Why People, Not Tech, Are the Biggest Barrier to AI Success

👉 Agentic AI is the third wave of AI, moving from predictive → generative → AGENTIC. 💥 High-quality data is key. 🧐 The report underscores that high-quality, relevant data is the foundation of effective Agentic AI systems. Unlike traditional AI, Agentic AI relies on precise, up-to-date information to autonomously reason, act, and adapt, making data accuracy non-negotiable. Poor or outdated data can lead to flawed outputs, while well-curated datasets enhance reliability and performance. Techniques like Retrieval-Augmented Generation (RAG) and adversarial testing help ground agents in trusted sources and mitigate risks. As the adage goes: "Garbage in, landfill out." This means prioritizing data governance, interoperability, and continuous refinement to unlock Agentic AI’s full potential. ✍️ Key takeaways: AI agents don’t just respond - they think, act, and reflect. Powered by LLMs, they can reason, set goals, navigate complex workflows, and adapt in real time. AI that handles multi-step customer queries, automates back-end operations, or even conducts proactive health check-ins.  It matters because: ✅ Beyond cost savings: Agentic AI drives revenue growth, innovation, and competitive moats. ✅ Enterprise transformation: 50% of enterprises could deploy AI agents by 2027 (up from 25% in 2025). ✅ Real-world impact: From healthcare (100 agents per patient?) to supply chains, customer service, and decision intelligence - agentic AI scales what humans can’t. ✅ But beware: Success isn’t just tech. It requires governance, high-quality data, interoperability, and a focus on precision over volume. 🚨 Key insight: “The easy thing is to think, ‘I can reduce costs with this.’ But most people are competing on top-line growth. If you’re not leveraging agentic AI for growth, someone else in your space is.” – Adrian McDermott, CTO, Zendesk 📗 Source: https://lnkd.in/dr5YZhm7 --------- ✨ The future of enterprise AI is here, and it’s agentic! 🌈 Learn more:  Join the industry's only AI Agent Ops Dedicated Linkedin Group:  https://lnkd.in/dMP_qg_N 👍🏻 Like | 💬 Comment | ♻️ Repost

👉 Agentic AI is the third wave of AI, moving from predictive → generative → AGENTIC. 💥 High-quality data is key. 🧐 The report underscores that high-quality, relevant data is the foundation of effective Agentic AI systems. Unlike traditional AI, Agentic AI relies on precise, up-to-date information to autonomously reason, act, and adapt, making data accuracy non-negotiable. Poor or outdated data can lead to flawed outputs, while well-curated datasets enhance reliability and performance. Techniques like Retrieval-Augmented Generation (RAG) and adversarial testing help ground agents in trusted sources and mitigate risks. As the adage goes: "Garbage in, landfill out." This means prioritizing data governance, interoperability, and continuous refinement to unlock Agentic AI’s full potential. ✍️ Key takeaways: AI agents don’t just respond - they think, act, and reflect. Powered by LLMs, they can reason, set goals, navigate complex workflows, and adapt in real time. AI that handles multi-step customer queries, automates back-end operations, or even conducts proactive health check-ins.  It matters because: ✅ Beyond cost savings: Agentic AI drives revenue growth, innovation, and competitive moats. ✅ Enterprise transformation: 50% of enterprises could deploy AI agents by 2027 (up from 25% in 2025). ✅ Real-world impact: From healthcare (100 agents per patient?) to supply chains, customer service, and decision intelligence - agentic AI scales what humans can’t. ✅ But beware: Success isn’t just tech. It requires governance, high-quality data, interoperability, and a focus on precision over volume. 🚨 Key insight: “The easy thing is to think, ‘I can reduce costs with this.’ But most people are competing on top-line growth. If you’re not leveraging agentic AI for growth, someone else in your space is.” – Adrian McDermott, CTO, Zendesk 📗 Source: https://lnkd.in/dk2Yerf2 --------- ✨ The future of enterprise AI is here, and it’s agentic! 🌈 Learn more:  Join the industry's only AI Agent Ops Dedicated Linkedin Group:  https://lnkd.in/dMDFZMJa 👍🏻 Like | 💬 Comment | ♻️ Repost

🚨 Agentic AI Is Great—But It’s Not a Substitute for Domain Expertise I have seen agents that deploy models, scale infra, and automate pipelines. But they still don’t know why your sales dropped last Tuesday. 💡 Common Criticism: “Just feed the right data and the AI will learn.” Sure—if the data is clean, labeled, and context-rich. But in the real world, it’s not. 🧩 Nuanced Failures I’ve Seen: A forecasting model missed a 12% spike in orders because the regional festival wasn’t in the calendar feed. The planner knew. The AI didn’t. A marketing mix model over-attributed ROI to paid search, ignoring a retail display campaign that wasn’t tagged in the data. The analyst caught it. The AI didn’t. A demand model failed in LATAM because it treated currency fluctuations as noise. The economist flagged it. The AI didn’t. A chatbot optimized for conversion—but misunderstood product descriptions in local dialects, hurting brand trust. The marketer knew. The AI didn’t. These aren’t data problems. They’re context problems. 🧠 What Agentic AI can do(as of today in my context world!) Automate MLOps Run experiments Generate code Scale infra 🧠 What it can’t do:(again pardon my ignorance or context!) Understand messy business data Spot domain-specific quirks Design proper experiments Think causality vs correlation 🔮 What’s Next? To scale AI responsibly, we need: Context-aware agents: LLMs grounded in domain knowledge graphs, not just APIs. Human-in-the-loop orchestration: Experts who guide, audit, and challenge AI outputs. Causal-first modeling: Systems that ask “why” before predicting “what.” 📉 The cost of ignoring this: Forecasting misses that disrupt supply chains Misallocated budgets in marketing AI systems that fail silently Talent burnout from misaligned expectations 💬 To AI leaders: Agentic AI is a tool. Domain expertise is the compass. Don’t trade one for the other. Are you seeing this disconnect in your org? Drop your stories below 👇 #AgenticAI #Forecasting #CausalInference #AILeadership #DataScience #TechHiring #EnterpriseAI #RetailAnalytics #TimeSeries #BusinessIntelligence #Innovation #FutureOfWork

🚨 FIXING ML INTERVIEWS FOR THE AGE OF AGENTIC AI 🚨 Thesis: Agentic AI changes how problems are solved — but not what we should measure. Hiring must evolve to surface judgment over just tool fluency. The problem today: Interviews still gate on infra checklists + LeetCode puzzles nobody uses in production. Take-homes are often AI-generated, with little transparency. Practitioners spend 40–60% of real time wrangling messy data — yet interviews rarely test data intuition. Result: orgs hire for plumbing, but filter out people who catch context, causal signals, and business risk. Practical fixes you can apply this week: ✅ Allow AI — but require an AI use log + one-line unique contribution. ✅ Replace one infra screen with a live case diagnostic (e.g., “Forecast dropped 25% — what do you check in 20 minutes?”). ✅ Short, realistic take-home (48–72h) → deliverable = notebook + exec summary on “What breaks this model in prod?” ✅ Score judgment over typing — rubric: Data intuition 30% / Modeling 25% / Business impact 20% / Repro 15% / AI provenance 10%. ✅ Require one domain-signal check (FX sanity, promo tags, calendar join). ✅ Add a human-in-loop question: “Which stakeholders would you involve, and why?” Why this matters: Agentic AI will automate plumbing — but it won’t spot an untagged promo, local festival, or causal confounder that kills a forecast. Hire for judgment, not just code. 💬 Micro-ask: What’s the strangest ML interview question you’ve faced? Drop it in comments — I’ll pin and summarize the best. #AgenticAI #MachineLearning #DataScience #Hiring #AIInterview #MLOps #CausalInference #AILeadership

Are you really an AI genius, or just acting the part? My LinkedIn feed paints a picture of everyone now as “prompt geniuses,” building AI agents, automating relentlessly, and claiming 1,000X productivity. But what’s genuinely happening?  How many are deep-diving into AI? How many dived in, had a paddle, jumped out, and are now quietly using it in modest, real‑world ways? I did some research so you don’t have to… 🧐 What the Latest Research Shows (I’ll drop all the links in the comments👇) - Despite the hype, only half of employees have ever been trained on AI tools- even though 93% of US leaders say AI is now essential. (Gallup Poll, via Business Insider, June 2025) - Among developers, 84% use AI - but almost half say they don’t trust the outputs and often revert to manual code. (ITPro, Developer Survey 2025) - As for agentic AI? Only 2% of firms have scaled anything, and just 15% of business processes are even semi-automated. (ITPro, Agentic AI Adoption Report 2025) 🧐 The Patterns That Don’t Get Posted - Small businesses are using AI more than ever (up from 39% to 55% in the past year) -but mainly for content, marketing and customer service. Useful? Yes. Transformational? Not yet. (Thryv SMB AI Adoption Survey 2025) - In the UK, just 29% of firms encourage AI use, and only 37% offer training. Compare that to almost double in the US. (The Times, UK AI Adoption Data 2025) 🧐 What’s Fueling the Gap? - Performative AI is real: 1 in 3 professionals admit to pretending they use AI - just to look competent. (Times of India, Survey via Microsoft Research 2025) - Most teams lack clear strategy, governance, or guardrails, so results vary wildly. (Arxiv org, Organisational Readiness Research 2025) - And let’s be honest: transformation is slow. It’s not “AI everywhere” overnight. (Harvard Business Review, June 2025) 🤔 So… where do you stand? Have you or your business been through the AI hype cycle? Maybe you started big - agents, automation, grand claims - then quietly settled into a prompt-based routine? How are you really using AI today? Where has it helped? Where has it fallen short? 👀 I’d love to hear some honest stories from the front lines, not the marketing decks.👇 #AIatWork #FutureOfWork #AIMaturity

😯😯😯 𝐑𝐞𝐚𝐥𝐥𝐲? 𝟗𝟓% 𝐨𝐟 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐢𝐧 𝐥𝐚𝐫𝐠𝐞 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬? 𝐍𝐨𝐭 𝐚 𝐬𝐮𝐫𝐩𝐫𝐢𝐬𝐞! This week the stock markets have put pressure on the AI sector after a new #MIT study has revealed that 95% of #GenAI pilots fail to generate real value. ➡️ Does this mean the AI bubble is about to burst? No! ➡️ Is this a surprise? Not much. The study reports that the learning gap is one of the main reasons of the failure. This refers to the gap between knowledge about AI and AI practical, business-driven applications. Of course, the hype has played a big role here. But is it just that? No! In my opinion:   ❌ General AI tools (like GPT-like chatbots) don’t contribute to generating real revenue or cost savings if they are not truly integrated into the company workflow. ❌ Not all AI use cases are generative AI use cases. For two years, nobody has been talking about DATA SCIENCE anymore, and this has not helped because, let’s be honest, the transformation triggered by data science in companies was not complete yet when GenAI started showing up.   So, what is the solution?   ✨ Mind the learning gap. ✨ Sometimes, GO BACK TO REAL DATA SCIENCE. ✨ Build partnerships with strategic, valuable vendors (not just marketers). ✨ Listen to and empower real AI experts within companies. #AI #GenAI #DataScience #ML 👉 https://lnkd.in/dECY7QVm

If you’re building AI products and still treating them as backend tools or narrow features – three recent papers show why that approach is quickly falling behind. They highlight where value is forming and how product strategies must evolve. These aren’t trends. They reveal how AI is outpacing team execution. I lead AI-focused product research and see how fast assumptions expire. Success depends on testing before building—because what works next month may look very different. For any product team: Where are people still working manually? Where do users mistrust automation? Where are we guessing? 1. OpenAI’s Agent Negotiation & Multimodal Demo Why this matters: OpenAI’s demo showed two AI agents negotiating and interpreting visuals – signaling a shift from single-answer bots to tools that collaborate and initiate. Why this should shift your strategy: Most products rely on users driving every step. But AI as the actor (not just assistant) means delegation, coordination, and autonomy are now in play. Ignore this, and faster-moving competitors will outpace you. Pinpointing value: Look for spots where users coordinate with others (scheduling, approvals, handoffs). Agent-based automation can save time and reduce friction. 2. DualSG: Semantic-Guided Forecasting Why this matters: DualSG combines historical data with real-world inputs, like news, trends, and sentiment, to generate smarter, more context-aware predictions. Why this should shift your strategy: Forecasting isn't just math anymore. AI can now factor in what’s happening around your business. Products that ignore context will feel outdated fast. Pinpointing value: Find metrics your team second-guesses: conversion, churn, demand. What real-world factors are missing from your models? 3. Symbiotic Epistemology & SynLang Protocol Why this matters: This paper proposes a model for collaborative reasoning – where users and AI co-develop answers, not just receive them. Why this should shift your strategy: AI tools without visible logic are losing trust. Products that show reasoning and adapt to user feedback will lead in high-stakes domains like finance, healthcare, and ops. Pinpointing value: Spot where users hesitate, override, or question AI suggestions. That’s where transparent, explainable AI can earn back confidence. ----------------------- I'm Brittany Hobbs – I lead strategic and product research to ensure AI investments succeed. If you're navigating where AI creates the most value or making critical product decisions, I can help you pressure-test your path forward. Comment or DM me to chat.

With frameworks like DORA, SPACE, and DevEx, one of the most common questions we hear is: This seems helpful, but how do I apply it? From the start, we wanted the DX AI Measurement Framework to be different. The goal wasn’t just to define concepts, but to give leaders a clear path to implementation through DX. In our latest post, we walk through exactly how to measure: Utilization — how widely AI tools are being used Impact — what they’re actually delivering to developers ROI — whether the investment is paying off all within DX. https://lnkd.in/gnD5taTn

95% of generative AI pilots are failing. A new MIT report shows the biggest culprit isn’t the models, it’s the data. Most enterprises are drowning in: ❌ Inconsistent and outdated records ❌ Duplicated files across silos ❌ Scattered information without context This messy data blocks AI from delivering accurate insights and ROI. The 5% who succeed take a different path: 👉 Unify siloed and unstructured data into a single source of truth 👉 Align AI initiatives with measurable business outcomes 👉 Work with expert partners to escape “pilot paralysis” At Allganize, we help enterprises master messy data and scale production-ready AI systems that deliver real impact from day one. Don’t let your data hold you back. Be among the 5% turning AI into ROI. Read the full guide: https://lnkd.in/eJ5QzkrP #MessyData #GenerativeAI #EnterpriseAI #Allganize

95% of generative AI pilots fail. It's a hard lesson many of the companies we work with have learned. The culprit isn't the tech—it's messy, conflicting, siloed data. Without a trusted data foundation, AI is a novelty, not a value driver. Success isn’t about more experiments. It's about rooting AI in your enterprise knowledge, integrating it into core workflows, and tying it to measurable ROI. At Allganize, our AI for real-world data enables companies to move beyond "pilot paralysis" and join the 5% that get it right. Our new blog breaks down the recent MIT study on this. Learn how to build for real business impact from day one.