Ankit Mathur’s Post

🚀 𝗘𝗺𝗯𝗮𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝘁𝗵𝗲 𝗽𝗮𝘁𝗵 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 requires a nuanced understanding of the landscape and a robust skill set. 𝗣𝗮𝗰𝗸𝗮𝗴𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗮𝘀 𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁: As an AI Product Manager, you'll shift your focus to selling data products packaged via APIs. Consider the myriad ways you can customize data, including access methods, refresh frequencies, schemas, and storage solutions. For instance, envision yourself tasked with developing a recommender system for a dating app like Bumble. Break down the problem into granular components such as ranking potential matches, defining criteria for "good" matches, and optimizing user interactions within the marketplace. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻: Delve deeply into the intricacies of system design for AI platforms. This entails considering various factors such as training data collection, feature engineering, model update frequencies, latency requirements, and prototyping timelines. For example, when designing a recommendation engine, meticulously define output metrics, evaluate system performance metrics, and strategically choose model types like neural networks or Bayesian methods based on the problem domain. 𝗙𝗼𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗧𝗲𝗮𝗺 𝗛𝗮𝗿𝗺𝗼𝗻𝘆: Effective management of diverse teams comprising researchers, engineers, and ML specialists necessitates adept interpersonal skills. Strive to bridge the gap between different team members' priorities and incentives. For instance, researchers may prioritize academic rigor, while engineers focus on scalability and performance. Aligning team efforts towards common product goals and fostering a culture of collaboration and mutual respect is imperative for success. 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝘁𝗼 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻: Transitioning algorithms from prototype to production entails navigating the complexities of MLOps. This includes considerations such as data pipeline construction, model documentation, resource optimization, and experiment tracking. For instance, ensure your AI product can adapt to changing data distributions, mitigate data staleness, and implement robust AB testing methodologies to measure product effectiveness. By immersing yourself in these 𝗳𝗼𝘂𝗿 𝗰𝗼𝗿𝗲 𝗽𝗶𝗹𝗹𝗮𝗿𝘀 and delving deep into each aspect, you'll equip yourself with the tools and knowledge necessary to excel as an AI Product Manager. Remember, practical insights and real-world examples will serve as invaluable guides on your journey to success! 🌟 #AIProductManagement #CareerGrowth #ProductManagerJourney

Struggling with data and data governance as a Product Manager? You're not alone. But common data mistakes will soon create serious risks for your product. Opaque AI and lack of oversight in how data is used will increase risks. However, data governance puts this afterthought first. Follow the guide below to avoid pitfalls, ensure compliance, and build customer trust Your executives may see governance as inhibiting velocity. But responsible data and data governance increases efficiency, adoption and innovation. Embedding data governance into your product lifecycle will enable competitive advantage and growth through trust. Quality data requires quality governance. You must embed it early and comprehensively. "My Engineers will handle this, it's fine." Robust governance needs to come from a product manager's involvement from the start. Product Managers must champion this. Believing governance comes post-launch is backwards. Weave it into your product lifecycle today. Want to lead confidently on AI? Let's connect on operationalising sustainable AI at scale. ________ Are you curious about the key steps to making the AI in your product more ethical and sustainable? Want to get them all? Go to my profile, hit follow, and the notification bell 🔔 👍 Like if you enjoyed this 💬 Comment if you have something to share P.S. ♻ Repost if you found this useful #artificialintelligence #ai #genai #productmanagement

💥 Quote from New Book FUTUREPROOF: ❝In an AI-first world, the critical path to solution delivery will no longer be the coding/development process, it will be the analysis. Deciding precisely what to do, and deciding between options, constraints, and other factors, will be paramount.❞ #BusinessAnalysis and BA/PO/PM roles are the pivotal role in helping business and implementation teams make decisions. As many development tasks leverage AI automation, the decisions (big and small) will pile up. BA roles are the ones with the relationships, knowledge, judgment, and analysis skills and techniques to enable the acceleration of these decisions. Decisions made out of urgency, anxiety, fear, and without strategic alignment, impact analysis, and data evidence will cripple organizations. Organizations that enable good analysis practices to make decisions in the AI augmented environment we are in will be the ones that thrive! #BusinessAnalyst #AI #ProductManagement #ProductOwner https://lnkd.in/gq3fzSuV

Unlocking Continuous #Transformation with MLOps Machine Learning Operations (#MLOps) is the practice of reliably deploying and managing machine learning models in production environments. It encompasses the entire lifecycle of an ML model, from data preparation to model deployment, monitoring, and retraining. To effectively implement MLOps and capture the full potential of AI-driven continuous innovation, organizations need to cultivate MLOps talent within their domain functions. For example, in the supply chain domain, having MLOps expertise is crucial for seamlessly integrating AI models into advanced planning solutions. Today, only 14% of job postings in Supply Chain include MLOps in desired experience and skills (Source: Zero100). Without embracing MLOps and a shift from the traditional Waterfall/ERP mindset and transition away from the more recently popular Agile/Advanced Planning Solution methodology, companies will struggle to realize the transformative power of machine learning, AI and other advances in compute. MLOps enables the agility and scalability required to continuously improve and adapt AI models based on real-world data and feedback loops. The future of technology-enabled transformations lies in the intersection of domain expertise and MLOps capabilities. By fostering a culture that values MLOps and attracts top talent in this field, organizations can unlock a continuous cycle of innovation, leveraging AI to drive efficiency, competitiveness, and growth. #MLOps #AI #SupplyChain #ContinuousInnovation #DigitalTransformation

Question : Are you ready to launch your first #AI proof of concept Before embarking on the ambitious undertaking of building an AI product, it might be prudent for product managers to conduct a thorough self-assessment to ensure they are truly prepared for the proof of concept (PoC) phase. Rushing headlong into AI product development without proper planning and preparation is a recipe for failure. The PoC stage is a critical juncture where fundamental questions must be answered, assumptions validated, and potential roadblocks identified. It is a time for experimentation, iteration, and course correction before committing significant resources to full-scale development. - Clear Problem Statement: Have you precisely defined the problem you're trying to solve? A well-articulated problem statement is crucial for scoping the PoC and aligning stakeholders. - Narrowed Scope: Have you narrowed down the scope of the PoC to a specific, achievable goal? Trying to solve too broad a problem can lead to scope creep and failure. - Hypotheses: Have you defined clear business and technical hypotheses to be validated during the PoC stage? These hypotheses will guide your experiments and success criteria. - Data Readiness: Have you identified and secured access to the necessary data for executing the PoC? Data is the lifeblood of any AI system. - Data Preparation: Have you performed initial data preprocessing, visualization, and cleaning? Understanding your data's characteristics is essential for selecting appropriate algorithms. - Concept Evaluation: Have you considered alternate solution concepts and produced a concept selection matrix? Evaluating multiple approaches can lead to better outcomes. - Timeline: Have you defined a realistic timeline for the PoC, including milestones and checkpoints? Proper planning prevents delays and ensures accountability. - Success Criteria: Have you established clear success criteria for the PoC? These criteria will determine whether to proceed with full-scale development or pivot. Only after thoroughly addressing these questions should you proceed with building your AI product's proof of concept. Skipping these crucial steps can lead to wasted efforts and failed projects. 💡 Quick Tip: Involve a diverse team of stakeholders, including subject matter experts, data scientists, and end-users, in the PoC planning process. Their insights can uncover blind spots and increase the chances of success. #artificialintelligence #productmanagement

The WORST advice for breaking into AI product management? Build a model or a Gen AI app! 🚫 Here's why 👇 👉 Most advice ends at building. You miss the chance to demonstrate real AI PM skills. 👉 Tuning parameters on a model using pre-cleaned datasets showcases great skills - for an entry-level data scientist. 👉Even if you're filling gaps from a non-tech background, creating genAI and no-code apps is becoming so straightforward that it doesn't stand out much. So, what should you do instead? If a candidate did any two of the following, I'd be far more impressed than if they just built a classifier on toy datasets or a simple chatbot: 🤔 Identify a real-world problem that's ripe for an AI solution. Ensure it's something people would pay to solve. ♻ Use low/no-code tools to create a generative AI prototype. Put it in users' hands, gather feedback, and refine it. 🧐 Observe how your system fails on edge cases and its limitations. Understand what it takes to scale it. Discuss how you'd develop a more robust, testable, scalable, and secure system. 💲 Craft a business case for your decisions. How many users at what price point would make this a viable business? AI business cases have different cost breakdowns! 📈 Define what metrics and KPIs you would monitor for the product. What technical metrics (like latency or accuracy) would you track? How do you translate those into product and business metrics that matter to leadership? 🔭Consider the system's long-term operation - would it continuously learn from user feedback? Does it need periodic retraining? 📢 Keen to learn more about nailing that AI PM job with your product management skills? Check out my free webinar replay, "The Path to AI PM." It highlights the 6 key skills you need to develop & showcase to succeed as an AI PM - sure to accelerate your learning AND clarity on your path forward! (🔗 Link in comments!) #AICareers #ProductManagement #TechTalk #CareerGrowth #AIProjects

Question : Why is #AI modeling just the tip of the iceberg❓ Many aspiring product managers and entrepreneurs are drawn to AI modeling, believing it to be the primary driver of success for AI-powered solutions. However, seasoned AI experts know that the actual modeling work represents only a small fraction of the overall effort required to build a successful AI product. In reality, AI modeling accounts for roughly 20% of the job 😯 . The remaining 80% involves a complex web of supporting systems and processes that are essential for transforming AI capabilities into tangible business value. From data collection and preprocessing to model infrastructure and deployment, each of these components plays a critical role in the AI product lifecycle. 👉 Data is the lifeblood of any AI system, and the effort required to source, clean, and prepare data for modeling can often exceed the time spent on the modeling itself. 👉 Once the data is ready, the next challenge lies in building the infrastructure to support the AI models. This includes everything from cloud-based compute resources and storage solutions to model versioning, monitoring, and scaling capabilities. Effective model deployment and integration with the broader product ecosystem are also essential for delivering a seamless user experience. 👉 Beyond the technical aspects, AI product managers must also consider the operational and organizational factors that contribute to the success of an AI-powered solution. This includes establishing processes for model governance, managing model drift, and ensuring ongoing model maintenance and retraining. 💡 Quick Tip: Adopt a systems-level perspective when developing your AI-powered products, ensuring that you allocate resources and attention across the full spectrum of requirements, not just the modeling component. #artificialintelligence #productmanagement #datascience #productmanager

Revolutionize Your Product Management Game with AI 🚀 As a Product Manager, you're responsible for developing and managing products that meet customer needs. That's why it's important to stay up-to-date with the latest technology trends. Here are some ways AI can help: 👉 No Code/Low Code AI AI platforms like Sway AI, Dialogflow and Hugging Face allow non-technical users to build and deploy AI models without extensive programming knowledge. Some real-life use cases include: creating personalized product recommendations, developing chatbots and analysing product usage data. 👉 Explainable AI As AI becomes more prevalent in product development, it's important to ensure that decisions made by AI algorithms are transparent and explainable. Tools like IBM Watson OpenScale and Microsoft Azure Machine Learning make it easy to monitor and interpret the decisions made by AI models. 👉 Sentiment Analysis Sentiment analysis algorithms can help you gauge how customers feel about your product by analyzing social media posts, reviews, and other customer feedback. From my personal experience, training data with MonkeyLearn has been a game-changer! 👉 Pricing Strategies AI tools like Sniffie and Accenture's Solutions.AI helps product managers determine the optimal pricing strategy for their products by analyzing customer behaviour and market trends. What other AI trends are you looking forward to?💡 #ai #data #technology #machinelearning #productmanagement

There are almost as many openings for AI Product Managers as there are for Data Engineers (8000 vs 8400). What does that tell us about the next 2-3 years of AI? Businesses are using curated datasets and building products to monetize them. Jobs are where the hype ends, and companies invest in what they believe will move the business forward for 2-3 years. Businesses are realizing that data is a novel asset class with quantifiable value. Each new curated dataset makes the business more valuable and increases the opportunities it can take advantage of. What’s novel about data is that one dataset can be monetized multiple times. One dataset powers multiple products without ever being depleted. The more use cases and models it supports, the more valuable it is. Business leaders are looking to cash in, and that’s an opportunity for people who can help. There aren’t enough AI Product Managers to meet the demand because we’re not training them fast enough. Product Owners, Data Scientists, and Data Leaders are pressed into service but aren’t given the frameworks to succeed. Internal promotions are a great way to fill the talent gap, but only if new AI Product Managers are given the training they need to deliver the new revenue business leaders expect. My book and courses teach lightweight frameworks for AI Strategy and Product Management. They help support new AI Product Managers and I’m happy to help business leaders develop their internal talent pipeline. You’ll find more insights and resources on my website. Click the link under my name to get started.

#AI isn’t a free lunch and data science isn’t alchemy that spins data into gold - but economies of scale and scope do exist with the right vision, strategy, and operating model: “Each new curated dataset makes the business more valuable and increases the opportunities it can take advantage of. What’s novel about data is that one dataset can be monetized multiple times. One dataset powers multiple products without ever being depleted. The more use cases and models it supports, the more valuable it is.” #datascience #strategy #innnovation #dataengineering