How CIOs are using AI to drive real-time decision-making

🤖 AI + Real-Time Intelligence = Business Reinvented Data isn’t just for analysts anymore — it’s democratized. From logistics to finance to search, AI is transforming how industries: ⚙️ Automate complex decisions 📊 Turn live data into instant insights 🚚 Adapt operations in real time Companies like UPS, Capital One, and Google are proving that when humans and AI collaborate, speed becomes strategy. How is your org using real-time intelligence to stay ahead? 👀 #AI #DataAnalytics #Automation #DigitalTransformation #CompTIA

Everyone’s talking about AI data analysts, but few have cracked what actually works 😅 In my latest Forbes Technology Council article, I break down: ✅ What’s working (and what’s not) in AI data analysis today 🧩 Practical strategies to make AI analysts useful in real teams 🚀 How to extract value from structured data even when text-to-SQL tools fall short Read here → https://lnkd.in/gpmkVwNk Teams often spend months chasing another 5% accuracy gain, instead of framing the problem so that AI accuracy isn’t the bottleneck and real impact is immediate. Big thanks to Prudhvi Vasa and Ravi Vijayaraghavan for their invaluable inputs while shaping this piece 🙏 #AI #DataEngineering #Analytics #ForbesTechCouncil

💡 What’s new in ML & Data Analytics and why it matters now A little while ago I remember being caught up in the usual analytics grind: cleaning data, building models, trying to make sense of the noise. Today, things are shifting and fast. Here are some of the key updates I’m tracking (and working with) in ML and data analytics: 🔍 1. From hype to results: agentic AI & generative AI go real We’re moving beyond “let’s test a chatbot” to solutions where autonomous AI agents perform meaningful tasks. These tools are not just buzz they’re helping data teams scale workflows and analyze data faster. 📊 2. Unstructured data takes centre stage Text, images, video data types once ignored are now front and centre. Cutting-edge analytics uses them to uncover insights beyond traditional spreadsheets. For me, this means rethinking how we prepare data and what kinds of questions we can ask. ⚙️ 3. Automation everywhere: feature engineering, data-cleaning, deployment No longer is it just about building models. The latest platforms are automating many of the prep steps for freeing up time for real strategy. More power for analysts, less time lost in messy data trenches. 📈 4. Real-time, edge, and scalable analytics Streaming analytics, edge computing & processing at the source are now essential. If insights don’t arrive fast enough, you’ve already lost. For those of us working on business impact: faster data = faster decisions. 🛡️ 5. Ethics, privacy & data governance are no longer optional As analytics power grows, so do responsibilities. More than ever: models must be built with ethics, bias-control and governance in mind. From my side: every model I build now includes a “check-in” for fairness and transparency. ✨ What this means for you and your team If you’re thinking of taking analytics and ML from being “nice to have” to strategic, these updates matter: Revisit your data strategy: Are you just working with structured tables, or also tapping into images, documents, video? Evaluate your tools: Are you relying on manual workflows that can be automated (and freed up)? Think impact and timing: Can your analytics deliver now, not in 3-6 months? Build with responsibility: Ensure your models are trustworthy, explainable and aligned with your business values. 💬 If you’re curious, I’d love to chat about how we’re integrating these into our work. Drop me a DM and I’ll share what’s worked (and what’s been tricky). #MachineLearning #DataAnalytics #AI #BusinessIntelligence #DataDriven #AnalyticsStrategy

🧠 AI can’t think without context. That was one of the biggest takeaways from our recent conversation with GigaOm’s Andrew J. Brust, who called semantics “no longer aspirational, but essential.” In our latest blog, AtScale CTO & Co-Founder David P. Mariani, explains why the semantic layer has become the foundation for trustworthy AI, and how pairing LLMs with governed semantics changes everything. Here’s what you’ll learn: ✅ Why context is the missing link in enterprise AI ✅ How the Model Context Protocol (MCP) connects LLMs to real governed data ✅ What the live Claude + AtScale demo revealed about reasoning vs. guessing ✅ Why GigaOm’s 2025 Radar recognized AtScale as a Leader and Fast Mover Read the full breakdown 👇 📘 The Golden Age of the Semantic Layer: Why AI Can’t Work Without Context:

My favourite BI tool just got better! I personally still think we vastly underestimate the worth of a well defined semantic layer. I mean AI is okay and all but we all know that context is what makes it great. The most advanced AI users I see will give their LLM a ton of information (i.e. connect to data, share links, give them a persona, ...) In the world of BI, that context is in your semantic layer. Yesterday, I also gave dbt Labs Cloud a spin, generates the semantic layer for you, with actually very decent descriptions I have to say. If you can make an MVP semantic layer and business users can modify/improve that themselves through chatting with an LLM, that's a gamechanger. Especially as all data folks know how much PM's like to ask questions. These are often extremely valuable questions, as they are full of business knowledge/logic that is not always - or very seldom - captured in documentation.

🤖 AI Stack: 7 Layers Transforming Data into Decision.✔️ Artificial Intelligence is no longer just a buzzword—it’s a strategic imperative. But to truly leverage AI, businesses must understand the full stack from raw data to actionable decision making.Breakdown of the seven layers that turn information into intelligence: 1️⃣ Data Layer The foundation of every AI system. Without high-quality data, even the most advanced models fail—garbage in, garbage out. This layer involves collecting, cleaning, labeling, and governing data from multiple sources. Investing in robust data pipelines and governance frameworks ensures that AI has a reliable base to learn from. 2️⃣ Model Layer This is the AI brain. Models range from pre-trained large language models like GPT to fine-tuned and custom-built models. Key considerations include accuracy, latency, cost, and adaptability to your specific use case. Choosing the right model architecture is critical to balancing performance with practicality. 3️⃣ Memory Layer Context matters. AI gains historical awareness through memory, storing past queries and interactions. Combining vector databases with caching systems like Pinecone allows efficient retrieval, ensuring AI responds intelligently, not blindly, to user input. 4️⃣ Tooling Layer Here, AI moves from suggestion to action. It connects to APIs, databases, and external applications like Slack, GitHub, or CRM systems, automating tasks and integrating insights into business operations. This layer bridges intelligence with execution. 5️⃣ Orchestration Layer The conductor of AI workflows. Orchestration tools such as LangGraph, AutoGen, and CrewAI manage multi-step processes, tool calls, and failure handling. Proper orchestration ensures AI systems operate cohesively and reliably across complex tasks. 6️⃣ Governance Layer AI without governance is risky governance. This layer monitors accuracy, bias, hallucinations, and model drift using frameworks like Weights & Biases, Arize, and NIST standards. Governance ensures AI is safe, fair, and trustworthy—critical for enterprise adoption. 7️⃣ Applications Layer Finally, the end-user experience. This layer turns AI capabilities into practical, usable tools agents, chatbots, copilots, dashboards, and internal applications. Without this layer, AI remains potential, not performance. ⚡ Key Insight; Each layer builds on the previous, transforming raw data into actionable intelligence that drives measurable business value. Organizations that master this stack don’t just automate—they innovate, predict, and create strategic advantage. The challenge for every professional today is Are you merely using AI tools, or are you architecting systems that integrate data, models, memory, tooling, orchestration, governance, and applications to make smarter, faster, and safer decisions? #AIStack #ArtificialIntelligence #DataScience #MachineLearning #BusinessIntelligence #DigitalTransformation #AIAdoption #Innovation #EnterpriseAI.

Unlock Business-Led AI with UBIX ModelSpace 🚀 Discover how UBIX ModelSpace is transforming data science by empowering business leaders—not just data scientists—to build, deploy, and optimize AI solutions with a no-code platform. This article explores how UBIX democratizes data, accelerates innovation, and enables true business-led AI. 📖 Spend 10 min with this article to learn how you can achieve: ✅ Empowerment for All: Learn how UBIX enables citizen data scientists to create predictive models and actionable insights—no coding required. ✅ Faster Time to Value: See how one-click productionization and intelligent defaults let you go from idea to production in hours, not months. ✅ Next-Gen AI: Understand how GenAI, Reinforcement Learning, and Agentic AI are integrated to drive continuous business optimization and smarter decision-making. https://lnkd.in/giYVPBQG #AI #NoCode #DataScience #BusinessIntelligence #DigitalTransformation #GenAI #ReinforcementLearning #AgenticAI #UBIX #DataDemocratization

🚀👏 Massive congratulations to John Burke and the outstanding UBIX.AI team on the launch of UBIX ModelSpace! This isn’t just another AI tool—it’s a major leap forward in the Democratization of AI. 🌍🤖✨ At PX42 Consulting, we are proud to partner with UBIX to extend the impact of ModelSpace by integrating it into the PX42 Agent Framework—creating enterprise-wide Agentic AI solutions that are business-led, trusted, and scalable. Together, we’re bringing AI out of the labs and into the hands of business leaders, operators, and innovators across every industry. 💡🤝 💫 The Democratization of AI in Action: UBIX ModelSpace removes the technical barriers that have traditionally kept AI in the domain of data scientists. Now, business leaders, citizen data scientists, and domain experts can design, deploy, and optimize AI models without writing a single line of code. When combined with PX42’s Agentic AI expertise, this means: 🚀 AI for Everyone: Empower entire organizations—not just IT—to participate in building, guiding, and using AI. ⚡ Faster Value Creation: Move from idea → production in hours instead of months, ensuring agility and speed. 🔐 Trust at Scale: Human-in-the-loop, Anti-Hallucination IP, and AI Optimization engines ensure outcomes are reliable and compliant. 🌍 Scalable Agentic AI: Enterprise-wide AI Agent swarms that continuously learn, adapt, and democratize decision-making. 📊 Use Cases Where Democratized AI Delivers Impact: ✅ Financial Services: Risk managers can launch new compliance models, while customer service leaders deploy fraud-detection agents—cutting compliance costs by up to 40%. 💳🔍 ✅ Healthcare: Clinicians and administrators can co-create models for patient engagement, claims, and care pathways—reducing admin costs by 25–30% and improving outcomes. 🏥❤️ ✅ Manufacturing & Supply Chain: Plant operators and logistics managers can design predictive maintenance and logistics optimization models, unlocking 10–15% cost savings. 🏭🚚 💰 The Financial Benefits of Democratized AI: ✅ Reduced Cost of Innovation: No-code AI means enterprises avoid the high costs of scarce technical talent. ✅ Accelerated ROI: Go live in weeks instead of quarters, lowering TCO while driving faster returns. ✅ Revenue Growth: Unlock new streams via personalized services, dynamic pricing, and AI-driven market insights. ✅ Workforce Enablement: Every employee becomes an AI-powered decision-maker, compounding productivity and innovation. 🌐 Together, PX42 + UBIX are proving that the future of AI is: 👉 Democratized. Trusted. Agentic. Scalable. We couldn’t be more excited to help organizations across industries embrace the Democratization of AI and build the intelligent, adaptive enterprises of tomorrow. 🚀 🔗 Dive into UBIX’s article here: https://lnkd.in/eiqsGpqa

ModelSpace from UBIX.AI Predictive solutions and related workspace data become inputs into no-code Reinforcement Learning, where you build intelligent agents that continuously learn from new conditions to optimize output for a business goal and generate recommendations. Then, use the results in AgenticSpace’s Action Agents to communicate specific recommendations back to source systems for automating business decisions.

In every industry, data is no longer just information, it is intelligence in motion. AI powered analytics is helping organizations uncover insights faster, predict trends with precision, and make smarter, data driven decisions. From finance to healthcare, retail to manufacturing, enterprises are transforming how they operate and compete. Explore how AI powered analytics is reshaping industries in our latest blog. https://bit.ly/49hc8XR #AI #Analytics #DataAnalytics #AIPoweredAnalytics #BusinessIntelligence #DataVisualization #EnterpriseAI #DigitalTransformation #DataDriven #NoCodeAnalytics #PredictiveAnalytics #AIInsights #DataStrategy #BigData #DataInnovation #AIAutomation #SmartAnalytics #DataCulture