tomer service, reduced operating costs and happier employees, all factors that improve a business’s performance relative to its competitors. In many cases, data will need to be sourced from outside the organisation and combined with internal assets. Infor- mation professionals have long played a central role in identifying, accessing and using external data sources going back to online database providers such as Dialog and DataStar in the 1970s. These skills will become even more important as a new open standard for linking LLMs to multiple data sources, internal and external, begins to take hold. This is the Model Context Protocol (MCP) released by Anthropic in late 2024 and which provides a simple, open standard for building two-way connec- tions between data sources and GenAI tools. In the same way that the HTTP standard powered the rise of the WWW and SMTP drove the widespread adoption of email, MCP promises to define the connections in the plumbing of a networked AI world. The standard is supported by Microsoft, Google and OpenAI with thousands of publicly available MCP servers allowing users to connect their LLMs to data from providers ranging from Spotify to Sales- force. MCP can also be used for internal purposes where an organisation needs to connect multiple data sources to their RAG workflows.
Advising and training users For most organisations, ignoring the opportunities and challenges presented by the current wave of AI innovations is not a viable option. This plays well to a number of skills many information pro- fessionals have developed over the years. Helping users identify and use new AI tools, particularly the selection of appropriate LLMs for specific tasks will be an impor- tant role. With new models from the major developers emerging every month, each designed for specific types of tasks, knowing when best to use Google’s Gemini 2.5 Pro, OpenAI’s GPT-4o or Anthropic’s Claude Opus 4 requires a high degree of knowl- edge about their strengths and weaknesses. This then leads into helping users structure appropriate prompts that will generate the best results. GPT-4o, for example, can han- dle input prompts of up to 128,000 tokens, equivalent to approximately 96,000 words, the size of a full novel. So called prompt engineering is a skill in itself and expertise in it makes an enormous difference to the effectiveness and relevance of GenAI results. I hope this article has provided some food for thought on the opportunities for information professionals presented by the rise of GenAI. There is undoubtedly considerable hype surrounding this new technology, much of it from companies and commentators who stand to benefit
16 INFORMATION PROFESSIONAL DIGITAL
from such rapid change. However, I firmly believe that the current wave of AI innovations will fundamentally change how we work with information and how organisations will transform many of their operating processes.
The dotcom bust at the beginning of the 21st century led to many claims that the internet was just a blip in the evolution of computing, a belief that quickly subsided when it became clear that an open network for connecting the world’s computers was transformational. Whatever happens with the current wave of AI companies and their offerings over the coming several years,
the technology will continue to evolve and offer advantages in data processing that should not be ignored. To order your copy of The AI and Data Revolution: Understanding the New Data Landscape visit
https://tinyurl.com/FacetAIRev. CILIP members can get a 35 per cent dis- count on all Facet books. IP
References 1
www.gartner.com/en/newsroom/press-releases/2025-03-31-gartner- forecasts-worldwide-genai-spending-to-reach-644-billion-in-2025
2
https://technologymagazine.com/ai-and-machine-learning/gartner-30- of-gen-ai-projects-to-be-abandoned-by-2025
3 Wang, J., Ding, W. and Zhu, X., 2025. Financial analysis: Intelligent financial data analysis system based on LLM- RAG. arXiv preprint arXiv:2504.06279.
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