Beyond Chatbots: AI-Driven Personalization at AGU
In the fast-moving landscape of artificial intelligence (AI), many associations have defaulted to implementing chatbots as their primary AI tool. However, the American Geophysical Union (AGU) envisions a broader application—leveraging AI to enhance research, improve personalization, and foster deeper member connections. Highland recently interviewed Thad Lurie, AGU's Senior Vice President of Digital and Technology, to learn more about his view on the role of AI, natural language process (NLP) and personalization in Associations. Lurie observes, "AI has come to the fore, and the association space is a little confused, I think, as to exactly how to use it."
AGU's innovative approach centers on utilizing AI to match members with relevant content, individuals, and research opportunities. This strategy moves away from outdated taxonomy checkboxes, instead employing natural language processing (NLP) to analyze the full text of members' publications and interests. Lurie explains, "Now what we're doing, instead of comparing your checkboxes to their checkboxes, we're comparing the entirety of every abstract and article you've ever published with AGU against the entirety of the text for each abstract or each other person's entirety of what they've published with AGU."
Rethinking Member Engagement with AI-Powered Personalization
To enhance member engagement, AGU launched Rex, a real-time AI-driven recommendation engine designed to provide:
- Personalized content recommendations on the AGU homepage
- AI-generated email suggestions for conference sessions and networking opportunities
- Future applications, such as job matching, grant alerts, and research funding connections
The impact has been notable. Early career professionals were 20% more likely to engage with AI-driven recommendations, indicating a significant boost in member interaction.
What This Means for Associations
AGU’s experience underscores a major shift for associations: AI and data shouldn’t be viewed as passive repositories but as active, strategic assets.
For associations considering AI adoption, Lurie offers one key piece of advice:
“You must have the data. AI is only as effective as the quality and depth of your existing datasets. Associations with research-driven, knowledge-rich archives have a unique opportunity to harness AI in ways that drive real value for members.”
What’s Next?
AGU's forward-thinking application of AI demonstrates that the technology's potential extends far beyond chatbots. By leveraging AI to provide contextually relevant and trustworthy content, AGU enhances member engagement and solidifies its position as a trusted authority in the scientific community. As associations navigate the complexities of AI, AGU's approach offers a compelling blueprint for meaningful and effective implementation.
AGU’s AI-powered approach is redefining the role of associations as connectors, facilitators, and trusted sources of knowledge. The future of associations won’t be about maintaining static membership models but rather creating dynamic, data-driven ecosystems that actively serve members at every stage of their career.
