When you hear about AI in the news today, most of the conversation revolves around future ideas on how technology can be used, but many people use AI daily and don’t even think about it. Consider those of us using Apple’s FaceID to open our phones or using a voice assistant like Alexa or Siri; that is AI working its magic. Voice assistants leverage natural language processing and AI-driven generators to return answers to us.
The use of AI in solving business problems has a profound impact on relationships with customers, employees, and the broader community—both positively and with some challenges. Here are just a few:
- Enhanced Personalization – AI allows businesses to offer tailored experiences, from personalized product recommendations to predictive customer service.
- 24/7 Support – AI chatbots and virtual assistants provide instant responses, improving accessibility.
- Faster, More Efficient Service – AI speeds up transactions, reduces wait times and offers better self-service options.
However, we must be mindful of some risks, including losing the human touch via an over-reliance on AI. There are also trust and data privacy concerns; customers worry about how AI collects and uses their data.
Business leaders should use AI to enhance, not replace, human interactions (e.g., AI can handle FAQs, but humans step in for complex issues) and ensure transparency in AI-driven decisions and data collection.
Businesses generate massive amounts of data daily. AI also helps process, analyze, and extract actionable insights from this data faster and more accurately than humans alone. This leads to better strategic decisions, increased efficiency, and competitive advantage.
In my industry, AI is transforming the way retailers handle excess returns. Rather than applying a one-size-fits-all approach, AI enables retailers to assess product conditions, analyze return patterns, track customer feedback, and gauge market demand in real-time. This allows them to determine the most profitable next step—whether that’s restocking, dynamically pricing resale items, routing to refurbishing facilities, donating for tax benefits, or recycling materials.
AI is also used for fraud detection and smart return approvals. Businesses are working to leverage anomaly detection models to flag suspicious return patterns (e.g., customers frequently returning high-value items or returning worn-out products as “defective”).
Retailers are using AI-powered automation, to categorize returns based on legitimacy, fast-tracking genuine returns while requiring additional verification for flagged cases. This strategy has shown potential to cut fraudulent returns by 25% or more, saving millions annually.
We have seen AI’s role in returns management extend beyond optimizing logistics—it also helps predict and prevent returns before they happen. By leveraging SKU-level data, retailers can proactively identify high-risk products, optimize product listings, and refine inventory decisions to reduce returns. This transition from reactive returns processing to predictive returns prevention is key to improving efficiency and customer experience. By leveraging AI, retailers can better align inventory with peak demand, reducing overstock and improving overall efficiency
By collecting detailed data—such as images, videos, and purchase history—at the moment of return, AI can make instant decisions, reducing reliance on customer service agents.
AI-powered returns systems can monitor requests in real-time to analyze patterns and identify fraudulent activities. For example, frequent returns on high-value items or mismatched purchases from a specific customer segment can be identified early, allowing retailers to take action or flag high-risk customers.
AI can help businesses support sustainability efforts by reducing waste throughout the returns process. For example, it can help the business determine which returned items are suitable for resale, donation, or recycling, moving them through the process more efficiently. Brands can also use AI to get eco-friendly packaging recommendations for return shipments, aligning with consumer demand for greener practices.
However, business leaders can still be hesitant to adopt AI because of fear, misconceptions, or lack of understanding. Here are just a few examples:
AI Will Replace All Jobs. AI augments human work rather than replaces it. While some repetitive tasks are automated, AI creates new job opportunities in AI management, data analysis, and innovation.
AI is Only for Big Companies with Huge Budgets. AI is more accessible than ever through cloud-based AI tools, no-code platforms, and affordable AI-as-a-service models.
I recommend that most companies start small, use no-code/low-code AI platforms (e.g., Zapier AI, OpenAI API), and experiment.
AI Needs to Be 100% Perfect to Be Useful. No AI is perfect, but it can significantly improve efficiency and decision-making. Businesses should focus on incremental improvements rather than perfection.
AI Can Work on Its Own Without Human Supervision. AI needs human guidance to be effective. Training, monitoring, and ethical oversight are required to prevent biases and errors.
Businesses are already using AI to make data-driven decisions with real-time predictive insights. This included AI-powered forecasting models will predict demand, optimize inventory, and reduce waste.
My advice is to educate yourself and your team. Misconceptions often stem from a lack of knowledge. Business leaders don’t need to become AI experts, but understand its basics can remove fear. There is tons of information and resources available. There are many free AI courses, lots of AI-focused newsletters, and webinars and conferences covering AI in many ways.
