The best way to think about improving AI for customer engagement comes from Neil Hoyne's approach on Ecommerce Coffee Break. He flips the question around: instead of starting with the technology and asking "Where can we put AI?", you should start with a specific, existing business problem and ask "Can AI solve this?". This "problem-first" framework keeps you grounded in real-world needs rather than chasing shiny objects.
The first step is to clearly articulate the problem you're trying to solve. As Hoyne puts it, you might find yourself saying, "I wish I could get customers more immediate responses. We don't have enough people that we can pay in the call center. We need to service these people at volume." These are perfect problems for AI. Maybe you need to provide 24/7 support, scale up for Black Friday without tripling your headcount, or automate repetitive inquiries to free up your human agents for more complex issues. Mike Wu from Monos, for example, talked on the DTC Podcast about using AI specifically to solve the problem of scaling customer service efficiently.
Once you have a well-defined problem, you can evaluate AI tools as potential solutions. If your problem is providing instant answers to common questions, an AI-powered chatbot might be the answer. If your goal is to increase average order value, you could use an AI personalization engine to deliver tailored product recommendations, a strategy Richard Harris of Black Crow AI discusses. The key is that the business need dictates the technology, not the other way around. This ensures you’re making a strategic investment rather than just experimenting.
After you've identified a potential AI solution, the next step is what Tim Peter often discusses on Thinks Out Loud: ensuring the implementation is actually helpful for the customer. A customer-centric approach to AI adoption is critical. Will your customers use this tool? Does it make their experience better, or does it just add frustrating friction? For instance, Payne Glasses found success using AI to enhance their customer experience by scaling personalized interactions, but they also emphasize the importance of maintaining a human element. The best AI applications feel intuitive and genuinely valuable to the end-user.
The place this framework breaks down is in building your brand's core identity and emotional connection. While AI is fantastic at solving logistical and operational problems, it can't replicate the humanity, empathy, and unique voice that makes people fall in love with a brand. As Tim Peter also warns, an over-reliance on AI can lead to depersonalization. The problem-first framework is for optimizing engagement and efficiency. It is not for creating the soul of your brand. You must still find a balance between automation and human touch, using AI as a tool to empower your team, not replace them entirely.