Best Practices For Building A Knowledge Sharing Platform

I am watching proposal content management experts evolve. Yet the foundation has not changed. AI cannot create accuracy from thin air. It amplifies the quality, or the weakness, of the content it consumes. This isn't anything new, either. Knowledge management has struggled when organizations focused on technology over substance. AI makes this tradeoff more visible. The wrong answer is now produced faster, at greater scale, and with more confidence. The result is an increased risk in front of customers, regulators, and partners if the underlying content is poorly managed. The path forward is not more content, but better content, and better connections between that content and the tools that use it. Three best practices stand out to me: ❶ Define a source of truth. Establish which assets are definitive versions of your company’s capabilities, policies, and proofs. Protect them as “gold copy” content. Anything less introduces doubt. ❷ Prioritize clarity over volume. Redundancy breeds confusion in AI models. Streamlined, unambiguous entries outperform sprawling archives. Think of content as signals to your AI-native proposal management software, not storage. ❸ Connect knowledge across systems. AI models excel when they can draw from integrated sources, like proposal libraries, CRM data, customer references, even product roadmaps. Siloed repositories lead to fractured outputs. Your AI-native content should and can retrieve context, not just snippets. ❹ Embed governance into workflows. Content should be reviewed, validated, and refreshed as part of business processes, like proposal submission, sales enablement, and product updates, not as occasional clean-up projects. And if your proposal content library still requires you to tag and create a content library that is more suitable to human keyword search? Well, it's not using AI-native technology :/ ✯ Implications for Content Professionals ✯ For content managers and librarians, this shift is quite literally career-defining. As AI adoption accelerates, organizations need professionals who can: ͢. Curate and maintain a knowledge base that AI can trust. ͢. Architect integrations across platforms so AI has access to context, not just isolated answers as we previously had with legacy systems. ͢. Translate complex business knowledge into clear, structured entries that AI can parse (not just for human search). In other words, content professionals move from “library caretakers” to strategic enablers of AI-driven performance. Their expertise in curation, governance, and integration becomes central to how the business competes and grows. Was that too much? Did I bore you? Luckily... stargazy is hosting an AMA with a proposal library expert until this Friday (tomorrow!). ↧↧↧↧ You can find the AMA below ↧↧↧↧

The first in a series of posts about knowledge graphs and ontology design patterns that I swear by. They will lead you through how at Yale we went from a challenge from leadership (build a system that allows discovery of cultural heritage objects across our libraries, archives and museums) to a fully functioning, easy to use, easy to maintain, extremely robust, public knowledge graph. *The 10 Design Principles to Live By* 1. Scope design through shared use cases 2. Design for international use 3. Make easy things easy, complex things possible 4. Avoid dependency on specific technologies 5. Use REST / Don’t break the web / Don’t fear the network 6. Design for JSON-LD, using LOD principles 7. Follow existing standards & best practices, when possible 8. Define success, not failure 9. Separate concerns, keep APIs & systems loosely coupled 10. Address concerns at the right level You must first agree on your design principles and priorities. These are crucial because when the inevitable conflicts of opinion arise, you have a set of neutral requirements to compare the different options against. (1) The first keeps you honest: good ideas are only ideas if they don't advance your business / use cases. Keeping to scope is critical as ontologies have a tendency to expand uncontrollably, reducing usability and maintainability. (2) Internationalization of knowledge is important because your audience and community doesn't just speak your language, or come from your culture. If you limit your language, you limit your potential. (3) Ensure that your in-scope edge cases aren't lost, but that in solving them, you haven't made the core functionality more complicated than it needs to be. If your KG isn't usable, then it won't be used. (4) Don't build for a specific software environment, because that environment is going to change. Probably before you get to production. Locking yourself in is quickest way to obsolescence and oblivion. (5) Don't try to pack everything a consuming application might need into a single package, browsers and apps deal just fine with hundreds of HTTP requests. Especially with web caches. (6) JSON-LD is the serialization to use, as devs use JSON all the time, and those devs need to build applications that consume your knowledge. Usability first! (7) Standards are great... especially as there are so many of them. Don't get all tied up trying to follow a standard that isn't right, but don't invent the wheel unnecessarily. (8) Define the ontology/API, but don't require errors for all other situations, as you've made versioning impossible. Allow extensions to co-exist, as tomorrow they might be core. (9) Don't require a single monolith if you can avoid it. If a consuming app only needs half of the functionality, don't make them implement everything. (10) If there's a problem with the API, don't work around it in the ontology, or vice versa. Solve model problems in the model, vocabulary problems in vocabulary, and API problems in the API.

Knowledge dies in silence. It grows when shared. McKinsey found that knowledge workers spend nearly 20% of their workweek just looking for internal information or tracking down colleagues who can help. That’s almost a full day lost – every week. Knowledge only creates power when it’s shared. And sharing doesn’t happen in one way – it happens everywhere: 👉🏻 Through communication modes: writing, speaking, documenting, teaching 👉🏻 Through work channels: meetings, memos, wikis, workshops, 1:1s 👉🏻 Through human practices: storytelling, feedback, mentoring, peer learning Here’s how that plays out across the layers of an organisation, and what you can check and try today: Public ✍ Writing, speaking, publishing Ask: Do we encourage people to share externally? Try: Post one lesson learned this week on LinkedIn or your Substack. Corporate 📢 Memos, all-hands, newsletters Ask: Does strategy truly reach everyone? Try: Replace one slide deck with a short memo people can re-read. Divisional 📓 Playbooks, wikis, dashboards Ask: Do we capture lessons, or keep repeating mistakes? Try: Start a simple wiki or Notion for recurring questions. Team 🤝 Retros, async updates, lunch & learns Ask: Do we have rituals where peers teach peers? Try: Run a 15-min lunch & learn on a recent win, failure or new area of knowledge. 1:1 👥 Buddying, mentoring, coaching Ask: Are we pairing people to accelerate growth? Try: Match two colleagues who rarely work together and create a buddy system. Every layer reinforces the others. Public sharing sharpens internal clarity. Internal sharing creates stories worth sharing more widely. Knowledge doesn’t just add up. It compounds – but only if you put it into circulation. 🔔 Follow Si Conroy and ♻️ Share if you like this. 📩 Weekly sanity in my Progressive Group Therapy newsletter: https://lnkd.in/eTZq6A5D

What's the lowest hanging fruit to improving a community experience? 1) Simplify the taxonomy and navigation. I can’t stress enough how powerful it is to get the taxonomy right. Are you going to structure the community by: → Product category. Product one, product two, product three, etc… → Visitor intent. Search, get help, learn, share advice, get started, etc… → Visitor type. Users, developers, resellers, partners, etc… I’d suggest structuring by product category for smaller communities and by visitor type for larger communities. The exact labelling you use for each section greatly impacts what people do. Often, simple tweaks help: → Ask Questions > Forum → Tutorials > Knowledge Base → Contact us > Get Help → Introduction to [topic] > Get Started Simplifying the navigation and taxonomy is always winner. 2) Reducing/removing clutter. Eliminating clutter can be a huge win.  → Remove features not used by at least 20% of active members. → Archive or merge categories which aren’t attracting dozens of posts per month. → Remove static pages which don’t attract at least 100 views per month.  → Consider removing members who haven’t posted in two years.  → Archive or merge groups which don’t attract regular discussions. → Archive discussions following these principles. → Remove the majority of profile fields from member profiles.  → Reduce the size of the navigation menu to a handful of simple options. The metrics are somewhat arbitrary – ensure you can stand behind whatever figures you use. 3) Sharing a regular ‘community best practices’ newsletter. The best newsletters do one of two things: → They become a beacon for what’s happening in the industry. This includes industry news, resources shared from experts across the web, upcoming events, etc… → They spread helpful tips and advice published in the community. What helpful advice in the community needs to be shared more widely? 4) Undertaking UX research to see where people get stuck and make improvements.  Some simple steps.. → Set up interviews with non-participants. → Give them learning tasks to do. → See where they get stuck and prioritise the severity of the issue.  → Identify solutions and time/effort required.  → Create a prioritised roadmap of issues to resolve. This process always identifies unforeseen problems you can solve. 5) Highlighting common issues/mistakes with 101 guides. A hugely underrated activity is creating community-generated 101 guides where members are invited to submit their best advice for newcomers to a product or a topic. My favourite questions include:  → What advice would you give to newcomers getting started in product/topic? → What’s the biggest challenge, and how did you overcome it? → What would you do differently if you were to go start again with product/topic? This has the dual impact of generating a huge amount of engagement while creating valuable content for members. 

Focus on Knowledge Management NOW I have been working on the ServiceNow platform for over six years, and one common mistake organizations make is neglecting to mature their knowledge bases and articles. A poor ServiceNow knowledge base can make your entire platform feel bleak. It can be very frustrating when you want to introduce new capabilities, like the virtual agent, or improve your service catalog, but your knowledge bases lack sufficient articles. Organizations need to invest time in building a strong knowledge base before they can successfully develop more comprehensive IT Service Management workflows. Here are several ways organizations can build a strong knowledge base: 1. Conduct an audit of existing knowledge articles to identify which articles should be retired or updated. 2. Hire a dedicated Knowledge Manager responsible for updating existing knowledge articles and creating new ones. 3. Develop a knowledge management governance process for creating new articles to ensure consistency in formatting, a clear content strategy, and proper meta tagging. Create a knowledge article template for this purpose. 4. Establish a review and approval process involving the Knowledge Manager, subject matter experts, and key stakeholders. 5. Ensure that knowledge articles are appropriately linked within service catalog items, virtual agents, and other relevant ServiceNow portals.  6. Gather valuable feedback from end users to ensure that knowledge articles are useful and effectively address their requests and incidents. 7. Review the knowledge management data to identify which articles are viewed the most. This will help you understand how to improve other ITSM workflows related to your service catalog items and request forms. 8. Knowledge Management is not a one-time task; become comfortable with making continuous improvements. Listen to your end users, as they can help you make your knowledge bases better. How do you improve your knowledge articles? Comment below #ITSM #ServiceNow #KnowledgeManagement #ITIL

Outdated knowledge assets aren't just an administrative issue—it's a safety and quality issue that affects everyone. Growing concerns and the loss of experienced personnel are creating more and more pain oints across manufacturing. Many recognize that knowledge management must be a strategic focus for accelerating workforce competency, protecting operational performance, and driving innovation. But moving from a strategic focus to embedding it within the cultute of an organization is where it gets tricky. In my conversations with manufacturing leaders, organizations that are most successful at maintaining current documentation have gone deeper than just building systems—they've created cultures that genuinely value knowledge sharing and accuracy. One way to support this cultural shift is by creating a "knowledge health index"—a dashboard showing the status of knowledge assets across operations. Some of The most effective systems include: 🔁 Collecting feedback from users on documentation quality and accuracy, in a non-intrusice way as soon as the job is done. 📈 Monitoring usage patterns to identify which procedures are most frequently accessed 🫶Mature capabilities that can automatically flag documentation that may need updates based on system changes 🤝Create a way to gauge how often knowledge assets are being reviewed and updated - and recognizing those who lead by example. One manufacturer who implementing this approach has maintained over 95% accuracy in their critical knowledge assets, compared to less than 60% before implementation. The difference between organizations that struggle with knowledge management and those that leverage knowledge to create value isn't just adopting technology—it's creating an environment where everyone understands that knowledge is a shared asset that requires collective stewardship. The LNS Research Industrial Knowledge Management framework provides a scalable approach for manufacturers, regardless of where you are in your journey. What approaches have you seen work for maintaining critical operational knowledge in your organization? 📣 I'd love to hear your experiences in the comments. ⬇️ #KnowledgeManagement #IndustrialTransformation #OperationalExcellence #ConnectedWorkforce #DigitalTransformation

Knowledge Management is hands down the most important factor for scalable GenAI adoption. Here’s a breakdown of the key components: 𝗖𝗲𝗻𝘁𝗿𝗮𝗹 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: The knowledge lifecycle spans the entire knowledge management process, interacting with all other components. It acts as the main decision-making and routing mechanism. 𝗖𝗿𝗲𝗮𝘁𝗲: Documenting knowledge and guiding users on how to capture their experiences with knowledge (both positive and negative). 𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗲: Structuring content and organising it in a way that ensures ease of access and effective retrieval. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲: Knowledge management relies on systems thinking. As systems evolve, knowledge must be continually improved. 𝗦𝗵𝗮𝗿𝗲: The way existing and new knowledge is presented to users determines its effectiveness. Every business must understand its knowledge-sharing practices—at its core, this is change management. 𝗥𝗲𝘂𝘀𝗲: Reducing redundant work is fundamental to knowledge management. Creating reusable knowledge leads to faster time-to-value for an organisation. For every instance of unsuccessful scaling of an AI solution, there is often a story of poor knowledge management. The more projects we complete at Insurgence, the clearer it becomes that effective and automated knowledge management is at the heart of successful AI adoption at scale. Yes, it’s not glamorous, but it drives progress for the initiatives that do capture attention. Step 1: Find great ideas for AI. Step 2: Build a mechanism to enable them to thrive throughout your organisation at scale. Mandatory component of Step 2: Knowledge Management. At Insurgence we're doing both. Feel free to reach out for a yarn on where AI could help out your team!

From Knowledge Hoarding to Knowledge Sharing: The Culture Shift L&D Needs. 💡 Companies don’t have a knowledge problem. They have a knowledge-sharing problem. Think about it—when an expert employee leaves, does their knowledge stay? Or does it leave with them? 📌 Why is knowledge hoarding a problem? 🚫 Employees don’t share what they know because they fear becoming "replaceable." 🚫 Teams work in silos, making cross-functional collaboration difficult. 🚫 Companies rely on outdated documentation that doesn’t capture real insights. 🔥 How some organizations solved this: One company, struggling with high dependency on senior employees, built an internal Knowledge Exchange System where employees: 1. Recorded their expertise through short video walkthroughs. 2. Created open forums for sharing best practices and lessons learned. 3. Integrated peer mentorship programs, where employees taught each other. 🚀 The impact? ✔️ Faster onboarding for new employees. ✔️ Less reliance on single experts—knowledge was accessible to all. ✔️ Teams collaborated more effectively, breaking down silos. 💡 What’s one way your company promotes knowledge-sharing? Drop your insights below! 👇