The fastest way I learned to stay relevant was experimenting with small AI tools hands-on, instead of waiting for structured training. In marketing, I'd test out AI-driven CRMs for campaign tasks, jot down what worked, and then offer that as proof-of-concept to my employer for bigger support. If librarians do the sameshowing small but useful winsthey'll likely get more backing to scale their skills, whether through funding for courses or internal workshops.
I think the most important step is developing a mindset around continuous learning, because in tech the only constant is change. Time after time, when my teams faced rapidly shifting SaaS technologies, the people who thrived were those that joined online cohorts, shared notes, and documented workflows together. For librarians and information specialists, asking employers to provide time and budget for workshops while also self-studying through platforms like Coursera or edX can strike a good balance.
As a Creative Strategist for Davincified, I have witnessed the impact of AI on various industries. I strongly believe that information professionals should develop skills in AI and the management of data in order to be prepared or stay competitive. I recommend starting with online training from Coursera, edX, and LinkedIn Learning to take courses in foundational AI, machine learning, and data science. Explore some of the tools available today (for example, an AI-powered search engine or an AI content curation tool) so they can play with them and use them with information they have integrated or understood through their training. Employer support will also be essential, as organizations will need to offer training and tools that can apply to their work to keep staff competitive. The employer builds a culture of continuous learning by doing this, which benefits the individual employee and the organization as a whole. Support can take shape as access to trainings focused on AI applications, hands-on training with AI technologies, and adopting AI into workforce processes. Another important support strategy is encouragement of team work to allow collaborative learning, as well as mentorship from other team members to enhance adaptation to new tools. Some institutions of higher education have indicated adapting their education strategy to AI, but we see a gap in institutions offering courses that reflect or combine skills in AI with information management. It would be advantageous for universities to offer courses that are practical, with critical engagements to AI that support research in areas such as, but not limited to, digital archiving, metadata curation, and data-driven decision making. I would imagine there are courses in graduate programs focused on data analysis employing AI, machine learning, digital archives and AI content management systems. Each course should include a strong topic in ethics and privacy at some level. In addition to taking courses, information professionals can engage and experiment with their industry professional communities, in the area of webinars and podcasts. Each venue presents current AI tools, processes and system changes in the industry, which allows for discovery and adjustment to the profession in an evolving field.
The past 18 months have caused librarians and knowledge managers to experience extreme mental strain. The formal education system and workplace training failed to provide employees with any preparation for working with GenAI. The quick-thinking professionals learned prompt engineering through YouTube tutorials and joined Discord communities and studied ChatGPT output patterns as if they were solving crosswords. Self-teaching methods become ineffective after a specific period of time. Employers along with institutions must provide their support for information professionals to learn new skills. The development of micro-certification programs for specific use cases including summary pipelines and AI-based classification systems and multimodal search functions would be my approach to L&D for university and library networks. The training should focus on practical applications of AI technology rather than basic explanations of its operation. Information professionals need to request workplace assistance for tool acquisition and research time and course funding because they want to maintain their job stability. The large museum established a dedicated budget for its archival team to explore GenAI tools through experimentation. The organization made an intelligent decision because employee retention expenses always prove more cost-effective than recruitment expenses.
The rise of generative AI (GenAI) is transforming the library and information professions, necessitating that professionals develop specific skills to stay relevant. Key competencies include data literacy, comprehending data management and analysis, recognizing biases, and critically evaluating AI outputs and proficiency in digital tools, including natural language processing and data visualization applications.
If you're checking credentials, they're easy to find on my site and LinkedIn (feel free to connect with me while you are there). What you won't find on LinkedIn is how many organizations I've watched stumble because they left employees to figure out AI on their own. Information professionals should absolutely seek employer support for AI skill development. Organizations have just as much responsibility to provide structured learning opportunities during work hours rather as professional should in freshening their skills through after-hours education. Based on my experience, the most effective approach an organization can take is when it links AI upskilling directly to visible career outcomes and compensation and creates clear(emphasis on clear) incentives for professional growth. Information professionals need to transition from process-holders to problem-solvers and should focus on training that will enhance their ability to leverage uniquely human skills like solving problems and ethical decision-making complement AI's process execution strengths. Dr. Thomas W. Faulkner, SPHR, LSSBB, CMHR-PIP
Information professionals should get ready to become AI orchestrators instead of information gatekeepers. Having built an educational model on AI software, I have been able to observe this transformation in many industries. The most important skill is learning prompt engineering. I utilize 30% of development time trying to learn ways to prompt AI to give me actionable ideas; before I can proceed to action, I need to prompt the AI provide me with exact ideas. Information professionals should be able to lean into the art of human-AI collaboration, including learning to turn complex research requests into systematic prompts that would yield copious, thoroughly researched outputs. Data literacy is a requirement. You want to know how models are trained, biases, and where they fall short. I've observed librarians who are skilled enough to spot AI hallucinations outperform others solely using AI-generated content. Start small using free materials like the documentation from OpenAI and guides from Anthropic. Practice daily with various AI tools and log or journal on what works and does not work. Find AI communities on Discord or Reddit where they exchange real-world stories of AI applications. Ask your employer to fund you to take courses on vendors beyond the vendor charter. The Digital Library Association and EDUCAUSE are great examples of organizations pushing out AI integration course work. One common theme I see is an information professional treating AI as competition. Smart information professionals consider themselves an AI advisor for others, and they conduct trainings on how to better the information search and retrieval process for information. Your subject-matter expertise + your expertise with AI, combined is irreplaceable for an organization!