Scaling Innovation Strategy Across The Organization

🏜️ 𝐆𝐞𝐨𝐟𝐟𝐫𝐞𝐲 𝐌𝐨𝐨𝐫𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐞𝐝 𝐭𝐡𝐢𝐬 𝐦𝐨𝐦𝐞𝐧𝐭 𝐢𝐧 𝟏𝟗𝟗𝟏. In 2024, GenAI took center stage—writing code, drafting content, streamlining workflows. The potential felt limitless. And yet… most organizations are waiting to see a significant impact. Why? 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐆𝐞𝐧𝐀𝐈 𝐡𝐚𝐬 𝐡𝐢𝐭 𝐭𝐡𝐞 𝐜𝐥𝐚𝐬𝐬𝐢𝐜 𝐜𝐡𝐚𝐬𝐦. The gap between early experimentation and scalable execution. Here are 5 strategies to help cross the chasm.⬇️ 𝟏. 𝐒𝐭𝐚𝐫𝐭 𝐖𝐢𝐭𝐡 𝐚 𝐁𝐞𝐚𝐜𝐡𝐡𝐞𝐚𝐝, 𝐍𝐨𝐭 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 Moore’s advice still holds: Focus beats breadth. 🔹Identify one high-value, high-urgency use case 🔹Ensure it has clean data, a clear ROI path, and defined success metrics 🔹Example: AI-powered claims processing that reduces cycle time by 40% ✅ Start where pain is highest, and impact is fastest. 𝟐. 𝐁𝐮𝐢𝐥𝐝 𝐖𝐡𝐨𝐥𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐚 𝐓𝐨𝐨𝐥 Organizations don’t buy AI—they buy solutions. 🔹Integration with workflows 🔹Training and change management 🔹 Governance and compliance from day one ✅ It’s not about what AI can do—it’s what it delivers. 𝟑. 𝐑𝐎𝐈 = 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐄𝐚𝐫𝐥𝐲 𝐌𝐚𝐣𝐨𝐫𝐢𝐭𝐲 Pragmatists need proof, not potential. 🔹Show measurable outcomes (e.g., time saved, cost avoided) 🔹Let peers speak—customer references carry more weight than marketing 🔹Reduce friction with phased rollouts and fast feedback loops ✅ Translate GenAI into business value, clearly and credibly. 𝟒. 𝐓𝐡𝐢𝐧𝐤 𝐢𝐧 𝐏𝐡𝐚𝐬𝐞𝐬, 𝐍𝐨𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 Scaling GenAI isn’t a single initiative; it’s a journey. 🔹Phase 1: Quick wins (task automation, productivity gains) 🔹Phase 2: Focused scaling (dashboards, team enablement) 🔹Phase 3: Enterprise transformation (AI-first products, governance frameworks) ✅ Maturity matters. Treat GenAI as a capability, not a campaign. 𝟓. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐅𝐫𝐢𝐜𝐭𝐢𝐨𝐧, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐇𝐲𝐩𝐞 The biggest blockers aren’t always technical. 🔹Talent gaps → solve with consistent micro-skilling 🔹Measurement struggles → tie GenAI outcomes to P&L metrics 🔹Risks → introduce scenario planning and AI governance ✅ Sustainable adoption requires confidence (beyond curiosity). 𝐆𝐞𝐧𝐀𝐈’𝐬 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐢𝐬𝐧’𝐭 𝐢𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧—𝐢𝐭𝐬 𝐩𝐚𝐭𝐡 𝐭𝐨 𝐢𝐦𝐩𝐚𝐜𝐭 𝐢𝐬. → Unlocking that value won’t happen by chance. → It requires clear use cases, strong foundations, and steady leadership. The encouraging part? 𝑾𝒆’𝒗𝒆 𝒇𝒂𝒄𝒆𝒅 𝒕𝒉𝒊𝒔 𝒎𝒐𝒎𝒆𝒏𝒕 𝒃𝒆𝒇𝒐𝒓𝒆—𝒘𝒊𝒕𝒉 𝒄𝒍𝒐𝒖𝒅, 𝒘𝒊𝒕𝒉 𝒅𝒂𝒕𝒂, 𝒘𝒊𝒕𝒉 𝒅𝒊𝒈𝒊𝒕𝒂𝒍 𝒕𝒓𝒂𝒏𝒔𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏. → The organizations that move with clarity and purpose now won’t just adopt GenAI. → They’ll shape how industries operate for years to come. 𝐈𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐭𝐡𝐚𝐭 𝐥𝐞𝐚𝐩: What’s the next best step you're focused on? 1️⃣ Choosing a beachhead 2️⃣ Building for integration 3️⃣ Making the ROI case Drop your thoughts below. ⬇️

Johnson & Johnson is zeroing in on GenAI use cases that it sees a strong ROI for and shutting down pilot projects for which it doesn't - and there are some powerful lessons here for all of us: 𝐖𝐡𝐚𝐭'𝐬 𝐭𝐡𝐢𝐬 𝐚𝐛𝐨𝐮𝐭?  - J&J initially encouraged employees across the company to experiment with AI, resulting in ~900 GenAI experiments across R&D, commercial, HR, and supply chain. - After reviewing, only 10–15% of these delivered 80% of the business value. - Now they're prioritizing and only scaling the high-value use cases and axing the rest. 𝐎𝐭𝐡𝐞𝐫 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐜𝐡𝐚𝐧𝐠𝐞𝐬:  - They're moving away from a centralized GenAI governance board. - And letting each business unit own their own AI agenda. - While setting up AI and Data Councils to ensure ethical use and scalability of AI tools. 📘 𝐇𝐚𝐯𝐢𝐧𝐠 𝐬𝐩𝐞𝐧𝐭 𝐲𝐞𝐚𝐫𝐬 𝐢𝐧 𝐂𝐨𝐫𝐩𝐨𝐫𝐚𝐭𝐞 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧, 𝐭𝐡𝐞𝐫𝐞'𝐬 𝐬𝐨𝐦𝐞 𝐭𝐞𝐱𝐭𝐛𝐨𝐨𝐤 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐩𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐦𝐨𝐯𝐞𝐬 𝐢𝐧 𝐡𝐞𝐫𝐞:  - Start broad. Learn fast. Then double down on what works. - Some people are calling this a failure. I completely disagree with that. This is what smart scaling looks like. - J&J mastered the Experiment → Validate → Prioritize → Operationalize cycle and built the real execution muscle around this. - Experimentation isn't just about wins; It's about building your path to value. 👉 𝐈𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧, 𝐮𝐬𝐞 𝐭𝐡𝐞𝐬𝐞 𝐥𝐞𝐬𝐬𝐨𝐧𝐬 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐚𝐧𝐝 𝐫𝐨𝐚𝐝𝐦𝐚𝐩: start broad, find what works, then scale proven value. ♻️ Share this with someone who needs to know this playbook. ➕ Follow me Heena Purohit for more AI news, insights, and real talk. 👉 Over to you: What aspects of this story stood out to you? --- 🔗 Full article: https://lnkd.in/dY_Mb4uE #EnterpriseAI #ArtificialIntelligence #AIforBusiness #GenerativeAI #AIRealTalk

The Innovation Squad Paradox: Speed vs. Scale in the Age of AI Leading over 200 engineers across three continents, I'm often asked: "Should we launch parallel innovation squads or invest in platform approaches?" The answer? It's not binary—and AI is changing the equation. The Innovation Squad Advantage: * 3-5x faster initial delivery * Laser focus on specific problems * Innovation without legacy constraints * Rapid MVP validation The Hidden Cost of Speed: After analyzing 50+ innovation squad initiatives across my tenure at Fortune 500 companies, here's what emerges: * The Frankenstein Effect: 6 innovation squads = 6 different architectures, authentication systems, and data models. One client ended up with 14 different logging frameworks! * The Scale Wall: That brilliant MVP handling 1,000 transactions? It melts at 1 million. I've seen teams rebuild from scratch 3x because squad solutions couldn't scale. * The Integration Tax: Integration costs often exceed initial development by 300%. Those "quick wins" become expensive technical debt. Enter AI as the Game Changer: AI can help innovation squads avoid becoming tomorrow's technical debt: * AI-Powered Standardization: GenAI code review ensures architectural consistency across parallel teams * Predictive Scale Analysis: ML models predict scaling bottlenecks before you hit them * Automated Integration Mapping: AI identifies integration points early, preventing Frankenstein architectures My Conway's Law Approach: Instead of choosing between innovation squads OR platforms, we structure hybrid models: * Innovation squads for 0→1 breakthroughs * Platform teams providing guardrails * AI ensuring coherence at scale The Bottom Line: Speed without strategy is just future complexity. But with AI-enabled governance, you can achieve both rapid innovation AND sustainable scale. The organizations that master this balance will define the next decade of software excellence. The future belongs to those who can innovate at startup speed while building at enterprise scale. What's your experience? Are you team innovation squad, team platform, or finding a third way? #DigitalTransformation #SoftwareLeadership #AI #EnterpriseArchitecture #TechnologyStrategy #Innovation #CTO #VPEngineering #ConwaysLaw #Agile #PlatformEngineering #TechLeadership #ScaleUp #SoftwareArchitecture #ArtificialIntelligence #TechExecutive