# Manufacturing AI Readiness Assessment > Source: https://netray.co/tools/manufacturing-ai-readiness-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score your manufacturing operation's readiness for AI across data, systems, people, and governance, and get a prioritized roadmap for closing the gaps. ## Category ai-automation ## Tool **Kind:** assessment **Questions:** #### How is production data captured on your shop floor? **Options:** ##### Paper travelers and clipboards **Score:** 0 ##### Spreadsheets keyed in after the fact **Score:** 1 ##### Digital capture in the ERP or MES, some delay **Score:** 2 ##### Real-time digital capture from machines and operators **Score:** 3 #### How would you rate the quality of your ERP master data (items, BOMs, routings, suppliers)? **Help:** AI models trained or grounded on bad master data amplify the errors. **Options:** ##### Widely mistrusted, workarounds everywhere **Score:** 0 ##### Usable but with known gaps and duplicates **Score:** 1 ##### Mostly clean, periodic audits **Score:** 2 ##### Clean, owned, and actively governed **Score:** 3 #### How integrated are your core systems (ERP, MES, quality, PLM, CRM)? **Options:** ##### Islands - data moves by spreadsheet and email **Score:** 0 ##### A few point-to-point file transfers **Score:** 1 ##### Key systems integrated, some manual bridges remain **Score:** 2 ##### Broad API-level integration across the stack **Score:** 3 #### Can your machines and equipment share data with your IT systems? **Options:** ##### No connectivity, operators transcribe readings **Score:** 0 ##### A few newer machines log data locally **Score:** 1 ##### Partial IT/OT connectivity on critical equipment **Score:** 2 ##### Most equipment networked with historian or IoT platform **Score:** 3 #### How does your organization use data for decisions today? **Options:** ##### Gut feel and tribal knowledge **Score:** 0 ##### Static monthly reports **Score:** 1 ##### Dashboards and KPIs reviewed regularly **Score:** 2 ##### Analytics drive daily decisions, some predictive use **Score:** 3 #### What AI or advanced automation is already in use? **Options:** ##### None **Score:** 0 ##### Individual employees experiment with AI tools **Score:** 1 ##### One or two sanctioned pilots underway **Score:** 2 ##### At least one AI or automation solution in production **Score:** 3 #### What internal skills exist to support AI initiatives? **Options:** ##### No IT beyond keeping systems running **Score:** 0 ##### Capable IT team, no data or AI experience **Score:** 1 ##### Some analytics skill, would need partner support for AI **Score:** 2 ##### Data engineering or data science capability in-house **Score:** 3 #### Where does executive leadership stand on AI? **Options:** ##### Skeptical or not on the agenda **Score:** 0 ##### Curious but no commitment **Score:** 1 ##### Supportive, evaluating where to invest **Score:** 2 ##### Committed sponsor with budget allocated **Score:** 3 #### Do you have a concrete list of AI use cases with business value attached? **Options:** ##### No, just a general sense AI could help **Score:** 0 ##### A brainstormed list, unquantified **Score:** 1 ##### A shortlist with rough value estimates **Score:** 2 ##### A prioritized pipeline with baselined ROI per use case **Score:** 3 #### How mature are your data security and AI governance practices? **Help:** Critical for regulated manufacturers handling ITAR, CUI, or customer IP. **Options:** ##### No formal policies on data handling or AI use **Score:** 0 ##### General IT security, nothing AI-specific **Score:** 1 ##### AI usage policy drafted, data classification underway **Score:** 2 ##### Enforced AI governance including data residency rules **Score:** 3 **Bands:** #### Early Stage: Build the Foundation First **Min:** 0 **Max:** 39 **Verdict:** Your operation is not yet positioned to get value from AI at scale, and jumping straight to AI projects would likely waste budget. The good news is the foundational work, which is digitizing data capture and cleaning master data, pays for itself even before any AI arrives. **Recommendations:** - Digitize shop floor data capture on one or two critical work centers as a starting point. - Launch a master data cleanup focused on items, BOMs, and routings, with named owners. - Pick one narrow, low-risk automation, such as document processing, to build organizational confidence. - Brief leadership on realistic AI economics using peer manufacturer case studies. - Reassess in six months; most manufacturers can move a full band in that time. #### Developing: Ready for Targeted Pilots **Min:** 40 **Max:** 69 **Verdict:** You have enough digital foundation to run focused AI pilots successfully, though gaps in integration, skills, or governance will limit how far you can scale. The right move is a narrow, high-ROI pilot that delivers a visible win while you close the specific gaps this assessment flagged. **Recommendations:** - Select one pilot in your strongest area, typically back-office automation like AP or order entry, where data is already digital. - Fix your lowest-scoring dimension in parallel; integration and governance gaps compound as you scale. - Baseline current-state metrics before the pilot so ROI is provable, not anecdotal. - Establish an AI usage and data-handling policy before pilots multiply informally. - Partner for skills you lack rather than pausing to hire; transfer knowledge during the engagement. #### AI-Ready: Scale With Confidence **Min:** 70 **Max:** 100 **Verdict:** Your data, systems, sponsorship, and governance are strong enough to deploy AI in production and scale it across processes. Your risk is no longer readiness but sequencing: choosing the highest-return use cases and building a repeatable delivery pipeline instead of scattered one-off projects. **Recommendations:** - Stand up a prioritized use case pipeline with baselined ROI, and review it quarterly. - Deploy your first production AI agents on high-volume ERP workflows where payback is fastest. - Build a reusable platform layer, covering integration, monitoring, and human review, so each new use case ships faster than the last. - Formalize model and agent governance: accuracy monitoring, escalation rules, and audit trails. - Evaluate on-prem AI infrastructure if you handle ITAR, CUI, or sensitive customer IP. ## Content **Intro:** This free assessment scores your manufacturing operation's readiness for AI across ten dimensions covering data capture, master data quality, system integration, skills, leadership, and governance. It is designed for plant managers, IT directors, and executives at discrete manufacturers who are being pushed to adopt AI but want an honest picture of their starting point first. In about three minutes you get a 0-100 readiness score, a band verdict, and specific recommendations that tell you whether to pilot now or build foundations first. **Sections:** #### What the assessment measures and why **Body:** The ten questions cover the four pillars that determine whether AI investments pay off in manufacturing: data (capture, quality, and accessibility), systems (integration and IT/OT connectivity), people (skills and executive sponsorship), and governance (security policy and use case discipline). The pillars are weighted equally because failure in any one of them sinks projects: brilliant models grounded on mistrusted master data produce mistrusted answers, and well-built pilots without executive sponsorship die in budget cycles. Your score is the percentage of the maximum across all ten questions, and the band verdicts are calibrated against patterns we see in real manufacturer engagements. #### Where mid-market manufacturers typically land **Body:** Most discrete manufacturers we assess score in the 40-65 range, which is genuinely good news: it means targeted pilots can succeed now while foundations improve in parallel. Patterns worth knowing: **Bullets:** - Back-office data readiness almost always outpaces shop floor readiness, which is why document and order automation usually make the best first pilots. - Master data quality is the most common hidden blocker; it scores low even at otherwise sophisticated plants. - Executive sponsorship has risen sharply since 2024, but use case pipelines with quantified ROI remain rare. - Governance scores lowest of all dimensions, which becomes urgent for ITAR and defense-adjacent manufacturers. #### How to act on your score **Body:** Treat your band as a sequencing instruction, not a grade. Early-stage operations should resist AI vendor pressure and spend two quarters on data capture and master data, which improves operations regardless of AI. Developing operations should run one narrow pilot in their strongest area while fixing their weakest dimension, because a visible win funds everything that follows. AI-ready operations should shift attention from readiness to throughput: a prioritized pipeline, a reusable delivery platform, and governance that scales. In every band, your lowest-scoring individual questions are your to-do list, and most manufacturers can move a full band within six to nine months of focused work. #### How Netray helps you close the gaps **Body:** Netray works with discrete manufacturers, including aerospace, defense, and electronics firms, at every readiness level. For early-stage operations we deliver data foundation work: ERP data cleanup, integration architecture, and digitized capture. For developing operations we build first pilots on high-ROI ERP workflows in SyteLine, LN, and Baan, structured so your team learns the delivery pattern. For AI-ready operations we design agent platforms and on-prem AI infrastructure that keep sensitive data inside your walls while scaling automation across the business. Wherever you scored, we can show you the next concrete step. **Cta:** Book a free readiness review with Netray and turn your score into a sequenced 12-month AI roadmap. **Faq:** #### We scored in the early band. Should we really wait on AI entirely? **Answer:** Not entirely, but be surgical. Avoid ambitious shop-floor AI, which will fail on data you do not yet capture, and instead run one contained back-office automation such as invoice or document processing, where the data is already digital. That single project builds skills, proves governance, and creates the internal credibility you will need later, while your foundational data work proceeds in parallel. What you should defer is anything requiring clean historical data or broad integration. #### How long does it take to move from one readiness band to the next? **Answer:** Six to nine months is typical for a focused mid-market manufacturer. The fastest movers pick two or three specific weaknesses from their assessment, such as master data ownership and an AI usage policy, and treat them as projects with owners and deadlines rather than aspirations. Moving from early to developing is mostly data and digitization work; moving from developing to AI-ready is mostly integration, governance, and building a quantified use case pipeline. #### Does AI readiness require expensive new infrastructure? **Answer:** Usually not at the pilot stage. Most first AI projects run on your existing ERP data and standard cloud or modest on-prem compute, and the real investments are in data quality and integration, which are labor rather than hardware. Infrastructure decisions become significant when you scale, particularly for regulated manufacturers who need on-prem GPU capacity to keep ITAR or CUI data resident. Even then, a single well-specified server often supports several production AI workloads. ## Search terms manufacturing AI readiness assessment, AI readiness checklist manufacturing, is my factory ready for AI, industrial AI maturity, smart manufacturing readiness, AI adoption manufacturing assessment, shop floor data readiness --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json