Strategies for Sustaining Long-Term AI Innovation

I had a sobering conversation with an AI founder last week. Her app was gaining traction, users loved it, and then... the model provider that powered her entire product released almost the exact same feature set. Overnight her competitive advantage vanished. This is becoming the new normal. Major model providers like OpenAI and Anthropic have evolved from infra companies into full-stack product companies. Think about that power dynamic for a moment. These providers have a direct line of sight to which prompts drive the most usage and which features gain traction. They have a panoramic view of the entire AI ecosystem. When they spot something working well, they can simply incorporate it into their own offerings. How do you build an enduring app-layer AI startup when the very platform you rely on might become your competitor? After speaking with dozens of founders navigating this challenge with Jaya Gupta, we noticed the most resilient teams are focusing on five key strategies: ➡️ First, master high-exception workflows. These are riddled with complexity that generic models can’t handle. ➡️ Second, create proprietary feedback loops in ambiguous domains where "correct" answers aren't clear-cut. ➡️ Third, own the execution layer - especially the parts of the tech stack that big providers won't bother with. ➡️ Fourth, hire domain experts and embed with customers to understand nuances the model providers never will. ➡️ Finally, move fast and stay in the details. Your speed and depth of understanding create compounding advantages. Build systems that continuously learn from real-world interactions in ways the major labs can't easily replicate. When you own unique data and feedback loops, each improvement in the underlying models becomes a tailwind rather than an existential threat. More here: https://lnkd.in/gyR-77a9

𝐌𝐨𝐬𝐭 𝐀𝐈 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐧𝐨𝐭 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐢𝐬 𝐢𝐦𝐦𝐚𝐭𝐮𝐫𝐞, but because they begin with tools and trends instead of business intent. Leaders don’t need more AI demos or vendor pitches. They need a practical way to decide where AI fits, what it should change, and how value will be measured over time. 𝐓𝐡𝐢𝐬 𝐯𝐢𝐬𝐮𝐚𝐥 𝐬𝐞𝐫𝐯𝐞𝐬 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐜𝐡𝐞𝐚𝐭 𝐬𝐡𝐞𝐞𝐭 𝐟𝐨𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬, 𝐠𝐫𝐨𝐮𝐧𝐝𝐞𝐝 𝐢𝐧 𝐥𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧: • Start with business outcomes like revenue, cost reduction, speed, or quality — not tools • Separate hype from value by prioritizing use cases with clear, measurable upside • Understand that adoption always comes before ROI • Focus on high-leverage, repetitive, and decision-heavy workflows where AI compounds value • Think in systems rather than standalone tools • Redesign workflows instead of layering AI on top of broken processes • Keep humans in the loop to preserve trust, accountability, and decision quality • Measure value beyond cost savings — including time saved, quality improved, and better decisions • Pilot small, learn fast, and scale what proves its impact • Avoid tool sprawl that increases cost, confusion, and governance risk When done right, AI isn’t a side project or experiment. It becomes a core operating capability embedded into how work actually gets done. Strategy first. Execution next. ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

When deciding how to acquire AI solutions, the focus must be on strategic alignment. This space moves incredibly fast, and staying ahead means making deliberate, informed choices. Off-the-shelf solutions offer speed and ease of implementation, but they often come with limitations: rigid capabilities, reduced control, and fewer opportunities for differentiation. On the other hand, building from services like APIs or open-source frameworks provides flexibility and the ability to tailor solutions to unique mission needs. However, this approach demands deeper technical expertise and ongoing investment in infrastructure and talent. For organizations committed to long-term competitive advantage, scalability, and speed of relevance, there are three critical areas of focus: 1️⃣ Grow Talent, Don’t Outsource Brainpower AI success isn’t about hiring contractors for temporary solutions. It’s about cultivating a workforce that understands the nuances of AI and drives innovation from within. Empower teams to become builders, not just users. Invest in upskilling, hands-on experimentation, and creating an innovation culture where boundaries are constantly pushed. 2️⃣ Invest in R&D to Understand Algorithms, Infrastructure, and Challenges AI isn’t a plug-and-play solution—it’s a complex system built on advanced algorithms, data pipelines, and constantly evolving infrastructure. To lead, organizations must prioritize R&D ecosystems that enable teams to explore AI’s foundations, uncover limitations, and unlock transformative applications. Experimentation isn’t optional; it’s essential for navigating challenges, improving efficiency, and ensuring ethical and impactful implementations. 3️⃣ Pilot Toward Smarter Build vs. Buy Decisions When faced with “off-the-shelf” AI solutions, quick wins may be tempting. But are they the right wins? Structured pilots provide clarity. By fostering internal expertise and experimenting through pilots, organizations can evaluate when to build custom solutions for differentiation and when to buy products to accelerate outcomes. This hands-on approach leads to smarter, more strategic investments that align with mission-critical goals. To succeed we won’t just adopt AI--we will master it from the inside out. #DataScience #AI #GenerativeAI #TalentDevelopment #Innovation #Leadership #Research #Development #Experimentation #Engineering

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

Most AI strategies fail for a simple reason: they optimise for the next breakthrough, not for what happens after deployment. The real challenge isn’t building models. It’s sustaining them. In the latest edition of The Data Science Decoder, I explore this idea in “The Long View: What Sustainable AI Leadership Looks Like.” The article reflects on a pattern many of us are seeing play out in real time. Organisations move quickly to adopt new AI capabilities, demonstrate early value, and then encounter a phase of complexity, model drift, governance gaps, and questions around trust and accountability. What separates those that progress from those that stall is not speed, but stewardship. The piece introduces a simple but important shift in thinking: AI systems are not static assets. They are living systems that evolve, degrade, and require ongoing oversight. Sustainable leadership means designing for that reality from the outset, balancing short-term capability with long-term resilience and trust. For practitioners, this changes how we think about model lifecycle, monitoring, and operationalisation. For leaders, it reframes the conversation from “how fast can we deploy?” to “how well can we sustain and govern at scale?” If you’re navigating AI strategy, governance, or scaling challenges, this perspective may resonate. You can read the full article, “The Long View: What Sustainable AI Leadership Looks Like,” in the latest edition of The Data Science Decoder.

Our team at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) responded to the U.S. Office of Science and Technology Policy’s call for input on the 2025 National AI Research and Development Strategic Plan with seven key recommendations. We believe this is a pivotal moment to shape the future of AI, as a technical achievement and as a force for positive societal progress. These recommendations aim to support a new generation of AI that advances our understanding of intelligence, supports scientific discovery, accelerates the deployment of AI across industry, strengthens public infrastructure, enhances national security, and helps humankind flourish. Here’s what we proposed: 1) Invest in Basic Research Toward Artificial Superintelligence (ASI): Move beyond today’s architectures to new models that combine reasoning, perception, and physical intelligence. 2) Advance AI for Scientific Discovery: Support “AI Co-Scientists” to accelerate breakthroughs in energy, medicine, materials, and more. 3) Ensure AI-Ready Data: Develop principles, architectures, and infrastructure for producing high-quality, AI-ready data by default. 4) Understand the Theoretical Foundations of Intelligence: Develop a scientific theory of mind to guide the next wave of AI architecture. 5) Invest in AI for Strengthening the Nation: Apply AI directly to public needs: defense, health, infrastructure, manufacturing, cybersecurity, and more. 6) Scale AI Education, Training & Reskilling: Build a diverse, AI-literate workforce with strong math foundations and ethical grounding. 7) Establish a National Frontier Lab: Create mission-driven partnerships across academia, industry, and government to accelerate transformative AI.   Please share your thoughts on these recommendations and others you may have

Most are building AI for today, not for what comes next, and that’s the real problem. There is little forward thinking. Mostly short-term survival. According to S&P Global Market Intelligence, 42% of companies abandoned most of their AI initiatives in 2025. If you’re in travel tech like I am, you can already see this happening. Long-term strategy is almost nonexistent. So if you want to build something that lasts: 1.⁠ ⁠Build personalized ecosystems, not single features. Create secure, subscription-based systems that store preferences, and habits to use them to make travel, errands, wellness, and daily life effortless. When you solve recurring needs automatically, you become irreplaceable. 2.⁠ ⁠Give users ownership of their experience. People trust companies that let them shape their own journey. User-owned customization, transparent models, and sustainable initiatives will define the next decade of loyalty. So, build systems that adapt, evolve, and stay relevant even as behavior changes. This applies everywhere, but especially in travel tech, where trust, personalization, and consistency matter more than ever. Don’t invest thousands just to survive the year. Build sustainably so you’re still here in ten.

Your organization is making a $100M+ strategic decision right now—and most leaders don’t realize it. The choice isn’t which AI model to adopt. It’s who owns your organization’s institutional memory. For 18 months I’ve watched organizations rush into AI deployments. Nearly all build on model-first architecture—stacking capabilities on OpenAI, Anthropic, or Google’s APIs. Building on rented land. What’s happening: Models commoditize in 18-month cycles. GPT-4 to Claude to Llama 3—each generation brings 90% cost compression. The model layer is becoming a utility. Memory appreciates. Your organization’s accumulated knowledge, relationships, and decision patterns compound in value. This is your only sustainable strategic advantage. Yet most organizations are: → Coupling AI architecture to specific model providers → Fragmenting institutional memory across silos → Exposing sensitive data through model APIs → Building exponentially increasing switching costs → Ceding control to vendors whose interests diverge from yours The economics: Model-first: 60%+ of AI investment flows to providers, depreciating each model generation. Memory-first: 95% of value compounds in your organization, with models as commodity infrastructure you control. The strategic question every leader must answer in 2025: “Are we building permanent organizational assets, or renting AI capabilities from vendors who profit from our dependency?” Most organizations choose by default—and that default is strategic disadvantage. The memory-first architecture inverts this: You own a unified memory layer—semantic knowledge across all sources, with governance and contextual intelligence built in. Models become interchangeable. Route tasks to the best model for each job. When a model fails or reprices, switch without re-architecture. Your memory gets smarter with every interaction. Your advantage deepens. Your vendor leverage increases. This is how you capture AI value rather than rent it. Why timing matters: Organizations establishing independent memory strategies in the next 12-18 months will build 5-10 year advantages. By 2027, migration costs from model-first to memory-first will increase 10x. The window is closing. Three questions to pressure-test your strategy: 1. If your model provider doubled prices tomorrow, what’s the impact? 2. Can you switch providers without re-architecting? 3. What % of AI investment builds permanent assets vs. vendor dependencies? If these create discomfort, you need an independent memory strategy. For executives and AI leaders navigating this: I’m sharing comprehensive analysis on memory-first vs. model-first architecture—the strategic, technical, and operational case for memory ownership. DM me “Memory Strategy” for the full analysis plus 30-day assessment framework. The choice between memory ownership and rental will determine winners and losers across sectors. Which side is your organization on? #AI #AIStrategy #Leadership #DigitalTransformation #FederalAI

After working with a number of organizations that have gone from AI crisis to competitive advantage, here's what I've seen separates success from disappointment: 1. Business Outcomes First, Technology Second Stop asking "How can we use AI?" Start asking "What business results do we need?" Leading with value creation gets you executive commitment. Leading with technology gets you pilot projects that often die. 2. Invest in People, Not Just Platforms The biggest barrier isn't technical - it's cultural. Organizations achieving significant improvements spend 10-15% of their budget on workforce transformation. Your people need to know not just HOW to use AI, but WHY and WHEN. 3. Don't Automate Yesterday's Problems Most processes were designed for information scarcity and human-only decisions. So before deploying any AI, ask: "If we were starting from scratch today, how would we solve this?" Adding AI to 10-year-old workflows is like putting a jet engine on a horse-drawn carriage. 4. Make Data Your Strategic Partner Traditional data sits passively in databases. "Intelligent data" understands context, validates itself, and prevents problems before they occur. This shift from "data management" to "intelligence orchestration" creates exponential - not linear - advantages. 5. Think Ecosystem, Not Just Efficiency While others focus on internal automation, successful organizations create network effects that benefit customers, partners, and suppliers. The pattern? Organizations that think exponentially, not incrementally, are building sustainable competitive moats while others optimize for yesterday's competition. What's your experience? Are you automating old processes or fundamentally rethinking how work gets done? #AI #DigitalTransformation #Leadership #Innovation #Strategy

This week I had a conversation with someone who has been building AI solutions for biopharma. He asked me a great question: “When will industry leaders move away from asking their teams to ‘do something with AI’ and start operating with a real strategy?” I believe in today’s world AI isn’t a strategy, it’s an accelerator. And like any accelerator, it only works if the fundamentals are strong. Across life sciences, too many teams are still running disconnected pilots, experimenting without clarity, or treating AI like an IT project. This is creating a gaps in execution and alignment. I appreciate the practical way Ruba Borno from Amazon Web Services breaks down what fundamental need to be in place to move to business need based execution. I took the advice she provided during this podcast episode of At the Edge and translated it to how LifeScience leaders can operationalize AI in their organizations: 1) Anchor AI to measurable business outcomes: Every AI initiative must tie back to the business case. Revenue growth, more robust customer insights, impact to patient access, increased field productivity, to name a few. If you can’t measure the impact, you can’t scale it. 2) Build data readiness with purpose: AI is only as strong as the data fueling it. Fragmented CRM insights, siloed customer data, disconnected access or patient services systems, these are the barriers to value. Data strategy is now a leadership topic. 3) Design an operating model, not a collection of pilots: Sustainable AI capability requires cross-functional orchestration. Only 20% of pilots move into implementation. Clear governance, decision rights, and shared accountability are key to building velocity beyond pilots. 4) Drive change management and adoption early: Technology doesn’t fail, adoption does. Teams need to learn how to translate and validate AI recommendations with their customers and business insights in order to embed them in workflows. Evangelists help, but enablement, training, and experience win. The companies that are leading aren’t the ones experimenting with AI. They are the ones operationalizing it across the business. Here’s the podcast I referenced if you want to listen: https://lnkd.in/e8H9RYnC