The role of information professionals is being redefined in the digital age, seamlessly bridging the gap between traditional knowledge management (KM) practices and cutting-edge AI applications.
At Data Summit 2026, IBM’s Fleur Levitz, principal consultant at FDL Consulting NYC LLC, former Wall Street data governance executive, senior management consultant and data governance practice leader, led the session “Information Professionals in the Age of AI,” which explored several classic approaches to organizing and streamlining human thinking: catalogs, classification schemes, and taxonomies.
The Annual Data Summit Conference was held in Boston on May 6-7, 2026, with a pre-conference workshop held on May 5th.
AI is more than just a technological innovation. It’s a transformation in the way information is created, interpreted and trusted, Levitz explained.
She highlighted how information professionals, especially librarians, play a critical role within the AI-driven KM space, not only as knowledge managers but also as ethics managers. The company’s commitment to transparency, inclusivity, and information integrity makes it an essential partner in shaping responsible AI systems.
Most organizations are asking themselves, “How can we build better AI?” But that’s the wrong question, she said.
A better question is: How do we manage information responsibly? Because the real challenge is not ability. It’s control, trust and responsibility.
To understand where this is going, we need to look back, she noted. For thousands of years, societies have relied on humans to manage knowledge.
Scribes, archivists, librarians. Each of them shaped what is recorded, what is preserved, and ultimately what is known.
Control of information always means control of decision making. Information has always been power, she explained. Who controls knowledge? Who controls decision-making? Institutions such as libraries and archives did more than simply store information; they shaped access, visibility, and truth. Today, AI is expanding its power in ways never seen before.
Something important happened in the 19th century. Information work has become a profession with standards, systems, and ethics.
That’s when we started formalizing the idea:
- fair access
- intellectual freedom
- responsible management
These ideas are not new. They have just been rediscovered in the AI era.
Information systems laid the foundation for AI, and as information grew, humans built systems to manage it (metadata, indexing, search). These were not just technological innovations, but ways of structuring reality. And they laid the foundation for data science.
Responsible AI is governance-ready. This is where Responsible AI comes in, not as a trend, but as a response to real harm.
And the principle is well known.
- fairness
- transparency
- accountability
- privacy
These are not engineering concepts. They are governance concepts.
The EU AI law is the first major attempt to regulate AI at scale. Deploy a risk-based framework. At the highest level, this means:
- Some AI systems Completely prohibited.
- Others are categorized as high risk And it’s strictly regulated.
- The rest fall into the low-risk category with lighter requirements.
The key idea is simple. The higher the risk to people, the stricter the rules.
Even if you don’t operate in Europe, this regulation will shape the way AI is built and managed around the world, she said. The EU AI law could become the de facto global standard, much like the GDPR did for data privacy.
Levitz asked, “If AI generates information, who is responsible for its truth?” These systems require governance. Key to this responsibility is human involvement.
At the highest level, this is about governance. Information professionals must lead, participate in decision-making bodies, and educate their organizations on responsible AI frameworks. They are uniquely positioned to bridge the gap between technological systems and human values.
She recommended three best practices:
- Develop information professionals.
- Build governance into your AI systems.
- Invest in AI literacy across your organization.
No new features required. You need to activate the one you already have.
“Responsible AI is an information problem, not a technology problem,” Levitz said.
We are entering a new era, she emphasized, where AI is not defined by who builds it, but by who takes responsibility for managing it. And the first organizations to understand this will lead what comes next. Those who control intelligence will lead.
“I like that because AI is a data use case, and data is at the heart of AI,” Levitz says. “AI governance is starting to become disconnected from everyone’s work because of all the tools we’re using.”
Many of the Data Summit 2026 presentations can be reviewed here: https://www.dbta.com/datasummit/2026/presentations.aspx.
