Muazma Zahid

Anthropic just launched their free online learning platform (anthropic.com/learn) where anyone can take structured courses to learn how to use Claude effectively. Here's a quick rundown: What it is: A collection of structured learning paths with video lessons and assessments to help you master AI collaboration, Claude development, and Model Context Protocol (MCP). (Claude) Who it's for? There are tracks for different audiences: - Developers who want to build with the Claude API or learn MCP (Model Context Protocol) (Anthropic Courses) - Faculty and educational leaders looking to apply AI in teaching and institutional strategy (Anthropic Courses) - Students looking to develop AI fluency for learning, career planning, and academic success (Anthropic Courses) - Nonprofit professionals looking to increase organizational impact with AI (Anthropic Courses) Sample courses available: Claude 101 – a beginner intro to using Claude Building with the Claude API – for developers integrating Claude into apps Claude Code in Action – for coders using Claude's agentic coding tool AI Fluency: Framework & Foundations – for general AI literacy MCP development courses – for building tools that connect Claude to external services Visit https://lnkd.in/gKpbP2dV to sign-up for free

6

Enterprise AI Strategy: Why Product Leaders Hold the Key Most enterprises today have AI pilots. Very few have an AI strategy. Even fewer have product leaders who can turn ambition into adoption. In my experience leading multi-cloud and AI strategy initiatives, I’ve seen one truth repeat itself: The biggest challenge isn’t building AI — it’s scaling it responsibly across the enterprise. That’s where Product Management becomes the game-changer. Here’s how the role of product leaders is evolving in the age of enterprise AI 👇 1. From Cloud Strategy → to AI Operating Model  Multi-cloud is just the foundation. The real value comes from orchestrating AI services, data, and governance across environments — ensuring scalability and compliance. 2. From AI Pilots → to Enterprise Platforms  It’s not about one chatbot or automation use case. It’s about building trusted, reusable AI capabilities that every team can innovate on safely. 3. From Innovation → to Responsible Adoption Speed matters — but trust scales. Embedding transparency, explainability, and fairness must be part of the product DNA. 4. From Features → to Outcomes AI success isn’t model accuracy — it’s business impact. Faster insights. Smarter operations. Richer customer experiences. The truth is — AI doesn’t transform enterprises. Product leaders do. We’re the bridge between vision and execution — aligning data, cloud, and AI into a cohesive, outcome-driven strategy that drives real change. Read my latest blog on this topic: https://lnkd.in/gGDum6Ft As enterprises move from AI exploration → AI transformation, the next wave of value will come from leaders who think in systems, not silos. If you’re leading AI or product strategy — How are you bridging the gap between innovation and enterprise adoption? Would love to hear what’s working (and what’s not) in your journey. #AIProductManagement #AIStrategy #EnterpriseAI #ProductManagement #AIPlatforms #AIProduct #ProductStrategy #DigitalTransformation #ResponsibleAI #ScalingAI #EnterpriseAIStrategy

37

1 Comment

It’s striking how this research maps the hidden choreography of our days. We imagine ourselves using AI for narrow tasks, yet the patterns say otherwise: at every hour, in every season, people are bringing their real interior lives to Copilot—health anxieties, late-night wonderings, weekend escapes, the Monday grind of code and craft. What emerges isn’t a tool profile but a human one. A reminder that beneath the metrics lies a person reaching for help, clarity, or simply a companion in the quiet spaces of their day. Work like this pushes us to design with greater moral imagination: not just what an AI can do, but who we are when we turn to it.

18

2 Comments

Excited to share my latest post where I dive into ways AI and human creativity  come together to spark real change. We’re in a world where AI is not our competitor, but our most powerful collaborator, amplifying our ideas, helping us solve problems faster, and freeing us to focus on what makes us truly human: empathy, curiosity, and purpose. Workplace where seamless teamwork between intelligent agents and humans drives innovation, agility and a new era of growth. Organizations and leaders that embrace this partnership will unlock new possibilities, transforming challenges into opportunities and data into actionable insights. Let’s be the builder who takes lead on this transformation. #AI #HumanIngenuity #Inspiration #FutureOfWork

19

2 Comments

I enjoyed this piece from a16z on why AI agents need context: https://lnkd.in/gWDxCYDX One of the big takeaways is agents fail without context. Even powerful models struggle if they don’t understand business definitions, data relationships, and how an organization actually operates. Many early “chat with your data” projects ran into exactly this problem. The models were fine. What was missing was the context layer that helps them interpret the business. What’s becoming clear is that agents need a system that can provide things like: • business definitions and semantics • entity relationships • operational state • relevant data sources • organizational knowledge In other words, a structured way to serve the right context to agents at the right time. This idea aligns closely with something we’ve been working on at Confluent: the Real-Time Context Engine. The goal is to provide a context engine that continuously assembles the information agents need from across operational systems and data streams, so agents can reason about what’s happening in the business and not just answer questions about static data. Models give agents reasoning while tools give them actions. The context is what grounds them in reality. And increasingly, that context needs to be assembled in real time.

60

🧑‍💼 For leaders, the real conversation today isn’t whether to use AI. It’s how different forms of AI show up across the organization, & how each one drives value in a different way. ⚙️ Some initiatives deliver immediate operational impact. 🧠 Some quietly strengthen existing systems. 🤝 Others reshape how teams think, decide, and collaborate. This deck captures perspective - not as theory, but as a practical way leaders are already approaching AI across functions and industries. #Leadership #DigitalTransformation #BusinessStrategy #FutureOfWork #TechStrategy

23

7 Comments

Shweta Shah, Ph.D. has suggested a concept that she calls "Content Quality Management". Shweta and I have been writing together for a while, and I am really impressed by this idea, that I think addresses exactly what the data community needs to consider, going forward in an era so indisputably dominated by AI. Shweta is adding to an ecosystem of thinkers like Bill Inmon (with his absolutely wonderful concept of the "textual warehouse") and Jessica Talisman (coiner of the "ontology pipeline"), and a wealth of people in the records- and information management space like tech entrepreneur Rachael Greaves, and skilled practitioners like Michael Upton, Andy Potter (deeply involved in ISO standards on AI records), and Stephen Clarke MRIM that are trained in thinking in the intersection of language and data. If you want ideas in how to succeed with AI, I suggest you follow Shweta Shah, Ph.D. #contentquality #contentqualitymanagement #ai #data #language #dataquality #dataqualitymanagement

42

6 Comments

Building on top of large language models is fun, but getting consistent performance and reliability is extremely challenging. I love this post from Kimberly Patron about how Sierra uses health and performance-based traffic routing and request hedging to get meaningfully better performance and tail latency from high scale LLMs https://lnkd.in/g4qWRCb5

284

13 Comments

The "skill" building in AI models is tending towards useless. Academic term "negative impact". Limited human curated skills provide most returns on investment. Good read. https://lnkd.in/g4hpTyKH

7

2 Comments

People hear “AI governance” and immediately imagine committees, PDFs, and 4-week review cycles. You don’t need any of that. You just need traceability. Every agent action in your system should log: - what it did - why it did it - the inputs - the outputs - and any validation checks It gives you accountability without slowing the workflow to a crawl. You shouldn’t need a meeting to explain how an agent made a decision. It should be obvious in the logs. Governance shouldn’t feel like friction. It should feel like clarity.

3

1 Comment