AI-Ready Data Strategies

Your SAP AI is only as good as your Data infrastructure. No clean data → No business impact. SAP is making headlines with AI innovations like Joule, its generative AI assistant. Yet, beneath the surface, a critical issue persists: Data Infrastructure. The Real Challenge: Data Silos and Quality Many enterprises rely on SAP systems - S/4HANA, SuccessFactors, Ariba, and more. However, these systems often operate in silos, leading to: Inconsistent Data: Disparate systems result in fragmented data. Poor Data Quality: Inaccurate or incomplete data hampers AI effectiveness. Integration Issues: Difficulty in unifying data across platforms. These challenges contribute to the failure of AI initiatives, with studies indicating that up to 85% of AI projects falter due to data-related issues. Historical Parallel: The Importance of Infrastructure Just as railroads were essential for the Industrial Revolution, robust data pipelines are crucial for the AI era. Without solid infrastructure, even the most advanced AI tools can't deliver value. Two Approaches to SAP Data Strategy 1. Integrated Stack Approach:   * Utilizing SAP's Business Technology Platform (BTP) for seamless integration.   * Leveraging native tools like SAP Data Intelligence for data management. 2. Open Ecosystem Approach:   * Incorporating third-party solutions like Snowflake or Databricks.   * Ensuring interoperability between SAP and other platforms. Recommendations for Enterprises * Audit Data Systems: Identify and map all data sources within the organization. * Enhance Data Quality: Implement data cleansing and validation processes. * Invest in Integration: Adopt tools that facilitate seamless data flow across systems. * Train Teams: Ensure staff are equipped to manage and utilize integrated data effectively. While SAP's AI capabilities are impressive, their success hinges on the underlying data infrastructure. Prioritizing data integration and quality is not just a technical necessity → It's a strategic imperative.

🚨 The real reason 60% of AI projects fail isn’t the algorithm, it’s the data. Despite 89% of business leaders believing their data is AI-ready, a staggering 84% of IT teams still spend hours each day fixing it. That disconnect? It’s killing your AI ROI. 💸 As CTO, I’ve seen this story unfold more times than I can count. Too often, teams rush to plug in models hoping for magic ✨ only to realize they’ve built castles on sand. I've lived that misalignment and fixed it. 🚀 How to Make Your Data AI-Ready 🔍 Start with use cases, not tech: Before you clean, ask: “Ready for what?” Align data prep with business objectives. 🧹 Clean as you go: Don't let bad data bottleneck great ideas. Hygiene and deduplication are foundational. 🔄 Integrate continuously: Break down silos. Automate and standardize data flow across platforms. 🧠 Context is king: Your AI can’t "guess" business meaning. Label, annotate, and enrich with metadata. 📊 Monitor relentlessly: Implement real-time checks to detect drift, decay, and anomalies early. 🔥 AI success doesn’t start with algorithms—it starts with accountability to your data.🔥 Quality in, quality out. Garbage in, garbage hallucinated. 🤯 👉 If you’re building your AI roadmap, prioritize a data readiness audit first. It’s the smartest investment you’ll make this year. #CTO #AIReadiness #DataStrategy #DigitalTransformation #GenAI

AI is only as smart as the data you feed it. Most HR teams already have the data. But it’s buried in the wrong formats. At Fig Learning, we help HR leaders unlock it. Here’s how to make your data AI-ready. Structured vs. Unstructured: What’s the difference? Structured = ready to use. Labeled, searchable, clean data in tools like LMSs. Unstructured = hidden value. Think emails, transcripts, PDFs, and feedback notes. Structured data is plug-and-play. Unstructured data needs work - but holds gold. Step 1: Audit your data sources Where does learning actually live right now? Start by mapping your tools, folders, and files: - LMS reports? - Post-training surveys? - Feedback forms? - Meeting notes? Inventory what you touch often but never analyze. Step 2: Prioritize what to work on Not all messy data is worth it. Start with content that’s high-volume and high-impact. Focus on: - Post-training feedback - Coaching and 1:1 notes - Workshop or debrief transcripts - Policy docs in unreadable formats This is where insights are hiding. Step 3: Structure the unstructured Use lightweight AI tools to make it usable. Try: - ChatGPT Enterprise to tag and summarize - Otter.ai / TLDV to transcribe and recap - Guidde to turn steps into searchable guides And tag docs with topic, team, and timestamp. Step 4: Train AI on what matters Once structured, your data becomes leverage. Use it to power SOPs, checklists, or internal bots. Let AI write based on your real examples. It will save time and multiply your reach. Good AI starts with good prep. Don’t feed it chaos. Feed it clarity. P.S. Want my free L&D strategy guide? 1. Scroll to the top 2. Click “Visit my website” 3. Download your free guide.

According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.

𝗪𝗵𝘆 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗜𝘀 𝗢𝗻𝗹𝘆 𝗮𝘀 𝗚𝗼𝗼𝗱 𝗮𝘀 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗰𝗸 I recently spoke with a mid-sized high tech company that had spent $250,000 on AI solutions last year. Their ROI? Almost nothing. When we dug deeper, the issue wasn't the AI technology they'd purchased. It was the foundation it was built upon. 𝗧𝗵𝗲 𝗨𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝗧𝗿𝘂𝘁𝗵 𝗳𝗼𝗿 𝗦𝗠𝗕𝘀 Many of us are rushing to implement AI while overlooking the unsexy but critical component: 𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. It's like building a sports car with a lawnmower engine. The exterior might look impressive, but the performance will always disappoint. 𝗧𝗵𝗲 𝟯 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗼𝗳 𝗮 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗰𝗸 After working with dozens of SMBs on their digital transformation, I've identified three non-negotiable elements: 𝟭. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 Before adding AI, ensure your existing systems talk to each other. One client discovered they had 7 different customer databases with conflicting information—no wonder their personalization efforts failed. 𝟮. 𝗖𝗹𝗲𝗮𝗻 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗞𝗶𝗻𝗴 In a recent project, we found that just cleaning contact data improved sales conversion by 23%—before implementing any AI. Start with basic data hygiene; the returns are immediate. 𝟯. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝘀 𝗚𝗿𝗼𝘄𝘁𝗵 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 The companies seeing the best AI results have clear data ownership and quality standards. This isn't just IT policy—it's business strategy that belongs in your leadership meetings. 𝗦𝘁𝗮𝗿𝘁 𝗦𝗺𝗮𝗹𝗹, 𝗦𝗰𝗮𝗹𝗲 𝗦𝗺𝗮𝗿𝘁 You don't need to overhaul everything at once. One retail client began by simply unifying their inventory and customer data systems. Six months later, their AI-powered recommendation engine was driving 17% more revenue per customer. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Your competitors are likely making the same mistake: chasing AI capabilities while neglecting data fundamentals. The SMBs that will thrive aren't necessarily those with the biggest AI budgets, but those who build on solid data foundations. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝗻𝗲 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝘀𝘂𝗲 𝘁𝗵𝗮𝘁'𝘀 𝗵𝗼𝗹𝗱𝗶𝗻𝗴 𝗯𝗮𝗰𝗸 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄? I'd love to hear your challenges in the comments—and maybe share some solutions. #DataStrategy #SMBgrowth #AIreadiness #BusinessIntelligence #DigitalTransformation

AI won't fix your bad data. But a solid data foundation will transform your AI... Too many companies rush to implement AI before organizing their data. It's like building a skyscraper on quicksand. No structure. No consistency.  No strategy. This approach leads directly to: • Unreliable insights that mislead decision-makers • Inefficient AI models that waste computing resources • Thousands of dollars spent with minimal return The hard truth: Data is an ingredient. Intelligence is the outcome. You can't cook a gourmet meal with spoiled ingredients. (I haven't tried it but I'm guessing) A strong data roadmap solves these fundamental problems by: → Breaking down organizational silos → Structuring data for optimal use → Creating consistency across systems → Enabling truly intelligent decision-making Companies that invest in data structure will lead the AI revolution. The rest will struggle to keep up, constantly wondering why their AI investments aren't delivering. The difference isn't in the AI tools. It's in the data foundation. Our team at Michigan Software Labs addresses this head-on: 1. Data Discovery - Uncover what data exists and pinpoint any gaps. ~3 weeks. 2. Data Structuring - Organize and refine your data for clarity and quality 3. System Connectivity - Link platforms and tools to break down silos 4. AI Enablement - Apply AI solutions to well-prepared, structured data Stop throwing good money after bad. Start building the foundation your AI initiatives need to thrive. p.s. - If you've been following me for a while but we've never connected directly, I'd love to hear from you. Drop me a comment or send a quick note. Whatever professional challenge you're facing, I'm here to help - and if I can't, I’ll point you to someone who can.

The majority of companies are not ready for AI and it's not why you think. Spoiler alert: It’s not the tech—it’s your data. Every time I present to a room of business leaders, I ask: “How many of you trust the data you have access to?” There is usually an awkward silence with folks looking around. Maybe one brave hand goes up. Maybe two, if I’m lucky. And I am never sure if they are confident or ignorant. Here’s the reality: AI outputs are only as good as the data they’re built on. And yet, when I ask leaders about their priorities for the year, Data Hygiene is nowhere to be found. But if you’ve got AI on your 2025 bingo card, you’d better add Data Clean-Up right next to it. Why? Because bad data leads to bad AI—and that’s a disaster waiting to happen. Here is why you need to prioritize your data: ➡️ Accuracy: AI that actually works (imagine that!). ➡️ Reduced Bias: No perpetuating societal stereotypes, thank you very much. ➡️ Efficiency: Faster training, faster results. ➡️ Smarter Decisions: Because mistakes are expensive. Trust me, I know. So if you’re ready to get your data in check, here are a few places you can start. 1. Get AI-Ready: Clean, accurate, structured data is the bare minimum. Data governance isn’t optional. 2. Unify Your Data: Silos are going to hurt you here, so you need to bring all your data together. 3. Leverage Metadata: Not enough time is spent thinking about this but it will be hugely beneficial. 4. Align with Goals: AI should be solving business problems, so make sure your data is structured around your objectives. 5. Upskill Your Team: Data literacy is critical. Help educate and enable your team. Data is or should be an organizational priority. If your CEO is hyped about AI, this is your time to shine. Raise your hand, speak up, and champion the essential work of data hygiene. Because here’s the hard truth: If your data’s a mess, AI isn’t going to save you. It’s going to expose you.

Two weeks ago, while I was off radar on LinkedIn. The concept of data readiness for AI hit me hard… Not just as a trend. But as a gap in how most professionals and organizations are approaching this AI race. I’ve been in this field for over a decade now ▸Working with data. ▸Teaching it. ▸Speaking about it. And what I’ve seen repeatedly is this: We’re moving fast with AI. But our data is not always ready. Most data professionals and organizations focus on: ✓ the AI model ✓ the use case ✓ the outcome But they often overlook the condition of the very thing feeding the system: the data. And when your data isn’t ready → AI doesn’t get smarter. → It gets scarier. → It becomes louder, faster... and wrong. But when we asked the most basic questions, ▸Where’s the data coming from? ▸Is it current? ▸Was it collected fairly? That’s when we show what we are ready for. That’s why I created the R.E.A.D. Framework. A practical way for any data leader or AI team to check their foundation before scaling solutions. The R.E.A.D. Framework: R – Relevance → Is this data aligned with the decision or problem you’re solving? → Or just convenient to use? E – Ethics → Who’s represented in the data and who isn’t? → What harm could result from using it without review? A – Accessibility → Can your teams access it responsibly, across departments and tools? → Or is it stuck in silos? D – Documentation → Do you have clear traceability of how, when, and why the data was collected? → Or is your system one exit away from collapse? AI is only as strong as the data it learns from. If the data is misaligned, outdated, or unchecked, → your output will mirror those flaws at scale. The benefit of getting it right? ✓ Better decisions ✓ Safer systems ✓ Greater trust ✓ Faster (and smarter) innovation So before you deploy your next AI tool, pause and ask: Is our data truly ready or are we hoping the tech will compensate for what we haven’t prepared?

As enterprises accelerate their deployment of GenAI agents and applications, data leaders must ensure their data pipelines are ready to meet the demands of real-time AI. When your chatbot needs to provide personalized responses or your recommendation engine needs to adapt to current user behavior, traditional batch processing simply isn't enough. We’re seeing three critical requirements emerge for AI-ready data infrastructure. We call them the 3 Rs: 1️⃣ Real-time: The era of batch processing is ending. When a customer interacts with your AI agent, it needs immediate access to their current context. Knowing what products they browsed six hours ago isn't good enough. AI applications need to understand and respond to customer behavior as it happens. 2️⃣ Reliable: Pipeline reliability has taken on new urgency. While a delayed BI dashboard update might have been inconvenient, AI application downtime directly impacts revenue and customer experience. When your website chatbot can't access customer data, it's not just an engineering problem. It's a business crisis. 3️⃣ Regulatory compliance: AI applications have raised the stakes for data compliance. Your chatbot might be capable of delivering highly personalized recommendations, but what if the customer has opted out of tracking? Privacy regulations aren't just about data collection anymore—they're about how AI systems use that data in real-time. Leading companies are already adapting their data infrastructure to meet these requirements. They're moving beyond traditional ETL to streaming architectures, implementing robust monitoring and failover systems, and building compliance checks directly into their data pipelines. The question for data leaders isn't whether to make these changes, but how quickly they can implement them. As AI becomes central to customer experience, the competitive advantage will go to companies with AI-ready data infrastructure. What challenges are you facing in preparing your data pipelines for AI? Share your experiences in the comments 👇 #DataEngineering #ArtificialIntelligence #DataInfrastructure #Innovation #Tech #RudderStack

Unlocking AI Success: Your Roadmap to Data Mastery & Readiness AI isn’t a “nice-to-have” anymore; it’s table stakes for competitive advantage. Yet too many organizations stumble at the start line, armed with ambition and budget but lacking the right data foundation and change-management playbook. Here’s how to bridge that gap: 1. Build a Rock-Solid Data Bedrock: - Data Quality & Governance: Automate validation checks, enforce clear policies, and empower dedicated data stewards. - Unified Platforms: Break down silos with cloud-native lakes and warehouses for real-time access. - Scalable Architecture: Future-proof your stack so it flexes with emerging AI agents and growing workloads. 2. Cultivate an AI-Ready Culture: People, not just technology, fuel transformation. - Leadership Alignment: Run executive workshops to nail down a shared AI vision. - Skill Building: Invest in data literacy, basic machine-learning know-how, and AI ethics. - Cross-Functional Teams: Stand up “AI Tiger Teams” that blend IT, analytics, and business experts. 3. Steer Transformation with Purpose: Digital change requires more than new tools; it demands a holistic strategy. - Strategic Roadmapping: Tie AI initiatives directly to business goals: revenue growth, cost reduction, or customer experience. - Change Management: Highlight early wins, gather feedback, and celebrate champions along the way. - Governance & Ethics: Set up oversight committees to safeguard compliance and responsible AI use. 4. Embrace AI Agents for Operational Excellence: Autonomous agents can revolutionize everything from support to supply-chain. - Use Case Identification: Start small! Think chatbots or predictive-maintenance alerts. - Pilot & Iterate: Launch MVPs, measure performance, and refine relentlessly. - Scale Responsibly: Monitor behaviors and embed guardrails to keep agents aligned with your values. By mastering your data, empowering your people, and marrying strategy with ethics, you turn AI from a buzzword into a business accelerator. Which part of this roadmap will you tackle first? —----------------- Ready to unlock AI success in your organization? Take our free AI Readiness Assessment Test: https://lnkd.in/efsUn89N Ensure you're positioned for AI success.