# Responsible AI Tools Checklist: Choose, Launch, Manage and Maintain AI Tools > Source: https://netray.co/tools/ai-tools-adoption-governance-checklist-for-manufacturers > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer A checklist to approve, launch and maintain AI tools safely in a manufacturing company, from data classification and ITAR to vendor review and ongoing monitoring. ## Category ai-automation ## Industry Manufacturing ## Audience - CIO - IT manager - Quality manager - Compliance officer ## Tool **Kind:** checklist **Scoring Note:** Use the checklist for any new AI tool, from a simple writing assistant to an advanced agent over your ERP. Beginner teams can complete groups 1 to 3 first; premium or advanced tools need all six. This is a governance aid, not legal advice. **Groups:** #### 1. Approve: purpose, owner and risk **Items:** **Text:** State the business purpose of the tool and the process it will support **Critical:** true Name a business owner and a technical owner **Text:** Rate the use case by risk: internal writing aid, decision support, or action in a system of record **Critical:** true Check whether a simpler tool, or your ERP vendor's built-in features, already covers the need Define what success looks like with measurable targets before purchase Record the approval decision and date in a central AI tool register #### 2. Data: classify before you connect **Items:** **Text:** List the data the tool will see: ERP records, drawings, customer data, employee data, supplier pricing **Critical:** true Classify each source as public, internal, confidential or controlled **Text:** Block export-controlled technical data (ITAR or EAR) and controlled unclassified information from any tool outside an approved boundary **Critical:** true **Text:** Check contract clauses with customers that restrict use of their data or drawings with third-party tools **Critical:** true Decide where prompts and outputs are stored, who can read them and how long they are kept Confirm personal data handling meets the laws that apply to your employees and customers #### 3. Vendor and deployment review **Items:** **Text:** Ask whether the vendor trains on your inputs, and get the answer in the contract **Critical:** true Review hosting location, sub-processors and the vendor's security attestations **Text:** Compare cloud, private cloud and on-prem options against your data classes **Critical:** true Check single sign-on, role-based access and audit logging Test an exit plan: how to export your data and stop using the tool Estimate the full cost: licences or tokens, integration, review time and support #### 4. Launch: pilot and rollout **Items:** **Text:** Run a pilot with a small group on real tasks and a written test set **Critical:** true **Text:** Keep a human review step for any output that reaches a customer, a regulator or a system of record **Critical:** true Give the tool read-only access first and require approval before any write-back Write a short acceptable use policy: what may and may not be entered Train users with real examples, including how to spot wrong answers Set a go or no-go review at the end of the pilot using the success targets #### 5. Manage: monitor and control **Items:** Log who used the tool, what they asked and what it returned, within privacy limits **Text:** Review a sample of outputs monthly for accuracy and policy breaches **Critical:** true Track cost against budget and usage against the licence or token plan Keep a channel for users to report wrong or unsafe outputs Re-check access rights when people change roles or leave Keep the AI tool register current as tools are added or retired #### 6. Maintain: change and retire **Items:** **Text:** Re-test the tool whenever the model, prompt or data source changes **Critical:** true Review vendor terms and security status at each renewal Re-run the risk rating when the use case expands Update training and the acceptable use policy at least yearly Retire unused tools and delete the data you no longer need ## Content **Intro:** AI tools arrive faster than policies. This checklist gives a manufacturer one repeatable process to approve, launch, manage and maintain any AI tool, from a beginner writing assistant to an advanced agent that reads your ERP. It puts data classification, ITAR and customer contract limits at the front. **Sections:** #### How to use the checklist **Body:** Run it once per tool. Beginners can complete the approve, data and vendor groups and stop there for low-risk tools. Tools that touch ERP, quality or export-controlled data need every group. Export the result to PDF as the approval record, or to Excel to build your AI tool register. Items marked critical should block approval until they are done. #### Why manufacturers need a stricter version **Body:** Plants hold data that general business tools do not: drawings, process parameters, supplier pricing and customer-owned technical data. Defense and aerospace contracts can forbid sending that data to outside services, and export rules such as ITAR and EAR restrict who may access technical data. The simplest control is to classify data first and match each class to an approved place where AI may run. **Bullets:** - Public and low-risk content: cloud tools are usually fine - Confidential business data: approved vendors with contract protections - Controlled technical data: only tools inside your own approved boundary - Anything that writes to a system of record: human approval required #### Reading your result **Body:** A high score means you have evidence for approval, controls and monitoring. A low score on data or vendor items is the biggest risk, because a pilot can leak data in the first week. A low score on manage and maintain items is more common, and shows up months later as drift, cost surprises and shadow tools. Fix the critical items first and re-score after the pilot. #### Common mistakes **Body:** Teams approve tools by feature demo with no data review, let staff paste drawings into public chatbots, skip the exit plan, and never retest after a model update. Another frequent error is giving an AI agent write access on day one. Start read-only, show the query or source behind every answer, and add write-back only with human approval. **Cta:** If some of your data cannot go to a public AI service, we build private AI that runs on your own servers over your ERP and documents, with read-only access and human approval for writes. **Faq:** #### What should an AI tools checklist include? **Answer:** At minimum: purpose and owner, data classification, vendor and contract review, a pilot with human review, monitoring after launch, and a retest process when things change. For manufacturers add export control and customer contract checks. #### Can I put drawings into a public AI chatbot? **Answer:** Usually no. Drawings and technical data may be export-controlled or covered by customer contracts that restrict third-party tools. Check your contracts and classification first, and use tools inside an approved boundary for that data. #### Do beginner AI tools need the full checklist? **Answer:** Low-risk tools that only see public or non-sensitive text can use a short version covering purpose, data, vendor terms and an acceptable use policy. Anything that touches ERP, quality or controlled data should use every group. #### How often should AI tools be reviewed? **Answer:** Sample outputs monthly, re-test whenever the model or data source changes, and review the full file at each contract renewal or at least once a year. Retire tools that are no longer used. ## Search terms responsible ai checklist creation, ai tool for checklist generation, ai to create checklist, essential ai tools checklist, launch ai tools checklist, manage ai tools checklist, maintain ai tools checklist, beginner ai tools checklist --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json