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Ryan Reynolds, ChatGPT, and The Reverse logistics mess

There was an advertisement on the internet that Ryan Reynolds creates using ChatGPT – an opensource AI chatbot from OpenAI that can generate natural language responses to user input. It is trained on a large dataset of conversations and can generate meaningful, relevant responses in a conversational context.  He asked the chatbot to create an ad from the voice of himself, he then read allowed the outputs of the AI engine.  I was astounded and amused so I decided to try for myself but regarding a topic I thought would be relevant to my network.  I wanted to see if ChatGPT Ryan Reynolds could write me a blog post, see for yourself, the issues are well highlighted with a touch of RR flavor.  This is also a demonstration of the new functions AI is presenting in our life.  This capability will change the way future generations learn, and how companies are able to function.  There is software, sortation, vision, and robotic solutions that can help solve for the repetitive tasks of returns processing.  Then there is AI and machine learning to improve the results.  I ran the model a couple times, changing the inputs slightly and taking the best outputs.  Mostly, I had to tweak the language inputs to keep it “PG” rated.  Although humorous, the instruction of some mild language got out of hand pretty quick. You’ll see this inspired from the original Ad shown below. Maybe I set Ryan aside but, is this how content will be created in the future?

Inputs to ChatGPT – “Write me a blog post in the tone of Ryan Reynolds on how returns are the retail industry’s quietly mounting logistics problem and how it could use AI and machine learning. Include facts and quantitative figures. Include some mild language”

Outputs from ChatGPT – Hey everyone, Ryan Reynolds here,

Let’s cut to the chase: Returns are becoming a major pain in the retail industry, and it’s time we do something about it. The numbers don’t lie: according to the National Retail Federation, returns are estimated to cost retailers $369 billion this year. That’s some major cheddar, a 25% increase from the previous year, and it’s only gonna get worse. 

But why? 

Well, for starters, people are buying more crap online than ever before. In 2018, e-commerce sales accounted for 10.9% of all retail sales, and that number is only projected to grow. But with this growth comes a surge in returns. A whopping 61% of online shoppers return at least one item per year, and 20% return more than five. That’s a lot of shit coming back. What’s even worse is that the process of dealing with returns is often costly and time consuming. It’s estimated that returns cost retailers an average of $25 per package. And on top of that, the process of processing a return can take up to 4 days. By using AI and machine learning, retailers can anticipate customer needs and tailor their return policies accordingly. They can also use AI and identify fraudulent returns and prevent them from happening in the first place.  

Bottom line is this: returns are a huge friggin problem for retailers, and it’s only getting worse. So, if you’re a retailer, the time to act is now. It’s time to get your return logistics in order, and start finding ways to make the process faster, cheaper and more efficient. Trust me: it’ll be well worth the effort.

Cheers, Ryan Reynolds

Auto-FC can help you use these, and other industry 4.0 tools to apply them to your reverse logistics problem.

Below is the original video that was referenced above:

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