Why In-House AI Projects Fail: The Role of Data

💡 Many enterprises chase AI success, but fall short—not because of the models, but because of the data foundation. At Infocepts, we believe data-first is the only way to make AI truly impactful. This perspective from our CEO & Founder Shashank Garg highlights why enterprises must focus on building trusted, unified, and real-time data systems to unlock the promise of predictive and agentic AI.   Read on to learn how we’re helping organizations bridge the gap between AI ambition and AI impact. 👇   #AITransformation #FutureOfAI #DataSolutions

AI + Graph Meets Unified, Contextualized IT Data Reading Charles’s excellent post on Solving Tech Debt With AI + Graph: Charles Betz [https://lnkd.in/gQTqsaSQ] “What I believe is a powerful new substrate: a unified IT knowledge graph. This may be the true successor to the long-troubled configuration management database (CMDB).” At Flexera, our data strategy is built on one idea: make IT data consistent, contextualized, and connected so it’s truly actionable. Our foundations: Normalize and unify IT assets into a single, consistent view  Add business context (ownership, risk, compliance, cost)  Build relationships across assets, contracts, and dependencies  Power it all with AI + analytics to deliver clear actions and recommendations As Forrester highlights, the future of IT isn’t just collecting data — it’s understanding dependencies and making smarter decisions. At Flexera, our mission is simple: help customers reduce risk, optimize spend, and govern with confidence — powered by trusted, connected data. #TechnologyIntelligence #Flexera #AI #DataStrategy #ITGovernance 

🌪️ The AI paradox: more than 40% (!) of enterprise AI initiatives were reportedly discontinued in 2025, despite ever growing investments by companies ❌ Top obstacles appear to be data readiness, cost (and ROI), and security concerns ☁️ When it comes to data, it's more than 65% of AI projects that fail due to data readiness issues At Oliver Wyman, our experience with the AI transformation of our clients demonstrated the necessity to put as much effort in improving the AI-readiness of their data as in defining and deploying a roadmap of AI use cases This will allow to avoid some traps and adopt the right approach: 🪤 Beware of the POC trap! Successful proofs of concept are encouraging... but they come with a bias: they are often run on an AI-ready subset of data, giving the impression that scaling will be an easy next step. In reality, companies usually face limited data continuity across systems or business units, and variable data quality 🧭AI-readiness of data should even be one of the top criteria to prioritize use cases: on top of business impact and technology maturity, make sure to start your roadmap with use cases fueled by AI-ready (or easy-to-improve) data 🗺️ Deploy a structured and systematic approach to make your full data landscape AI-ready: initial mapping of data landscape, assessment of datasets on 3 main dimensions (accessibility, connectivity, quality), pragmatic action plans 🎆 To succeed in your AI transformation, discover our 7 key principles to successfully scale AI: https://owy.mn/4c91Rwb #AI #GenAI #transformation #digital #data

I believe every company should have its own aligned AI. Generic AI is fine for boilerplate. But the moment you ask for decisions that carry your brand and risk - prioritize, forecast, explain, alert, recommend, then generic breaks. It doesn’t share your definitions, incentives, or guardrails. Trust needs control, and control means AI shaped by your data, values, and culture. Aligned AI should be sovereign to your organization: transparent enough to audit, adaptable enough to “fork” as you evolve, private enough to keep your advantage, and opinionated in your language. Alignment isn’t a prompt. It’s an architecture. And architecture starts with the data layer. If you can’t capture, connect, and govern the full story - numbers and narratives - no model will give you true competitive advantage or earn your trust. At B EYE, we’re building toward this in two tracks: 1. a strong data foundation - the platform and pipework that makes context reliable; and 2. a new product (stealth) that turns that context into everyday intelligence. #DataStrategy #DataPlatform #ModernDataStack #EnterpriseAI #AlignedAI #DataDrivenAI #BEYE

Staying ahead in knowledge management can be demanding, especially when your data comes in many forms—text, images, and graphs. The right tools can dramatically enhance how teams build, search, and reason over large, multimodal information repositories. ApeRAG tackles these challenges head-on. By combining advanced GraphRAG retrieval, vector and full-text search, and vision features, it transforms how organizations process and query their knowledge. Built-in AI agents and Model Context Protocol (MCP) support allow users to interact naturally and intelligently with their data, while enterprise needs are met with audit logging, visualization, and robust agent management. For teams deploying at scale, ApeRAG’s Kubernetes integration—complete with Helm charts and KubeBlocks—simplifies moving from prototype to production, ensuring high availability and flexibility across infrastructure setups. Industries such as research, healthcare, and finance can particularly benefit from these capabilities, empowering more effective insights from vast or evolving information. Curious to see how ApeRAG’s blend of multi-modal retrieval and scalable deployment could support your work? Visit the project repository to dive deeper: https://lnkd.in/d8G48xFg What challenges have you faced managing complex data? Share your experiences or join the discussion. #ai #andai #&ai

Here’s the reality: Only a fifth of organizations have an architecture fully equipped to support AI workloads. Without the right foundation, AI initiatives will struggle to reach their full potential.    So, how do you build the right infrastructure to support AI at scale?    ➡️ Start with a Solid Data Strategy: Align your data architecture with your AI goals. Define your data sources, integration points, and storage needs.  ➡️ Invest in Scalable Solutions: Choose flexible platforms that can grow with your AI ambitions—cloud-based systems are a good place to start.  ➡️ Leverage AI-Ready Platforms: Ensure your platform supports AI initiatives by being able to process and analyze data efficiently, securely, and in real time.  ➡️ Focus on Data Governance: Implement strong data governance frameworks to ensure your data is clean, compliant, and actionable.    AI is a powerful tool, but it’s only as good as the infrastructure behind it. It’s time to refit your data systems, simplify your approach, and ensure you're truly ready to unlock AI’s potential in your supply chain. Let's future-proof your data systems and accelerate your AI journey. Contact us today to get started! https://lnkd.in/gM5MSSbd #SFLTech #digitallyelelvate #AI #supplychainmanagement #Innovation #efficiency #AI  

🚨 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗔𝗜 𝗕𝗮𝘁𝘁𝗹𝗲: 𝗚𝗲𝗻𝗲𝗿𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝘀 𝘃𝘀. 𝗗𝗼𝗺𝗮𝗶𝗻-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 🚨 Big AI players like 𝘖𝘱𝘦𝘯𝘈𝘐, 𝘔𝘦𝘵𝘢, 𝘢𝘯𝘥 𝘰𝘵𝘩𝘦𝘳𝘴 𝘢𝘳𝘦 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘧𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯 𝘮𝘰𝘥𝘦𝘭𝘴 trained on vast, general purpose data. These models are powerful, but by design, they target a 𝗯𝗿𝗼𝗮𝗱 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲 𝗹𝗶𝗸𝗲 𝗰𝗵𝗮𝘁, 𝗰𝗼𝗱𝗲, 𝗶𝗺𝗮𝗴𝗲𝘀, 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲. On the other side, 𝘀𝗲𝗿𝘃𝗶𝗰𝗲-𝗯𝗮𝘀𝗲𝗱 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 have deep knowledge of 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗻𝗲𝗲𝗱𝘀: • Regulatory constraints • Domain-specific data (banking, pharma, retail, telecom…) • Complex legacy architectures • Mission critical workflows This creates an interesting tension: 👉 Foundation models are 𝗯𝗿𝗼𝗮𝗱 𝗯𝘂𝘁 𝘀𝗵𝗮𝗹𝗹𝗼𝘄 in enterprise context. 👉 Service companies are 𝗻𝗮𝗿𝗿𝗼𝘄 𝗯𝘂𝘁 𝗱𝗲𝗲𝗽, solving for industry specific use cases. Why This Matters We’re starting to see OpenAI and others open-source models and move into service style offerings. Why? Because they realize that general intelligence isn’t enough to win in enterprises. Enterprises don’t just want a clever chatbot. They want: • AI systems integrated into 𝘀𝗲𝗰𝘂𝗿𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 • Models fine-tuned on 𝗽𝗿𝗼𝗽𝗿𝗶𝗲𝘁𝗮𝗿𝘆, 𝗱𝗼𝗺𝗮𝗶𝗻 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗱𝗮𝘁𝗮 • 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲, 𝗮𝘂𝗱𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 by design • Service partners who understand their workflows, not just their prompts The Takeaway The future of AI in enterprises won’t be won by “generic” vs. “specialist.” It will be a 𝗵𝘆𝗯𝗿𝗶𝗱: • Foundation model companies providing the raw 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 • Service companies tailoring, fine tuning, and embedding those capabilities into enterprise ecosystems 💡 𝘋𝘰 𝘺𝘰𝘶 𝘵𝘩𝘪𝘯𝘬 𝘧𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯 𝘮𝘰𝘥𝘦𝘭 𝘱𝘭𝘢𝘺𝘦𝘳𝘴 𝘸𝘪𝘭𝘭 𝘴𝘶𝘤𝘤𝘦𝘦𝘥 𝘪𝘯 𝘣𝘦𝘤𝘰𝘮𝘪𝘯𝘨 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘱𝘳𝘰𝘷𝘪𝘥𝘦𝘳𝘴? 𝘖𝘳 𝘸𝘪𝘭𝘭 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦𝘴 𝘤𝘰𝘯𝘵𝘪𝘯𝘶𝘦 𝘵𝘰 𝘳𝘦𝘭𝘺 𝘰𝘯 𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘪𝘴𝘵𝘴 𝘸𝘩𝘰 𝘬𝘯𝘰𝘸 𝘵𝘩𝘦𝘪𝘳 𝘥𝘰𝘮𝘢𝘪𝘯 𝘪𝘯𝘴𝘪𝘥𝘦 𝘰𝘶𝘵? #𝗔𝗜 #𝗚𝗲𝗻𝗔𝗜 #𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝗔𝗜 #𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 #𝗧𝗲𝗰𝗵𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽

Quest Software Unveils Strategy & Unified Data Platform for AI Why this matters: - A recent MIT study highlighted that 95% of generative AI pilots fail, underscoring the critical need for robust data foundations to support AI initiatives. - Quest's platform addresses common barriers in AI adoption by providing enterprises with a scalable solution that ensures data accuracy, trustworthiness, and faster time-to-market for AI applications. Our take: Quest Software's strategic shift towards AI-focused solutions, backed by a $350 million investment in R&D and a rebranding effort, positions the company to meet the growing demand for integrated data management platforms that support AI at scale. What do you think? How can organizations effectively integrate Quest's erwin platform into their existing data ecosystems to enhance AI readiness and drive innovation? Tim Page Read More:- https://lnkd.in/dg7KhSax #QuestSoftware #AI #DataManagement #erwinPlatform #DataGovernance #AIReadiness #EnterpriseAI #TechInnovation #aitp #aitechnology #artificialintelligence #technology #aitech #machinelearning

If you’ve been following the enterprise AI space, you already know the harsh reality: nearly **90% of AI initiatives never make it into the hands of business users**. They stall in endless data preparation, complex integrations, and slow adoption — often taking **8–12 months** to even get a solution in production. By then, business needs may have already shifted, and the investment risks becoming just another expensive “science experiment.” That’s why I find Turinton’s approach so compelling. Their **Insights AI platform** is cutting AI implementation timelines by **80%** — taking projects that once took nearly a year and delivering them in **8–12 weeks**. The secret? A **zero-movement, zero-ETL architecture** that connects to enterprise data in real time, without moving sensitive information. This not only accelerates deployment but also reduces compliance and security risks. What’s equally powerful is how the platform addresses the “last mile” challenge. Instead of AI being locked away for data scientists, Turinton builds **AI accessibility, governance, explainability, and cost control** into its core. That means decision-makers and frontline teams actually get to use AI insights — whether it’s a 24/7 digital co-pilot for heavy industry or predictive maintenance deployed in weeks for manufacturing. In a space where many companies are still running pilots, Turinton’s clients are already seeing measurable business value within weeks, not months or years. And as AI adoption shifts from experimental to operational, the real winners will be those who can operationalize AI at scale and put it directly into the hands of the people making decisions every day. I’m curious — what’s the biggest barrier you’ve seen to operationalizing AI in your organization, and how are you tackling it? #ArtificialIntelligence #DigitalTransformation