Mani Grewal’s Post

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Digital Transformation Leader: Solving Chaos in Product Engineering | Specializing in IaaS, PaaS, and SaaS | Driving Excellence Across Health, Retail, K12, and Auto Sectors | Expert in Cross-Functional Leadership

As a seasoned Engg and Product Leader , I'm often asked: Is AI right for my product? There is NOT a simple "yes" or "no" answer to it! Because, It requires a very careful assessment and evaluation. Do not just blindly follow the train, till you calculate everything around it to aboard. Let's understand with some examples : -Assessing AI for the product means, understanding its potential benefits and drawbacks. Take, e.g., the 📊Recommendation Systems. AI-powered recommendations can significantly enhance user experience by personalizing content. However, they require vast amounts of data and sophisticated algorithms, which may not always be feasible or necessary for every product. -On other hand, let's consider 🔒Cybersecurity. AI has changed the way of threat detection, enabling real-time analysis of complex patterns to identify and mitigate risks. In this case, integrating AI isn't just beneficial but essential for staying ahead of evolving security threats. -Now let's look at chatbots: While AI-driven 🤖 chatbots can automate customer support and streamline interactions, they may "still" fall short in handling nuanced Q/A or providing empathetic responses. In such cases, a human touch might be more effective. -On the other hand, in industries like healthcare , 🏥AI-powered diagnostic tools have demonstrated remarkable accuracy in detecting diseases from medical images. In this domain, the benefits of AI in improving healthcare outcomes outweigh concerns about privacy or algorithmic biases. 👩🎓 As a Technical Product Manager, my role is to guide 🗺 product decisions based on rigorous analysis and strategic foresight. Before jumping on the AI bandwagon, let's critically evaluate its relevance and potential impact on our product's success. When deciding 🛠️whether to incorporate AI, it's imp to weigh in factors like data availability, computational resources, and the problem domain's complexity. Blindly following the AI trend without proper evaluation can lead to wasted resources and missed opportunities for innovation. It is also 🎯crucial to educate leadership authentically, about the true status of AI research and its impact on consumers. Blindly saying "yes" to every AI implementation request, isn't beneficial to informed decision-making. By providing a balanced view of AI's capabilities, limitations, & risks, we can foster discussions grounded in reality. Ultimately, empowering leadership with knowledge and fostering a culture of critical thinking enables us to make responsible decisions aligned with our product's goals & values. 💡 What are your thoughts on incorporating AI into product development? 👩🏫 How do you approach educating leadership about AI in your organization? Share your strategies and insights in the comments below #ProductManagement #AI #TechInnovation #DataDrivenDecisionMaking #StrategicThinking #Engineering #Leadership #AI #Leadership #ProductStrategy #EthicalAI #ContinuousLearning #InformedDecisionMaking

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