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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

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

As companies transition from the honeymoon phase with generative AI to real-world application, the focus is shifting towards creating tangible value. Like it or not, AI is becoming an integral part of business strategy (looking at you, LinkedIn, for opting everyone in to their data being used to train their generative AI models...). How you embrace it into your strategy is the key to ensure measurable business outcomes. Many organizations are still in the pilot stage, but success hinges on aligning technology with strategic goals, avoiding pitfalls like tech-for-tech’s-sake and disjointed trial-and-error approaches. By fostering a well-defined operating model, businesses can truly harness the transformative power of gen AI for lasting impact. #IT #Business #GenAI #HolisticStrategy

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

A brilliant piece of content by Alex Singla and the team McKinsey & Company and QuantumBlack, AI by McKinsey. Immediately, the phrase that catches my eye is "Honeymoon phase of AI," referring to leaders investing for productizing and monetizing Proof-of-concepts (POCs) and experiments. Centralized, Federated, and Decentralized is often a good question. I agree with some views here and my POV is that the measure of maturity of the next generation Data platforms will do it all with AI core. And as I always say, with this the next generation CompanyOSs will be born from the fundamental learnings of SaaS. #data #ai #saas #productmanagement

After almost two years of infatuation with generative AI (gen AI), companies are moving past the honeymoon phase to embrace the work that matters most: creating value from this tantalizing technology. Expectations are high. A recent McKinsey Global Survey found that 65 percent of companies across sizes, geographies, and industries now use gen AI regularly, twice […] www.Cyprus-CEO.com https://lnkd.in/dm4vF6n4 #CEO #business #management #marketing #tech #AI #legal #money Cyprus CEO

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:

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

After 2 years of infatuation with #genAI, life sciences companies have identified many priority use cases for #2025, e.g. AI agents, content creation, personalization, planning ideation and strategy, media optimization including social, etc. But many continue to lack #AI operating models, which are key to speed to market, impact and ROI. In this piece, McKinsey & Company provides 2 powerful frameworks to help with this https://lnkd.in/gAWvj_et #businesstransformation #strategy #valuecreation #beyondthehype

💡 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