inSTRATEGIA’s Post

As gen #AI advances from experimentation to implementation, companies are urged to establish robust operating and technical models to steer deployments effectively. Central to these models is #data organization, allowing gen AI applications secure and scalable access. Data leaders play a pivotal role in orchestrating gen AI rollouts as genuine #digitaltransformations, ensuring clear #governance, objectives, and progress monitoring. By executing a well-coordinated strategy, companies can swiftly launch #genAI use cases from a centralized Center of Excellence (#CoE). This approach positions them for a gradual shift towards decentralized development, underpinned by shared technology infrastructure. This evolution enables #agile gen AI deployments, essential for competing in today's rapidly evolving AI landscape. Read more about scaling gen AI in the digital era: https://lnkd.in/dhwAjMQd

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

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

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

Interesting posts from friends of our McKinsey members. Whether you’re a data leader looking to enhance your organization's AI strategy or an executive aiming to stay ahead in the digital transformation race, the guide offers valuable insights on: - Building a solid foundation for AI adoption: How to create an environment that fosters Gen AI implementation, from establishing the right infrastructure to nurturing a data-driven culture. - Identifying high-impact use cases: How to pinpoint the areas where Gen AI can deliver the most value, ensuring a focus on initiatives that align with business objectives. - Scaling AI capabilities effectively: Understanding the key elements for scaling AI initiatives across your organization, including talent, governance, and technology considerations. Article here: https://lnkd.in/gf3Rr7UN

💡 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

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

Thanks Julio Lema for sharing this article. While discussing this with our healthcare clients, there is hesitation on starting on the journey, not because they don´t think is relevant for them, but because they are aware of the change management involved needed, the governance and how to apply common sense so that they don´t embark on a journey of continuous POC´s without a long term vision. It is about how we bring actual and tangible value and how do we uncover the most impactful use cases for their business. #aihealthcare #valtechhealthcare #datadrivendecisions

The (very good) article from McKinsey highlights the necessity for businesses to reevaluate their approach to GenAI to fully unlock its potential. I really enjoyed it. While initial excitement was high, companies are finding it challenging to scale GenAI initiatives into tangible value without significant organizational changes. It is important to integrate GenAI into broader business strategies and the need for comprehensive training, a focus on data quality, and establishing standards for responsible AI use. Success in GenAI requires a blend of technical skills, strategic vision, and an ability to adapt to new learning and operational models. It highlights McK's own lessons learned during the development of their own GenAI solution. https://lnkd.in/enKiGkFS

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: