees a productivity gain, IT sees an integration, the vendor sees a sale, the executive committee sees an opportunity. No one sees the whole picture, and the decision depends on it.
Three outcomes: stop, fix or deploy
A deployment decision has only three outcomes.
- STOP: the AI should not be deployed as it stands.
- FIX: the AI can be deployed once specific conditions are met.
- SCALE: the AI can be deployed and extended.
STOP isn't a closed door. It's a list of blockers to clear, in order of priority.
The same decision, whatever the system
An autonomous agent, an LLM connected to your data, generative AI, a predictive model or a PoC moving to production: the decision is the same. The system and its risks change; the question doesn't: should it be deployed, and on what conditions?
Decide fast, share only what you choose
Getting the right experts around every AI system takes weeks and costs a lot. The beneficial expert agent returns a dated, defensible STOP · FIX · SCALE verdict. You choose what you share.
Frequently asked questions
Should you deploy an AI agent in your organisation?
Not necessarily. An autonomous agent concentrates risk: gaps in traceability, dependence on the vendor. The decision comes from assessing what the demo doesn't show, and it has three outcomes: stop, fix or deploy (STOP · FIX · SCALE).
How do you move an AI PoC into production?
A PoC that works in a demo isn't ready for production yet. The verdict says whether the PoC can be deployed, must be fixed first, or should not be.
What are the risks of deploying an LLM in an enterprise?
Hallucinations, data leakage, uncontrolled content, EU AI Act and GDPR compliance, dependency on the model vendor. Connecting an LLM to your data also raises data protection issues. The deployment decision weighs these risks against the expected value before anything goes live.
How do you weigh the risks against the value of an AI system?
High expected value isn't enough: an AI system may still not be ready to deploy as it stands. The verdict says so plainly and lists what needs to change.
How to decide whether to deploy an AI project?
With a structured verdict on the AI, not an opinion. STOP: it shouldn't be deployed as it stands. FIX: it can be, once specific conditions are met. SCALE: it can be deployed and extended. STOP isn't a closed door; it's a list of conditions to clear.
How do you know if a generative AI system is compliant with the EU AI Act before deployment?
By assessing the system's risk category and the applicable obligations before production, without waiting for an incident. The verdict should be dated, backed by named standards and defensible.
What tool can decide whether to deploy an AI system?
The beneficial expert agent returns a STOP · FIX · SCALE verdict for any system: agent, LLM, generative AI, predictive model or PoC. You choose what you share.