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Four changes GenAI is (and isn't) making to stakeholder engagement

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It's widely agreed that artificial intelligence (AI) will change the face of the project profession. In fact, a 2019 Gartner study predicted that 80% of today’s project management tasks will be done using AI by 2030.

While it's easy to see the benefits of AI for, e.g., task automation, administration and data analysis, what isn’t clear is how it will affect stakeholder engagement. After all, this is inherently a people-focused skill, so what will robots and software bring to the table?

To answer this question, I considered the current capabilities of AI within the four stages of the stakeholder management process. My aim? To give a view on the things that will and won’t change in the stakeholder management space in the next few years, and where I’d recommend putting your focus right now.

1. Identifying stakeholders

To set the foundation of your stakeholder management approach, you need to identify those with an interest in, and those who can impact, your project. There are many classic ways to identify internal and external stakeholders, but how can AI help?

I jumped onto ChatGPT, imagining I was an early career project manager in an entirely new sector. With some fairly basic prompts, I instantly identified 40 stakeholder groups (e.g., local authority, IT director) related to my imaginary project. In practice, this would save time, reduce the risk of stakeholder gaps and provide a foundation for a more detailed stakeholder identification workshop with the team.

But AI won’t truly revolutionise this process until these tools are integrated into company networks and can identify stakeholders by name. That said, it’s a great start and an easy way for you to save some time.

2. Analysing stakeholders

Once you’ve identified your stakeholders, analysing their opinions, wants, needs and philosophies will help you shape your engagement approach.

I asked three separate GenAI tools (ChatGPT, Claude and Google Gemini) to:

  • rank five stakeholders in terms of their ability to impact a project
  • provide a justification for the ranking
  • predict the consequences for the project if they weren’t correctly engaged

All three gave slightly different answers, with reasonable justifications and consequences — I was impressed! As with stakeholder identification, this would provide a great baseline to build from as a team.

The problem? The data these tools are built on is generic; it doesn’t have the context of your project and your organisation to create a bespoke analysis you could truly rely on.

So, for now, keep focusing on traditional analysis techniques, but use GenAI to spark new ideas or validate your thinking.

3. Planning and executing stakeholder engagement

Once you’ve got deeper insight on your stakeholders, it’s time to begin planning your engagement approach.

I tried using Taskade to create a stakeholder engagement plan based on specific stakeholder communication preferences and calendar availability. It created a reasonable plan, but didn't have the power or integration to automatically schedule the tasks and meetings for me.

For creating communications, there are various AI tools (e.g., Prowly, Grammarly and SlidesGo) that can help craft emails, build presentations and exchange Teams messages. While they take away a lot of legwork, they lack a human touch, risking your engagement coming off as robotic.

So, continue scheduling and overseeing your engagement plans, but use AI tools to help you save time and craft consistent, error-free communications.

4. Monitoring stakeholder feedback

Regularly capturing stakeholder feedback helps you adjust your approach as the project progresses.

The game-changer here could be sentiment analysis — the ability to analyse the language used by stakeholders to understand their attitudes and predict future behaviour.

Big hitters like Microsoft are beginning to build sentiment analysis into tools such as Outlook, but right now, they only provide a high-level assessment of sentiment (e.g., positive, negative, neutral). Unfortunately, that doesn’t offer greater insight than you could.

In the future, tools such as sentiment analysis will help you to understand your stakeholders better and predict future thoughts and feelings, but AI can only make assessments with the data it has. Face-to-face and virtual engagements are essential parts of project management, so you need to continue fostering strong interpersonal relationships away from your inbox.

For monitoring stakeholder feedback, AI isn’t offering much value yet, so continue to invest time in gathering feedback yourself to gauge the mood of your stakeholders and drive project success.


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