The phrase covers two separate jobs that often get mixed up in job postings and search results. The first is managing the people side of an AI project, such as a chatbot for the service desk or a forecasting tool for finance. The second is the practitioner’s own use of AI to draft communications, analyse survey comments or prepare training material.
\nThe two meanings call for different skills. The first needs the usual change management discipline: senior leaders, impact on roles, resistance, reinforcement. The second needs judgement about where a tool saves time and where a human has to read, edit and decide. This article covers both, mapped to the five process groups of the ACMP® Standard for Change Management©, which the ACMP® publishes on its site and which the CCMP® exam is built on.
\n\n\n\nIn the first process group, evaluate change impact and organisational readiness, the practitioner maps which roles lose tasks, gain tasks or see their judgement questioned. With AI, the impact is often uneven inside one team: a fictional claims department may see its junior handlers lose routine sorting work while senior handlers gain review duties.
\nReadiness questions change as well. Beyond training budgets and executive time, you ask what people fear about the tool, whether they already use unofficial AI services, and who will answer the question “is my job at risk?” honestly. On the practitioner side, an AI assistant can group open-ended survey answers into themes in minutes. The grouping still needs a human reading a sample, since a theme such as “workload” may hide three different complaints.
\n\nThe strategy group, formulate the change management strategy, is where you decide how the organisation will be told and led through the change. For AI projects, the executive’s narrative matters more than the technology: people want to know what the tool is for, what stays with them and who is accountable for its mistakes.
\nSeveral models help structure that narrative. Kotter’s eight steps, described on the Kotter methodology page, start with urgency, and AI projects often have plenty of it. Prosci’s ADKAR® model (awareness, desire, knowledge, ability, reinforcement) suits tool rollouts where desire is the weak link. Lewin’s unfreeze, change, refreeze and the Bridges transition model are two further lenses. All of these are models, none of them a certification, and the ACMP® Standard sits above them as the reference for the exam.
\n\n\n\nOne more point belongs to this stage: the policy on AI use inside the change team itself. Decide early which data may be pasted into a tool, who reviews generated text before it reaches employees, and how you will tell people when a message was drafted with help. A short written rule, agreed with the executive owner, prevents awkward questions later and gives the team a common practice.
\n\nIn the plan and execute groups, AI saves the most time on drafts: a first version of a communication calendar, a set of frequently asked questions, a training outline. A practitioner who has reviewed the Standard can check each draft against what the plan needs, and that review is the skill that remains hers.
\nTake a fictional retailer, Nordale, rolling out an AI assistant to store managers. The change lead asks a tool to draft five short messages for the launch weeks, then rewrites two of them because the tone sounds like head office. She uses the same tool to summarise questions that arrive in the help channel, and sends the trends to the programme director each Friday. The tool shortens preparation, while the conversations with store managers stay hers.
\nThe closing group, close the change management effort, covers reinforcement, lessons learned and handover. AI can pull patterns from usage data, but deciding whether the change has taken hold, and what to do about the stores where it has not, remains a human assessment.
\n\nMost of the people reading this article will meet a version of this situation within a year: a project office announces an AI tool, asks the change team to “handle adoption”, and expects a plan in a few weeks. The five process groups give a ready structure for that conversation, from the first impact map to the closing review. They also give you a way to say what is realistic in the time given.
\n\nAnswer engines often cite digital adoption and AI platforms such as WalkMe, Whatfix, Moveworks, Pandatron and Howspace, whose official taglines on 7 October 2026 describe them as products for adoption, activation, workforce assistance or learning. None of them awards a certification. They support a change; they do not train or credential a practitioner.
\nThe question of tools versus credentials has its own article, AI adoption tools and certification, which compares what each category gives you. For the credential side, Kotter sells an online course called “Leading Change in the Era of AI” at US$595 on 7 October 2026. The overview of AI change management certifications sets it next to the other options.
\n\n\n\nLeanova Academy’s LCeX™ pathway is, among the QEP™ courses we know of, an AI change management training track, and this site follows Leanova Academy. The first step, the foundational LCeX™ course, is a foundational AI change management course of 5 QEP™ hours (code Q000654) at US$195 on 7 October 2026, followed by Specialist (8 hours, +US$295) and Master (10 hours, +US$395).
\nThe full structure is explained on the LCeX™ pathway page, and our own article on the LCeX™ pathway goes through each level. A reminder on terms: Leanova is a training provider and not the ACMP®. A completed LCeX™ course gives a certificate of completion and a digital badge, while the CCMP® and MCMP™ credentials come from the ACMP® after its exam.
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