In parallel to F500 leadership roles/ CX-Social Commerce-AI Think Tank / GDN’s Post

Scaling Gen AI: A Guide for Data Leaders - Core Challenge: Companies often struggle to scale generative AI (gen AI) beyond pilots due to unclear operating models. - Operating Models: Successful gen AI scaling requires a structured model that integrates technology, data, and teams. - Common Pitfalls: Avoid “tech for tech’s sake” and uncoordinated trials. Focus on creating business value. - Team Structure: Choose between extending existing teams or forming distinct gen AI units. - Risk Management: Emphasize compliance, governance, and regular risk assessments. Read more: https://lnkd.in/gf3Rr7UN #GenerativeAI #DataLeadership #AIManagement

Data leaders struggling with #GenAI integration are running into common traps like: * Tech for tech's sake: spending on tools without clear business goals * Trial and error: disjointed projects lacking coordination @McKinsey outlines how a cohesive operating model that integrates people, processes and technology can set organizations up for GenAI success. #AI #BusinessStrategy #datagovernance https://lnkd.in/gf3Rr7UN

Scaling Gen AI: A Guide for Data Leaders - Core Challenge: Companies often struggle to scale generative AI (gen AI) beyond pilots due to unclear operating models. - Operating Models: Successful gen AI scaling requires a structured model that integrates technology, data, and teams. - Common Pitfalls: Avoid “tech for tech’s sake” and uncoordinated trials. Focus on creating business value. - Team Structure: Choose between extending existing teams or forming distinct gen AI units. - Risk Management: Emphasize compliance, governance, and regular risk assessments. Read more: https://lnkd.in/gaxg3NY3 #GenerativeAI #DataLeadership #AIManagement

Two traps often ensnare data leaders when creating #genAI operating models - tech for tech's sake and uncoordinated trial and error. McKinsey & Company recommends structuring talent teams, organizing data assets, and determining whether a centralized or domain-centric development is the best approach to avoid this fate. #operatingmodels #AI #scaling https://smpl.is/9mv5w

After almost two years of excitement, companies are transitioning from the honeymoon phase of generative AI to scaling its real-world value. With 65% of companies already using generative AI regularly, the focus is now on building robust AI operating models that deliver measurable business results. But while many organizations have started integrating AI into their tech stacks, the challenge is: How do you scale AI to create lasting value? The key lies in effective data management, governance, and building a flexible operating model that evolves with the fast-paced world of AI. 🔑 Key insights include: Designing for Flexibility: Adopt a component-based model that allows quick updates without overhauling your tech stack. Decentralizing Development: Start with centralized teams, but plan for federated or decentralized AI teams as your capabilities mature. Data is the Backbone: Effective data management and governance are critical—especially when handling vast amounts of unstructured data. Risk & Compliance: Implement robust AI risk management to mitigate misinformation, hallucinations, and data leaks. If you're looking to implement scalable generative AI, this practical guide offers actionable steps, from team structures to compliance governance. 🔗 Dive into the full article to learn how your organization can build an AI operating model that drives success. #GenerativeAI #AIInnovation #DataGovernance #TechStrategy #DigitalTransformation #AIAdoption #FutureOfWork #McKinsey

💡 Working with DTC Brands across industries, AI Is Top of everyone's Mind! 💡 I’m frequently asked about AI implementation in their respective functions. According to the latest estimates from McKinsey, 65% of companies worldwide have implemented AI models—either in the pilot or production stages. 🌍🤖 But with a plethora of AI tools and platforms out there, businesses can feel overwhelmed. How do you choose the right solution for your functional and infrastructure use cases? 📝 This McKinsey report offers a great framework for CDOs and Chief AI Officers to scale GenAI successfully. Here are my key takeaways: 1️⃣ Operating Model: Develop a model that aligns people, processes, and technology to deliver the most value. In addition, build continuous Measure, Test, & Learn cycles. 2️⃣ Component-Based GenAI: Focus on critical components in the early phases—think APIs for data inputs, vector databases, Agents, Hallucination Checkers, and more. 3️⃣ Team Org Structure: Whether you choose a centralized or decentralized AI strategy depends on your organization’s structure and communication practices. 4️⃣ Risk & Compliance Management: Establish a baseline risk assessment and compliance framework, revising it as new risks are identified. check out the full report from McKinsey here: https://lnkd.in/gbnBYefR #AI #GenAI #DataScience #DigitalTransformation #McKinsey #Innovation #Leadership

I stumbled across this article that highlights how companies are moving beyond the initial excitement around generative AI (gen AI) to focus on generating real business value. The key takeaway is that organizations need a well-structured operating model to scale gen AI successfully, with data playing a central role. This involves creating a framework that aligns technology, people, and processes to ensure AI solutions are impactful, not just experimental. It’s crucial for companies to avoid common traps, like investing in AI without a clear purpose or running disjointed pilot projects. Instead, they should focus on building adaptable AI components, creating cohesive teams, and ensuring risk and compliance management is integrated into the process. Additionally, many organizations are shifting from centralized AI teams to more decentralized models as they mature. This need for structure and organization directly aligns with industrial engineering, where the goal is to optimize systems by streamlining processes and improving efficiency. In the future, I aim to contribute by integrating AI and data analytics into continuous improvement strategies. I want to help design systems that not only leverage AI for innovation but also ensure that the technology is scalable, compliant, and aligned with business goals. #FutureOfWork #DataAnalytics

Really insightful article from McKinsey discussing "a data leaders operating guide to scaling Gen AI". The article discusses how companies are moving beyond the initial hype of Gen AI and are now focused on how to create real business value. My top 5 takeaways: 1. Design an AI operating model around flexible components that can be scaled, flexed, turned on/off, or adapted as needed, vs one static capture all solution. 2. Choose between extending an existing data/IT team with AI skills, or building a COE, being clear on the trade-offs of each approach. 3. Prioritise data management and governance as the foundation for any successful AI adoption. 4. Plan for a future state, moving from a centralised AI model to an embedded approach enabling regional teams to own their own AI use cases. 5. Maintain robust procedures to address risk and compliance requirements, mitigating issues such as misinformation as AI is scaled across the organisation. #ai #data #governance #dataleader #mckinsey https://lnkd.in/e-V9-mxm

📢 Scaling Generative AI in Enterprises: A Practical Guide for Data Leaders 📢 Generative AI (GenAI) is rapidly becoming an essential technology in the corporate world, but scaling it effectively requires more than just tech integration. This guide highlights the importance of a data-centric operating model to unlock GenAI’s full potential. 🚀 Key Takeaways: - Data-Centric Approach: GenAI success depends on structured data management, governance, and integration into business processes. - Avoid Common Pitfalls: Stay clear of “tech for tech” investments and disconnected GenAI pilots. Focus on real business value. - Operating Model Essentials: Align people, processes, and technology under a well-defined plan that evolves with your GenAI components. - Risk and Compliance: Implement robust governance to mitigate risks like AI hallucinations and data misuse, while complying with evolving regulations such as the EU AI Act. We can help Closer Consulting #GenAI #DataLeadership #AITransformation #TechStrategy #AIAct

Ready to take #AI to the next level? As teams get better with #genAI, they can shift from #centralized to #decentralized models, letting each domain create its own custom tools. This means faster #innovation and more tailored solutions. Discover how: