By Holly Zanville, Founder/Executive Director, Learn & Work Ecosystem Library
When we created the Learn & Work Ecosystem Library, one of our challenges was to develop a framework that would help people better understand the nation’s complex learn-and-work ecosystem. We drafted the framework around 12 key components such as employers and workforce, career navigation, policy, credentials and providers, communications and technology, transparency, and other essential building blocks.
We expected these components to evolve over time. We imagined new organizations would emerge. New initiatives would be launched. Definitions would become more refined or entirely new terms would emerge. And reports would come and go on topics relevant to the ecosystem.
What we did not anticipate was how quickly artificial intelligence (AI) would begin impacting nearly every one of those components. We did anticipate that AI would impact the components differently and the impacts would occur unevenly; i.e., some components would likely be impacted sooner than others.
Recently, we began updating our components, beginning with “communications and technology” and some light bulbs switched on. As we read what we had written only three years ago, we recognized that the document wasn’t wrong but it was incomplete. Our original description discussed websites, webinars, conferences, journalism, translation services, digital repositories, and communication platforms such as Zoom and Microsoft Teams.
Yes, those technologies still remain important today. But what was missing was AI. That omission says less about our original work than it does about the extraordinary pace of change. When we first drafted the key component document, generative AI had not yet become part of everyday work for educators, employers, researchers, journalists, credential providers, workforce organizations, or policymakers. Today, AI is rapidly becoming part of nearly every conversation.
That realization led us to ask a much larger question. If the communications and technology section needed updating, what about everything else?
The more we discussed that question, the more obvious the answer became. AI is no longer changing just one or a few parts of the learn-and-work ecosystem. It is becoming woven throughout the entire ecosystem.
Consider these examples:
Journalism provides one of the clearest examples. This has come to light with the hiring of our first Journalist-in-Residence, Matthew Arrojas. Matthew describes for us the changing scenario within journalism:
“Many news organizations now use AI to transcribe interviews, translate stories, monitor legislative activity, analyze large document collections, draft headlines, summarize reports, and automate highly structured content such as earnings reports and sports recaps.”
How are these changes on the ground impacting the specific jobs of journalists,” I asked Matthew recently.
He explained, “Newsrooms are setting the standard but not replacing journalists. They are redefining how journalists work while emphasizing that editorial judgment, verification, and accountability remain human responsibilities.”
“What do you think it will be like going forward?” I asked.
Matthew: “Everything I’m reading suggests AI is likely to become a permanent component of modern journalism/media operations. That could look like more sophisticated multilingual publishing, personalized news delivery, advanced investigative analysis, automated fact-checking support, and AI-assisted verification tools. I think the core question is no longer whether AI will be used in journalism /media operations, but how it can be used responsibly while preserving the core values of accuracy, independence, accountability, and public trust that have historically defined the field of professional journalism.”
We agreed the story here is not that AI is replacing communication but communication itself is changing. Information is no longer simply written and published. Now it’s searched by AI, summarized by AI, translated by AI, recommended by AI, and reused by AI. People may never visit the original website or read the original reports hosted there. Instead, they encounter information through conversational assistants/Chatbots, personalized summaries, or AI-generated responses.
What does this mean for organizations like the Library, with a mission to collect, organize, preserve, connect, and make reliable information easier to discover?
First, we can recognize that technology alone does not improve communication. Shared vocabulary, trusted sources, transparent methods, and human judgment remain essential.
Second, we can acknowledge that AI can help us find information faster though it cannot determine which information deserves our trust. That responsibility still belongs to people.
These realizations come at an appropriate time. The Library is embarking on a comprehensive review of its collection to identify where AI is reshaping concepts, terminology, initiatives, organizations, policies, and practices across the ecosystem. Some entries will require only modest revisions. Others may need to be substantially rethought. New glossary terms, Topic Briefs, and initiative profiles will undoubtedly emerge.
If we don’t update, our tech group reminds us: “You will have stale information, as in stale lettuce.”
Not a comforting metaphor but telling for an information organization whose reputation rests, in part, on our ability to remain current.
Updating just a single key component reminded us of something every library eventually learns. Collections are snapshots of knowledge at a particular point in time. As knowledge changes, the collection must change with it.
And as the collection changes, our staffing arrangements will likely change. When we began the Library, we staffed it with experienced researchers to build the collection. Then we added library and information science expertise. Recently, we added a journalist to understand how a Library interrelates with journalism in the rapidly changing information world.
Going forward, we expect our organizational chart to change. We expect there will be AI agents to inform our collection, especially to help keep our collection updated. But humans will still be core to the organizational chart
Three years ago, we built a framework (our ecosystem “wheel”) for understanding the complicated and rapidly changing learn-and-work ecosystem. Today, AI is reshaping nearly every part of that 12-component framework. The Library, like other information organizations must continue evolving alongside it.
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See Topic Brief: Artificial Intelligence in Media / Journalism | Learn & Work Ecosystem Library
For the ecosystem to function effectively, all parts of the system must be connected and coordinated.