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AI adoption should never feel like a “set it and forget it” strategy. Instead, it should be approached as a way to solve real business problems. It starts with asking questions like: What are the most time-consuming tasks for the team? What frustrates customers the most? For instance, a restaurant might automate table reservations and menu queries, while a small online retailer could deploy AI to handle order status updates or product recommendations. These targeted use cases ensure that AI adds
AI adoption should never feel like a “set it and forget it” strategy. Instead, it should be approached as a way to solve real business problems. It starts with asking questions like: What are the most time-consuming tasks for the team? What frustrates customers the most? For instance, a restaurant might automate table reservations and menu queries, while a small online retailer could deploy AI to handle order status updates or product recommendations.
These targeted use cases ensure that AI adds tangible value without overwhelming operations. Take the example of Driva, a fast-growing online finance broker that deployed AI-powered customer service tools to answer routine enquiries and provide immediate assistance while customers wait in the call queue. By automating common interactions, Driva reduced the volume of requests requiring human intervention and achieved a 5% uplift in conversion rates at key points in the customer journey.
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