Challenges of AI Adoption

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

AI handled 75% of customer chats at Klarna… and they still brought humans back. Why? Because speed isn’t the same as quality! Because customers noticed the difference. And it wasn’t good. Speed? Great. Empathy? Missing. Trust? Slipping. After a year of leaning heavily on AI, they’re rehiring human support agents. Real people. Not because AI failed—but because it wasn’t enough. AI can answer your question. But only a human can make you feel heard. Klarna is now hiring in rural areas and among student communities—betting on empathy, not just efficiency. This should be a wake-up call. You can automate tasks. But relationships? They still need people! This is why the future isn’t human vs AI. It’s human with AI. And the companies who get that balance right? They’ll win customer loyalty, and talent, faster than any chatbot ever could.

This image captures a pattern I keep seeing in real AI projects. We blame AI for being unreliable, unpredictable, or hallucinating. In practice, it is usually doing exactly what we asked, just without the context we assumed was obvious. After years of working with automation, one thing has become very clear to me. AI agents are exceptional at execution, and terrible at inferring intent. We speak to them like humans. We skip assumptions. We expect mind reading. Then we are surprised when the system delivers something technically correct and practically useless. This is why so many AI initiatives disappoint. Not because the models are weak, but because the context is. The real skill shift is not better prompts. It is learning how to design context. So here is the question I keep coming back to. When AI fails, is it really the technology, or the way we explain the problem to it? #AI #ArtificialIntelligence #AIAgents #Automation #FutureOfWork #ContextEngineering #TechLeadership

We shut down Kapstan. There’s been chatter, so let me say it plainly:  We worked hard with a superb, committed team and built a strong product. But it didn’t work. Entrepreneurship is packed with highlight reels. What’s missing are the postmortems. So here’s mine. Kapstan was designed to solve a problem we felt again and again at super{set}: infrastructure chaos, complexity, and redundancy. Every time we launched a new company, we pretended we’d never stood up a cloud-based company, rebuilt clusters from scratch, and chased the same five DevOps experts to help us build them. If we were feeling this pain, others had to be, too. So we built what we wished we had: an opinionated, infra orchestrator that worked out of the box. Simpler deployments. A path to simplicity, speed, and cost reduction in cloud ops. The problem was real. The product was solid. And we still failed. The outcome doesn’t reflect a problem with product architecture or customer need. What we underestimated was the psychological architecture inside the companies we were selling to. The economic buyer — often a CEO — didn’t understand infrastructure well enough to question the status quo, and didn’t want to look impetuous by shaking it up. The user — typically a DevOps lead — saw our solution as encroaching on their patch. It wasn’t, but it *felt* like it was. When someone’s job identity is tied to building and tuning the system themselves, plug-and-play orchestration can feel like a power grab. So even with clear ROI and a best-in-class product, the most common reaction was: “We’ve got it handled.” And the places where we were able to convince the buyer that they didn’t have it handled? Sales cycles that turned into long, soul-sucking, pride-swallowing sieges. The surge in AI made a hard thing even harder. Without a path to AI-ifying our offering beyond a few product marketing flourishes, it was clear that future investors chasing AI were unlikely to catch what we were pitching. We assumed real pain would lead to real adoption. But adoption isn’t just logical. It’s political. It’s emotional. You can be right about the problem and still lose. You can build a better mouse trap and still get left outside the gate. So what do you do? You learn, and you lace up again tomorrow.

Many engineers can build an AI agent. But designing an AI agent that is scalable, reliable, and truly autonomous? That’s a whole different challenge.  AI agents are more than just fancy chatbots—they are the backbone of automated workflows, intelligent decision-making, and next-gen AI systems. However, many projects fail because they overlook critical components of agent design.  So, what separates an experimental AI from a production-ready one?  This Cheat Sheet for Designing AI Agents breaks it down into 10 key pillars:  🔹 AI Failure Recovery & Debugging – Your AI will fail. The question is, can it recover? Implement self-healing mechanisms and stress testing to ensure resilience.  🔹 Scalability & Deployment – What works in a sandbox often breaks at scale. Using containerized workloads and serverless architectures ensures high availability.  🔹 Authentication & Access Control – AI agents need proper security layers. OAuth, MFA, and role-based access aren’t just best practices—they’re essential.  🔹 Data Ingestion & Processing – Real-time AI requires efficient ETL pipelines and vector storage for retrieval—structured and unstructured data must work together.  🔹 Knowledge & Context Management – AI must remember and reason across interactions. RAG (Retrieval-Augmented Generation) and structured knowledge graphs help with long-term memory.  🔹 Model Selection & Reasoning – Picking the right model isn't just about LLM size. Hybrid AI approaches (symbolic + LLM) can dramatically improve reasoning.  🔹 Action Execution & Automation – AI isn't useful if it just predicts—it must act. Multi-agent orchestration and real-world automation (Zapier, LangChain) are key.  🔹 Monitoring & Performance Optimization – AI drift and hallucinations are inevitable. Continuous tracking and retraining keeps your AI reliable.  🔹 Personalization & Adaptive Learning – AI must learn dynamically from user behavior. Reinforcement learning from human feedback (RHLF) improves responses over time.  🔹 Compliance & Ethical AI – AI must be explainable, auditable, and regulation-compliant (GDPR, HIPAA, CCPA). Otherwise, your AI can’t be trusted.  An AI agent isn’t just a model—it’s an ecosystem. Designing it well means balancing performance, reliability, security, and compliance.  The gap between an experimental AI and a production-ready AI is strategy and execution.  Which of these areas do you think is the hardest to get right?

🔥 Most “AI Engineers” today aren’t building intelligence, they’re building demos. In the last 2 years, everyone’s been talking about RAG, agents, orchestration, and tool use. But when you actually look under the hood… most of it is just OpenAI + a vector DB + vibes. I’ve spent the past 6 months collaborating with teams build AI systems, handling real users, messy data, latency budgets, compliance, and cost ceilings. And here’s the truth most people don’t want to hear 👇 1️⃣ Prompting ≠ Engineering A clever prompt won’t save a bad architecture. If you can’t manage context, logging, and feedback loops, you’re not doing AI engineering, you’re doing UX for LLMs. Learn systems. Learn scaling. Learn evaluation. 2️⃣ “RAG” isn’t about retrieval, it’s about representation. The hard part isn’t embedding text; it’s designing knowledge that’s searchable, ranked, contextualized, and trustworthy. Good RAG isn’t “add a vector DB.” It’s: → hybrid search (BM25 + dense) → structured metadata → retrieval evaluation → adaptive chunking and reranking 3️⃣ “Agents” aren’t chatbots. Real agents plan, remember, and adapt. They have states, goals, and policies. They know when they’re wrong. The biggest failure I see: companies confuse AI wrappers with autonomous systems. A true agent doesn’t just respond, it decides. You can read my posts about autonomous agents here: https://lnkd.in/gmcefXc4 4️⃣ Evaluation is the new debugging. You can’t debug an LLM like software. You need metrics: faithfulness, coherence, factuality, cost-per-task, token efficiency. If you’re not measuring these, you’re flying blind. Eval-first culture is what separates production teams from prototype teams. 5️⃣ Deployment is where 99% fail. Demos run on laptops. Products run with SLAs, governance, and monitoring. You need CI/CD, observability, fallback models, tracing, and incident playbooks. Read more about the state of AI in 2025: https://lnkd.in/gPb3SyNH Otherwise, your “AI platform” is a very expensive toy. The companies that are actually pulling ahead? They’re treating LLMs like infrastructure, not inspiration. They’re building systems, not slides. They’re deploying, observing, iterating. Every week. wdyt?

I've watched 3 "revolutionary" healthcare technologies fail spectacularly. Each time, the technology was perfect. The implementation was disastrous. Google Health (shut down twice). Microsoft HealthVault (lasted 12 years, then folded). IBM Watson for Oncology (massively overpromised). Billions invested. Solid technology. Total failure. Not because the vision was wrong, but because healthcare adoption follows different rules than consumer tech. Here's what I learned building healthcare tech for 15 years: 1/ Healthcare moves at the speed of trust, not innovation ↳ Lives are at stake, so skepticism is protective ↳ Regulatory approval takes years usually for good reason ↳ Doctors need extensive validation before adoption ↳ Patients want proven solutions, not beta testing 2/ Integration trumps innovation every time ↳ The best tool that no one uses is worthless ↳ Workflow integration matters more than features ↳ EMR compatibility determines adoption rates ↳ Training time is always underestimated 3/ The "cool factor" doesn't predict success ↳ Flashy demos rarely translate to daily use ↳ Simple solutions often outperform complex ones ↳ User interface design beats artificial intelligence ↳ Reliability matters more than cutting-edge features 4/ Reimbursement determines everything ↳ No CPT code = no sustainable business model ↳ Insurance coverage drives provider adoption ↳ Value-based care is changing this slowly ↳ Free trials don't create lasting change 5/ Clinical champions make or break technology ↳ One enthusiastic doctor can drive adoption ↳ Early adopters must see immediate benefits ↳ Word-of-mouth beats marketing every time ↳ Resistance from key stakeholders kills innovations The pattern I've seen: companies build technology for the healthcare system they wish existed, not the one that actually exists. They optimize for TechCrunch headlines instead of clinic workflows. They design for Silicon Valley investors instead of 65-year-old physicians. A successful healthcare technology I've implemented? A simple visit summarization app that saved me time and let me focus on the patient. No fancy interface, very lightweight, integrated into my clinical workflow, effortless to use. Just solved an problem that users had. Healthcare doesn't need more revolutionary technology. It needs evolutionary technology that works within existing systems. ⁉️ What's the simplest technology that's made the biggest difference in your healthcare experience? Sometimes basic beats brilliant. ♻️ Repost if you believe implementation beats innovation in healthcare 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for realistic perspectives on healthcare technology

AI adoption is accelerating faster than the energy systems built to support it. Data centers are already among the most power-intensive assets on the grid and are seeing demand rise at rates that legacy infrastructure, static operating models, and fragmented regional grids were simply not designed to handle. The consequence is predictable: higher costs, growing emissions, and mounting pressure on utilities and operators trying to maintain reliability while integrating renewables. I’ve spent much of my career working at the intersection of technology, energy policy, and industrial systems, and this challenge is proving to be one of the defining infrastructure questions of the decade. It’s increasingly clear that the sector needs new ways to manage load, forecast demand, and coordinate resources across highly variable conditions. This week, I had the opportunity to hear from senior leaders at Hanwha Qcells about a model they are developing that aims to address these pressures. What stood out to me was the architectural shift behind the technology: using AI, interoperable language, and digital twins to unify diverse equipment, link operations to real-time grid signals, and automate many of the repetitive, checklist-style decisions that currently consume operator time. This broader concept of treating data centers as intelligent, grid-aware assets aligns with conversations happening across industry and government. The framework they described integrates clean generation, storage, and control software into a single adaptive system. The goal is straightforward but ambitious: reduce wasted energy, cut emissions, and improve resilience as AI demand grows. Their lofty projections (20–30% cost reductions, up to 35% emissions cuts, faster response times through agentic operations) reflect why approaches like this are gaining momentum. What interests me most is how these ideas fit into the larger trend: the shift toward an “Intelligent Age” where digital growth and energy management are inseparable... remember when VPPs were unheard of? Solutions that improve transparency, interoperability, and operational flexibility will be essential, and not just for data centers, but for manufacturing, transportation, and other power-intensive sectors facing similar constraints. As we look ahead, the real opportunity is in building systems that scale, adapt, and operate with far greater situational awareness. The conversation with Qcells underscored how quickly this space is evolving and why collaboration across utilities, technology developers, operators, and policymakers will be critical in the years ahead. Article link: https://bit.ly/4qggMLd #Hanwha | #HanwhaQcells | #Microsoft | #AI | #DataCenters | #EnergyManagement | #GridModernization | #CleanEnergy | #Innovation

A board member at a top-10 pharma company said something interesting to me last week: "I'm convinced your tech is superior. The question is: how do I get my scientists to actually use it?" Even if you solve a technical problem to some degree, that doesnt mean you automatically get adopted. Here's what I'm seeing across pharma: → Executives understand AI will transform drug development → Scientists are skeptical of "black box" solutions → IT departments worry about data security and compliance → Everyone's stuck in spreadsheet workflows that "just work" The result? Tens of millions-dollar AI budgets are spent while teams revert to Excel. This isn't a technology challenge - it's a change management challenge. The companies winning at AI adoption aren't necessarily building the best models. They're building the best bridges between cutting-edge technology and day-to-day scientific workflows. They're answering questions like: → How does this fit into my existing lab process and workflows? → Can I trust the tool enough to stake my career on them? → Will this make my job easier or just add more complexity? Technology is only as good as the adoption it drives. Learn more about how we enable scientists to engineer better proteins faster at Cradle: https://cradle.bio/

ChatGPT’s new reasoning models are hallucinating more often than previous generations, and the cause is still under investigation. It appears that adding the ability to break tasks down degraded OpenAI’s LLM reliability. On the PersonaQA and SimpleQA benchmarks, OpenAI’s new reasoning models (o3 and o4-mini) hallucinated between 33% and 79% of the time. The problem is likely to be impacting Google and DeepSeek’s reasoning models. It may be a cascading failure where multiple calls to the LLM amplify minor issues and inaccuracies as more steps are completed. The result of piling all those small inaccuracies on top of each other could be more noticeable errors. In any case, reasoning models fail at rates that make them unusable for consumer-facing products. It’s another setback to productizing LLMs, and there’s no timeline for when reliability will improve. For now, small language models (SLMs) are the best option for generative AI products. They cost less and are easier to put guardrails around. Post-training SLMs with domain-specific data helps them achieve higher reliability than LLMs. SLMs lack the horizontal breadth of knowledge but make up for it with vertical depth, enabling a narrow set of capabilities. They can support a few workflows well, but don’t generalize like LLMs are intended to. However, LLMs don’t meet the reliability requirements for most use cases. When they generalize, users can’t trust the output, so they can’t be integrated into AI products, especially agents that take action independently. As Anthropic recently discovered, we can’t trust the LLM’s explanations of how they arrived at the answers and output they generate. LLMs will often provide an explanation that doesn’t fit the reality of their internal processes. The fact that LLMs are unexplainable and unreliable means they aren’t ready for prime time. That doesn’t mean the technology is useless, and it’s essential not to overlook what does work (SLMs) just because some things don’t.