Real-World Examples Of Successful AI Scaling

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 more I engage with organizations navigating AI transformation, the more I’m seeing a number of “flavors” 🍦 of AI deployment. Amidst this variety, several patterns are emerging, from activating functionality of tools embedded in daily workflows to bespoke, large-scale systems transforming operations. Here are the common approaches I’m seeing: A) Small, Focused Add-On to Current Tools: Many teams start by experimenting with AI features embedded in familiar tools, often within a single team or department. This approach is quick, low-risk, and delivers measurable early wins. Example: A sales team uses Salesforce Einstein AI to identify high-potential leads and prioritize follow-ups effectively. B) Scaling Pre-Built Tools Across Functions: Some organizations roll out ready-made AI solutions across entire functions—like HR, marketing, or customer service—to tackle specific challenges. Example: An HR team adopts HireVue’s AI platform to screen resumes and shortlist candidates, reducing time-to-hire and improving consistency. C) Localized, Nimble AI Tools for Targeted Needs: Some teams deploy focused AI tools for specific tasks or localized needs. These are quick to adopt but can face challenges scaling. Example: A marketing team uses Jasper AI to rapidly generate campaign content, streamlining creative workflows. D) Collaborating with Technology Partners: Partnering with tech providers allows organizations to co-create tailored AI solutions for cross-functional challenges. Example: A global manufacturer collaborates with IBM Watson to predict equipment failures, minimizing costly downtime. E) Building Fully Custom, Organization-Wide AI Solutions: Some enterprises invest heavily in custom AI systems aligned with their unique strategies and needs. While resource-intensive, this approach offers unparalleled control and integration. Example: JPMorgan Chase develops proprietary AI systems for fraud detection and financial forecasting across global operations. F) Scaling External Tools Across the Enterprise: Organizations sometimes deploy external AI tools organization-wide, prioritizing consistency and ease of adoption. Example: ChatGPT Enterprise is integrated across an organization’s productivity suite, standardizing AI-powered efficiency gains. G) Enterprise-Wide AI Solutions Developed Through Partnerships: For systemic challenges, organizations collaborate with partners to design AI solutions spanning departments and regions. Example: Google Cloud AI works with healthcare networks to optimize diagnostics and treatment pathways across hospital systems. Which approaches resonate most with your organization’s journey? Or are you blending them into something uniquely yours? With so many ways for this technology to transform jobs, processes, and organizations, it’s important we get clear about what flavor we’re trying 🍨 so we know how to do it right. #AIAdoption #ChangeManagement #AIIntegration #Leadership

JPMorganChase has already deployed 400+ AI use cases, according to Jamie Dimon's latest shareholder letter. That shows the momentum many companies are still aiming for. AI has moved beyond testing. It’s now part of core operations and reshaping how top organizations run. In recent client conversations, I keep getting asked: “Where can I find more AI use cases for inspiration?” So I curated this list of 12 directories that features examples that help teams automate and solve real business problems across industries, from finance and healthcare to logistics and manufacturing. Together, they showcase more than 2,265 practical use cases, from legal ops and customer service to supply chains, compliance, sales and marketing. 📚 The list (in alphabetical order): 1) Amazon – GenAI Customer Stories (280+ use cases) https://lnkd.in/g-GzGaUD 2) Capgemini – Harnessing GenAI Potential (54) https://lnkd.in/gmCuy6i8 3) Deloitte – GenAI Dossier (73) https://lnkd.in/gzjwNz4F 4) EY – AI Use Cases Suite (15) https://lnkd.in/gn4eZnq8 5) Google – 601 Real-World GenAI Use Cases https://lnkd.in/g_Cx7eD3 6) IBM – The Most Valuable AI Use Cases (27) https://lnkd.in/ghy8bcNf 7) Intel Corporation – AI Applications Across Industries (35) https://lnkd.in/gm9hVV2f 8) McKinsey & Company – GenAI in TMT (63+) https://lnkd.in/gaBb6ydz 9) Microsoft – 700+ AI Customer Stories (use cases) https://lnkd.in/gFD2cUhf 10) Oracle – GenAI for Enterprise Apps (17) https://lnkd.in/gTBfEkZP 11) PwC – Applied AI Compass (200+) https://lnkd.in/gyUjZEtQ 12) SAP – AI Use Cases by Department (200) https://lnkd.in/g43WR--i How Tech Leaders Can Use These Directories: 1️⃣ Prioritize what’s proven: Start with repeatable use cases that have scaled in your industry. 2️⃣ Align use cases with business goals: Map examples to your OKRs, not just your tech roadmap. 3️⃣ Use them to shape your GenAI backlog. Turn inspiration into action by feeding use cases into your AI delivery pipeline. 💡 For startup founders: Use these directories to validate product ideas, identify whitespace, or benchmark against how enterprise teams are solving similar problems. If this sparked an idea, save it or pass it along. Appreciate a repost or tag when you share it! I will be sharing more on Agile AI adoption, automation blueprints, and use cases. Follow for the next drop. #GenAI #EnterpriseAI #Startups