Why AI agents fail: The data foundation gap

Top 5 on G2 out of 250+ AI platforms-RapidCanvas earned momentum status from real user outcomes, not hype. As a student exploring AI transformation, this recognition caught my attention. It's not just about the technology - it's about results. What makes this different? RapidCanvas focuses on practical implementation. Not flashy demos or theoretical promises. The article about enterprise AI agents highlights a critical point: data quality makes or breaks AI success. Most AI initiatives fail because of: • Siloed information • Inconsistent data • Poor governance • Lack of real-time access Companies like Radisson Hotel Group and Eaton Corporation are already seeing results. They're using platforms that connect AI agents with unified data foundations. The lesson here is clear. Successful AI isn't about the smartest algorithms. It's about the quality of data feeding those algorithms. For students like me entering this field, this perspective is valuable. We need to understand that AI transformation requires more than coding skills. It requires understanding data governance. System integration. Business processes. The momentum RapidCanvas gained comes from solving real problems. Not creating impressive tech demos. What aspects of AI implementation do you think are most overlooked by newcomers to the field? #AITransformation #DataGovernance #StudentPerspective 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gciAM7JM

From Models to Machines — Turning AI Innovation into Business Reality Every enterprise today is racing to build smarter AI and Data Science teams. But the truth is — great models alone don’t drive business impact. What matters is how those models are operationalized — scaled, governed, and embedded into production systems. That’s where Data Engineering becomes the unsung hero. It’s the bridge that turns AI from a research project into a real, measurable business engine. In my latest article on Medium, I break down: 🔹 The “last mile” challenge between Data Science and production systems 🔹 How Data Engineering transforms experimentation into reliable, enterprise-grade AI 🔹 Why collaboration between DS + DE is critical for sustainable, scalable AI 🔹 How Generative AI adds a new layer of complexity — and opportunity Key takeaway: “Data Science builds intelligence; Data Engineering builds the ecosystem that powers it.” If your organization is investing in AI, this read will help you understand what it really takes to move from notebooks to impact. 👉 Read the full article here: https://lnkd.in/geEd7ZRr #DataEngineering #AI #DataScience #MachineLearning #GenerativeAI #MLOps #AIOps #DigitalTransformation #AnalyticsLeadership

2026: The Year AI Observability Becomes Business Observability The Towards Data Science article calls 2026 the “year of data and AI observability.” That prediction resonates deeply with what we are building at NetGain Systems. As AI models increasingly power critical business decisions, observability is no longer just about keeping systems online. It is also about making sure the intelligence driving them remains reliable, explainable, and secure. At NetGain, our Astra AI framework embodies this shift. It acts as an AI-driven COO for IT operations, turning observability data into structured, actionable insight. With Astra AI, we move from monitoring metrics to understanding intent and correlating system health with business outcomes. From anomaly detection and predictive capacity planning to autonomous remediation, Astra AI enables IT teams to stay ahead of incidents while explaining why they happen. This alignment of data, AI, and operations is what will define next-generation observability. 2026 will not just be the year of data and AI observability. 2026 will be the year where observability becomes inseparable from business performance. 🔍 Read the full piece here: https://lnkd.in/gTEcaT29 How are you preparing your organisation for this convergence of AI and observability? #AstraAI #NetGainSystems #AIObservability #ITOperations #DigitalResilience #AIforIT

According to IBM, data silos pose the most significant barrier to scaling enterprise AI, overshadowing technological challenges. Ed Lovely, IBM’s VP and Chief Data Officer, labels these silos as the “Achilles’ heel” of data strategy. A recent study highlights that essential business functions like finance and HR operate in isolation, hindering AI initiatives that require integrated data. Although 92% of Chief Data Officers (CDOs) aim for business value from data, only 29% possess clear metrics to assess this value. AI agents are emerging as solutions to bridge this gap. Organizations must adopt modern data architectures and foster a data-driven culture to enhance accessibility and governance. Emphasizing data literacy across teams can transition enterprises from isolated AI experiments to comprehensive intelligent automation, enhancing decision-making and competitiveness.

As enterprises keep leveraging artificial intelligence across business operations, it’s important to remember that AI efficiency is dependent on the framework it’s placed in. AI doesn’t work alone — in fact, Gartner predicts that more than 75 percent of generative AI deployments will use containers by 2027. 😜 That’s where ModelOps comes in....... What Is ModelOps and How Does It Work? 🤔

🔍 The Future of Trustworthy Data: Why Schema, Lineage, and Drift Matter More Than Dashboards AI has changed today’s data world in ways that dashboards alone can’t explain. The real transformation isn’t in visualization — it’s in validation. For years, organizations measured success by the number of dashboards they had. Now, with AI powering decisions at scale, the question isn’t “Can we see the data?” It’s “Can we trust the data?” 🧠 The Three Pillars of Trustworthy AI Data 1️⃣ Schema Validation – Your first line of defense. Automated schema checks catch drifts, null floods, and unannounced source changes before they corrupt downstream models. 2️⃣ Lineage Tracking – The DNA of your data. When you automate lineage — from ingestion to inference — every metric, feature, and model output becomes traceable, explainable, and auditable. 3️⃣ Drift & Reconciliation – The continuous reality check. Monitoring statistical drift and reconciling anomalies ensures AI doesn’t silently degrade in production. It’s not just about accuracy — it’s about accountability. ⚙️ The New Mindset I’ve seen this firsthand while automating quality, lineage, and reconciliation frameworks across multi-cloud environments (AWS & GCP). When data reliability is automated — not assumed — pipelines become self-healing, models stay governed, and insights remain trusted. Dashboards show what happened. But schema, lineage, and drift explain why — and ensure it keeps happening the right way. Because the future of AI isn’t just intelligent. It’s trustworthy by design. 🧩 #AI #DataEngineering #DataGovernance #MLOps #DataLineage #DataQuality #DataDrift #Automation #CloudComputing #Databricks #Snowflake #AWS #GCP #Analytics #MachineLearning #DataOps #AIOps #DevOps #DataArchitecture #ResponsibleAI #Recruiting #TechLeadership #Innovation

The 2026 Open-Source Data Quality and Data Observability Landscape https://lnkd.in/e8SegygF. Explore the new generation of open source software that uses AI to police AI, automate test generation at scale, and provide transparency with control. And no embarrassing errors! The terrifying truth is that AI amplifies data quality and data observability failures exponentially. A single schema drift that once meant a broken report now means thousands of incorrect predictions per second. That missing data validation you postponed? It just trained your model to be confidently wrong at scale. Your data engineers are in full panic mode, manually spot-checking tables while AI models consume data faster than any human can validate it. The executives who demanded “AI transformation” are now demanding answers for why their million-dollar models produce nonsense. And those expensive observability platforms that promised to solve everything? They’re just telling you what broke after your AI has already made 10,000 bad decisions. The cruel irony of the AI revolution is that it demands perfect data quality at the exact moment when data volumes, sources, and complexity have made quality impossible to achieve through traditional means. Modern data pipelines feed voracious AI systems that retrain hourly, consume from hundreds of sources, and make decisions in milliseconds—all while your team is still writing SQL tests like it’s 2015. The old guard of enterprise solutions wants six figures to tell you what you already know: your data is broken. But in the age of DataOps, where deployment cycles are measured in hours, not months, you need tools that move at AI speed and don’t require selling a kidney to afford. We’ll explore the new generation of solutions that use AI to police AI, automate test generation at scale, and provide the transparency and control that proprietary platforms can’t match—all while keeping your CFO happy. Because in 2026, the question isn’t whether you need data quality for AI; it’s whether you’ll solve it before bad data destroys everything you’ve built. This guide explores that landscape. It defines the categories, compares the tools, and explains how teams are combining them—often with DataOps practices in the age of AI —to create truly reliable, end-to-end systems. #dataquality #dataobservability #opensource #dataops

The Silent Killer of Enterprise GenAI: Your Stale Data Pipeline We're past the 'demo phase' of Generative AI. The real challenge today isn't training a foundational model—it's ensuring that the model, once deployed, operates on data that is fresh. As someone who’s built data systems for three decades, I can tell you: Batch processing is the silent killer of enterprise-grade AI performance. A $10 million GenAI investment will deliver $10 results if it's fed 24-hour-old data in a dynamic environment like finance or logistics. The shift is non-negotiable. To achieve the sub-second latency and fidelity required for competitive RAG and real-time decision-making, we must move to event-driven architectures and embrace Data Mesh principles for governance. Here’s why I believe your Data Engineering roadmap needs a hard reset now: Real-Time RAG Imperative: Retrieval Augmented Generation demands instant access to current, domain-specific context. If your pipeline can’t populate a Vector Database in minutes, your AI's answers are already obsolete. From ETL to CDC: The focus must shift from traditional Extract, Transform, Load jobs to Change Data Capture (CDC) to stream data updates continuously, ensuring the feature stores are always current. Data Mesh for Trust: Data-as-a-Product governance is crucial for GenAI. We need clear domain ownership for the high-quality data used for fine-tuning, not another centralized data swamp. This is the hard, unsexy truth of production AI. It's an Engineering challenge first, and an Algorithm challenge second. Engagement Question: What's the biggest Data Engineering bottleneck slowing down your organization's Generative AI deployment right now? Is it governance, streaming adoption, or cost? #DataEngineering #GenerativeAI #ArtificialIntelligence #DataMesh #RealTimeData

Context engineers are already obsolete. If you are a CIO, CDO or CTO at a Fortune 1000 company, you have been racing to deploy AI. More likely than not your initiatives have been stalling, delivering inconsistent, untrustworthy results,  because your structured data was not AI-ready.  The reason? AI doesn’t understand your business context. Your data may be complete, but your AI doesn’t know how you define “revenue,” how “customer” differs between CRM and ERP, or how “churn” is calculated by marketing versus finance. These are not database problems, they’re context problems. For many organizations the solution has been a rush to hire context engineers, specialists who align semantics, lineage, and KPI definitions so AI systems can reason accurately. In essence, they build your company’s data reasoning layer. But here’s the problem: enterprise data never sleeps. New sources, schema changes, metric updates, and naming conflicts emerge constantly. To maintain trustworthy context, you’d need multiple engineers working around the clock — reconciling definitions, remapping relationships, and debugging logic 24/7. That model doesn’t scale. But luckily WALT has built a solution that does. WALT: The AI Context Engineer Instead of building a human army, WALT automates the entire process through AI agents designed to continuously learn,  understand, reconcile, and maintain context across your data ecosystem. WALT builds what we call a ReasonBase™,  a living, adaptive reasoning layer that sits above your structured data and ensures every AI answer is accurate, explainable, and consistent. Automatic introspection: WALT scans warehouses, pipelines, and catalogs to discover relationships and definitions. Autonomous canonicalization: Conflicting metrics and lineage paths are reconciled into trusted, canonical forms. Evaluation-driven reinforcement: Each AI response is evaluated and improved overnight, strengthening the ReasonBase. Agentic collaboration: Governance, Quality, and Lineage Agents operate continuously — scaling faster than human teams ever could. The Result With WALT, your organization moves from Search → Explore → Build to Build → Prompt → Deliver. -Data engineers spend less time firefighting and more time innovating. -AI finally delivers answers executives can trust. -In the AI era, context is your new infrastructure. -And you need an AI context engineer that never sleeps. Read more at https://lnkd.in/gfPuGpib

https://ow.ly/QKC350XrT6n IBM announced that a new global study from their Institute for Business Value reveals enterprise data strategies are rapidly evolving as organizations race to scale #AI across their business.