AI is now making its way into transformation projects. Deliverable production, communication, access to information, support for managers, analysis of field feedback: use cases are multiplying in change management practices. But behind the promised productivity gains, the challenge remains above all organisational and human. Because integrating AI into a transformation project is not just about using a new tool. It also changes how you support, communicate, train and steer adoption. This momentum is part of a rapidly accelerating market: Gartner estimates that global spending on generative AI will reach $644 billion in 2025, an increase of 76.4% compared with 2024.
1. Produce change management deliverables faster
One of the first visible impacts of AI in transformation projects concerns document production. Change teams often have to produce, within short timeframes, communication materials, manager toolkits, FAQs, training plans, operating procedures, executive summaries or presentation decks.
AI makes it possible to generate a first draft deliverable more quickly, rephrase content, summarise workshops or tailor a message to different audiences.
This development is part of a broader automation trend already underway since the 2010s with RPA (Robotic Process Automation). These are software tools capable of automating repetitive, standardised tasks, such as data processing or certain administrative workflows.
Generative AI changes the scale, however: it now automates certain so-called cognitive tasks, such as drafting, summarising or rephrasing.
The time savings are real, but they also shift where the value lies. Yesterday, the effort was mainly focused on producing content. Today, it is more about validation, consistency, contextualisation, message quality and adaptation to the field.
AI can produce a well-structured document, but it still needs to be adjusted to the company culture, the project context and the teams’ real concerns.
The role of change teams therefore remains essential: creating meaning, maintaining consistency and preserving the human dimension of transformations.
2. Personalise communications by audience segment
In a transformation project, the same message cannot always be addressed in the same way to all audiences. The expectations of a frontline manager, a business leadership team or a field employee may differ.
AI now makes it possible to adapt content more quickly: rephrase a message, create multiple levels of detail, adjust the tone, generate different formats or produce targeted communications.
In practical terms, a change team can more easily tailor a project communication for several functions or hierarchical levels without starting from scratch each time.
But this personalisation cannot be purely automatic. AI can adapt the form. It does not replace an understanding of organisational challenges, business constraints, on-the-ground pain points, teams’ maturity level or change fatigue.
The risk would be producing smooth communications that are too generic or insufficiently connected to the reality experienced by teams.
Change management retains an essential role here: listening, arbitrating and contextualising.
3. Improve access to project information
In large transformation projects, information is often scattered across meeting minutes, collaborative spaces, training materials, procedures, project documentation, FAQs, emails or user guides.
Internal AI assistants can help employees find information faster or get an answer more easily. For example, they can help locate a procedure, understand a new rule, identify a point of contact, access project documentation or prepare a change-related action.
The potential is significant, particularly to streamline the employee experience during rollout phases.
This acceleration in usage is already visible in organisations. According to Microsoft and LinkedIn’s Work Trend Index 2024, 75% of employees who primarily use digital tools in their work already use AI.
But an AI assistant is never better than the content it is given. If project documentation is not maintained, structured or made reliable, the answers produced may be approximate or contradictory.
The topic then becomes knowledge governance and the quality of transformation content.
AI can make information more accessible. It does not replace the structuring work carried out by project and change teams.
4. Equip managers and project teams
Managers play a central role in adopting change. They must explain, reassure, answer questions, relay messages and sometimes manage local resistance.
AI can help them prepare certain useful materials or formats more quickly, such as team meeting agendas, facilitation scripts, answers to frequently asked questions, manager action plans, follow-up messages or impact summaries for their teams.
It can also help project teams prepare workshops, structure meeting minutes, formalise action plans or consolidate points of attention raised from the field.
The benefit is to make preparation easier and reduce the time spent on certain documentation tasks.
But AI does not replace the managerial role. A manager remains the one who embodies the change, addresses concerns and adapts the message to their team. Likewise, a project team remains responsible for trade-offs, prioritisation and the quality of the support approach.
AI can therefore support change actors. It does not replace their ability to listen, decide and build relationships.
As AI becomes integrated into the day-to-day practices of managers and project teams, organisations must also structure its use more rigorously. The AI Act, which entered into force in the European Union on 1 August 2024, aims to regulate the development and use of AI through a risk-based approach. For companies, this reinforces the importance of defining clear rules of use and supporting employees in adopting these tools.
5. Analyse field feedback and detect weak signals
Transformation projects generate a significant amount of data: surveys, feedback, verbatims, user feedback, recurring questions or adoption indicators.
AI can help change teams analyse this information more quickly to identify pain points, detect resistance, spot misunderstandings, track how perceptions evolve or prioritise support actions.
This is an interesting lever to make change management more responsive and more data-driven.
But caution is needed. AI can surface trends. It replaces neither listening on the ground, nor human exchanges, nor a nuanced understanding of organisational dynamics.
An indicator never tells the whole story of a transformation.
Technology therefore remains a decision-support tool, not a substitute for relational and managerial work.
In summary
AI is already transforming change management practices. It does not replace transformation teams, but it changes how they produce, communicate, equip, analyse and support.
The most tangible gains are often the most operational: producing certain deliverables faster, better personalising communications, streamlining access to information, supporting managers and project teams, or analysing field feedback more effectively.
But AI is also becoming a change management topic in its own right. Its uses must be explained, framed and supported so they are understood and adopted sustainably.
Because the success of a transformation never relies solely on tools. It depends on the ability to build buy-in, understand on-the-ground realities and support changes in practices.
AI is transforming transformation projects. Change management remains what enables transformations to be truly adopted.
Writing
Emmanuelle Rassek
People & Transformation Director
