Sujay Kar’s Post

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

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

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

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:

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

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

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

Most companies have made steps to integrate AI into their tech stacks at some level. Yet a technical integration model is only part of what is necessary to generate lasting value from gen AI. Companies must also create gen AI operating models to ensure their technology implementations deliver measurable business results... #strategy #genai #growth #digitaltransformation

Matthew Fitzpatrick, Mohammed ElNabawy, and Rohit Rathod from QuantumBlack, AI by McKinsey shed light on the challenges organisations face when measuring the impact of #AI in this article featured by DataIQ. Challenges include aligning stakeholders, defining metrics, isolating financial impact, and ensuring #data accuracy, and yet some organisations are starting to crack the code. What do they do differently? Top performers have been found to prioritise business impact tracking, following a four-step approach to ensure effectiveness: 🔹 Define and align on leading and lagging KPIs 🔹 Ensure the chosen indicators are feasible 🔹 Automate tracking to rigorously measure value across the portfolio 🔹 Clearly communicate the impact to stakeholders 👉 Discover how measuring the impact of AI use cases can benefit your business and leadership in today's AI-driven operations: https://lnkd.in/euhSdj_i