Evaluating AI investments with the 5M Framework

AI-Powered Metadata Discovery — Governance at Machine Speed Metadata has always been the backbone of governance, but as data volumes explode, traditional manual approaches can’t keep up. This is where AI-powered metadata discovery transforms the game: 🔹 Faster CDE Identification — AI automatically scans across systems to detect critical data elements and tag them based on sensitivity and regulatory requirements. 🔹 End-to-End Lineage Mapping — Instead of months of manual effort, AI tools map lineage across complex data estates in hours, enabling traceability at scale. 🔹 Automated Classification — With built-in learning models, AI can detect PII, PHI, and confidential data across structured and unstructured sources seamlessly. For enterprises, this isn’t just efficiency—it’s the foundation for regulatory confidence and innovation at speed. 💡 Pro Tip: Start small. Automate lineage for your top 10 critical data flows. The impact on compliance readiness and reporting accuracy is immediate. #DataGovernance #AIinData #MetadataDiscovery #LineageAutomation #DataTrust

AI is entering a new era of intelligence. In IBM IBV’s newly released 2025 COO Study, findings reveal that AI has moved far beyond automation and productivity gains. Seven out of ten COOs are already embracing agentic AI—intelligent systems that don’t just follow commands but reason, plan, and autonomously execute complex business strategies. This signals a major shift in the operating model of enterprises worldwide. But what are the architectural foundations enabling this transformation? From adaptive data frameworks to orchestrated cloud-native environments, the next generation of enterprise architecture is being redefined around autonomy and intelligence. 👉 Contact us to learn more: https://lnkd.in/es_GVQK2 #AgenticAI #InnovationLeadership #COOInsights #IBMPartners #MESolutions #IBMAmplify

𝐘𝐨𝐮𝐫 𝐀𝐈 𝐈𝐬𝐧’𝐭 𝐅𝐚𝐢𝐥𝐢𝐧𝐠 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐈𝐭’𝐬 𝐃𝐮𝐦𝐛 𝐈𝐭’𝐬 𝐅𝐚𝐢𝐥𝐢𝐧𝐠 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐈𝐭’𝐬 𝐃𝐢𝐬𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 Most AI systems don’t break because the model is weak. They break because they forget. Or misinterpret. Or pass context in ways that don’t scale. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭’𝐬 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐨𝐢𝐧𝐠 𝐰𝐫𝐨𝐧𝐠: ▪️Your agent can’t remember what happened three steps ago ▪️The tools it uses don’t share memory or task state ▪️Prompts are being passed like duct tape instead of structured handoffs ▪️The system works in a notebook, but collapses in production That’s where 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 (𝐌𝐂𝐏) comes in. MCP isn’t just another acronym. It’s a way to structure how information moves between models, tools, agents, and environments so that context isn’t just passed, but understood and reused. Think of it as the API contract for your AI system’s memory and reasoning. 𝐖𝐡𝐞𝐧 𝐲𝐨𝐮 𝐠𝐞𝐭 𝐭𝐡𝐢𝐬 𝐫𝐢𝐠𝐡𝐭: 🔸Agents can coordinate across long workflows 🔸Tools can execute actions without needing every detail repeated 🔸Systems can recover, retry, and reflect without losing their place 🔸And your AI becomes more than a prompt machine — it becomes a collaborator At 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐆𝐫𝐨𝐮𝐧𝐝, we help teams implement real context protocols, not just clever hacks. We design interoperable AI systems that can share memory, track state, and operate across components like a real team — not isolated bots. Because at scale, it’s not enough to be smart. You need to be connected. If you’re hitting the ceiling with brittle agent chains or prompt spaghetti, we should talk. 👉 𝐁𝐨𝐨𝐤 𝐚 𝐬𝐞𝐬𝐬𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐨𝐮𝐫 𝐭𝐞𝐚𝐦: https://lnkd.in/dJ_--DwU #AgenticAI #ModelContextProtocol #AIInterop #EnterpriseAI #AIAgents #AIArchitecture #ServicesGround

Ascend.io Unveils Custom Data Engineering Agents: Build Powerful Agents for Data ... - AiThority ... Agents, a breakthrough feature that empowers data teams to build intelligent, context-aware AI agents in under 10 minutes using simple markdown files. https://lnkd.in/dJf8CxZ3

A refreshing read on 5 strategies to shatter enterprise inertia from a COO’s lens from IBM Institute of Business Value. Loved the future-ready perspective! From a techie’s viewpoint, here are my key takeaways: 1.Prioritize transformational growth : embrace uncertainty, anticipate market shifts 2.Make operations the data epicenter : design apps with unified, connected data 3.From process architect ➝ operational disruptor : question norms & simplify 4.Embrace AI : explore agentic AI first-use cases 5.Move from AI adopters ➝ AI orchestrators : enable human + AI collaboration for measurable impact Inspiring vision for building resilient, future-ready enterprises! Read more here: https://lnkd.in/g8_FRVDa

𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗥𝗮𝗽𝗶𝗱𝗹𝘆 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗠𝗼𝘀𝘁 𝗔𝗰𝘁𝗶𝘃𝗲 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 🧠⚙️ Stop treating data as a passive asset to be stored and start treating it as your most active and intelligent software. Leading organizations are evolving from passive data lakes to active intelligence platforms where insights trigger actions, not just dashboards. The old model was about running queries against static data. The new model is about creating an environment where data, powered by AI, proactively identifies opportunities, flags risks, and triggers automated actions. The edge isn’t about owning the most data—it’s about enabling data that learns, adapts, and acts. 🚀 #Data #AI #DigitalTransformation #IBM

More than half of enterprise leaders plan to boost their data infrastructure spending and in some industries, that’s over $30M a year. Falling behind isn’t an option. Your data stack can be the engine that powers AI, agility, and growth. Explore the 9 proven moves top enterprises are using to build a resilient, real-time, and AI-ready stack. 👉 https://ow.ly/Y2rY50WVG1F #DataEngineering #DataInfrastructure #DataStack #AIReady #EnterpriseAI #DigitalTransformation #grazitti

Announcing AI CEO — An Open-Source, Policy-Aware Executive System "The AI discussion keeps optimizing “individual contributors,” while executive decision-making largely stays manual. AI CEO is a public, remixable baseline that brings policy-checked automation to strategy, finance, and operations — with human-in-the-loop escalation where it matters. It’s a practical codebase for testing how far an “AI executive” can go — openly, transparently, and with guardrails." Sundar Pichai Satya Nadella Marc Benioff https://lnkd.in/eZirgPTg

🚗 AI has powerful engines, but where are the highways? Gartner's analysis of GPT-5 reveals a critical truth: While AI models are becoming incredibly sophisticated, we're missing the infrastructure to unlock their true potential. Think about it - we had Ferraris and Porsches decades before the U.S. interstate highway system in 1956. Today's AI landscape mirrors this perfectly. GPT-5 brings impressive advances: • Enhanced coding capabilities rivaling Anthropic • Expanded context windows up to 128K tokens • 65% reduction in hallucination rates • Significant API cost reductions Yet, according to Gartner's Arun Chandrasekaran, we're seeing "incremental progress, rather than radical improvement." The missing piece? Infrastructure for true agentic AI: ✗ Lack of enterprise tool integration standards ✗ Insufficient identity and access management systems ✗ Limited agent-to-agent communication protocols ✗ Absence of robust governance frameworks Currently, agentic deployments exist only in "small, narrow pockets" - mostly semi-autonomous workflows in software engineering or procurement. For enterprise leaders considering GPT-5: • Run side-by-side evaluations against current models • Audit API quotas and multimodal data pipelines • Update governance policies for expanded capabilities • Maintain human oversight for critical workflows The bottom line: We're "very, very far away from AGI." Success requires more than just better models - it demands a revolution in how we build the supporting ecosystem. What infrastructure gaps are you seeing in your AI implementations? #AIInfrastructure #EnterpriseAI #GPT5 #AgenticAI #DigitalTransformation Reference: https://lnkd.in/dt86eDXA

The integration of data engineering and AI is transforming how organizations manage and utilize data. Key advancements include real-time processing capabilities, AI-enhanced workflows, and the rise of low-code tools, making data engineering more accessible. As generative AI automates tasks and improves data governance, the future promises a more efficient and autonomous data ecosystem. Read more in this article: https://heyor.ca/sX2xqf #DataEngineering2025 #AIIntegration #RealTimeProcessing #AIEnhancedWorkflows