The Role of AI in Data Strategy

The fastest way to lose credibility with senior leaders is to say ‘AI strategy’ but deliver tactics. I’ve taught data and AI strategy for 6 years. Few understand strategy well enough to work with C-level leaders. Do you? Run the exercise I use in my courses and see for yourself: Give me an example of something you’ve heard called a data or AI strategy, then tell me if it’s a strategy or a tactic. Data Management, Quality, Governance, Modelling, CoE, Readiness Assessments, Generative AI, Knowledge Graphs, Buy vs. Build, AI Literacy. Which are strategies, and which are tactics? All could be part of implementing and executing a strategy, but all are tactics. A step must come first: proving there’s positive value, not the assumption of it. Strategy is a statement of why that forms a thesis for action. Strategy enables an evaluation of tradeoffs by revealing multiple paths to success. Business leaders can pick the best one, not the most obvious one. Data and AI strategy allow CxOs to ask a critical question. Why are we using data and AI in the first place? Businesses with AI strategies can explain why they use the technology with use cases, not buzzwords. Without an AI Strategy, AI increases costs without delivering new revenue or efficiency. Business goals come first. Opportunities second. Use cases third. Then, the business can document an AI strategy that defines what value all those tactics are expected to deliver. Data and AI strategy means one thing inside the data team but something entirely different for the business. If you say ‘AI strategy’ and go straight to LLMs or data engineering, business leaders put you on a lower level, and it’s a tough road to reestablish credibility. #DataScience #DataEngineering #DataStrategy #AIStrategy

There is no AI strategy without a data strategy. 1/ AI is only as good as the inputs it can access. If critical data is fragmented across spreadsheets, PDFs, emails, and SaaS tools, AI can’t generate meaningful insights or automate workflows effectively. But AI agents are starting to change that—every browsable UI can become an API, closing the data fragmentation gap. 2/ Data moats naturally emerge at the application layer. Product usage generates data that no one else has, which can be re-injected into the next generation of your custom model. This feedback loop compounds over time, creating defensibility. 3/ The next frontier of data moats is personalization. Even within the same industry, two companies—or even two individuals—will develop unique workflows, preferences, and decision-making patterns. Two lawyers at different firms may interpret the same contract clause differently based on their firm’s risk tolerance, past case history, or internal legal philosophy. Capturing highly specific user interactions creates fine-grained user data making AI truly differentiated and deeply integrated. 4/ Foundational model companies only have distribution advantages. They train on similar data, go through similar vendors, use relatively similar methods, and follow parallel scaling trends. Their competitive edge comes from distribution and the usage data it generates—though much of their focus is on topping benchmarks in math, science, and code. The biggest opportunities in the AI app layer aren’t just about model progress—they’re first and foremost about structuring, collecting, and leveraging product-specific data in a way that compounds over time and creates true differentiation.

I've been having more conversations with fellow CEOs and board members about AI implementation lately, and one thing keeps coming up: we're not just facing an AI challenge - we're facing a fundamental data readiness crisis. I talk about this in my latest piece for Fast Company and the numbers don’t lie: 45% of organizations cite data quality and lineage as their biggest obstacles to AI success. That's not a technology problem - that's a foundation problem. Here's what else I'm hearing in these boardroom conversations: leaders know AI is essential, but many are discovering their data landscape isn't ready for prime time. The questions that come up again and again are practical ones: Which data assets are truly essential? How much has actually migrated to the cloud? And crucially, how do we measure progress without getting lost in the complexity? The reality is that every AI strategy is fundamentally a data strategy. The path to refine complex, fragmented data systems includes three critical steps: review your data landscape with clear eyes, align your AI strategies with specific business goals, and deploy with the ability to monitor, measure and improve. Given AI's expanding role in business, the companies that combine their AI ambitions with business goals will thrive, all while building the measurement capabilities to track real outcomes. Read more: https://lnkd.in/gXG44C6n

Everyone says they want a strategic data & AI function. Here’s what they expect: → Choose vendors → Draw architecture diagrams → Build pipelines → Launch dashboards → Sprinkle AI on top like parmesan But here’s what actually works: - Problems: Deeply understand what (business) problems you are solving for whom - Users: Define Roles and Responsibilities between data and business - UVP: Describe how you solve your user’s problems better than existing alternatives (e.g. Excel, GA4, Hubspot) - Solution: Describe your tool stack & architecture (yes, it still matters - just not first) - Distribution: Just like every product, data products need a go-to-market strategy - Systems: Your automations and SOPs that help you create value consistently - Outcomes: Which business outcomes (and not only outputs) will you create? - Costs: Focus 80% on Outcomes and 20% on Costs (most teams do it the other way around) - People: Hire and grow people who create business value with data One approach looks impressive in a slide deck. The other actually moves the needle. The hard truth? Most companies don’t have a data strategy. They have a shopping list and a POC graveyard. The solution isn’t more tooling. It’s more clarity, more empathy, and more focus. ♻️ Repost if you’ve ever watched a 100-slide deck on the data & AI strategy and still had no idea what problem was being solved 👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/geQvfc9h

🚨 The Stanford 2025 AI Index Report is out - and it’s one of the most comprehensive looks at where AI is heading. As a CTO and data & AI leader, a few key takeaways stand out: 🔍 AI has moved from exploration to execution. In 2024, 78% of U.S. organizations reported using AI - up from 55% in 2023. Even more striking: 71% now use generative AI in at least one business function, more than double from the year prior. GenAI has gone from hype to operational reality, and the pressure to deliver business outcomes is on. 💰 Capital flows to AI continue to surge. Private AI investment hit $252B globally, a 26% YoY increase. Capital is shifting from experimentation to enablement, with major spend on model development, infrastructure, and data platforms. Strategy: Build once, scale fast, govern continuously. 🛠️ Open-source models are closing the performance gap. Benchmark results show open models gaining fast on closed systems, unlocking cost-effective, transparent, and customizable enterprise AI. But it raises the bar on data readiness, observability, and model risk management. 🌍 Global AI dynamics are evolving. The U.S. still leads in foundational model output, but China is accelerating rapidly. This creates a more competitive landscape, and new complexity in compliance, localization, and governance. 🔑 What matters most? Data strategy. From our own experience, building trusted AI starts with data quality, governance, and reuse. You can’t scale AI without scalable, trustworthy data infrastructure and a clear line of sight from use case to outcome. 📌As AI evolves at record speed, it’s no longer about chasing the next model, it’s about scaling responsible AI with the right data, infrastructure, and cross-functional culture behind it. Explore the full report here 👉 https://lnkd.in/eadyPzqw #StanfordAIIndex2025 #CTOInsights #ArtificialIntelligence #GenAI #DataDriven #ResponsibleAI #AIInvestments #OpenSourceAI

𝗗𝗮𝘁𝗮 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗜𝗧 𝗵𝘆𝗴𝗶𝗲𝗻𝗲 𝗮𝗻𝘆𝗺𝗼𝗿𝗲—𝗶𝘁’𝘀 𝗮 𝗯𝗼𝗮𝗿𝗱-𝗹𝗲𝘃𝗲𝗹 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝘆. AI is moving faster than most organizations can govern it. The real differentiator won’t be who builds the biggest models, but who builds the strongest data foundation. Data is no longer just the fuel for AI, it’s the chassis that determines whether your enterprise accelerates or stalls. 𝗙𝗿𝗼𝗺 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝘁𝗼 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Collecting data for its own sake only creates noise. The winners will be those who engineer clarity: - 𝗔𝗹𝗶𝗴𝗻 𝗱𝗮𝘁𝗮 𝘁𝗼 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗻𝘁𝗲𝗻𝘁. Every dataset should serve a measurable outcome such as growth, resilience, or speed to decision. - 𝗙𝗹𝗮𝘁𝘁𝗲𝗻 𝘀𝗶𝗹𝗼𝘀. Data needs to move freely across domains so AI systems can learn context, not chaos. - 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝘄𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁. Build flexibility into your data stack to handle use cases that may not exist yet. Retrofitting is far more expensive than readiness. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗡𝗼𝘄 𝟭. 𝗔𝗴𝗶𝗹𝗶𝘁𝘆 𝗯𝗲𝗮𝘁𝘀 𝘀𝗰𝗮𝗹𝗲. Well-organized data lets AI models pivot as markets shift, without the lag of re-engineering. 𝟮. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗯𝘂𝗶𝗹𝗱𝘀 𝘁𝗿𝘂𝘀𝘁. Strong lineage and transparency reduce compliance risk while reinforcing credibility in AI outcomes. 𝟯. 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗶𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆. Data prepared for AI keeps operations running through supply shocks, system outages, and market volatility. 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗗𝗲𝗰𝗮𝗱𝗲 Forward-looking boards are reframing AI readiness as a leadership mandate: - Make data strategy part of C-suite scorecards, with KPIs tied to outcomes like time-to-insight or audit efficiency. - Adopt modular, federated architectures so business units own their data but share it through standardized APIs. - Create cross-functional data guilds consisting of analysts, engineers, and business owners who co-design AI roadmaps and ethics frameworks. - Invest in metadata, lineage, and interoperability to future-proof your infrastructure. - Elevate governance from a checkbox to a catalyst for innovation and accountability. 𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗟𝗲𝗻𝘀 Data readiness is the quiet determinant of who thrives in the AI economy. Organizations that weave AI into their data strategy today will become adaptive, insight-driven enterprises tomorrow. Those who treat it as an afterthought will spend the next decade catching up. 𝗪𝗵𝗮𝘁’𝘀 𝗼𝗻𝗲 𝗱𝗮𝘁𝗮 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗮𝗰𝗶𝗻𝗴 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄? 𝗜’𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝘁𝗵𝗲𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝘁. 𝗣𝗹𝗲𝗮𝘀𝗲 𝘀𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀 𝗯𝗲𝗹𝗼𝘄. #NavigatingNext #AIReadyData #DataStrategy #DigitalTransformation #EnterpriseAI #Leadership

When I started working in strategy, I hand collated data on custom-printed BCG worksheets, and manually plotted out the data points on graph paper. It was slow. But it gave me a deep intuitive feel for the data and patterns that were emerging. And helped me find insights in the variance. But today, there's a new player on the field. And it just... processes. Billions of data points. In seconds. It maps the data. It analyzes the variance. It finds patterns. And it automatically generates slightly above-average insights. But it still lacks human intuition, creativity, and common sense. Here's the thing --> While there's lots of hype about AI replacing the strategy firms, there's a more accurate truth: AI is changing how strategy is done. But it's not replacing the strategists. It's transforming the strategist's toolkit and the way we work. Some recent work by McKinsey & Co. shows what that looks like in practice: 🔹 AI as Researcher Need to size a market or find under-the-radar M&A targets? AI can crawl 40M+ companies, analyze trends, and return insight-rich shortlists in minutes. 🔹 AI as Interpreter You’ve got 10,000 reviews, 80 annual reports, and 15 years of pricing data. Good luck reading that. But AI can. And it can spot growth adjacencies you might miss. 🔹 AI as Thought Partner Think of it as your no-ego brainstorming buddy. It challenges assumptions, pressure-tests options, and forces you to think wider, deeper, smarter. 🔹 AI as Simulator Want to know what happens if your competitor drops prices 15% next quarter? Or how a new regulation changes your cost structure? AI models can simulate it. 🔹 AI as Communicator A good strategy means nothing if no one gets it. AI can translate dense insights into decks, briefs, even podcasts tailored for investors, teams, or regulators. It frees us from grunt work and cognitive overload. But strategists still need to do what we do best: → Explore that random outlier. → Find insight in the variance. → Craft non-linear bets. → And build an advantage from unique insights. AI is a fantastic tool. But never forget -- AI is finding patterns in the past. 𝐈𝐭'𝐬 𝐍𝐎𝐓 𝐟𝐢𝐧𝐝𝐢𝐧𝐠 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐢𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐭𝐡𝐚𝐭 𝐝𝐨𝐧'𝐭 𝐲𝐞𝐭 𝐞𝐱𝐢𝐬𝐭. 👉 That's your job as a strategist. How are you using AI? — I'm Alex Nesbitt. I help CEOs, founders, and executives accelerate strategy. 🎯 Like this? Join the waitlist for the 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫 – Link in comments ♻️ Share with your network

Most organizations today are racing to deploy AI – chatbots here, forecasting there, a recommendation engine somewhere else. But deploying isolated targeted AI tools isn’t transformation. Just think back to the RPA days. Real transformation starts with Intelligent Data at the center. Because no matter how advanced your algorithms or workflows, if your data isn’t contextual, self-aware, and reliable, everything downstream suffers. Intelligent Data isn’t just better quality – it’s data that thinks, connects, and acts as the strategic asset it is. That’s why the future belongs to organizations that build an Intelligent Data Mesh - a foundation where data is: ✅ Always contextual and up to date ✅ Self-describing and self-optimizing ✅ Ready to power AI and workflows instantly From there, two essential frameworks help you turn Intelligent Data into sustainable competitive advantage: The Four Operational Dimensions – the infrastructure where work happens: • People collaborating with AI • Workflows that adapt themselves • Data that participates in operations • Algorithms that continuously learn The Five Intelligence Dimensions - the capabilities that compound: ✨ Data Intelligence: Prevents errors before they occur, provides instant context, and generates insights that humans miss – reducing decision time from hours to seconds. ✨ Human Intelligence: Frees people to focus on strategy and creativity while AI handles routine analysis – boosting productivity 3–5x and increasing job satisfaction. ✨ Operational Intelligence: Creates compound effects – improvements in one area amplify all others, making 1+1+1+1 = exponential value instead of just 4. ✨ Strategic Intelligence: Anticipates change so you lead markets, not just react to them. ✨ Network Intelligence: Turns your ecosystem into a strategic force – where partners, suppliers, and customers all contribute to your advantage. When these dimensions are optimized, the results are transformative: → People: Become strategic partners with AI, not just task executors –evolving from cost centers into revenue drivers. → Workflows: Self-optimize and adapt based on outcomes – eliminating bottlenecks without human intervention. → Data: Becomes an active participant in operations – thinking and acting to create value. → Algorithms: Coordinate seamlessly with humans – enabling coherent, confident decisions. The multiplier effect: When Intelligent Data powers every dimension, you achieve capabilities your competitors using traditional approaches simply can’t replicate or buy off the shelf. In the end, the question isn’t whether algorithms will become your partners –it’s whether you’ll govern that partnership effectively or let it govern you. Is your organization putting Intelligent Data at the center of your AI efforts? Are you building the mesh and frameworks needed to turn data into the engine of an intelligent organization? If not, let me know - we can help you out.

There's no AI strategy without a data strategy. Enterprises are redesigning their operating models around agents, yet most are doing so on data foundations that were never engineered for autonomous execution. This reveals a tension that is only going to get worse. Agentic systems synthesize data across domains, apply reasoning, trigger downstream actions, and create second and third order effects across systems. The integrity of those actions depends entirely on the integrity of the underlying data – and how AI systems interpret that data. Accurate interpretation requires data context. The majority of enterprise data foundations in place today were built to support analytics, reporting, and human operated applications. They were not designed to supply AI agents with a shared, machine readable understanding of how to interpret data: where did it come from, how do data entities relate, what constraints apply, and under which conditions can information be used.  Making data AI-ready means making context explicit, with relationships, constraints, and business meaning expressed as runtime signals that systems can evaluate at the point of action. It means treating data context as a first class property and asking the critical question of whether an agent can act on it safely. That is why there is no AI strategy without a data strategy. Enterprises that want AI to scale need a shared, contextualized data layer that enables consistent interpretation across systems and grounds every action in the right constraints, along with runtime enforcement. Without it, AI will stagnate, produce inconsistent results and act in unpredictable ways.  The data strategy that wins is the one that makes context explicit, shared, and enforceable. Learn more at IndyKite.ai IndyKite

In the gold rush for AI, many leaders are overlooking the actual gold: their data foundation. After 25+ years in the B2B data trenches, from ERP to GTM, I've seen this pattern before. New technology promises transformation, but the fundamentals remain the same. AI is powerful, but it's a mirror reflecting the quality of the data you feed it. Relying solely on your first-party data for these sophisticated models is like navigating the open ocean with a map of your local pond. Before you scale your AI initiatives, ask if your data provides a complete picture: A Myopic Worldview: Your first-party data only shows your slice of the pie, creating massive blind spots to new markets and competitive threats. Incomplete Customer Profiles: You know what they bought, but do you know their full tech stack, recent intent signals, or true growth trajectory? Inherent Bias: Training AI exclusively on your past successes can trap you in a self-reinforcing loop, stifling true innovation and market expansion. The solution isn't just buying more data; it's about creating authoritative source of truth. This requires strategically enriching your first-party data with high-quality, compliant intelligence from a trusted data partner. Pick one who provides the complete, contextual fuel your GTM engine truly needs to win. Let's ensure your data strategy is an accelerator, not an anchor, for your AI ambitions. #DataStrategy #AI #B2B #GoToMarket #Leadership #BusinessIntelligence #DataPartner