Alex G.’s Post

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

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

65% of companies now use generative AI regularly, though most haven't yet seen a significant impact. Today, businesses are working to create gen AI operating models that align tech, processes, and people to deliver measurable value. Organizations that scale AI effectively will lead innovation and unlock new growth opportunities. #AI #BusinessDevelopment

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

Deploying generative AI in the enterprise requires a data-centric road map. Leaders can use a well-defined operating model to successfully scale the technology.

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

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:

💡 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

Deploying generative AI in the enterprise necessitates a data-centric road map. Leaders can leverage a well-defined operating model to effectively scale the technology. #AI #DataCentric #TechnologyLeadership

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