Doing more with less

Doing More with Less: How AI Can Help Change Managers Work Smarter

We are joined by Eva Beiner from BearingPoint, who guides us through practical ways Change Managers can use AI, especially Copilot, to reduce repetitive work, support better Change Management practice, and help more people deliver quality change outcomes. With a background in teaching rather than technology, Eva brings a grounded perspective to AI adoption: it does not need to begin with coding or advanced technical knowledge. It can begin step by step, with small tasks, simple prompts and a willingness to experiment.

For Change Managers, the promise of AI is not about replacing judgement, experience or human insight. It is about spending less time on administrative tasks, repetitive work and activities that do not need to be recreated from scratch every time. The practical message is clear: start small, learn how the tool works, build confidence, and then move towards more advanced uses such as agents, libraries and AI-supported ecosystems.

Start With Simple, Useful Tasks

The baseline for using AI is reducing the time spent on administrative and repetitive tasks. This includes activities such as translations, meeting notes, facilitation mock-ups, organising emails and preparing minutes.

At BearingPoint, the starting point is Copilot. It is the only tool used, and it is integrated into the organisation’s own infrastructure. Other tools are not allowed, which becomes important when considering data protection, legal requirements and where information is stored.

The first step is not advanced or complicated. It is deliberately simple:

• Use Copilot for facilitation mock-ups
• Record meetings
• Have the template ready
• Ask Copilot to combine the recording and the template
• Use it to prepare meeting minutes
• Start with organising emails or other easy-to-go tasks

Not every basic use case feels personally relevant to every Change Manager. Organising a calendar, checking emails or being told what to do during the day may not suit every working style. But simple tasks still create useful practice. They help people understand how the tools work, how they respond, and how to shape the input to improve the output.

The value is in using the tool regularly enough to learn how it behaves. As with any skill, confidence builds through use.

Find the Use Cases That Matter in Your Day

Once the basics are understood, the next step is to move from “what can this tool do?” to “what is my use case?”

If sorting emails is not useful, there are likely other use cases where AI can help during the day. BearingPoint explored this through a “day in the life of a Change Manager” approach, identifying practical situations where someone new to AI could try using the tool in normal work.

One example is creating a PowerPoint structure. A Change Manager can ask for a slide template with specific chapters and a defined structure. The result may not be perfect, but it removes the need to begin with a blank page.

That matters when it is late in the evening, the head is not as fresh as it was in the morning, and the blank page is not helping. AI can provide a starting structure in five minutes, giving you something to work from instead of starting at zero.

Other practical use cases include:

• Analysing large Excel files
• Reviewing concepts and documents
• Combining examples from different clients
• Creating a best practice example from existing materials
• Reducing repetitive, less creative work that still needs to be done well

This is not only a technical learning path. It is also a methodology learning path. Change Managers need to learn how AI “thinks”, how it works, and how to treat AI in order to get useful results.

Move Beyond Documents Into More Creative Outputs

After small administrative tasks and documents, the next step is using AI for more advanced outputs.

One example is creating short videos to show a client what something could look like. In a project where two companies need to merge and clean up their data before moving together, the change story is about cleaning up. It is a simple idea, and one that people can recognise from the experience of moving in with somebody else: before moving together, you need to clean up.

Using Copilot, a 10-second video can be created with relevant scenes in only a few minutes. This gives people who may not be deeply involved in communication or creativity a clearer idea of what is meant.

Another example is a key user concept. A Change Manager may have 10 or 20 versions of a concept from different clients, with only minor differences. When working with client change teams, those teams may not always be mature in Change Management. They may be young people, working on their first project, who have never created a concept before.

By bringing together best-in-class concepts, Copilot can be asked to create one best practice concept using the common elements across them. The result can include an agenda and guidance on what to fill in for each chapter.

This creates a practical starting point that can be used with client change teams. It also makes the work more democratic. More people can contribute to better change work, even if they are not Change Managers.

Build Agents for Repeated Change Tasks

The next step is moving from individual prompts to agents.

A prompt may support one task. An agent can support multiple tasks around a repeated activity. A project kick-off is a useful example. Kick-offs happen regularly and often have similar conditions:

• Work out who needs to be in the room
• Prepare the agenda
• Build repeatable task blocks
• Support preparation for the event

The important point is that these agents are not being built by people who know how to code. They are being built by Change Managers. That creates a real opportunity for Change Practitioners to use tools such as Copilot to create small helpers that support better results.

The development path is clear:

1. Start with simple prompting
2. Use prompts for documents and videos
3. Build agents that support multiple tasks
4. Move towards ecosystems that bring tools, prompts and agents together around a purpose

Create Libraries So More People Can Do Better Change Management

AI adoption becomes more powerful when it moves beyond individual productivity and into community enablement.

Within BearingPoint, there is a dedicated global Change Management team of around 50 people. These are people with Change Management education or long experience. But the organisation delivers more than 50 projects a year, which means more people need to understand what Change Management really involves.

Change Management is not just writing a newsletter. To help colleagues and clients do better change work, BearingPoint is building libraries organised around the Change Management framework and the tasks within it.

These libraries can include:

• Tested prompts
• Different prompt versions
• Agents
• Tools from other teams that can support Change Management
• Use cases showing where AI can help or complete parts of the work

The purpose is not to force everyone to use the same prompt. It is about giving people, especially AI beginners, a tested starting point so they do not have to do all the testing and learning alone.

The library also needs to grow and improve. If someone uses a prompt and finds a better way, that improvement can be added back in. The quality grows with every usage, and it must also keep pace with the development of the tools. The first version of BearingPoint’s library is around one and a half years old, and Copilot has improved so much in that time that updates are now needed.

Build AI-Supported Ecosystems Around Change Activities

The next development is the creation of “agentic ecosystems”. These bring together different prompts, tools and agents around one purpose.

Training is a strong example. Training is already well developed, and many training tools already have AI components. The use cases are also relatively easy to identify.

In a training ecosystem, AI can support:

• Planning training events
• Planning courses
• Analysing data from Excel files
• Matching project data with HR system data
• Combining skill analysis from the project with company data
• Defining course outlines
• Supporting content creation
• Connecting with learning platforms and HR systems
• Using avatars for frequently asked question sections, support or smaller training parts

AI can help define courses by bringing together information about project processes, roles and training needs. It can support data analysis across different sources and help build course outlines.

There are still limits. For recordings or longer process-based training, the results are not yet as strong and humans are still needed. A two-hour process training delivered only by an avatar may not be the right experience for everyone. That is also partly a matter of personal preference.

The benefit of an ecosystem approach is that people can work with one data set, compare data within that set, reduce repeated copying, and avoid errors. But this also makes quality, data protection and legal requirements even more important.

Keep Humans in the Quality and Judgement Loop

AI tools are built to give answers. They will give an answer no matter what. Sometimes, that answer is simply wrong.

That means Change Managers must still check the output. In settings involving data, quality issues are critical. Humans need to assess the data, confirm whether it is high quality, and decide whether the output is right or wrong.

This becomes especially important when working with personal data, client data, legal requirements and global teams. BearingPoint uses only Copilot because it can make sure the data used in Copilot stays within its environment. That cannot be secured in the same way when other tools are used.

Data protection becomes even more complex when work is global. Europe has different data privacy rights from the US, and if a system has a database in the US, it may not be suitable to use in Europe.

A simple example makes the point clearly: a person jogging on a warship uploaded the run to the cloud, making the ship’s location visible. If you are not sure where the data is going, you are not sure who will see it.

For Change Managers working with client data or people data, this matters. If client data or people data ends up somewhere it should not, no one will be happy if you are the reason it appeared on the internet.

Use AI Creatively, While Understanding the Limits

AI also creates significant opportunities for creativity, especially in marketing, video, training and communication.

Video has traditionally required high effort. Someone may need to film, an agency may be involved, and production can be time-consuming. It is now possible to create very good and very nice videos with the tool alone.

This opens up opportunities for:

• Training videos
• Communication material
• Illustrations
• Visuals for presentations
• More interactive workshops
• Creative ways to support change activities

AI can also help with background administrative work, creating more time for different varieties of training and more interactive workshops.

There is a lot of opportunity. The sky may feel like the limit, but legal regulations and data protection rules are real boundaries.

Make AI Education Constant and Practical

Successful AI adoption needs ongoing learning.

At BearingPoint, a team in Romania is responsible for spreading the word about AI across the company. They create learning sessions, provide opportunities to check work, and help people when they have trouble.

The support includes:

• Monthly sessions to check agents
• Weekly 15-minute sessions on new AI features
• Learning coaches who explain what works and why
• Constant opportunities to learn more

This ongoing learning is what makes AI adoption successful. It is not about spending four hours a day learning AI and then having only four hours for client work. It may be one hour or one and a half hours exploring something new, while the rest remains normal project work.

Adoption can also be gradual. Some people need to find the use cases that genuinely help them. Organising emails or planning may not be useful for everyone. Illustrations, videos and visible outputs may be far more valuable for others.

The key is to find the use cases that make the tool useful in real work.

Understand How the Tool Works in the Background

Using AI well also requires learning what happens behind the scenes.

For example, Copilot searches in particular ways. It may look for newer content. It may prioritise content written by you. It will only return results from content you have access to. This means two people can write a similar prompt and receive different results because they have access to different information.

That back-end environment matters. When AI is used with Change Managers, client teams or colleagues, enablement cannot focus only on Change Management content. It also needs to explain how AI tools work, what rules need to be considered, and how to use the tools in a trustworthy way.

This is a new task for Change Managers involved in enablement. In the past, an enablement session might focus mainly on the role of leadership or how to develop communication. Now, people also need to understand the tool environment, data protection, access rights and the way AI retrieves information.

Start Small and Keep Learning

The practical advice is simple: start small.

It is easy to feel too busy to learn something new. Heavy project work can make AI learning feel impossible. But this is the classic trap. If you want to learn something new, you need to find 15 minutes somewhere in the week.

That is enough to start learning prompting.

A practical starting point looks like this:

• Start with easy prompts
• Try things out
• Do not worry if the first result is not good
• Create your first useful picture, template or output
• Build from there
• Learn about data protection and governance as you go
• Use mentoring where available
• Keep experimenting

Technical knowledge is not required to begin. Coding is not needed. The important thing is to try, learn, improve and understand the responsibilities that come with using AI well.

For Change Managers, the opportunity is not to hand over the work that requires experience. The value of a good Change Manager still sits in interpreting data, interpreting interviews, seeing behind the curtain, managing the political environment, and applying experience over time.

AI can help more people learn parts of Change Management faster. It can reduce repetitive work, support better starting points, and help spread quality practices more widely. But human judgement remains essential.

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