Simon De Forni’s Post

This week, I had the chance to dive deeper into one of the most exciting topics in our industry: Data Management for AI and AI for Data Management. 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐟𝐨𝐫 𝐀𝐈 – It became clearer to me how critical trusted, well-governed, and high-quality data is for any AI initiative. AI models are only as good as the data they’re trained on, and without the right foundations (governance, lineage, integration, quality), scaling AI responsibly is nearly impossible. 𝐀𝐈 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 – On the other hand, AI itself is becoming an enabler to simplify and accelerate how we manage data. From automating metadata discovery to improving data quality and even assisting in governance, AI is now helping us tackle some of the long-standing challenges in the data world. For me, the combination of these two perspectives is where the future lies: - Strong data management practices to empower AI. - Smart AI-powered capabilities to make data management more efficient and intelligent. Curious to hear: how are you seeing the intersection of AI and data management play out in your own business? #ai #datamanagement #learning #weekly #futureofwork

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 

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

The Strategic Blind Spot. Why In-House AI Projects Fall Short of Impact.   As AI adoption matures, many enterprises are eager to go beyond off-the-shelf tools and build internal AI solutions. But for most businesses, especially those outside the tech world, this approach rarely delivers the intended business value.   The issue isn’t ambition - it is the execution. And more often than not, the gap lies in the data, not the AI model.   Data solutions truly transform your AI outcomes because they are only as good as the data foundation beneath them. Without unified, trusted, and timely data, even the most sophisticated models fall flat. This is where enterprise-grade data solutions become the differentiator. Predictive and agentic AI systems must be able to analyze and act on streaming or near real-time data. Traditional systems aren’t built for this. Enterprise data solutions enable the shift from passive reports to proactive intelligence.   At Infocepts, we help organizations bridge the gap between AI ambition and impact through intelligent data solutions.   Whether you're modernizing legacy environments, integrating cloud-native platforms like Databricks, or preparing for agentic AI, our data-first approach ensures your foundation is future-ready. In today’s AI-driven world, smart decisions start with smarter data. I'm happy to connect. Please feel free to reach out if you are struggling. #AITransformation #FutureOfAI #DataSolutions

💡 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

🚨 Everyone’s chasing GenAI, AgenticAI, and the next big AI breakthrough… But after spending 15+ years in Data & AI consulting, here’s what I’ve consistently seen: 🔍 The true value of analytics isn’t in flashy GenAI tools — it’s in good DATA. Every other company today wants to be at the forefront of AI-led transformation. GenAI POCs are popping up everywhere. But behind the scenes? Many are still struggling with foundational elements — no proper data lake, poor data quality, scattered governance, and no unified data model. 💡 You can’t run if you haven’t learned to walk. What I’ve learned is simple but powerful: ✅ One table — One grain ✅ All facts and dimensions interlinked A robust data model and strong data governance are the real engines behind any successful AI/analytics program. Without that foundation, no amount of AI layering will magically generate business value. Yet, too often, analytics teams are blamed when insights don’t deliver — when the root issue is actually the underlying data chaos. ✨ Want GenAI to work for your business? Start with your data. Clean it. Model it. Govern it. Then watch the magic happen — for real. #DataStrategy #Analytics #DataGovernance #GenAI #DigitalTransformation #DataFirst #AI #EnterpriseAI

When enterprises express enthusiasm for implementing AI to address operational inefficiencies, they're often unknowingly articulating a deeper, more fundamental challenge. What appears to be a forward-looking technology adoption strategy, frequently masks the accumulated consequences of architectural decisions and compromises made years earlier. The smartest organizations are recognizing that the most successful AI implementations happen when technical debt is addressed simultaneously with AI adoption - not as an afterthought, but as a strategic prerequisite. This integrated approach explains why many AI initiatives fail to deliver their promised value. Recent MIT research reveals that 95% of enterprise generative AI pilots fail to deliver measurable ROI, while S&P Global Market Intelligence found that 42% of businesses are scrapping most of their AI initiatives, up dramatically from 17% in 2024. The financial implications are staggering: Gartner projected that by 2025, organizations implementing formal tech-debt quantification methods will release features 35% faster, while a 2022 McKinsey report estimates that technical debt can consume up to 40% of an organization's IT capacity. These statistics aren't coincidental - they reflect a fundamental misunderstanding of what being #AIFirst actually requires. The core issue isn't the sophistication of AI models, but the learning gap between AI tools and enterprise systems. When AI is implemented on top of fragmented systems, manual workarounds, and disconnected data sources, it essentially automates dysfunction rather than resolve it. Being #AIFirst means treating technical debt like financial debt - tracking, prioritizing, and systematically eliminating it, instead of bolting AI onto existing problems. The solution lies in adopting an approach that treats AI implementation as an opportunity to modernize the underlying architecture. Organizations that set aside around 15% of their IT budgets specifically for tech debt remediation while simultaneously implementing AI report significantly higher success rates. This strategy recognizes that the problems organizations want AI to solve—manual data reconciliation, workflow bottlenecks, and reporting inefficiencies—are symptoms of technical architecture decisions that accumulated over years of expedient choices; finally delivering the transformative value that AI promises. #AI #ArtificialIntelligence #DigitalTransformation #ITStrategy #DigitalStrategy #SystemArchitecture #EnterpriseIT

❌ Struggling to overcome data silos and align your strategy for maximum impact? Data, AI, and analytics integration is critical for modern enterprises, but fragmented ecosystems can hold organizations back. According to Forrester Research, 45% of global tech leaders in data disciplines plan to increase budgets for data, analytics, governance, and AI by 5% or more in the next year. Download the report to learn how to: ✅ Break down data silos and scale AI across your enterprise ✅ Enable data literacy and self-service for all teams ✅ Build trust in your data systems ✅ Implement the 7 components of an effective AI strategy ✅ Activate AI with data excellence 🖇️ Get the full report here: https://lnkd.in/dbw8Mnv9 #DataStrategy #AIandBI #DataGovernance #BusinessIntelligence #StrategySoftware #Forrester

✦ Contrarian Insight: Many decision-makers assume that successful AI initiatives require massive amounts of data. In reality, just 10% of enterprise data can provide actionable, high-impact insights. ✦ Problem: The misconception that “more data equals more value” leads companies to pour resources into vast data lakes. Yet, up to 90% of this data often goes unused — overwhelming teams, increasing storage and management costs, and diverting focus from high-value opportunities. ✦ Solution: Shift your approach to data and AI by aligning strategy with specificity and impact. Here's how: ☑ Prioritize Data Relevance: Start with cleaner, curated data. AI thrives on signal, not noise. ☑ Create a Targeted Data Strategy: Define clear business objectives and map out which data types matter most to your AI use cases. ☑ Strengthen Data Ops: Invest in scalable data governance and infrastructure. Trusted, accessible data accelerates AI ROI. ☑ Apply AI Intelligently: Deploy AI models where the data is strongest and the outcomes are measurable—be it forecasting, process optimization, or customer insights. ✦ Let’s Talk: Effective AI doesn’t start with more data — it starts with the right data. At Promptly Solutions, we help organizations design smart data strategies that make AI work. Book a consultation → promptlysolutions.com. #AI #EnterpriseAI #TechStrategy #BusinessConsulting

🔥 Board pressure is real: "We need an AI strategy. Yesterday." .........Here's what's actually happening in boardrooms across the country: Panic-driven AI initiatives launched without the slightest clue about foundational requirements. The conversation sounds like: "Competition is doing AI, we're falling behind, just get some AI projects started.". This isn't strategy. It's expensive theatre. 📊 While 87% of companies have AI ambitions, only 23% report successful enterprise-wide deployments. The other 77%? They're drowning in: - Millions in sunk costs with nothing to show for it - Regulatory nightmares they never saw coming - Data breaches from rushed implementations - Teams actively sabotaging AI tools that disrupt everything - Board meetings where promised ROI becomes an awkward silence 💡 THE BRUTUAL TRUTH: Your competitors aren't winning because they deployed AI faster. They're winning because they got the fundamentals right first. ⚡ Stop the madness. Think about it: You wouldn't jump into a race car and hit the track without proper preparation, safety checks, and optimization. So why rush into AI that way? 🏎️ Before you even think about AI, master these fundamental business pillars: 🔄 PROCESS OPTIMIZATION Map your current workflows. Where are the bottlenecks? What manual processes exist that shouldn't? Fix the basics before automating broken processes. ✂️ WASTE ELIMINATION Conduct a lean analysis of your operations. Are you running efficiently? Look for: Redundant systems Unnecessary approval layers Outdated procedures Unused software licenses 🏗️ DATA INFRASTRUCTURE If your data is scattered across spreadsheets and legacy systems, AI won't save you—it'll amplify the chaos. Clean house first. 👥 HUMAN CAPITAL OPTIMIZATION Are your teams structured effectively? Do you have the right skills in the right places? Is tribal knowledge documented? Are decisions made at the appropriate level? 🎯 CUSTOMER JOURNEY MAPPING Understand where real value is created. Often, the solution isn't AI—it's simply removing friction points in your customer experience. ⚠️ Here's the truth: AI won't fix inefficient operations—it'll just make them fail faster. Address the above as foundational as only then, can you harness AI's true potential and take the lead. 🏎️ 🏁 👉 Follow me for more insights on business transformation, programme delivery and the thoughtful integration of AI in business. #BoardStrategy #DigitalTransformation #AI #BusinessOptimization #LeadershipInsights #OperationalExcellence #BusinessEfficiency #FutureOfBusiness #StrategicLeadership