Knowledge Management Consulting

Most are sleeping on the power of 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻, and every company should have a Distillation Factory to stay competitive This technique is reshaping how companies build efficient, scalable, and cost-effective AI. First, 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻? Also known as knowledge distillation, is a machine learning technique where a smaller, more efficient "student" model is trained to replicate the behavior and performance of a larger, more complex "teacher" model. Think of it as a master chef (the teacher) passing down their culinary expertise to an apprentice (the student) without sharing the exact recipe. The student learns by observing the teacher’s outputs and mimicking their decision-making process, resulting in a lightweight model that retains much of the teacher’s capabilities but requires fewer resources. Introduced by Geoffrey Hinton in his 2015 paper, “Distilling the Knowledge in a Neural Network,” the process involves: 1/ Teacher Model: A large, powerful model trained on massive datasets. 2/ Student Model: A smaller, efficient model built for faster, cheaper deployment. 3/ Knowledge Transfer: The student learns from the teacher’s outputs—distilling its intelligence into a lighter version. There are several types of distillation: 1/ Response-Based: The student mimics the teacher’s final outputs 2/ Feature-Based: The student learns from the teacher’s intermediate layer representations. 3/ Relation-Based: The student captures relationships between the teacher’s outputs or features. The result? A student model that’s faster, cheaper to run, and nearly as accurate as the teacher, making it ideal for real-world applications. 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗡𝗲𝗲𝗱𝘀 𝗮 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗙𝗮𝗰𝘁𝗼𝗿𝘆? In today’s AI landscape, very large LLMs are incredibly powerful but come with significant drawbacks: high computational costs, massive energy consumption, and complex deployment requirements. A Distillation Factory is a dedicated process or team focused on creating distilled models, addressing these challenges and unlocking transformative benefits. Here’s why every company should invest in one: 1/ Cost Efficiency: Distilled models cut costs, running on minimal GPUs or smartphones, not data centers. 2/ Scalability: Smaller models deploy easily. 3/ Faster Inference: Quick responses suit real-time apps. 4/ Customization: Tailor models for healthcare or finance with proprietary data, no full retraining. 5/ Sustainability: Lower compute needs reduce carbon footprints, aligning with green goals. 6/ Competitive Edge: Rapid AI deployment via distillation outpaces costly proprietary models. A Distillation Factory isn’t just a technical process; it’s a strategic move.

Managers Don’t Support Innovation Unless You Align It to Strategy—Here’s How Innovation sounds exciting—until it clashes with corporate priorities. The hard truth? Most managers won’t support innovation unless it directly contributes to strategic goals. That’s why aligning innovation to strategy is a game-changer. When innovation becomes an enabler of strategic success, leaders see it as a necessity, not a distraction. In my book Breaking Innovation Barriers, I introduce 15 different ways to align innovation with strategy, from cost leadership and differentiation to digital transformation and sustainability. Each approach comes with a clear innovation assignment—one that defines what needs to happen, why it matters, and how success will be measured. For example, if a company pursues cost leadership, its innovation assignment might be: “Generate innovative ways to reduce costs by 50% in five years in a sustainable way, making us the cost leader in our niche.” If a company focuses on market development, its innovation assignment could be: “Generate new, easy-to-access markets for our current offerings to grow revenues by 15% over three years.” Seizing the Right Moment A key opportunity to align innovation with strategy? When a new CEO or corporate strategy shift happens. These moments create a natural opening for fresh, ambitious ideas that move the company in the right direction. To make this happen, I recommend running an Innovation Focus Workshop—a structured session that turns vague ambitions into a concrete innovation assignment. This approach, which I developed as part of the FORTH Innovation Method, ensures senior managers buy in by clearly defining what innovation should achieve. Your Next Step If you want management support for innovation, don’t just push for “new ideas.” Tie innovation directly to your company’s strategic objectives. That’s when leaders listen, invest, and actively champion innovation. Want to see how this works in practice? Check out the full story in Breaking Innovation Barriers. Let’s make innovation strategically unavoidable. #innovation #strategy #innovationstrategy #Breakinginnovationbarriers

Why is the Records and Information Management Function Crucial to Good AI Governance? The RIM function is crucial to effective AI governance due to its integral role in managing the lifecycle of information, which forms the backbone of AI systems. Key reasons why RIM is indispensable for robust AI governance: 1.     Data Quality Assurance: AI systems depend on the quality of data they process. RIM ensures that the data feeding into AI systems is accurate, complete, and reliable. By maintaining high standards for data quality, RIM helps ensure that AI outputs are based on the best available information, reducing the risk of errors and enhancing the system's reliability. 2.     Compliance with Data Regulations: AI systems must comply with various data protection regulations such as GDPR, HIPAA, or CCPA. RIM manages these aspects by ensuring that data is handled in compliance with legal and regulatory requirements, thereby safeguarding the organization from legal risks and penalties. 3.     Information Lifecycle Management: RIM professionals are experts in managing the lifecycle of records from creation, use, storage, and retrieval to disposition. In AI governance, managing the lifecycle of datasets used for training and operationalizing AI is crucial. This ensures that data is retained only as long as necessary and disposed of securely to prevent unauthorized access or breaches. 4.     Facilitating Audits and Transparency: RIM helps in creating an audit trail for data and decisions made by AI systems. This is essential for transparency, allowing stakeholders to understand how decisions are made. Audit trails also facilitate compliance checks. 5.     Risk Management: By managing records and information properly, RIM reduces risks associated with information mismanagement, such as data breaches, loss of data integrity, and failure to comply with retention policies. This is particularly important in AI systems where data sensitivity and security are paramount. 6.     Supporting Data Accessibility and Retrieval: AI systems require seamless access to relevant data. RIM ensures that data is organized, classified, and stored in a manner that facilitates easy retrieval and efficient use. This not only enhances the efficiency of AI systems but also supports scalability and management of data resources. 7.     Enhancing Ethical Considerations: Ethical AI governance involves ensuring that data usage respects individual rights and societal norms. RIM contributes to ethical governance by managing personal and sensitive information in line with ethical standards and best practices, thus supporting the ethical deployment of AI technologies. By integrating RIM into AI governance frameworks, organizations can ensure that their AI initiatives are responsibly managed, legally compliant, and aligned with broader business and ethical standards. Learn more at InfoGov World https://lnkd.in/gRwtkExh

Your company has amnesia. Someone on your team needs an answer: • Where is Client X's order? • What did we agree on in the last meeting? • What's included in Package Z? • What did we bill, and when? Cue the ritual: Open the CRM. Scroll Airtable. Hunt for meeting notes. Ask a colleague. Wait. Ask again. 5 minutes per request. 20 requests per person. 5 people. 220 workdays. That’s ~1,800 hours per year. Almost two full-time salaries… Paid to your team to search for data you already own. No CEO would ever say: “Let’s hire two people to look for information we already have.” But that’s exactly what’s happening. Quietly. Every day. On your payroll. That’s why we’re building the Intelligence Hub inside LearningSuite. The interface is boring on purpose: Open a chat. Ask a question. Get the answer. The architecture behind it isn’t. Layer 1: Data Synchronization ===== Airtable, HubSpot, SalesSuite, meeting transcripts, invoices, project notes. Continuously ingested, normalized, and connected. The agent doesn’t just know your data. It understands how it relates. Layer 2: Vector Database ===== Your data becomes embeddings. Meaning > keywords. “What did Client X push back on last time?” → Still returns the right answer. Layer 3: Retrieval + Guardrails ===== Every answer is grounded in your actual company data. No hallucinations. No guessing. No generic AI fluff. If it’s not in your systems, the agent says so. What this means for you: → Internal search time cut in half → Zero knowledge loss when people leave → Customer responses in minutes, not hours → Onboarding in days, not weeks → Scale operations without scaling headcount Your team doesn’t need to be smarter. They need a system that remembers. The smartest person in your company shouldn’t be the one who’s been there longest. It should be anyone who opens the chat. https://lnkd.in/edgi6E9Y

Exciting New Research: Injecting Domain-Specific Knowledge into Large Language Models I just came across a fascinating comprehensive survey on enhancing Large Language Models (LLMs) with domain-specific knowledge. While LLMs like GPT-4 have shown remarkable general capabilities, they often struggle with specialized domains such as healthcare, chemistry, and legal analysis that require deep expertise. The researchers (Song, Yan, Liu, and colleagues) have systematically categorized knowledge injection methods into four key paradigms: 1. Dynamic Knowledge Injection - This approach retrieves information from external knowledge bases in real-time during inference, combining it with the input for enhanced reasoning. It offers flexibility and easy updates without retraining, though it depends heavily on retrieval quality and can slow inference. 2. Static Knowledge Embedding - This method embeds domain knowledge directly into model parameters through fine-tuning. PMC-LLaMA, for instance, extends LLaMA 7B by pretraining on 4.9 million PubMed Central articles. While offering faster inference without retrieval steps, it requires costly updates when knowledge changes. 3. Modular Knowledge Adapters - These introduce small, trainable modules that plug into the base model while keeping original parameters frozen. This parameter-efficient approach preserves general capabilities while adding domain expertise, striking a balance between flexibility and computational efficiency. 4. Prompt Optimization - Rather than retrieving external knowledge, this technique focuses on crafting prompts that guide LLMs to leverage their internal knowledge more effectively. It requires no training but depends on careful prompt engineering. The survey also highlights impressive domain-specific applications across biomedicine, finance, materials science, and human-centered domains. For example, in biomedicine, domain-specific models like PMC-LLaMA-13B significantly outperform general models like LLaMA2-70B by over 10 points on the MedQA dataset, despite having far fewer parameters. Looking ahead, the researchers identify key challenges including maintaining knowledge consistency when integrating multiple sources and enabling cross-domain knowledge transfer between distinct fields with different terminologies and reasoning patterns. This research provides a valuable roadmap for developing more specialized AI systems that combine the broad capabilities of LLMs with the precision and depth required for expert domains. As we continue to advance AI systems, this balance between generality and specialization will be crucial.

UK Government Modern Industrial Strategy launched in the last 24 hours: what does it mean? I’ve been exploring this using #systemsthinking and a causal loop diagram (CLD) to map its feedback structures. A few key takeaways which might be relevant #business schools… Systemic Insights via CLD: – Investment → R\&D → Innovation → Productivity → Economic Growth → Investment – Skills ↔ Innovation & Infrastructure → Tech Adoption → Innovation → Productivity Key “hubs” include **Innovation**, **Productivity**, & **Economic Growth**, with **Collaboration** and **Skills** as powerful levers. Negative links (e.g., regulatory uncertainty) can weaken investment, while peripheral nodes (e.g., Net-Zero in our simplified map) may need stronger connections to reflect real-world influence. This underscores the need for aligning R&D, #skills, infrastructure, and #sustainability objectives. So, what should business schools do? 🤝 Strengthen Industry Partnerships: Collaborate with firms & regional clusters on real projects. Connect students/faculty to innovation initiatives, boosting learning and local impact. 💡 Focus on Emerging Skills: Update programs for digital literacy, clean-energy management, & advanced manufacturing basics. Equip grads with in-demand skills that feed productivity and innovation loops. 🚀 Foster Entrepreneurship & Scale-Ups: Offer incubators, mentorship, and finance guidance. “Entrepreneurship → Scale-ups → Innovation” will help startups grow and energize the wider economy 🤝🔬Promote Cross-Disciplinary Collaboration: Bridge business, engineering, sustainability, etc. Joint projects mirror how “Collaboration → Innovation/Skills/Infrastructure” drives broader outcomes. 📜 Short Courses on Policy Signals: Run workshops on navigating regulatory certainty/uncertainty. Helping leaders anticipate policy shifts reduces investment hesitation. 🌍 Champion Regional Engagement: Partner with local authorities & SMEs to tailor programs to regional needs. Reinforce “Regional Clusters → Growth → Inclusive Growth” and support levelling-up. ♻️ Embed Sustainability & Net-Zero Goals: Integrate clean energy case studies & net zero strategy in courses. Aligns with “Net-Zero → Clean Energy → Investment/Innovation,” preparing leaders for green transitions. 📊 Leverage Data & Analytics: Track outcomes of partnerships, alumni ventures, and skills placement. Measurable impact reinforces further investment and collaboration. 🌐 Build Innovation-Focused Alumni Networks : Create forums where grads in high-growth sectors share insights with current students. Sustains knowledge transfer and industry connections. #IndustrialStrategy #SystemsThinking #Innovation #EconomicGrowth #UK #CLD #Policy #Sustainability #Collaboration #Skills

Continuing with the GenAI series, I am excited to share how we revolutionised the knowledge management system (KMS) for a leading client in the manufacturing industry. R&D teams in manufacturing often face the tedious task of manually sifting through complex engineering documents and standard operating procedures to ensure compliance, uphold safety standards, and drive innovation. This manual process is not only time-consuming but also prone to errors. To address this, we collaborated with our client to automate their R&D function’s KMS using Generative AI (GenAI). By allowing precise querying of specific sections of documents, our solution sped up access to critical information, reducing search time from hours to mere seconds. Our Generative AI team processed over 110 R&D-related documents, leveraging Large Language Models (LLMs) to generate accurate responses to complex queries. Hosted on a leading cloud platform with an Angular-based UI, the solution delivered remarkable benefits, including: - Significant accuracy in generated answers - Faster and more accurate data search and summarisation - Enhanced decision-making with easier access to critical R&D information - Improved overall employee productivity By implementing GenAI for knowledge management, the client's R&D function was also able to improve its competitive edge by tracking and responding quickly to market trends and consumer behavior. With plans to scale the solution to process over 1,500 documents across multiple departments, the client is creating a centralised hub for all their information needs. Taking advantage of GenAI can revolutionize knowledge management by delivering the right information to the right person on demand and enabling strategic impact. #GenAI #ManufacturingInnovation #KnowledgeManagement #GenAIseries #GenAIcasestudy #Innovation #R&D #DigitalTransformation #AI #Deloitte

Can You Trust Your Data the Way You Trust Your Best Team Member? Do you know the feeling when you walk into a meeting and rely on that colleague who always has the correct information? You trust them to steer the conversation, to answer tough questions, and to keep everyone on track. What if data could be the same way—reliable, trustworthy, always there when you need it? In business, we often talk about data being "the new oil," but let’s be honest: without proper management, it’s more like a messy garage full of random bits and pieces. It’s easy to forget how essential data trust is until something goes wrong—decisions are based on faulty numbers, reports are incomplete, and suddenly, you’re stuck cleaning up a mess. So, how do we ensure data is as trustworthy as that colleague you rely on? It starts with building a solid foundation through these nine pillars: ➤ Master Data Management (MDM): Consider MDM the colleague who always keeps the big picture in check, ensuring everything aligns and everyone is on the same page.     ➤ Reference Data Management (RDM): Have you ever been in a meeting where everyone uses a different term for the same thing? RDM removes the confusion by standardising key data categories across your business. ➤ Metadata Management: Metadata is like the notes and context we make on a project. It tracks how, when, and why decisions were made, so you can always refer to them later.     ➤ Data Catalog: Imagine a digital filing cabinet that’s not only organised but searchable, easy to navigate, and quick to find exactly what you need.     ➤ Data Lineage: This is your project’s timeline, tracking each step of the data’s journey so you always know where it has been and is going.     ➤ Data Versioning: Data evolves as we update project plans. Versioning keeps track of every change so you can revisit previous versions or understand shifts when needed.     ➤ Data Provenance: Provenance is the backstory—understanding where your data originated helps you assess its trustworthiness and quality.     ➤ Data Lifecycle Management: Data doesn’t last forever, just like projects have deadlines. Lifecycle management ensures your data is used and protected appropriately throughout its life.     ➤ Data Profiling: Consider profiling a health check for your data, spotting potential errors or inconsistencies before they affect business decisions. When we get these pillars right, data goes from being just a tool to being a trusted ally—one you can count on to help make decisions, drive strategies, and ultimately support growth. So, what pillar would you focus on to make your data more trustworthy? Cheers! Deepak Bhardwaj

Most knowledge workers end up duct-taping tools together. - Notion for notes. - Something else for collaboration. - Another tool for publishing or monetizing what they build. I recently came across Buildin, and it’s one of the cleaner attempts I’ve seen at collapsing all of that into a single AI workspace. What stood out isn’t chat or real-time messaging. It’s how collaboration happens inside the content itself. Teams work through structured documents, knowledge bases, and mind maps. Ideas evolve in-place. Context stays intact. It feels much closer to how people already collaborate in Notion just more opinionated and more AI-native. The other interesting layer is monetization. Buildin treats knowledge like an asset, not just a note. You can turn internal thinking, frameworks, or templates into publishable content and offer it directly to paid subscribers, without exporting anything elsewhere. Creators get a way to compound their expertise. Teams get private, enterprise-grade deployments for sensitive work. It’s not just note-taking, and it’s not another “all-in-one” pitch. It’s a workspace designed around building, collaborating, and eventually shipping value from the same place. Worth a look if you’re tired of juggling tools just to get real work done. 👉 https://tryit.cc/BxUooc9

Your CMS is holding your docs hostage. Powerful search, version control, and a WYSIWYG editor. That's great for 2020. In 2025, if your platform isn't offering these 8 features, you're not just writing docs—you're managing a museum. 1️⃣ Semantic Search. Users don't search for the exact words you used. They search for the problem they have. Your CMS needs AI-powered semantic search that understands intent, not just keywords. It should answer natural language questions like, "How do I connect to a new database?". 2️⃣ Content Health & ROT Analysis. Your docs are full of ROT (Redundant, Obsolete, Trivial) content. A modern CMS should proactively flag it. Imagine a dashboard showing: "These 15 pages haven't been viewed in 6 months," or "This code snippet is likely outdated based on our latest release." An automated content gardener. 3️⃣ User Journey Playbacks. You see a page has high views, but is it successful? This feature shows you anonymized recordings of user sessions in your docs. You can see where they get stuck, what they copy, and where they rage-quit. Like having a UX researcher looking over your user's shoulder, 24/7. 4️⃣ Proactive Content Recommendations (In-App). Don't wait for the user to search. A great CMS integrates with your product to offer contextual help. If a user is struggling on the billing page for more than 30s, a small pop-up should offer them the "Billing FAQs" article. It brings the help to them. 5️⃣ AI-Assisted SME Reviews. The biggest bottleneck is getting Subject Matter Expert reviews. This feature uses AI to pre-process content for SMEs. It highlights the specific technical claims that need verification and even formulates direct questions like, "Is this parameter name still correct for the v2.5 API?" It respects their time, so you get faster approvals. 6️⃣ Trust Score & Verified Snippets. Not all content is created equal. This feature adds a "trust score" to articles, based on how recently they've been updated and verified by an expert. Crucially, code snippets get a "Verified for version X.X" badge, automatically tested via CI/CD. It tells devs what they can trust at a glance. 7️⃣ Search Query-to-Article Pipeline. Your search analytics show 100 people searched for "how to integrate with Slack," but you have no article on it. A smart CMS doesn't just show you that data; it automatically creates a draft article with that title and assigns it to your team. It turns missed opportunities into a content pipeline. 8️⃣ Low-Code Interactivity. You shouldn't need a UI developer to make your docs engaging. A modern CMS needs a library of low-code interactive components: add a quiz, an editable code block, a pricing slider, or an interactive diagram as easily as you'd add a screenshot. This is why we're building Archbee (YC S21) (we shipped some of these features already). So, for all the tech writers and doc managers building the future: What's the #1 "dream feature" you wish your CMS had right now?