GenAI Implementation and Impact

🚨 MIT Study: 95% of GenAI pilots are failing. MIT just confirmed what’s been building under the surface: most GenAI projects inside companies are stalling. Only 5% are driving revenue. The reason? It’s not the models. It’s not the tech. It’s leadership. Too many executives push GenAI to “keep up.” They delegate it to innovation labs, pilot teams, or external vendors without understanding what it takes to deliver real value. Let’s be clear: GenAI can transform your business. But only if leaders stop treating it like a feature and start leading like operators. Here's my recommendation: 𝟭. 𝗚𝗲𝘁 𝗰𝗹𝗼𝘀𝗲𝗿 𝘁𝗼 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵. You don’t need to code, but you do need to understand the basics. Learn enough to ask the right questions and build the strategy 𝟮. 𝗧𝗶𝗲 𝗚𝗲𝗻𝗔𝗜 𝘁𝗼 𝗣&𝗟. If your AI pilot isn’t aligned to a core metric like cost reduction, revenue growth, time-to-value... then it’s a science project. Kill it or redirect it. 𝟯. 𝗦𝘁𝗮𝗿𝘁 𝘀𝗺𝗮𝗹𝗹, 𝗯𝘂𝘁 𝗯𝘂𝗶𝗹𝗱 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱. A chatbot demo is not a deployment. Pick one real workflow, build it fully, measure impact, then scale. 𝟰. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗵𝘂𝗺𝗮𝗻𝘀. Most failed projects ignore how people actually work. Don’t just build for the workflow but also build for user adoption. Change management is half the game. Not every problem needs AI. But the ones that do, need tooling, observability, governance, and iteration cycles; just like any platform. We’re past the “try it and see” phase. Business leaders need to lead AI like they lead any critical transformation: with accountability, literacy, and focus. Link to news: https://lnkd.in/gJ-Yk5sv ♻️ Repost to share these insights! ➕ Follow Armand Ruiz for more

GenAI is easy to start but hard to scale. Too many companies are stuck in endless pilots. Here’s what it takes to build GenAI capability. McKinsey has recently published their findings from working with 150+ companies on their GenAI programs over two years. Two hurdles stand out: 𝟭. 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝘁𝗼 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗲: Teams waste time on duplicate experiments, wait on compliance processes, and solve problems that don’t matter. 30% - 50% of innovation time is spent trying to meet compliance - not building. 𝟮. 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲: Even when a prototype works, most companies can’t get it into production. Risk, security, and cost barriers overwhelm teams, leading to stalled or cancelled deployments. According to McKinsey the most successful GenAI platforms contains three core components: 𝟭. 𝗔 𝘀𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗼𝗿𝘁𝗮𝗹: To support both innovation and scale, companies need a secure, centralized portal that gives teams easy access to pre-approved gen AI tools, services, and documentation. It should enable developers to quickly build with reusable patterns, while also offering governance features like observability, cost controls, and access management. The best portals promote contribution and reuse across the organization, reducing friction and accelerating development at scale. 𝟮.𝗔𝗻 𝗼𝗽𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗼 𝗿𝗲𝘂𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: Scaling GenAI requires modular, open architecture that enables teams to reuse services, application patterns, and data products across use cases. Leading companies build libraries of common components (like RAG, embeddings, or chat workflows) and focus on integration via APIs - not vendor lock-in. Infrastructure and policy as code ensure changes can propagate quickly and securely across the platform, reducing cost and accelerating deployment. 𝟯. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: To scale safely, GenAI platforms must embed automated governance that enforces compliance, manages risk, and tracks costs. This includes microservices that audit prompts, detect policy violations (like sharing sensitive personal data or generating inaccurate responses), and attribute usage to specific teams. A centralized AI gateway enforces access controls, logs interactions, and routes traffic through security filters - allowing flexibility where needed. These guardrails accelerate approval processes, reduce setup time, and let teams focus on building value - not managing risk manually. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲? Source: McKinsey & Company 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

The Alan Turing Institute 𝗮𝗻𝗱 the LEGO Group 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗰𝗵𝗶𝗹𝗱-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗔𝗜 𝘀𝘁𝘂𝗱𝘆! ⬇️ (𝘈 𝘮𝘶𝘴𝘵-𝘳𝘦𝘢𝘥 — 𝘦𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘭𝘺 𝘪𝘧 𝘺𝘰𝘶 𝘩𝘢𝘷𝘦 𝘤𝘩𝘪𝘭𝘥𝘳𝘦𝘯.) While most AI debates and studies focus on models, chips, and jobs — this one zooms in on something far more personal: 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝗰𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗴𝗿𝗼𝘄 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜? They surveyed 1,700+ kids, parents, and teachers across the UK — and what they found is both powerful and concerning. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 9 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝘀𝘁𝗼𝗼𝗱 𝗼𝘂𝘁 𝘁𝗼 𝗺𝗲 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁: ⬇️ 1. 1 𝗶𝗻 4 𝗸𝗶𝗱𝘀 (8–12 𝘆𝗿𝘀) 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 — 𝗺𝗼𝘀𝘁 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝗮𝗳𝗲𝗴𝘂𝗮𝗿𝗱𝘀 → ChatGPT, Gemini, and even MyAI on Snapchat are now part of daily digital play. 2. 𝗔𝗜 𝗶𝘀 𝗵𝗲𝗹𝗽𝗶𝗻𝗴 𝗸𝗶𝗱𝘀 𝗲𝘅𝗽𝗿𝗲𝘀𝘀 𝘁𝗵𝗲𝗺𝘀𝗲𝗹𝘃𝗲𝘀 — 𝗲𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗹𝘆 𝘁𝗵𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗻𝗲𝗲𝗱𝘀 → 78% of neurodiverse kids use ChatGPT to communicate ideas they struggle to express otherwise. 3. 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗶𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 — 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 → Kids still prefer offline tools (arts, crafts, games), even when they enjoy AI-assisted play. Digital is not (yet) the default. 4. 𝗔𝗜 𝗮𝗰𝗰𝗲𝘀𝘀 𝗶𝘀 𝗵𝗶𝗴𝗵𝗹𝘆 𝘂𝗻𝗲𝗾𝘂𝗮𝗹 → 52% of private school students use GenAI, compared to only 18% in public schools. The next digital divide is already here. 5. 𝗖𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗮𝗿𝗲 𝘄𝗼𝗿𝗿𝗶𝗲𝗱 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜’𝘀 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 → Some kids refused to use GenAI after learning about water and energy costs. Let that sink in. 6. 𝗣𝗮𝗿𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰 — 𝗯𝘂𝘁 𝗱𝗲𝗲𝗽𝗹𝘆 𝘄𝗼𝗿𝗿𝗶𝗲𝗱 → 76% support AI use, but 82% are scared of inappropriate content and misinformation. Only 41% fear cheating. 7. 𝗧𝗲𝗮𝗰𝗵𝗲𝗿𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗮𝗻𝗱 𝗹𝗼𝘃𝗶𝗻𝗴 𝗶𝘁 → 85% say GenAI boosts their productivity, 88% feel confident using it. They’re ahead of the curve. 8. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝘀 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗿𝗲𝗮𝘁 → 76% of parents and 72% of teachers fear kids are becoming too trusting of GenAI outputs. 9. 𝗕𝗶𝗮𝘀 𝗮𝗻𝗱 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗮 𝗯𝗹𝗶𝗻𝗱𝘀𝗽𝗼𝘁 → Children of color felt less seen and less motivated to use tools that didn’t reflect them. Representation matters. The next generation isn’t just using AI. They’re being shaped by it. That’s why we need a more focused, intentional approach: Teaching them not just how to use these tools — but how to question them. To navigate the benefits, the risks, and the blindspots. 𝗪𝗮𝗻𝘁 𝗺𝗼𝗿𝗲 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀? Subscribe to Human in the Loop — my new weekly deep dive on AI agents, real-world tools, and strategic insights: https://lnkd.in/dbf74Y9E

If you’re an AI engineer building a full-stack GenAI application, this one’s for you. The open agentic stack has evolved. It’s no longer just about choosing the “best” foundation model. It’s about designing an interoperable pipeline, from serving to safety- that can scale, adapt, and ship. Let’s break it down 👇 🧠 1. Foundation Models Start with open, performant base models. → LLaMA 4 Maverick, Mistral‑Next‑22B, Qwen 3 Fusion, DeepSeek‑Coder 33B These models offer high capability-per-dollar and robust support for multi-turn reasoning, tool use, and fine-grained control. ⚙️ 2. Serving & Fine-Tuning You can’t scale without efficient inference. → vLLM, Text Generation Inference, BentoML for blazing-fast throughput → LoRA (PEFT) and Ollama for cost-effective fine-tuning If you’re not using adapter-based fine-tuning in 2025, you’re overpaying and underperforming. 🧩 3. Memory & Retrieval RAG isn’t enough, you need persistent agent memory. → Mem0, Weaviate, LanceDB, Qdrant support both vector retrieval and structured memory → Tools like Marqo and Qdrant simplify dense+metadata retrieval at scale → Model Context Protocol (MCP) is quickly becoming the new memory-sharing standard 🤖 4. Orchestration & Agent Frameworks Multi-agent systems are moving from research to production. → LangGraph = workflow-level control → AutoGen = goal-driven multi-agent conversations → CrewAI = role-based task delegation → Flowise + OpenDevin for visual, developer-friendly pipelines Pick based on agent complexity and latency budget, not popularity. 🛡️ 5. Evaluation & Safety Don’t ship without it. → AgentBench 2025, RAGAS, TruLens for benchmark-grade evals → PromptGuard 2, Zeno for dynamic prompt defense and human-in-the-loop observability → Safety-first isn’t optional, it’s operationally essential 👩💻 My Two Cents for AI Engineers: If you’re assembling your GenAI stack, here’s what I recommend: ✅ Start with open models like Qwen3 or DeepSeek R1, not just for cost, but because you’ll want to fine-tune and debug them freely ✅ Use vLLM or TGI for inference, and plug in LoRA adapters for rapid iteration ✅ Integrate Mem0 or Zep as your long-term memory layer and implement MCP to allow agents to share memory contextually ✅ Choose LangGraph for orchestration if you’re building structured flows; go with AutoGen or CrewAI for more autonomous agent behavior ✅ Evaluate everything, use AgentBench for capability, RAGAS for RAG quality, and PromptGuard2 for runtime security The stack is mature. The tools are open. The workflows are real. This is the best time to go from prototype to production. ----- Share this with your network ♻️ I write deep-dive blogs on Substack, follow along :) https://lnkd.in/dpBNr6Jg

There’s a lot of excitement about generative AI in pharmaceutical R&D — and rightly so. But alongside the enthusiasm, there’s still real uncertainty about where & how it’s delivering value today, and what it will actually take to scale impact. A recent paper from the DISRUPT–Data Science Industry Roundtable — a cross-industry consortium I co-founded — offers a useful, data-based snapshot of the current state of play. The analysis is grounded in aggregated, anonymized benchmarking across multiple large pharma companies, which makes the insights especially practical. What I appreciate most about the paper is that it doesn’t oversell where the industry is. Where GenAI is actively being used today: ◆ Knowledge search, summarization, and synthesis across biological, chemical, and clinical data ◆ Drafting & supporting research, clinical, and regulatory-adjacent documents ◆ Assistive productivity tools for scientists, statisticians, and data teams ◆ Early traction in molecule design, though maturity & consistency remain mixed across organizations These use cases are real and, in many organizations, already moving into routine use — particularly for knowledge workflows & document generation. Where proof points are still emerging: ◆ Clear, reproducible examples of GenAI directly driving clinical-stage success ◆ Demonstrated impact beyond efficiency gains into durable biological insight ◆ Scaled, end-to-end integration into core R&D decision-making processes Much of today’s GenAI value in pharma remains assistive rather than decisive — an important & often overlooked distinction. What continues to limit scale: ◆ Data quality & availability: models amplify what they’re trained on, and biology remains noisy & incomplete ◆ Process integration: embedding GenAI into existing R&D workflows is as hard as building the models themselves ◆ Trust, robustness, and oversight: human-in-the-loop remains essential in regulated environments ◆ Organizational readiness: governance, operating models, and adoption matter as much as algorithms The takeaway for me isn’t skepticism — it’s focus. Early wins matter because they build confidence & momentum. But the step-change impact many hope for will come from doing the harder work: investing in high-quality data foundations, tightly coupling AI systems to experimental feedback, and building operating models that earn trust over time. That’s where efforts like DISRUPT are valuable — not because they provide all the answers, but because they create shared visibility. They help the industry separate real progress from noise & have more honest conversations about what it will actually take to scale impact responsibly. We’re entering a more mature phase of generative AI in medicine — one where optimism is paired with discipline, and success is measured not by what’s possible, but by what’s proven. Read more:  https://lnkd.in/e5ZZkWRE #GenerativeAI #AIinMedicine #DrugDiscovery

🧠 12 open-source GenAI tools that actually deliver (and scale) Not every tool with a GitHub repo deserves your trust. These ones do. 👉 If you're building real GenAI systems—not just demos—save this list. I grouped them into Build, Orchestrate, and Monitor so you know when to use what. GenAI AgentOS: (NEW) 📎 Agent registry → memory handoff → orchestration layer → HITL toggle ✅ Focused on production reliability and audit trails ⭐ https://lnkd.in/gyzMnnjw 🔧 BUILD – For devs building GenAI-powered apps LangChain – The Swiss army knife for chains, RAG, agents, and tools. ⭐ 70k+ stars | https://lnkd.in/gun-rmdj LlamaIndex – Clean integration layer between LLMs and your data. Great for structured docs + flexible vector backends ⭐ 30k+ stars | https://lnkd.in/gW-iBKR2 Flowise – Drag-and-drop LLM orchestration (perfect for demos & MVPs) UI-first, deploy fast, iterate even faster ⭐ 19k+ stars | https://lnkd.in/gA8J3Tr5 Embedchain – Minimalist RAG framework that just works Perfect if you’re tired of config overkill ⭐ 8.5k+ stars | https://lnkd.in/g8DnHQg2 RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. 🔁 ORCHESTRATE – For managing agents, workflows & system logic LangGraph – Declarative, stateful agent workflows built on top of LangChain Role-based agents + memory + edge control ⭐ 2.5k+ stars | https://lnkd.in/gveKVfE4 Superagent – Plug-and-play LLM agent framework API + UI, works with OpenAI, Claude, Mistral ⭐ 5.5k+ stars | https://lnkd.in/gtsy5CQ3 CrewAI – Multi-agent task planning + collaboration Gives each agent purpose, tool access, and autonomy ⭐ 9k+ stars | https://lnkd.in/gUpwvbn9 📊 MONITOR – For logging, debugging, and scaling safely Langfuse – Logging, tracing, and evals for GenAI pipelines Inspect every token and decision ⭐ 4.5k+ stars | https://lnkd.in/g6BEnVyA Phoenix – Open-source observability for LLM workflows Error tracking, token usage, monitoring ⭐ 3k+ stars | https://lnkd.in/gT3ERHgm PromptLayer – Prompt logging + analytics Simple but powerful tracking for prompt performance ⭐ 4k+ stars | https://lnkd.in/gGSRRBrH Helicone – Open-source alternative to OpenAI’s usage dashboard Understand cost, latency, and user behavior ⭐ 6k+ stars | https://lnkd.in/gCgcy7Kd 🔍 Why these matter: Too many GenAI teams waste time gluing together 20 tools, only to discover they can’t scale. These 12 tools are: ✅ Well-maintained ✅ Actively used in production ✅ Community-supported ✅ Actually helpful when you go beyond a chatbot Don’t just play with LLMs. Build systems that can grow. 🔖 Save this. ♻️ Repost this.

The 2025 GenAI Developer Learning Path: A Step-by-Step Guide After implementing numerous GenAI solutions, here's the proven path to becoming a successful GenAI developer. Follow both tracks simultaneously for the best results: Technical Journey: Start with the Core Foundation - Master Python & ML basics - Build Deep Learning fundamentals - Understand Transformer architecture Move to LLM Fundamentals - Learn HuggingFace ecosystem - Practice fine-tuning techniques - Master prompt engineering Advance to RAG Development - Implement Vector Databases - Build Hybrid Search Systems - Design Multi-Vector Retrieval Tackle Advanced Techniques - Study Constitutional AI - Implement Chain of Verification - Develop Agent Systems Focus on Production - Learn Model Optimization - Deploy Inference Servers - Set up Monitoring Systems Explore Future Tech - Study Multimodal AI - Understand MoE Architecture - Implement Cross Encoders Professional Growth: Start with AI Ethics - Address Bias & Fairness - Ensure Privacy - Practice Responsible AI Add Business Perspective - Analyze Use Cases - Calculate ROI - Handle Stakeholders Manage Risks - Implement Security - Ensure Compliance - Set up Governance Establish Quality - Design Testing Strategies - Track Performance - Collect User Feedback Document Everything - Create API Docs - Map System Architecture - Maintain Clear Guides Develop Leadership - Manage Teams - Plan Projects - Share Knowledge I'd like you to move through both tracks in parallel. The magic happens when technical expertise meets business acumen.

GenAI isn’t just challenging the ideas but also organizational structure/frameworks. The meticulously designed structures within companies, characterized by defined roles, responsibilities, competencies, culture, and a shared vision, are being challenged. One element of this structure is the management layers that typically come with deep context about the business. GenAI has created a divide in excitement levels across the organization. Senior leaders are engrossed in strategizing for GenAI's integration, fascinated by its potential. The ground team and engineers are eager to learn more about this technology and run experiments to evaluate it. However, this enthusiasm presents a conundrum for the middle and mid-senior management tiers, particularly for those in people management roles. It's crucial for them to not only grasp the technical nuances of GenAI but also to understand its broader business implications. This mid-management layer is where strategy meets execution. Any misses here will either create a situation of over-promises and then push the ground teams to achieve the impossible or miss the execution by not understanding the potential of this technology. Both of which could prompt precarious business decisions. In this transformative period, promoting a supportive culture is essential. Success hinges on how well an organization can equip its current managers with new skills while judiciously integrating external leaders (as new hires) to bolster the transition. If not handled properly, there's a risk of territorial behavior that might push real problem-solving out of the window. #ExperienceFromTheField #WrittenByHuman

Automation, augmentation, or maybe AMPLIFICATION? Here's a thought experiment for you: what if we considered GenAI an idea amplifier for your teams? Picture this: just as a sound amplifier takes a faint audio signal and boosts it to fill a room with powerful sound, an "idea amplification" does the same for strategic thought and creativity. It takes the seeds of ideas that your team brings to the table and enriches them, enhancing their clarity, impact, and reach. How would it work? Input Stage: Your team brainstorms and puts forward initial concepts—the foundational ideas. Amplification Process: Enter GenAI. Just like an amplifier uses electronic components to elevate sound, GenAI processes these initial ideas, adding depth, breadth, and entirely new angles. It’s like having an additional team member who consistently surprises you with innovative extensions and unexpected insights. Output Stage: The result? A set of richer, more expansive ideas that resonate far beyond the original thought—ideas that empower your teams to think broader, tackle challenges from different perspectives, and bring more ambitious solutions to the table. GenAI as an idea amplifier isn’t about replacing human creativity; it’s about boosting it. The machines of the Industrial Revolution amplified our strength and manual skills. The machines of the AI revolution have started to amplify our thoughts and mental skills. It’s about taking what’s already there and transforming it into something more impactful. I had a great conversation about it with Michal Krawczyk 🚀 of Int4. And, together with Dr Graham Kenny and Dr Kim Oosthuizen, we wrote about the potential of GenAI for strategic planning in Harvard Business Review ("How CEOs Are Using Gen AI for Strategic Planning") #genAI #technology #business #strategy #creativity

GenAI adoption is all about people, not about tools. Pharma giant Novo Nordisk offers a great case study of working out what supports useful uptake of AI across a large organization. A case study in MIT Sloan Management Review uncovers a range of useful lessons. Here are some of the most interesting. 🚀 Recognize a mid-cycle drop as normal. Novo Nordisk grew Copilot use from a few hundred to 20,000 users in just over a year, with 23% becoming frequent users within one month. However, by month three or four, 15% of early adopters dropped off and average time saved per week declined. Recognizing this dip as natural helped avoid panic and kept the focus on re-engagement strategies rather than getting staff to try tools for the first time. 🛠 Deliver function-specific training through champion networks. Generic AI onboarding failed to meet the needs of specialized roles. Novo Nordisk succeeded by creating domain-specific training, leveraging internal champions to contextualize AI use, and allowing teams to shape guidance based on their actual work. This addressed “AI shaming” and bridged confidence gaps across functions. 🤝 Use internal champions to overcome cultural resistance. Skepticism wasn’t solved by policy, it was shifted by influence. Novo Nordisk identified trusted, high-status employees to openly adopt and advocate for AI tools. Their visible endorsement encouraged hesitant peers to try AI without fear of judgment or failure. 📈 Treat adoption as a change process, not a tech rollout. Rather than pushing a one-time launch, Novo Nordisk framed GenAI as a long-term transformation. This meant investing in ongoing communication, support structures, and iterative learning. The approach acknowledged that adoption would ebb and flow, and prepared the organization to adapt accordingly. 🎯 Emphasize strategic value over time saved. Though average users saved about 2 hours per week, the most meaningful wins came from higher-quality work—more strategic thinking, clearer writing, and better planning. By highlighting these human-centric gains, Novo Nordisk built a stronger case for AI’s workplace relevance beyond mere productivity. 📊 Use employee data to shape the deployment strategy. Over 3,000 employee surveys and interviews helped Novo Nordisk spot where and why adoption lagged. This feedback guided real-time adjustments—like where to invest in new use cases, where to scale back, and how to tailor messaging. It also surfaced which functions became tool-reliant versus those needing more support.