How data readiness challenges generative AI

Trying to keep up with AI — or even thinking about going Agentic? The truth is: it all starts with data. ⸻ I’ve spent over 12 years at Capgemini in Insights & Data, so I’ve seen first-hand what separates successful data programmes from those that stall. Right now, every organisation is on a journey with AI. For some it’s about sharper decision-making. For others it’s cutting costs, running operations more efficiently, or getting a clearer picture of customers and supply chains — so they or a system can make the next step with the most impact. But here’s the common thread: without strong data foundations, AI doesn’t deliver impact — it delivers incorrect insights which could lead to wrong decisions made, negative impact on your business and wasted budget. At Capgemini, we’ve spent more than two decades helping organisations build those foundations. Today, we’re combining that experience with the latest in AI and Agentic AI to deliver solutions that land quickly and scale with the business. If you’re already experimenting with AI — or just trying to figure out where to start — I’d love to have that conversation. 👉 Drop me a message and let’s explore how we can unlock more value from your data. #ArtificialIntelligence #DataStrategy

Data science isn’t just about building models anymore—it’s about building trust. We’ve moved past the stage where executives asked, “Can AI do this?” Now the question is, “Should we trust AI to do this?” Accuracy alone isn’t enough. We need explainability, governance, and a culture where people actually use the insights instead of filing them away in a dashboard no one opens. The hardest part of my job isn’t pushing technology forward—it’s translating complexity into clarity. When a business leader sees a recommendation and knows why it matters, that’s when data becomes a decision, not just a chart. The next era of data science leadership won’t be measured by how advanced our algorithms are, but by how deeply our work is embedded in business strategy and culture. 📊 Question to peers: what do you think is the bigger challenge today—advancing the technology itself, or driving adoption across the organization? #DataScience #AI #Analytics #Leadership #DigitalTransformation

The latest McKinsey Global Survey on AI finds that organizations are beginning to take steps that drive bottom-line impact—for example, redesigning workflows as they deploy gen AI and putting senior leaders in critical roles, such as overseeing AI governance. The findings also show that organizations are working to mitigate a growing set of gen-AI-related risks and are hiring for new AI-related roles while they retrain employees to participate in AI deployment. Companies with at least $500 million in annual revenue are changing more quickly than smaller organizations. Overall, the use of AI—that is, gen AI as well as analytical AI—continues to build momentum: More than three-quarters of respondents now say that their organizations use AI in at least one business function. The use of gen AI in particular is rapidly increasing. What do you think about a world where enterprises can actually know themselves by answering any question instantly through data and AI? Our Co-founder & CTO Ed Huang dives into this vision of the self-aware enterprise, powered by LLMs, RAG, and unified data platforms like TiDB. It’s a fascinating look at where enterprise AI is headed and how data architecture is evolving to keep up. Check it out 👇 https://ow.ly/wZI730sQGtI

CEOs don’t need to be data scientists to lead in AI, but they do need to own the data conversation. This piece explains why executives who anchor AI transformations in business outcomes and data quality are the ones turning hype into real results. It’s a thought-provoking read on how leaders can avoid common pitfalls and use AI to reimagine their businesses. 👉 Read the full article here: https://lnkd.in/g-GkwQyq

CEOs + Data: A Match Made for AI AI transformations succeed when CEOs lead. Boston Consulting Group (BCG)’s Vladimir Lukic explains why executives must own the data conversation, set direction, and anchor every data and AI initiative to clear business outcomes. https://lnkd.in/gJvy-axN

# The Reproducibility Paradox in AI Testing In the enterprise AI landscape, we're facing a profound methodological challenge: how do we validate systems whose outputs inherently change with each execution? As a data scientist specializing in organizational analytics, this reproducibility paradox has significant implications for how we implement AI solutions in enterprise environments. Traditional software testing follows deterministic paths—input A always produces output B. But with modern AI systems, particularly generative models, identical prompts can yield varying responses, making conventional testing frameworks fundamentally insufficient. This isn't merely a technical concern. For organizations integrating AI into critical business functions—whether for ESG performance metrics, executive sentiment analysis, or customer churn prediction—this variability introduces governance and compliance challenges that our existing frameworks weren't designed to address. The solution lies in statistical testing approaches that measure distributions of outcomes rather than binary pass/fail criteria. Organizations must develop probabilistic quality thresholds and implement continuous evaluation pipelines that monitor drift over time. For enterprise leaders, this represents a paradigm shift in how we conceptualize system reliability. The question isn't "Does our AI system work?" but rather "Within what parameters and confidence intervals does our system perform acceptably?" How is your organization adapting its testing methodologies to account for the inherent variability in AI system outputs? #AITesting #EnterpriseAI #DataScience #OrganizationalAnalytics

🧠 The Next Frontier of Analytics Isn’t Dashboards — It’s Decisions For years, organizations have invested heavily in dashboards, KPIs, and reports. But here’s the truth: more data doesn’t always mean better decisions. That’s where Decision Intelligence (DI) comes in — the next evolution in analytics maturity. Decision Intelligence connects data, AI, and human context to help leaders make smarter, faster, and more explainable decisions. It’s not about replacing intuition with algorithms — it’s about augmenting human judgment with intelligence at scale. From my experience leading analytics strategy, I’ve seen this shift firsthand: Dashboards inform. Decision Intelligence guides. Data tells you what happened. DI helps you decide what to do next. Traditional BI answers descriptive questions. DI tackles prescriptive and strategic ones. The real challenge for organizations now isn’t just collecting or visualizing data — it’s embedding intelligence into every decision, every process, every outcome. 💡 My view: The future belongs to leaders who can bridge analytics, AI, and business context — creating systems that don’t just report performance, but shape it. #DecisionIntelligence #AnalyticsLeadership #AI #DigitalTransformation #DataStrategy #BusinessIntelligence

AI is everywhere right now 🤖. But here’s the stat that should stop you in your tracks: 👉 60% of AI projects will fail without the right data foundation. That’s exactly why 3 out of 4 leaders are doubling down on AI-ready data. The question isn’t “Should we invest?” --> It’s “Are we ready?” Gartner recently published a free guide that breaks down what “AI-ready” actually means and how to get there: A Journey Guide to Deliver AI Success Through AI-Ready Data https://lnkd.in/gMDgt27G

Everyone’s excited about AI, but few are asking the hard question: is our data ready? This latest article by Vladimir Lukic really resonated with me. It highlights something I’ve seen play out numerous times: AI outcomes improve dramatically when data is treated not just as a tech asset, but as a strategic one. When business and tech leaders align early around data quality, governance, and ownership, the results are faster, more scalable, and more impactful. That’s where real transformation begins. A great read: https://lnkd.in/gR9fETa6 #AI #DataLeadership #DigitalTransformation #TechStrategy

I recently watched an insightful episode of The AI Daily Brief that analyzed what over a thousand executives are saying about AI agents, based on data from Superintelligent’s Agent Readiness and Opportunity Mapping audits. It’s one of the clearest looks I've seen at where organizations truly stand on enterprise AI today. A few insights stood out: → “By far the number one blocker… is data fragmentation. Even the most agent-ready organizations still deal with fragmented, unstructured, unorganized data.” → “There’s really no such thing as off-the-shelf AI. Every agent still needs customization to fit into existing systems.” → “Better documented processes mean faster pilots, more results, quicker.” → “One of the biggest factors for ROI? Better context. Moving away from flashy agent pilots to seeing data foundation work as exciting again.” These findings align closely with what I’m hearing in conversations with data and AI leaders. 2025 was the year of the flashy AI demo. Now, the focus is shifting to what really matters: building strong foundations, in data and in context. Organizations that invest here are the ones crossing the AI value chasm and realizing measurable impact. We’ll be exploring this further in our upcoming State of Data and AI report, launching at Atlan #ReGovern, our community conference for data & AI leaders on November 5th. I hope you’ll join us.