How to maintain an AI system after launch

What does it take to keep an AI system working well after it goes live?

Treat launch as the start of operations, not the end of the project. Name an owner, keep watching a few quality measures against the baseline, review a sample of outputs and corrections every week, and retest whenever the data, the prompts, the model or the process changes. Keep the documentation, credentials and code with your company so the system can be supported by your team or another partner.

What this means in practice

Name an owner who reviews quality and handles issues.
Track a few measures against the pre-launch baseline.
Review a sample of outputs and human corrections each week.
Retest after any change to data, prompts, models or the process.
Keep code, credentials and documentation under company control.

How Lid Vizion approaches it

We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.

--- title: "How to maintain an AI system after launch" description: "Treat launch as the start of operations, not the end of the project. Name an owner, keep watching a few quality measures against the baseline, review a sample of outputs and corrections every week, and retest whenever the data, the prompts, the model or the process changes. Keep the documentation, credentials and code with your company so the system can be supported by your team or another partner." url: https://lidvizion.ai/answers/how-to-maintain-an-ai-system-after-launch/ site: Lid Vizion index: https://lidvizion.ai/llms.txt --- [Direct answers](https://lidvizion.ai/answers/) # What does it take to keep an AI system working well after it goes live? Treat launch as the start of operations, not the end of the project. Name an owner, keep watching a few quality measures against the baseline, review a sample of outputs and corrections every week, and retest whenever the data, the prompts, the model or the process changes. Keep the documentation, credentials and code with your company so the system can be supported by your team or another partner. ## What this means in practice - Name an owner who reviews quality and handles issues. - Track a few measures against the pre-launch baseline. - Review a sample of outputs and human corrections each week. - Retest after any change to data, prompts, models or the process. - Keep code, credentials and documentation under company control. ## How Lid Vizion approaches it We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place. ## More on Automation & custom AI - [How can a team automate its weekly status report without losing what matters?](https://lidvizion.ai/answers/how-to-automate-a-weekly-status-report/) - [How can operations teams make shift handovers reliable?](https://lidvizion.ai/answers/how-to-run-a-good-shift-handover/) - [How can a business automate inventory reorder alerts without over-ordering?](https://lidvizion.ai/answers/how-to-automate-inventory-reorder-alerts/) [Compare services](https://lidvizion.ai/services/) · [Use a planning tool](https://lidvizion.ai/tools/) · [Explore workflow use cases](https://lidvizion.ai/use-cases/) · [More direct answers](https://lidvizion.ai/answers/) Talk it through ## Have a version of this question inside your company? Bring the decision, the constraints, and what a good outcome looks like. [Talk through the decision](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fanswers%2Fhow-to-maintain-an-ai-system-after-launch%2F#write) · [Book a call](https://calendar.app.google/bB26LdA9e2gmGFp69)

What this means in practice

How Lid Vizion approaches it

More on Automation & custom AI

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