An algorithm is only useful if it helps the business. A smart model without a real purpose is just theory. Here’s what I do: 1. Focus on key business goals—if it doesn’t help, it’s just extra code. 2. Balance accuracy with speed—a perfect but slow model is a problem. 3. Keep improving based on real-world feedback. 4. Make it easy to understand—if people don’t trust it, they won’t use it.

How do you align technology with business goals? Everyone will say collaboration, taking feedback, Understand Stakeholders but the real problem arises when there is Techie guy with chatGPT or bots, newcomers like me don't have real strategy we try to do what have already done: Either we combine historically done things and bring some good or we will try steps given from these AI bots "What I think strategy should be connect to different age groups and make that a team this might take time to come on same page but if we have intelligent brain with a brain using intelligence from new tech will be the best business strategy." Even for hiring if you have a young guy will know about new techniques to cheat and will help in better hiring

Some ways to do this is as follows - Align algorithm goals with business KPIs. - Test rigorously on data. - Involve stakeholders for strategic fit. - Prioritise revenue-driving features. - Use explainable AI for trust. - Ensure scalability and data privacy. - Monitor, adapt, and audit for bias.

If you consider your algorithm to be a tactic to help you reach your goals that align with your strategy, then updating and evaluating your algorithm should be done at regular intervals. A way to make effective changes without losing the vision is by asking key questions 1. Is my current algorithm meeting my goals? 2. Is there a better alternative or competitors doing better? 2. What changes can be made to make it better? 3. Will these changes align with the product vision? After asking these questions when you build and test an algorithm don't forget to ask experts in the domain for their opinions. For the best way to serve the mass audience is to first get approval of the industry experts.

Throughout my career, I’ve realized that growth happens when we step out of our comfort zone. There were times when I faced challenges—be it solving complex DSA problems, designing scalable systems, or debugging unexpected production issues. But every challenge taught me something new