AI Current Opportunities For Better Decisions And More Shopping

The question of how to leverage AI puzzles all of us. How will AI help retailers perform quicker, better, and safer. Such questions pose challenges, so much so that questions are being raised in the Congress of the United States since AI may invade privacy. This is a new phase of technology that both tantalizes and challenges retailers.

A recent NRF study asked how generative AI is revolutionizing retail. There were some good examples – one notably for big ticket retailing. For instance, furniture retailer Ashley Global Retail has used generative AI to target promotions to those customers most likely to make a purchase.

Beyond that, Generative AI can significantly impact the retail industry in many other ways and provide numerous benefits. One of the ways AI can transform retail is through greater personalization. Generative AI algorithms can analyze consumer behavior and preferences data to create personalized consumer experiences. It frequently pays off in a purchase.

AI, and all its different forms, can be confusing so we will look at the topic in a few ways.

Here are some definitions of AI as defined by Dynatrace
DT
:

1. Predictive AI: provides continuous forecasting and anomaly prediction across multidimensional baselines, application traffic and service load. In doing so, it takes into account all seasonality and patterns.

2. Causal AI: Analyzes observability and security data in the context of topology information groups anomalies, pinpoints root causes and priorities based on business impact automatically and ad hoc.

3. Generative AI: This is fueled by both Predictive AI and by Causal AI. Dynatrace’s Co-Pilot creates queries, notebooks and dashboards to simplify analytics and provides workflow and automatic recommendations.

I then checked Google and found these descriptions for AI. Overall, AI is a form of a form of machine learning that is able to produces text, ideas, images and other types of content

Traditional AI focuses on performing the task at hand more intelligently. It reviews available input data, learns from it, and then makes decisions or predictions based on the analysis of everything it has reviewed. The goal is to find the best option for completing a task, making a decision, etc. but it does not create anything new.

In contrast, Generative AI takes AI to the next level. It enables users to quickly generate new content based on a variety of inputs and outputs to share models for new actions. It can include text, images, sounds animals, 3D, models or other type of data. Chat GPT, DALL-E, and Bard are examples of generative AI applications that produce text or images based on user-given prompts or dialogues.

The Key Difference between Traditional and Generative AI:

Traditional AI systems are primarily used to analyze data and make predictions, while generative AI goes a step further. It creates new data similar to its original data to generate additional predictions. In other words, traditional AI excels at pattern recognition, while generative AI builds upon that and excels at pattern creation.

David Dorf, a world-wide specialist who focuses on retail at AWS, presents another way to think about the use of AI. He wrote in his brilliant essay that there are four main areas where using the power of AI can leverage natural language interactions. They are:

A. Enhanced chatbots that enable customers can to ask more complex questions about their orders and product recommendations. This can ultimately lead to more sales and higher customer satisfaction as well.

B. Summarizations could provide bulk data like weekly sales, inventory reports, and more, while providing a summary. These summaries can help management understand business activity and lead to better decision-making.

C. Real -time language translations could bring international users to the website. A more global customer base can generate more sales.

D. Enhanced search will be possible by allowing complex requests to be posed that can then providing detailed results.

Professor Carm Taglienti of Insight Enterprises
NSIT
points out that we are still at an early stage with all of this AI and there are likely great benefits as refinements evolve. He acknowledged that many people question the accuracy of AI procedures and worry that AI could make erroneous decisions, but the overall benefits should outweigh these possible issues.

POSTSCRIPT: Looking ahead, seeing that there will be to new ways to speed information to the customer is exciting. Retailers must rely on experts to help them develop systems that will communicate new, targeted promotions to customers in language that will hit home with them. It does not have to be a promotion based on price, but could easily promote a fashion idea for the home. What’s important is that the marketing efforts deliver messages that entice a customer to take action. As Professor Taglienti said, we are at the infancy stage and more improvements will come in the next few years to make Generative AI a valued aide.