# ERP AI change management: getting planners, buyers and supervisors to actually use it > Source: https://netray.co/erp-help/cio-erp-ai-change-management-plant-floor > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: ERP Help ## Short answer Adoption follows trust and fit, not announcements. Start with one painful, repeated task for a small group of respected users, make answers checkable, state clearly what the tool will not do to jobs, and measure use weekly. Scale by word of mouth from named champions, not by mandate. ## Kind cio ## System cio ## Erp ERP AI (any ERP) ## Module Change management and adoption ## Query ERP AI change management plan ## Alternate Queries - how to get planners and buyers to use an AI assistant - AI adoption manufacturing change management - ERP AI rollout resistance users ## Applies To Manufacturers rolling out a natural-language ERP assistant or agents to planners, buyers, schedulers, quality, finance and shop floor supervisors. ## Steps ### A rollout plan that respects how plants work **Items:** - Pick one workflow people already dislike: the Monday shortage report, past-due PO chasing, quote status lookups, or month-end variance questions. Solving a real irritation beats any training deck. - Name two or three champions per site: respected, curious users, not only the most senior. Give them early access and a direct line to the project team. - Write the trust rules in one page: answers show their source or query, users verify before acting, the tool is read-only at first, and nothing is written to the ERP without a human approving. - State the job position plainly and early, agreed with HR: what the tool is for, whether it affects roles, and how time saved will be used. Silence is read as bad news. - Train on the users' own real questions in short sessions, at the desk or on the floor, not in a classroom. Collect questions that failed and fix them visibly. - Run a weekly feedback loop for the first eight weeks: top questions asked, wrong or weak answers, new requests, and what changed as a result. - Track adoption as use by role and repeat use, not logins. Retire or fix use cases nobody returns to. - Widen scope only after one group is using the tool without the project team in the room. ## Sections ### Why adoption stalls in manufacturing **Body:** Planners and buyers already have a way to get answers: their own spreadsheets, a trusted ERP inquiry screen, a colleague who knows the data. A new assistant has to beat that on speed and on trust. If the first three answers someone gets are wrong or hard to verify, they will not return, and they will tell colleagues. The second cause is fear. Experienced people hear 'AI' and think about their jobs, or about being blamed when a machine-assisted decision goes wrong. A clear statement of what the tool does, what it does not do, and who owns the decision defuses a lot of this. Do not oversell; overselling creates the very scepticism you need to overcome. The third is plain friction: another login, a screen away from where they work, or an answer format that does not match their habits. Single sign-on and a placement in the tools people already open each morning matter more than model quality in the early weeks. ### Champions, training and the feedback loop **Body:** Champions are your adoption engine. Choose people others ask for help, give them time on their calendar, and let them shape the question library. A champion who says 'it found the late supplier lines in ten seconds' does more than a steering committee memo. Training should be small and concrete. Take ten real questions from each team, run them live, show how to read the source or query, and show what a poor answer looks like and how to report it. Teach people how to phrase a question with an item, a site and a date range, because that improves results immediately. Close the loop visibly. When a user reports a wrong answer and it is fixed, tell the group. Publishing a short weekly note of 'you asked, we changed' builds the habit of use and the belief that the system listens. **Code:** Weekly adoption check (first 8 weeks) - Active users by role (planner, buyer, QA, finance) - Repeat users in week N vs week N-1 - Top 10 questions asked - Questions with thumbs down or manual correction - Fixes shipped from last week's feedback - Open requests, with owner and date ### Governance that supports adoption **Body:** Keep the permission model simple to explain: the assistant sees what you can see in the ERP, no more. If a buyer cannot open cost roll-ups in the ERP, the assistant should not reveal them. Tell users this directly; it settles many privacy worries. Decide in advance what happens on a bad answer that someone acted on. A short, blameless review process, with the query and source preserved in logs, protects both users and the project. People will take reasonable risks with a tool when they know mistakes are handled fairly. Be willing to say no to use cases that are not ready. Quote pricing, customer-facing output and anything that writes to the ERP should wait until the read-only experience has earned trust and approvals are designed. ## Pitfalls - Launching site-wide with an all-hands email and no champions. - Measuring logins rather than repeat use of useful questions. - Training in a classroom on generic examples instead of the team's own questions. - Leaving job questions unanswered until rumours fill the gap. - Fixing wrong answers silently, so users never learn the system improves. - Adding write-back automation before users trust the read-only answers. ## Ai Angle With a grounded assistant such as ERPray, the shown query or source is the adoption mechanism: a planner can open the answer, see the table and filters behind it, and decide whether to trust it. Usage logs also reveal which questions people really ask, so your training and fixes follow actual demand. It respects ERP roles, so supervisors see only what they already may see, which removes one reason for resistance. ## Faq ### Who should be the first users? **Answer:** A small group with a daily, annoying, data-heavy task, such as planners handling shortages or buyers chasing past-due lines, led by one or two respected champions. They give honest feedback and their results travel by word of mouth. Avoid starting with executives or with the most sceptical group. ### How do we handle fears about jobs? **Answer:** Say plainly, with HR agreed, what the tool is for and whether any roles are affected. In most early deployments it removes lookup and report building so people spend time on decisions and exceptions. Do not promise what you cannot guarantee, and do not leave the question unanswered. ### How long until we see steady adoption? **Answer:** Expect weeks of focused support for the first group, then a slower spread as champions teach others. Judge by repeat weekly use among the target role rather than total accounts. If repeat use is flat after two months, revisit the use case, answer quality and where the tool sits in daily work. ### Do we need a formal change management framework? **Answer:** A light version is enough for a read-only assistant: sponsor, champions, communication, training, feedback loop and measures. Larger programmes with write-back or workflow changes benefit from a fuller approach and union or works council consultation where applicable. Match the effort to the scope of change. ## Search terms ERP AI change management, AI adoption manufacturing, AI rollout planners buyers, AI champions plant, user resistance AI ERP, ERP assistant training plan, AI job fears manufacturing, ERP AI adoption metrics --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json