In the AI era, time-to-Snowflake is a strategic competitive advantage. As models and autonomous agents evolve on weekly cycles, migration programs that move at project speed fall behind. Next Pathway CEO Chetan Mathur explains why velocity is now the most important asset an enterprise can possess and how Next Pathway's automation technology discovers, translates, and validates legacy logic into Snowflake-native code so the data foundation is production-ready at the pace AI demands.    The constraint is no longer what the platform can do. It is how quickly your organization can arrive there. Read why speed, automation, and data foundation now determine who leads: https://bit.ly/4sQw4I3   #NextPathway #AIReadiness #TimeToSnowflake #DataModernization #CloudMigration #Snowflake

We've been working with clients in EMEA & APAC managing AI-based services for nearly two years. It's become evident that data, agents, models and use cases are inextricably linked, even if today, our customers spread this responsibility across several departments. AI Command Center lets everyone collaboarate, from data source to application agent to ensure safe, efficient deployment of AI-based apps and agents. We know the full context of the data used in these applications, it makes sense to build the AI control on that existing knowledge-base. Collibra 91% of tech leaders are deploying agentic AI. Fewer than half have the governance to control it. The new Collibra AI Command Center was built to close that gap as the end-to-end control plane for enterprise AI. Real time visibility. Continuous control. One place to govern every model, agent and decision across the AI lifecycle. We could tell you why it matters, but the industry already is. Kevin Petrie, VP of Research at BARC on why this matters now: "AI adopters must extend their data governance programs to address new agentic AI risks with continuous monitoring and oversight. Collibra's new capabilities offer granular, real-time controls so that AI, data and security leaders gain confidence in their AI driven workflows." Robin Sinclair, Data Governance Product Owner at The Weir Group PLC, on what it unlocks in practice: "By integrating MCP Server, AI agents can operate directly on trusted data and context, turning governance into an enabler of faster, safer agent-led innovation." Two voices. Same conclusion. The Collibra AI Command Center isn't the brake on agentic AI, it's what makes scaling it possible. Learn more: https://ow.ly/nk2550YYfNO #UnifiedGovernance #DataConfidence #AICommandCenter #AIGovernance #AgenticAI #Collibra #Innovation

𝐒𝐭𝐨𝐩 𝐜𝐡𝐚𝐬𝐢𝐧𝐠 𝐀𝐈 𝐡𝐲𝐩𝐞. 𝐒𝐭𝐚𝐫𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐯𝐚𝐥𝐮𝐞. Most AI initiatives fail because of poor data foundations. 𝐒𝐎𝐋𝐈𝐗𝐄𝐦𝐩𝐨𝐰𝐞𝐫 is designed to help CIOs and data leaders solve the "data mess" once and for all. From healthcare-specific AI use cases to autonomous finance operations, learn how the world's leading companies are leveraging the Solix Common Data Platform to drive digital transformation. Full details here: https://empower.solix.com/ #DigitalTransformation #CIO #DataGovernance #SolixEmpower2025 #Solix Technologies

Data is everywhere—but without good data, enterprise AI doesn’t stand a chance. That’s been the biggest takeaway from IBM Think 2026. Yes, the conversation is full of agents, automation, productivity, and time-to-value. And those things matter. But they often assume a perfect world. Real enterprises aren’t perfect. AI doesn’t run in clean demos—it runs on messy data, legacy systems, outdated reports, multiple clouds, tangled APIs, strict governance, and teams that still have to deliver results every morning. So the challenge isn’t adding AI. The real challenge is making the enterprise ready for AI. That means getting serious about: * Data quality * Integration * Governance * Engineering productivity Before scaling AI, we need to fix the foundation. It’s time to rethink and modernize the data landscape. #IBMThink #EnterpriseAI #DataModernization #Integration #AgenticAI

The model is not the moat. The operating layer is. Most enterprise AI conversations still start with model choice: - Which model is best? - Which benchmark matters? - Which provider is faster? - Which one is cheaper today? Those questions matter, but they are not enough. Once AI moves into real enterprise workflows, the harder questions shift: - Who is allowed to use which model? - Which data can enter context? - What memory is available, and under what boundary? - Which tools can the AI execute? - What gets logged, audited, evaluated, and rolled back? - How do we keep cost predictable when agentic usage expands? That is where the operating layer becomes the enterprise advantage. The model layer will keep changing. But the system around the model — identity, policy, routing, memory, evaluation, audit, observability, deployment, rollback, and cost control — is what determines whether AI becomes usable inside serious organizations. That is the lane Srasta is built for. Private/open-weight AI infrastructure. Governed memory and tool execution. Controlled inference paths. Operator workflows that make AI governable, observable, and recoverable. If your team is thinking about private AI, governed agents, or deterministic AI infrastructure economics, this is the conversation we want to have. https://lnkd.in/e5mhui8f #EnterpriseAI #AIInfrastructure #AIGovernance

𝗧𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲: 𝗪𝗵𝘆 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗡𝗲𝗲𝗱 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗮 𝗠𝗼𝗱𝗲𝗹 AI agents do not need just another model. They need 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. That is the gap many organizations are underestimating. Large language models are becoming widely accessible. Copilots are becoming easier to deploy. AI productivity tools are being embedded into everyday workflows. 𝗕𝘂𝘁 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗔𝗜 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗰𝗿𝗲𝗮𝘁𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀. To move from experimentation to trusted autonomy, enterprises need to think in 𝘁𝗵𝗿𝗲𝗲 𝗹𝗮𝘆𝗲𝗿𝘀. 𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 This is what the business knows. This is the operating truth of the enterprise.  • Your SOPs  • Your policies  • Your process history  • Your exception patterns  • Your customer commitments  • Your institutional judgment 𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗧𝗵𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 This is how knowledge becomes usable and trusted. It is where enterprise knowledge is structured, contextualized, governed, refreshed, accessed, and validated. This layer turns fragmented information into a reliable foundation for decision-making. 𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 This is where trusted knowledge creates advantage. AI agents can support faster decisions, safer autonomy, better recommendations, reduced operational risk, and human oversight where it matters most. Without 𝗟𝗮𝘆𝗲𝗿 𝟭, agents lack business truth. Without 𝗟𝗮𝘆𝗲𝗿 𝟮, knowledge remains fragmented. Without 𝗟𝗮𝘆𝗲𝗿 𝟯, AI stays trapped in assistance rather than becoming part of the digital operating model. The model is not the moat. The knowledge layer is. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: 𝗡𝗼 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲. 𝗡𝗼 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆. From Automation to Autonomy requires more than model access. It requires an enterprise knowledge backbone that allows AI to operate with context, control, and confidence. What layer do you think most enterprises are underinvesting in today? #AgenticAI #EnterpriseAI #AIReadiness #AutonomousEnterprise #AIGovernance #DigitalOperatingModel #EnterpriseArchitecture #IntelligentAutomation #FutureOfWork #AutomationToAutonomy

AI agents are getting cheaper. The state they act on is getting more expensive. That is the shift I think many enterprise teams are still underestimating. Soon, every team will be able to build agents. Agents that write. Agents that search. Agents that call tools. Agents that update records. Agents that trigger workflows. The harder question is not whether the agent can act. It is what the agent is standing on when it acts. Is the data usable? Is the business meaning attached? Is the sensitive context on an approved path? Is the data state fixed enough to reproduce the run later? Did the definition of “customer”, “risk”, or “eligible transaction” change between runs? This is where many AI systems become fragile. The model may be the same. The agent may be the same. The prompt may be the same. But the data window moved. The schema changed. The preprocessing logic shifted. The permission boundary changed. The business definition was updated quietly. Then the output changes, and everyone starts debugging the model. I think this is why the next important AI infrastructure layer will not just be about models, agents, or dashboards. It will be about trusted execution state. The layer that makes enterprise data usable, contextual, protected where needed, and stable enough for AI to act on. Storage stores. Monitoring alerts. Governance documents. But autonomous work needs a trusted state behind every run. If agents are becoming workers, the data state is their workplace. Bad workplace, bad work. Where do you see the biggest gap today: restricted data access, missing business context, unstable data state, or replayability? #EnterpriseAI #AIInfrastructure #AIGovernance

Most enterprises have AI activity across teams and use cases, but very few have turned that activity into a consistent operating model that delivers measurable business results. The gap between experimentation and production‑ready AI is where value gets left behind. The AI Enablement Layer for Databricks changes how teams design, govern, and deploy AI workflows. It brings structure, security, and scalability to enterprise AI without adding complexity. The business impact speaks for itself: 96% of unsafe prompts blocked – real‑time protection against data leaks and compliance violations ≥99.5% improvement in AI output reliability – fewer hallucinations, more trustworthy results No vendor lock‑in – flexible model routing across LLMs to optimize cost and performance Complete auditability – full traceability from prompt to response, ready for internal and external review Swipe through to see how projects, pipelines, and governed execution come together in one unified platform. If you are attending the Data + AI Summit in San Francisco from June 15 through June 18, let us connect. We would be glad to discuss how a governed AI Enablement layer can accelerate your ROI while decreasing risk. https://worldlink-us.ai/ #DataAISummit #Databricks #EnterpriseAI #AIGovernance #WorldLink

AI vendor lock-in doesn't happen in one place. It accumulates across five distinct layers: 1. Model layer - fine-tuned models, prompt libraries, calibrated outputs. Switching costs are real but actually decreasing as models commoditize. 2. Orchestration layer - the fastest-growing category. Agentic workflows, tool-calling conventions, agent architectures. This is where most companies get locked in without realizing it. 3. Data layer - embeddings, RAG indexes, training datasets. Technically portable. Operationally expensive. The institutional knowledge behind curation? Often impossible. 4. Governance evidence layer - every compliance audit, evaluation benchmark, and risk assessment calibrated to a specific provider. Switch models, start compliance from scratch. 5. Organizational knowledge layer - how your team prompts, interprets confidence signals, works around limitations. Months of trial and error, documented nowhere. Here's the trap: LLM API prices dropped 80% last year. That creates a false sense of portability. You can swap the model endpoint cheaply. But the orchestration, data, governance, and organizational layers? Those are where the real switching costs live. Besides, have you heard all tokens are subsidized right now? When a PE/VC firm encourages AI adoption across portfolio companies without assessing these layers, it inherits the concentration risk of every tool those companies adopt. The portfolio-level exposure compounds in ways no individual company's risk assessment reveals. #VentureCapital #PrivateEquity #AIRisk #VendorManagement #TechDueDiligence #VenturFlow

Most enterprises are deploying AI. Very few are deploying it safely. Here's what I keep seeing across regulated industries — banks, insurers, healthcare systems, defense contractors: the AI conversation gets hijacked by capability hype, and sovereignty gets treated as an afterthought. That's a fundamental strategic error. 🔑 **What actually matters for regulated enterprises:** 1. 🏛️ **Data Sovereignty** → Your data never leaves your perimeter. Not negotiable. 2. ⚙️ **Model Control** → You own, audit, and govern the models running in your environment. 3. 🔒 **Governance Architecture** → Explainability, access controls, and audit trails built in — not bolted on. The enterprises winning with GenAI and Agentic AI aren't the ones moving fastest. They're the ones moving with **architectural discipline**. On-premises infrastructure isn't legacy thinking. In regulated environments, it's the only thinking that survives regulatory scrutiny. I've observed across 20+ advisory engagements that when AI fails inside a regulated enterprise, it rarely fails because of the model. It fails because nobody owned the governance layer. At MRC, we've built our enterprise AI practice around exactly this conviction — sovereign infrastructure, controlled deployment, deep governance integration. If you want to see how this comes together in practice, explore what we're doing at https://lnkd.in/dD8aKcSD What's your biggest barrier to sovereign AI deployment — infrastructure, talent, or regulatory clarity? Book a demo session https://lnkd.in/dy8hve2d #EnterpriseAI #GenerativeAI #AgenticAI #DataSovereignty #RegulatoryCompliance