# AI Pilot-to-Production Readiness Assessment: Will Your Prototype Survive Deployment? > Source: https://netray.co/tools/ai-pilot-to-production-readiness-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score your AI pilot against the ten gates that decide whether it reaches production, and get a prioritized list of the gaps blocking deployment. ## Category ai-automation ## Tool **Kind:** assessment **Questions:** #### Has the pilot been measured against a documented accuracy or quality threshold? **Help:** A pilot without a pass/fail bar cannot be approved or rejected on evidence, so it stalls in permanent evaluation. **Options:** ##### Yes, a written threshold agreed with the business, measured on a held-out set **Score:** 3 ##### We measure accuracy but the acceptable threshold was never formally agreed **Score:** 2 ##### Only informal spot checks by the build team **Score:** 1 ##### No quality measurement beyond demo impressions **Score:** 0 #### Is there a named production owner with a funded run budget? **Options:** ##### Named owner, funded operating budget, and an SLA in writing **Score:** 3 ##### Named owner but run costs are still coming from project funds **Score:** 2 ##### Ownership is assumed to sit with IT but nothing is documented **Score:** 1 ##### No owner identified; the pilot team disperses after the demo **Score:** 0 #### How does the pilot get its data today? **Help:** Hand-curated extracts are the single most common reason a working demo cannot be scheduled in production. **Options:** ##### Automated pipeline from systems of record with monitoring and retries **Score:** 3 ##### Scheduled extract that runs unattended but has no alerting **Score:** 2 ##### Semi-manual refresh a person runs weekly **Score:** 1 ##### A one-time hand-curated extract prepared for the demo **Score:** 0 #### What security review has the solution passed? **Options:** ##### Full review completed: data classification, access model, and threat model signed off **Score:** 3 ##### Review underway with no blocking findings so far **Score:** 2 ##### Informal conversation with security; nothing documented **Score:** 1 ##### No security involvement yet **Score:** 0 #### How will model output quality be monitored after go-live? **Help:** Model behavior drifts as source data, prompts, and vendor models change underneath you. **Options:** ##### Automated evaluation on a golden set plus alerting on drift and refusal rates **Score:** 3 ##### Sampled human review on a fixed cadence **Score:** 2 ##### We rely on users reporting bad output **Score:** 1 ##### No monitoring plan exists **Score:** 0 #### Do you know the fully loaded cost per transaction at production volume? **Options:** ##### Yes, modeled at peak volume including inference, storage, and support labor **Score:** 3 ##### Inference cost is known but support and infrastructure costs are not **Score:** 2 ##### Rough guess based on pilot spend **Score:** 1 ##### Unit economics have not been calculated **Score:** 0 #### How is the solution integrated with your systems of record? **Help:** For ERP-adjacent use cases, read-only integration is easy; writing back safely is where projects stall. **Options:** ##### Bidirectional integration through supported APIs with transaction logging **Score:** 3 ##### Read-only integration into SyteLine, LN, or M3 through a supported interface **Score:** 2 ##### Screen scraping, file drops, or an unsupported direct database read **Score:** 1 ##### No integration; users copy and paste between systems **Score:** 0 #### Is there a designed human-in-the-loop and escalation path? **Options:** ##### Confidence thresholds route uncertain cases to named humans with tracked resolution **Score:** 3 ##### Humans review all output before it takes effect **Score:** 2 ##### Users are told to check the output but nothing enforces it **Score:** 1 ##### Output is applied automatically with no review path **Score:** 0 #### What change management and training is planned for end users? **Options:** ##### Role-based training, updated work instructions, and adoption tracked as a KPI **Score:** 3 ##### A training session and documentation, but adoption is not measured **Score:** 2 ##### An announcement email and a link to the tool **Score:** 1 ##### Nothing planned; we expect users to figure it out **Score:** 0 #### Can you roll back a bad model, prompt, or configuration change? **Help:** Prompts and model versions are production code and need the same controls as any other release. **Options:** ##### Everything is versioned in source control with tested rollback and staged releases **Score:** 3 ##### Versioned but rollback has never been rehearsed **Score:** 2 ##### Prompts and configs live in a console with no version history **Score:** 1 ##### Changes are made directly in production by whoever has access **Score:** 0 **Bands:** #### Demo stage: not deployable yet **Min:** 0 **Max:** 39 **Verdict:** What you have is a convincing demonstration, not a system. The gaps are almost certainly in ownership, data plumbing, and monitoring rather than in model quality. Pushing this into production now creates an unsupported service that quietly degrades and erodes executive confidence in AI generally. **Recommendations:** - Name a single production owner and a run budget before writing another line of code. - Replace the hand-curated data extract with an automated, monitored pipeline from your system of record. - Agree a written accuracy threshold with the business sponsor and measure against a held-out set. - Book a security review now so data classification findings do not surface at the last minute. - Reframe the next 60 days as hardening rather than new features. #### Pilot stage: real gaps remain **Min:** 40 **Max:** 64 **Verdict:** You have a functioning pilot with a few genuine production blockers, typically monitoring, unit economics, or rollback discipline. This is the most dangerous band because the solution looks ready and stakeholders expect a date. Close the specific gaps below before committing to one. **Recommendations:** - Model fully loaded cost per transaction at peak volume, including support labor, not just inference. - Stand up automated evaluation against a golden set so quality regressions are caught by machines. - Put prompts, model versions, and configuration under source control with a rehearsed rollback. - Design an explicit escalation path with confidence thresholds and named human reviewers. - Pilot with 10-20 real users for four weeks and track adoption, not just satisfaction. #### Production candidate: tighten and go **Min:** 65 **Max:** 84 **Verdict:** This solution is close. The engineering fundamentals are in place and the remaining gaps are usually operational: adoption tracking, formal SLAs, or writeback integration. Manufacturers in this band typically reach production within a quarter if they resist the temptation to add scope. **Recommendations:** - Freeze scope and convert the remaining gaps into a dated hardening backlog. - Publish an SLA covering availability, response time, and quality, and agree who is paged when it breaks. - Upgrade read-only integration to supported bidirectional APIs if writeback delivers the real value. - Make adoption a tracked KPI with a named owner in the business, not just in IT. - Run a production readiness review with security, infrastructure, and the business sponsor in one room. #### Production ready: scale the pattern **Min:** 85 **Max:** 100 **Verdict:** Your pilot clears the gates that stop most enterprise AI projects. The highest-value move now is not this use case but the next five: turn what you built into a repeatable platform pattern so the second and third deployments cost a fraction of the first. **Recommendations:** - Document the deployment as a reference architecture other teams can copy. - Extract shared components (retrieval, evaluation, logging, auth) into a common internal platform. - Publish the unit economics so future use cases are prioritized on real numbers. - Set a quarterly evaluation cadence so drift is caught before users notice it. - Build an intake process so new AI requests are scored against value and effort, not enthusiasm. ## Content **Intro:** This free AI pilot-to-production readiness assessment scores a working AI prototype against the ten gates that decide whether it survives contact with real users. It is built for enterprise IT directors, ERP leaders, and AI product owners at manufacturers who have a promising demo and no clear path to deployment. You answer ten questions covering accuracy thresholds, ownership, data pipelines, security review, monitoring, unit economics, ERP integration, escalation design, change management, and rollback. The result is a percentage score, a maturity band, and a prioritized list of the specific gaps blocking production. Most pilots fail on ownership and monitoring, not on model quality. **Sections:** #### How the assessment is scored **Body:** Each of the ten questions carries four options worth zero to three points, so the maximum raw score is thirty. Your answers are converted to a percentage and matched to one of four bands: demo stage below 40%, pilot stage from 40 to 64%, production candidate from 65 to 84%, and production ready at 85% and above. The questions are deliberately weighted equally because in practice a single unaddressed gate is enough to stop a deployment. A team with a state-of-the-art model and no named production owner will not ship. A team with a modest model, clean data plumbing, and a funded owner usually will. #### The benchmarks behind the ten gates **Body:** The gates come from published industry failure analysis and from what we see across enterprise AI engagements in aerospace, defense, and discrete manufacturing. Roughly three quarters of enterprise AI pilots never reach sustained production, and the failure modes cluster tightly. Use these reference points when you interpret your band. **Bullets:** - Hand-curated demo data is the most common single blocker: the pipeline, not the model, is the hard part. - Pilots without a funded run budget almost never survive the first budget cycle after the project closes. - Unmonitored models degrade silently as source data, prompts, and vendor model versions change. - Solutions with no rollback path get frozen after their first bad release and quietly abandoned. #### How to read your score **Body:** Read the individual zeros before the headline number. A score of 70% with a zero on security review is riskier than a score of 55% spread evenly, because a hard blocker cannot be averaged away. Look for the pattern too. Low scores concentrated in questions three, five, and ten mean an engineering discipline problem that a platform investment fixes once for every future use case. Low scores in questions two, nine, and eight mean an organizational problem that no amount of engineering will solve. Fix the category with the most zeros first, then re-take the assessment after each sprint to watch the trend move. #### How Netray helps you cross the gap **Body:** Netray builds enterprise AI that runs inside your firewall and connects to the systems your business actually runs on: Infor SyteLine and CloudSuite Industrial, Infor LN and Baan, Infor M3, ServiceMax, and Salesforce. We take pilots that work on a laptop and turn them into monitored, owned, integrated services with real unit economics. Because we deploy on-prem and air-gapped, ITAR, EAR, and CMMC constraints in aerospace and defense are a design input rather than a project blocker. A typical engagement starts by hardening one pilot to production in 60-90 days, then extracting the reusable platform components so the next five use cases cost far less than the first. **Cta:** Get a personalized production readiness review and a dated hardening plan from Netray's enterprise AI engineers. **Faq:** #### Why do so many enterprise AI pilots never reach production? **Answer:** The dominant reasons are organizational rather than technical. Pilots are funded as projects with no operating budget, run on hand-prepared data that nobody automated, and have no named owner once the build team disperses. Model quality is rarely the binding constraint. Teams that treat the pilot as the first ten percent of the work, and budget for pipelines, monitoring, and support from day one, ship far more reliably than teams chasing accuracy points. #### How accurate does an AI system need to be before it can go live? **Answer:** There is no universal threshold, only a threshold relative to the process it replaces and the cost of an error. An assistant drafting a first-pass response can ship at accuracy that would be unacceptable for a system posting inventory transactions. Agree the bar in writing with the business owner before you build, measure it on data the model has never seen, and design an escalation path so uncertain cases reach a human instead of being guessed. #### Should we harden the current pilot or rebuild it properly? **Answer:** Rebuild when the pilot depends on manual data preparation, has no version control, or was built on a platform you cannot secure or support. Harden when the data pipeline and integration are sound and the gaps are monitoring, ownership, or documentation. In practice most pilots need a partial rebuild of the data and integration layers while the prompts, evaluation sets, and user interface carry forward intact. ## Search terms AI pilot to production, AI production readiness assessment, why AI pilots fail, enterprise AI deployment checklist, AI proof of concept to production, AI readiness score, moving AI from pilot to scale --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json