7 practical AI ideas for anyone working in communications or change management

For anyone who wants to use AI for more than first drafts and meeting summaries

Like most people, those who work in communications and change management almost always have more on their list than they have the capacity to deliver. This is especially true for internal communications and internal change projects, which usually run on tighter budgets than external communications or marketing. That said, several of the tips in this article work just as well in both areas.

AI has opened up new possibilities across most business functions, but communications and messaging are perhaps among the areas with the most low-threshold and genuinely exciting use cases.

This article is not a technical guide. The goal is to give you an overview of relevant use cases, along with a few practical tips.

It is also not just about working more efficiently with AI in communications. The main point is how you can do more of the useful and interesting things you'd love to do if only you had a bigger budget or more people.

AI opens doors to many of the things you've always wanted to do but never had the capacity or budget for. A lot of what used to require outside help, or simply couldn't be done because you lacked design tools, is suddenly within reach.

Every idea here assumes you review the output and often make adjustments afterwards. But running it through AI still takes a fraction of the time it would take to do the same thing by hand.

Here are seven ideas worth considering, none of which require a big IT project or deep technical skills.

1. Tailor your message to different audiences

It's no secret that a one-size-fits-all approach to communication doesn't work. So one of the key jobs for anyone in communications or change management is adapting the message to different audiences. You rarely have time to do this properly for every group, which is exactly where it helps that AI can take on some of the work.

This assumes you already have a solid, well-written text with all the relevant information, something that can serve as a starting point.

2. Analyze open-text survey responses

Open-text responses in surveys hold valuable insight you won't get from quantitative analysis. Reading through hundreds of answers by hand is slow, and it often ends in a shallow skim because no one has the time.

AI makes the job more manageable.