Expensive training costs
Training AI models is time-consuming and costly. The cost of a single training session for GPT-3 is estimated to be around $1.4 million and, for some larger LLMs, the training cost ranges from $2 million to $12 million. Even in basic scenarios, ongoing training of AI models requires infrastructure that supports ingesting and feeding the right context into LLM models so they can generate high-quality answers while remaining cost-effective at a large scale.
This can be a challenge, especially for smaller brands. Think about how often your product SKUs are updated, the new iPhone launches and the new line of Nike’s. Keeping up isn’t as easy as you may think.
Lack of personalization
LLMs are not designed to get to know customers better over time. In an in-store scenario, a sales clerk can “read” the customer’s intent based on the choices they make and what items they are gravitating towards in a store. Some of these can even be nonverbal signals, such as the shopper’s facial expression or body orientation during the selection process.
Generative AI tools may be stumped by complex problem-solving situations, such as a distressed customer, but beyond that, they also cannot factor in the history of the customer’s interactions to make contextually relevant recommendations. LLMs have a vast amount of linguistic knowledge, but they lack behavioral data, which can result in opportunity costs or be off-putting. Unless a shopper specifically shares their preferences, there is bound to be a lack of personalized interactions, which can negatively affect the brand experience.
Privacy and security vulnerabilities
Customers may be sharing sensitive information, such as personal or financial information in an ecommerce interaction. Where is the shared information stored? Where might it pop up next?
A lot can go wrong here if the proper data protection measures aren’t taken. A company would need to have the right security infrastructure in place (i.e.: a cloud play with a common data source, ingestion and secure framework).
A cautious balance of pros and cons
So, how should brands weigh these downsides in the context of making decisions to leverage what generative AI has to offer their customers?
Consider the sensitivities in your brand’s space. If your brand has to contend with strict standards, regulations and other elements where sudden changes can impact customer safety (i.e.: medication, safety equipment, etc.), it is important to consider the lack of data freshness seriously in your decision-making about generative AI tools. An antique jewelry company may find the freshness of information less critical than an aircraft or car parts manufacturer, though both could theoretically utilize the LLM’s linguistic abilities for customer service without relying on their internal memory.
