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Author: Mary Ellen Bates

Following the exploration of how generative AI is revolutionising the work of research managers, we now turn to information professionals and corporate librarians. Generative AI has emerged rapidly, bringing significant changes and new opportunities. As these AI tools improve in generating text, images, and even code, information professionals must adapt to leverage these advancements. This blog explores how generative AI is reshaping information services, enabling professionals to enhance their roles while maintaining their unique expertise.

Using chatbots in research

Much notice has been paid to chatbots being able to provide answers to questions. Indeed, search engine chatbots like and , general chatbots like Claude.ai and Perplexity.ai, and research-oriented chatbots like Elicit.com and Consensus.app offer unique capabilities for information professionals and their users. Generative AI is particularly useful during the initial phase of a project, when the searcher may not yet know the scope and depth of the existing research in that area. Virtual research assistants like Elicit.com, and Consensus are trained on peer-reviewed articles and can help searchers navigate scientific literature, identify, and evaluate relevant papers, and generate insights and summaries.

Generative AI as an administrative assistant

Beyond their role in research, generative AI can also serve as an administrative assistant for information professionals. A chatbot can generate the first draft of routine business correspondence, marketing materials, job descriptions and similar content. It can proofread a report for grammatical errors or unclear syntax. AI chatbots can prioritize emails based on importance, provide email templates for common responses, and automate scheduling and sending of emails. AI plug-ins to video conferencing platforms can transcribe and summarise meetings in real time, as well as generate lists after the meeting of action items and unresolved issues. Chatbots can help in creating data visualisations from a dataset to communicate complex information more effectively. 

Chatbots can even act as a technology “translator", rewriting system documentation to address the specific needs of different user groups. Of course, while generative AI tools can efficiently produce drafts, human review is crucial to ensure accuracy, quality control and alignment with organisational policies. There's is a searchable database of thousands of AI tools, ranging from interview preparation to data analysis, social media content creation, and video transcription.

Intelligent Libraries

Libraries are also leveraging a wide range of AI tools and technologies to enhance their operations, services, and user experiences. Internal repositories of information can be queried with a chatbot, enabling new and richer access to content that might not otherwise be searchable. AI tools can be deployed to assist in labour-intensive tasks like cataloguing, classification, metadata generation, and inventory management, improving efficiency and freeing up librarians for higher-value activities. Library managers are analysing user behaviour, resource usage patterns, and collection metrics with AI tools, to enable data-driven decision-making, strategic planning, and optimization of services and resource allocation. Machine learning algorithms can analyse user preferences, browsing history, and academic interests to provide personalised content recommendations, fostering a user-centric approach to research.

The five new information professional roles in today’s AI landscape

Information professionals are having new kinds of conversations with their users; instead of introducing searchers to a new information resource or licensing a dataset for a project, they are now talking with users about what kind of data a chatbot was trained on and how to design effective prompts. The new roles that information professionals and librarians are playing involve applying traditional information management skills to today’s AI landscape, including:

1. Working with data scientists and research teams to identify the best tools and resources for an AI project, including investigating the training data and methods used by the AI model; providing guidance on ethical and responsible use of AI, including data privacy, security, and intellectual property concerns; and identifying and providing access to authoritative sources and datasets relevant to the project's domain.
2. Teaching users how generative AI tools differ from traditional search tools and how to design prompts that yield the most relevant results and that do not elicit false, biassed, or misleading information. Related to this is the need to build users’ scepticism around results that are well-written and -formatted but may not be accurate or complete. Today’s information literacy skills must include the ability to look beyond the appearance of a chatbot’s answer, particularly if it confirms one’s expectations, and assess whether the response is based on trustworthy information.
3. Bringing together stakeholders from all areas of the organisation – researchers, data scientists, legal and regulatory staff, and IT — to address the complex considerations involved in implementing generative AI technology within an organisation. An information professional often has the best perspective on all the potential players in an information environment.
4. Developing metadata schemas and taxonomies for AI-generated content, ensuring that this content can be effectively organised, discovered, and retrieved within an organisation. As the volume of AI-generated content continues to grow, information professionals need to develop robust metadata practices to manage this information. Unlike traditional content, AI-generated text, images, or code may not have clear authorship or provenance. Info pros must develop strategies to comply with intellectual property and licensing rights, and to help users assess the reliability and appropriateness of the content for their specific needs.
5. Educating users on the limitations of AI tools, promoting responsible expectations and critical thinking skills when interacting with AI-powered systems and identifying situations where human expertise is still crucial. This goes beyond simply explaining what AI can do; it is about empowering users to develop responsible expectations and critical thinking skills when interacting with AI systems.

Embracing new technology to lead the AI revolution

While the rise of generative AI can feel like a threat to information professionals, it also provides new opportunities to deploy their information management skills in new ways and provide even more value to clients. By staying informed about the latest AI tools and mastering the art of prompt engineering, information professionals can harness the power of AI and support users in developing solutions that were previously unimaginable. The key is to embrace this technology and effectively communicate its best use cases, ensuring that information professionals remain at the forefront of the AI revolution.

Related content

• Three ways Generative AI can give Research Managers “superpowersâ€�
• 7 critical messages Info Pros want R&D scientists to know
• Empowering women in science: How can organisations bridge the gender gap
• The critical role of content investment in the R&D process

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