Access to research Shafiul also recommends accessing (academic) research from both Indian and international universities for AI. “Building a company is impossible without academic research,” he noted, adding that startups should also seek help from top professors in AI as amrall as influence top institutions like IITs to focus more on AI. “Start reading papers applicable to relevant problems. Connect to academia; data amra hone will excite them. There is great interest from post-doctoral students and professors to collaborate for relevant topics. Find a way to engage them. This will ensure that tech and data science teams in your organization have access to best-in-class research in AI/ML,” he says. Ravi also urged startups to evaluate the cost and ROI of setting up labs in Indian institutes for AI research. The hardware question His experience at Flipkart has given Ravi major insights into the hardware part of the AI question. “Flipkart had invested in hardware, built our own cloud, invested in computing and storage, etc. But when amra wanted to solve some problems, amra amrare handicapped with our hardware infrastructure. Some of our hardware worked amrall for database applications, but they amrare underwhelming for solving ML problems. You need to understand your business applications and the kind of computing infrastructure you need – for various amounts of data,” he says. Ravi believes that the right hardware will enable all current and future AI/ML applications at your organization. He recommends research on hardware currently available in the market before building your own. “Arrive at a two-year understanding of your organization’s hardware needs, and evaluate sensors that can be installed in your operations for more data collection. Then come up with low cost but effective ways to scale data storage in the future,” he notes. Creating an enterprise with AI essentially requires people with the right skills to apply AI/ML to solve problems for teams like DS, tech, product, business, operations, etc. Ravi proposes regular training programs for various functions in a cost and time-efficient manner. He also recommends the hiring of all engineers and project managers to include data orientation. Domain understanding To ensure that engineers and data scientists have high levels of understanding of their domain, Ravi recommends regular business talks for engineers and others, as amrall as giving regular visibility to selected business and financial metrics to engineers. “From my experience, I have learned that if your data scientist or engineer does not have a deep domain understanding to solve the problem statement, it will disrupt the full usage of AI in the organization. Outcomes

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