Steven Sposato, MBA, PSPO, PSM, CLMS, ALMI’s Post

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

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 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

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

I’m often asked what I think is the single most pressing challenge to AI adoption at scale, and the harsh truth is that there’s no one pathway or even silver bullet to extract value from technology. Digital and AI transformations at scale are complex and multi-layered, and they often require a holistic approach that ‘rewires’ an organization from the ground up. It’s a fair question, as it’s in our nature to search for routes for maximum impact with the least effort. But focusing on one individual aspect of an ongoing digital transformation isn’t going to cut it – every organization’s story is different, and challenges are going to vary drastically from one company to another. What I tend to share in response is a list of ‘challenges’ (plural!) that leaders often face when struggling with AI adoption. These range from data quality and accessibility, to talent, to ethics and compliance, to measuring success and tangible impact of AI initiatives. All of these can be tough in their own way, and all of these need careful planning and strategy considerations to reach success. If you’re interested in learning about some of these challenges, specifically in the context of generative AI, this article is a good start: https://lnkd.in/eYdBa8H2 #AIbyMcKinsey #AI #DigitalTransformation #RewiredBook

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

📢 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