How AMR Is Approaching AI: A Framework Built for People, Not Just Technology

This is Part 1 of an ongoing series on AI strategy and leadership at AMR Management Services

Artificial intelligence is not going away. Most of us already know that. What is less clear for a lot of organizations, including association management companies like ours, is how to approach it in a way that is responsible, sustainable, and actually useful to the people we serve.

At AMR, we are in the early stages of building that approach and working towards operationalizing AI. This is the first post in an ongoing series where we will share how we are thinking about AI, what we are working on, and what we are learning along the way. We do not have all the answers yet. But we have a plan, and we think it is worth sharing.

Starting with a Foundation, Not a Shortcut

The first thing we decided early on was that we were not going to chase AI tools for the sake of it. There are a lot of options out there, and new ones appear every week. Without a clear framework, it is easy to end up with a patchwork of tools, inconsistent practices, and real risk to the clients who trust us with their data.

So, we are starting with the foundation. That means defining which tools our team is approved to use, how we handle client data within those tools, and what guardrails need to be in place before anyone uses AI in a client-facing context. We are also developing a formal internal AI security and data privacy policy, as well as a set of recommendations that our clients can adapt for their own organizations.

This is not about limiting what people can do. It is about creating a structure that makes it safe to experiment, so that creativity has room to operate without putting anyone at risk.

Meeting Employees Where They Are

We also recognize that not everyone on our team feels the same way about AI. Some people are excited and already using tools regularly. Others are skeptical, overwhelmed, or worried about what AI means for their role. Both of those reactions are completely valid.

Our goal is not to force the same level of adoption. It is to make sure everyone has access to the same information, the same approved tools, and the same opportunities to learn. We are building out a regular cadence of knowledge-sharing sessions and creating a simple way for staff to submit ideas for how AI could help with their day-to-day work. If you have an idea for how a repetitive task could be handled differently, we want to hear it. When there are opportunities to operationalize a process with the support of AI, we want to explore those to make everyone’s lives easier.

We also take seriously the real environmental impact of AI, including energy consumption and resource use. These are legitimate concerns and ones we factor into how we roll out AI. The way we see it, using AI more intentionally and efficiently is part of using it responsibly. That also means being knowledgeable on tools already built into the platforms we are using today.

Building Custom Solutions That Actually Serve Our Clients

Beyond internal adoption, we see a real opportunity to deliver meaningful value to our clients through AI. Not generic tools dropped into their operations, but thoughtfully scoped solutions designed around their specific needs.

We are developing a structured process for this. Before any client-facing AI project moves forward, it goes through a clear evaluation: What problem are we solving? What does success look like? How complex is the build, and what does ongoing support require? Does this solution have value for other clients, or is it unique to this one?

That last question matters a lot. When we build something that could serve multiple clients, we invest accordingly. When a solution is highly specific, we price and scope it that way. The goal is to be efficient without being generic, and to make sure every client-facing AI project is grounded in real value, not novelty.

What Comes Next

Over the coming months, we will be sharing updates on this effort, including what we learn from our first internal pilot projects, how our AI security and data privacy policy comes together, and early results from any client solutions we develop.

We will also be asking questions of our employees and our client leaders, working with consultative partners, and engaging with our vendors to leave no stone unturned. This is a process we are building in the open, and the people closest to the work will help shape it.

There is a lot more to come. Stay tuned.

Have questions about AMR’s AI strategy or want to share feedback? Contact us today. We will address common questions in future posts in this series.