Why AI Fails to Solve Big Data Challenges in Supply Chain
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This CIO Online article by Pat Brans concisely summarizes the key ingredients to unlock AI's full potential (all are also key to CBRE's AI strategic pillars). It starts with a focus on data. 1. Treat data as a product 2. Build trust through observability 3. Bake in governance 4. Make data literacy universal 5. Unify structured + unstructured data to deliver richer insights 🔗 cio.com/article/4049233 #AI #DataCulture #DigitalTransformation
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Enterprise Data & AI Executive | Fortune 500 & PE-backed Transformation | Board & C-Suite Partner
Great CIO.com article on building an AI-ready data culture: treat data as a product, bake in governance, make observability and traceability core, push data literacy, and unify structured with unstructured sources. From my lens, success depends not just on tech, but on creating ownership, trust, and everyday habits across the organization. How is your org preparing its data culture for AI? Let's discuss! #DataStrategy #AI #DigitalTransformation #DataCulture #Leadership https://lnkd.in/gcjuay7G
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Most enterprises leave nearly 90% of their data untouched, trapped in unstructured formats like documents, emails, and videos. This “dark data” holds huge potential that, when combined with AI, can unlock powerful business insights. To realize this value, organizations must break down data silos, modernize legacy systems, and reframe compliance as a source of insight rather than just an obligation. By building structured pipelines and intelligent AI agents, companies can transform dark data into real-time, actionable intelligence. The future belongs to those who move beyond simply collecting data to truly leveraging it. Read the full article: https://lnkd.in/gguCXBd9 #DarkData #AIInfrastructure #DataStrategy #REEAGlobalInsights #ReadNow
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For enterprises seeking to unlock the value of gen AI, it’s not enough to have data or even a model. What matters is how data is created, managed, shared, and trusted. (Story by Pat Brans) With thanks to Mike K., Dinesh Nirmal, Andrew Crisp, and Magan Naidoo for sharing your insights. https://lnkd.in/eQqK-y3Q
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You don’t need a separate AI strategy. You need a better data strategy—one that includes AI. Mallory Busch shares how Sigma helps teams stay flexible, governed, and ready for whatever comes next.
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You don’t need a separate AI strategy. You need a better data strategy, one that includes AI. Mallory Busch shares how Sigma helps teams stay flexible, governed, and ready for whatever comes next.
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Customer Success Leader | EHR & SaaS Implementation Expert | Healthcare Tech Strategist | Driving Retention + Operational Value
Great insights on modernizing and unlocking value with data from one of my many newsletter reads, Navigating AI by Launch. The article explores the transformative impact of data modernization on organizational efficiency and value creation. It emphasizes the shift from traditional data management systems to agile, cloud-based solutions that enable real-time analytics and improved decision-making. #DigitalTransformation #AI #DataModernization #CloudComputing #RealTimeAnalytics Launch Consulting Group
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AGI Explorer | Ontology & Knowledge Graph–Driven GenAI Platforms | Recommender Systems | Semantic Search & RAG | PMP
I couldn’t agree more. Much of what makes great companies succeed lies in the experience and mental models inside employees’ heads—knowledge that’s hard to capture, easy to lose, and critical for decision-making. This is why the Semantics Layer, Ontology Layer, and Knowledge Graph are so essential: they connect AI’s intelligence with real business context, turning abstract capability into trusted, actionable value. It’s the “last mile” of enterprise AI—and the decisive one. KamiwazaAI
The real bottleneck to enterprise AI isn’t how good the model is. It’s your messy, buried, disconnected enterprise knowledge. I’ve seen it firsthand. Years of building data and analytics platforms taught me that adoption stalls in the “last mile,” when the nuances and edge cases of business context can’t be integrated fast enough. AI will only magnify this gap. Here’s the problem: 📌 Business knowledge lives in people’s heads 📌 Capturing and operationalizing it is slow, manual, and disruptive 📌 Without it, AI can’t make decisions you can actually trust I'm excited to finally share what I've spent months thinking about. In this piece, I explore: 🧠 Why internal knowledge is the control point 🔄 How the context flywheel compounds value over time 🚀 The traits of startups positioned to win this market Huge thanks to the data leaders, engineers, and AI builders whose insights shaped this POV. Next up: a market map of the startups tackling this problem. If you’re building in this space (or know someone who is), let’s chat! 🔗 https://lnkd.in/egkeMY5g
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Account Executive, Large Enterprise @ Alteryx | Empowering Data-Driven Decisions with Analytics Automation
AI can’t thrive without trusted data. Data silos, poor governance, and missing context hold back GenAI success. That’s why enterprises are turning to the AI Data Clearinghouse. It ensures your data is clean, explainable, and governed BEFORE it powers your AI models. With Alteryx, you can: ✨ Unify structured & unstructured data ✨ Enrich it with business logic ✨ Secure & audit workflows seamlessly ✨ Accelerate AI readiness without the guesswork Don’t gamble on black-box AI. Build a foundation you can trust. Read our latest blog to learn more: https://ow.ly/9mhj30sPOLL #AIDataClearinghouse