Information professionals are on the front line of AI disruption, and the speed of change has outpaced most formal training pathways. From what I've seen, the most essential competencies for librarians and information specialists today fall into three areas: data literacy, AI-assisted research, and content evaluation. AI tools are powerful, but without a critical understanding of how they generate, source, and sometimes distort information, professionals risk misguiding the very audiences they serve. Self-driven learning is currently the most practical path. Free or low-cost platforms like Coursera and LinkedIn Learning offer introductory courses on prompt engineering, data ethics, and applied AI in information sciences. Pairing that with peer-led learning groups can help bridge the gap until educational institutions catch up with more specialized programs. Asking for employer support is wise. Training budgets, access to premium AI tools, and time carved out for learning are investments that benefit both the professional and the organization. Looking ahead, courses that combine AI with human-centered skills like fact-checking, critical analysis, and information design will become increasingly important. __ Name: Eugene Leow Zhao Wei Position: Director Site: https://www.marketingagency.sg/ Headshot: https://imgur.com/a/JM5Iisz Email: eugene@marketingagency.sg Linkedin: https://www.linkedin.com/in/eugene-leow/
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.
From my experience working at the intersection of AI and creative industries, the most valuable skill information professionals can build is learning how to experiment quickly with new tools. I remember testing early text-to-video models and realizing the real value wasn't in mastering one software, but in knowing how to adapt workflows as tools kept changing. I'd suggest librarians identify internal 'AI champions' who train peersthis way knowledge isn't siloed, and the institution evolves together.
From managing distributed teams, I've noticed that skills like prompt engineering and workflow automation go a long way in keeping professionals ahead of AI changes. The big takeaway I've had is that you can't just learn onceyou need to create personal systems for ongoing updates, like setting aside an hour weekly to explore new tools and reflect on what's useful. I'd also suggest librarians ask their organizations for structured practice spaces, where experimenting with AI doesn't feel like high-stakes trial and error.
When I started integrating AI copilots into content workflows, I realized that the strongest skill wasn't coding, but knowing how to prompt, refine, and fact-check results. At Elementor, for example, we saw huge gains when teams treated AI like a collaborator for organization and drafting rather than a replacement for expertise. My suggestion would be to focus on these collaboration skills first, since tech will always evolve, but the ability to guide and interpret AI stays timeless.
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.
In my experience building Tutorbase, one of the key lessons has been that adaptability matters more than predefined expertise. For information professionals, the same holds trueskills like mastering AI-based curation workflows and learning ethical use of GenAI are becoming indispensable. Drawing on my background in SaaS, I've leaned on open-source communities and hands-on experimentation more times than I can count, and they provide practical understanding much faster than traditional programs alone. Educational institutions are beginning to add AI modules, yet the most helpful learning often comes from hybrid modelspairing theory with experimentation inside your daily workflow. Employer support works best when it funds access to flexible tools and ongoing microlearning, since this space evolves rapidly and demands lifelong updating.
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.
In the industrial real estate business at Mack Industrial, where we manage and develop self-storage, small bay warehouses, and industrial outdoor storage across South Florida, information is the backbone of everything. While we are not librarians by trade, we work closely with information specialists in planning, legal, and operational roles, and we've seen firsthand how the rapid shift to generative AI is reshaping how professionals interact with data. For information professionals to stay relevant, the most essential skills are rooted in digital literacy and the ability to evaluate and apply AI tools critically. This means understanding not just how to use generative AI, but when and why to use it. In our space, that could include using AI to draft lease summaries or extract actionable insights from market reports. But someone still needs to guide the tool, interpret results, and ensure accuracy. Professionals can take the initiative to build these skills through platforms like LinkedIn Learning, which now offer courses in prompt engineering and information ethics. But real growth happens when employers support this upskilling with time, funding, and internal knowledge sharing. At Mack Industrial, we've encouraged our team to explore new tools and set aside time to learn for operations and leasing teams who benefit from streamlined workflows. Educational institutions are starting to catch up, but many programs are still catching their breath. What's needed are interdisciplinary courses that blend information science, ethics, and applied AI. Real-world case studies, hands-on labs, and AI tool literacy should be core components. Outside of formal programs, tools like ChatGPT, Claude, or Perplexity can be used for experimentation and learning, as long as they're used responsibly. In a field where data is everywhere but trusted insight is rare, professionals who can combine domain knowledge with responsible AI usage will become more valuable than ever.
As someone who works closely with leaders on content and thought leadership, I see generative AI as both a disruptor and an amplifier for information professionals. The abundance of AI-generated material makes curation, clarity, and credibility more valuable than ever. The skills that matter most now include: 1. Content discernment - the ability to identify what is original, credible, and strategically useful in an environment where information is plentiful but trust is scarce. 2. Narrative building - moving beyond aggregation to interpretation: connecting insights, context, and strategy to create meaning. 3. AI-augmented writing and research - not just using AI to draft or summarise, but understanding when to rely on it, when to challenge it, and how to integrate it into a larger content workflow. Where can these skills be developed? A blend of structured courses (short programmes in AI literacy, research methods, and content strategy), community learning (peer groups, professional networks, industry webinars), and self-driven practice. In my experience, experimenting with the tools is itself a form of training - skills sharpen fastest when applied to live projects. The real differentiator will not be in mastering every tool - that's impossible in a shifting technology environment. Employer support is critical: access to tools, training budgets, and above all, the encouragement to experiment without fear of failure. At the same time, professionals should take ownership of their learning paths, and building a personal "AI toolkit" of practices, platforms, and sources they can trust. Educational institutions are beginning to catch up, but content and information professionals cannot wait for the perfect curriculum. Some resources that will be useful: 1. Coursera — AI For Everyone (Andrew Ng) - https://www.coursera.org/learn/ai-for-everyone? 2. ChatGPT Prompt Engineering for Developers - https://learn.deeplearning.ai/courses/chatgpt-prompt-eng/lesson/dfbds/introduction 3. Practical Applications of AI in Libraries - https://www.youtube.com/watch?v=8lfH-2fPR64 4. Generative AI: What Librarians Need to Know - https://www.youtube.com/watch?v=GViQxwfkp8I 5. Prompt Engineering 101 - Crash Course & Tips - https://www.youtube.com/watch?v=aOm75o2Z5-o
Generative AI has fundamentally shifted what librarians and information specialists need to excel in their roles. While we once primarily focused on curation and organization, today's information professionals must also evaluate AI outputs, integrate these tools appropriately, and guide ethical implementation. To remain relevant in this rapidly evolving landscape, information professionals should develop several key competencies: First, build genuine AI literacy by understanding how these tools function, their inherent limitations, and potential bias risks. Second, master prompt engineering to craft queries that yield accurate, relevant results. Third, develop expertise in data ethics and governance to protect privacy, ensure copyright compliance, and maintain transparency. Fourth, sharpen critical evaluation skills to verify AI-generated content against trusted sources. Finally, become adept at change facilitation to guide patrons and colleagues in responsible AI adoption. These skills can be acquired through multiple channels: targeted short courses from professional organizations like ALA and Library Juice Academy; university continuing education programs; free MOOCs covering AI fundamentals; and peer learning networks. Individuals can take initiative by experimenting with AI tools in controlled environments and documenting their findings. Organizations should support this transition by funding professional development, providing access to AI platforms for testing, and allocating dedicated time for skill-building. While educational institutions are incorporating AI into their curricula—with some LIS programs now offering specialized electives—this integration remains inconsistent across the field. Additional resources include policy toolkits from international library associations, practice datasets, and vendor-provided training on AI-enhanced systems. The most successful information professionals will blend their human expertise with ongoing AI skill development.
Begin with skills rather than buzzwords: prompt engineering, metadata & taxonomy design, the basics of vector search/embedding, data literacy (how to validate sources), and AI ethics/privacy. Bring along tool fluency: a note-taking workflow (Python or no-code), a vector DB, and a LLM interface so you can prototype. Where to learn: short, project-based courses (Coursera/edX microcredentials, Library Carpentry, and vendor sandboxes), plus professional bodies (ALA, SLA) for domain-specific training. Self-study works: build a tiny portfolio (index 1,000 docs, add embeddings, run searches) - nothing teaches faster than a broken proof-of-concept. Ask employers to support you concretely: with paid time to experiment, a sandbox environment, paid models (or on-prem options), and a mentorship budget. The best ROI is a funded "practice" project that does real work (search, triage, FAQ automation).
Based on our experience at Zapiy, information professionals should seek employer support through learning stipends and dedicated time for skill development. We've found success implementing a skills-based talent development approach where team formation focuses on skill sets rather than traditional job titles, encouraging cross-functional learning to adapt to AI-driven changes. Organizations benefit from investing in their information specialists by providing structured learning opportunities and resources for emerging technologies. This approach creates a continuous learning environment where professionals can develop relevant AI skills while maintaining their core expertise in information management.
The rapid rise of generative AI has made it clear that information professionals need to prioritize skills that go beyond traditional cataloging or data organization. In my work with digital marketing, I've seen firsthand how quickly tools like ChatGPT and other AI platforms reshape the way we research, synthesize, and present information. The most valuable skill now is learning how to frame the right questions and verify AI-generated answers against credible sources. Early on, I had to "learn on the fly" when Google began rolling out AI-driven updates in search. I quickly realized that fact-checking and cross-referencing would become more important than ever, and that's the same shift librarians and information specialists are facing today. I'd suggest starting with free or low-cost resources like Coursera, LinkedIn Learning, or even YouTube tutorials to build a foundation in prompt engineering, data literacy, and critical evaluation of AI outputs. Educational institutions are catching up, but waiting for a formal degree program isn't realistic when the tools change monthly. A step I recommend—and one I take myself—is setting aside time each week to experiment with new AI platforms, compare outputs, and document what works. Employer support is helpful, especially in the form of paid training or dedicated time to test tools, but professionals shouldn't rely solely on that. By combining continuous self-study with organizational backing, librarians and information managers can stay relevant in an AI-first environment.
Client Relations Specialist at GO Technology Group Managed IT Services
Answered 6 months ago
As generative AI reshapes how information is discovered and managed, the most valuable skills for librarians and information professionals extend beyond technical fluency. They need AI literacy, data evaluation, and cybersecurity awareness so they can assess tools critically, protect sensitive information, and guide others in using these platforms responsibly. Much of this learning can be done through short-form courses, vendor-led training, and peer networks, but the most effective path is a blended approach where professionals both self-educate and receive structured support from their employers. A supportive organization can provide access to workshops, curated training platforms, and expert partners to ensure that staff aren't left "learning on the fly." Educational institutions are making progress, but many offerings remain theory-heavy. In my experience working with schools and community organizations in Chicago, what resonates most are practical, applied workshops on topics like responsible AI adoption, cloud collaboration, and threat mitigation. Partnering with a trusted provider of IT consulting and managed services in Chicago can accelerate this learning, because it connects information professionals with experts who are already deploying these tools across industries. With the right mix of employer backing, hands-on training, and community collaboration, librarians and other specialists can remain indispensable guides in the evolving information landscape.
I'm Muhammad Mustafa, SEO Manager at Flipdish. Information professionals today can start by picking one short, practical AI course, like the Prompt Engineering class on Coursera or the AI for Everyone series from DeepLearning.AI, and then immediately trying out what you learn. At Flipdish we built an internal chatbot that pulls training documents and case-study data from our own knowledge base, which taught us more in a week of tinkering than we'd get from months of theory. You can do something similar by signing up for a free OpenAI API key, collecting a handful of your most common guides or reports, and writing a simple script to index and query them in a vector database like Pinecone or Weaviate. Or just use notebook llm by Google. You should ask your employer for support. Explain that a small monthly budget for API credits and a couple of dedicated "learning hours" each week will let you prototype features without dipping into your personal resources. Most universities are only just adding AI modules and often lag the industry by six to nine months, so look instead for bootcamps or micro-credentials in data curation, AI ethics or metadata management to catch up faster.
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
Educational institutions are starting to catch up, but the pace is uneven. Some universities and library science programs are weaving AI literacy and digital ethics into their curricula, while others are still figuring out where to begin. The most promising moves are the creation of micro-credential courses, certificate programs, and hands-on workshops that focus on real-world applications of generative AI rather than just theory. These offerings give librarians and information specialists practical skills they can use immediately in their daily work. The challenge now is scaling these programs quickly enough so professionals aren't left learning in isolation, but instead supported by structured, future-ready education.