Why AI Isn’t Working for Everyone

Digital code behind a digitized face

Welcome back to our series with Nicholas Wegman, Ph.D., Senior Director of Artificial Intelligence and Alex Barnes, Senior Director of Product Management, as they continue to discuss the science behind Artificial Intelligence (AI) and reveal how it can specifically increase retail/CPG margin. In the second part of our three-part series, we’ll delve deeper into the data science of AI, and why it’s not working for every business.  

In our last article, we explored some of the transformative capabilities of AI, specifically how they relate to retail and CPG industries. AI can analyze large datasets, automate mundane tasks, and improve business decision-making processes. We also discussed AI-powered demand forecasting and planning and how it can be used to streamline the buying process, manage in-season markdowns more effectively, and provide accurate demand forecasts. All of which ultimately translate into stronger margins.  

Today we’ll look at the data science behind AI and answer the question “how does it work”. But first, let’s examine the ethics of AI, and why some see concern. In light of some recent ominous headlines, there’s still some trepidation surrounding AI. By understanding the concerns and using what we can leverage from the emerging data science, we can develop both confidence and discernment when utilizing AI. 

The Issue of AI Ethics 

Recently, there have been several articles that call into question the ethics of AI. Take this article, describing when a law professor was named on a list of those who’d committed sexual harassment. It cited an article in which OpenAI chatbot named a law professor on a list of legal scholars who had sexually harassed a student. It gave evidence citing a supposed March 2018 article in the Washington Post. But the article didn’t exist. Who do you hold accountable? How could AI cite something that doesn’t exist? Is this a reason to avoid AI? 

While this is one extreme example, a more subtle is from a colleague who prompted ChatGPT to find an optimal reorder point. The answer it gave was close, and to a non-retailer could have appeared correct, but was slightly off. ChatGPT is not ideal for calculations but seemed to have been able to generate reasonable reasoning for this problem given the likelihood that it had encountered similar problems in its training data.  

So how do you ensure that you’re getting accurate answers that don’t cause you, at minimum, to reveal a lack of expertise, or at worst, falsely accuse someone of a crime? Understanding that the data you put in, is the key to accuracy and confidence in the tool that you’re working with.  

’Know Before You Go’—Can AI Work for You? 

To understand AI or Machine Learning (ML), first consider that it’s not a “one-size-fits-all” tool.  According to an article in Dataconomy, there are several types of AI models, including supervised learning, unsupervised learning, and reinforcement learning. These models enable AI systems to recognize patterns, classify information, and make decisions based on data inputs.  

This article from DZone, reviews the top 10 most popular AI models— the algorithms and mathematical representations that allow machines to learn from data and make predictions or decisions. As DZone explains, "by selecting the appropriate model for a particular task or application, developers and data scientists can create powerful AI systems that can automate tasks, improve efficiency, and provide insights that would be difficult or impossible for humans to obtain through manual analysis." 

To select the right model, there are several key topics to understand: 

  1. Data collection and preprocessing: Before we can build AI models, we need to gather relevant data and clean it. This may involve handling missing values, dealing with outliers, and normalizing data to ensure that the input features are on a similar scale. 
  2. Exploratory data analysis (EDA): This is the process of visualizing and summarizing the data to gain insights, identify patterns, and formulate hypotheses for further analysis. EDA helps us understand the underlying structure of the data and informs the selection of appropriate machine learning algorithms. 
  3. Feature engineering: This involves the process of selecting and transforming the most relevant variables, or "features," from the raw data to improve model performance. Feature engineering may include techniques such as dimensionality reduction, feature scaling, and feature extraction. 
  4. Machine learning algorithms: These are the core of AI systems and can be divided into three main categories – supervised learning, unsupervised learning, and reinforcement learning. Some popular algorithms include linear regression, decision trees, support vector machines, clustering algorithms, and neural networks. 
  5. Model selection and evaluation: Choosing the right model and evaluating its performance is crucial. This involves splitting the data into training and testing sets, selecting the best model based on performance metrics (e.g., accuracy, precision, recall), and fine-tuning the model to achieve optimal results. 
  6. Model deployment: Once a suitable model has been developed, it must be deployed for real-world use. This may involve integrating the model into existing systems, monitoring its performance, and updating it as new data becomes available. 
  7. Ethical considerations: AI systems have the potential to impact society in significant ways. It is important for data scientists to consider the ethical implications of their work, such as fairness, transparency, and privacy.

However, if companies CPG and retailers don't ask the right business questions or have inaccurate data, they can use AI incorrectly—and it won’t work the way they intended. Here are some hypothetical examples for CPG and retail concerns: 


  1. Incorrect Data Inputs: A CPG company may try to use AI to predict consumer demand for every location and SKU, but if the data inputs are incorrect or incomplete, the predictions may be inaccurate. For example, if the company doesn't consider local events or promotions that may affect demand, the AI system may not be able to accurately predict demand in that location. 
  2. Overreliance on Legacy Solutions: CPG companies may be using legacy solutions that aren't able to keep up with the demands of modern business. If they rely too heavily on these solutions, they may miss out on the benefits of AI. For example, if a company is using a legacy demand forecasting solution that can't handle large datasets or provide real-time updates, they may miss out on opportunities to improve their margins. 
  3. Lack of Understanding of AI: CPG companies may not fully understand how AI works or how to use it effectively. For example, they may not understand the importance of data quality or how to select the right AI model for a particular task.


  1. Failure to Ask the Right Business Questions: Retailers may fail to ask the right business questions, leading to inaccurate or irrelevant data inputs. For example, if a retailer is trying to predict omnichannel demand for all products across channels and time, but they don't consider changing market conditions or customer preferences, the predictions may not be useful. 
  2. Inaccurate Data Inputs: Retailers may also face challenges with inaccurate data inputs. For example, if a retailer is trying to ensure that they have the right product, at the right place, time, and price, but they're relying on inaccurate or incomplete data, they may miss opportunities to improve their margins. 
  3. Lack of Integration with Legacy Systems: Retailers may also struggle to integrate AI with their legacy systems. For example, if a retailer is using an outdated inventory management system, they may not be able to take advantage of the benefits of AI-powered demand forecasting or pricing optimization. 

To avoid these pitfalls, it's important for companies to approach AI strategically and with a clear understanding of their business needs. They should take steps to ensure that their data inputs are accurate, that they're using the right AI models for their specific tasks, and that they're integrating AI effectively with their legacy systems. 

In the final installment of our series, we'll explore practical case studies in the retail and CPG sectors to highlight AI's significant impact on a company's profits. We'll analyze the essential elements that businesses, specifically CPG and retailers, require to implement, adopt, and execute AI technologies effectively. 

To learn more if AI and ML solutions are right for your business, contact us