Most technology platforms have made significant investments in varying degrees of AI features, with a significant focus on Generative AI to match the hype of AI in 2023 and 2024. However, many organizations are left wondering, "What use cases will work for me, and what best practices and pitfalls should I pay attention to?"  

In this blog, we'll touch on some of the most common use cases and how to best get started to maximize the value of your AI investments. 

Unique Features of Generative AI 

Generative AI leverages Large Language Models (LLMs) to understand natural language inputs and, more importantly, the intent of the input and respond with useful and relevant content. This could be a large, unstructured data set consisting of common text but also images, code, sound, and more. The data sets available to it are what it bases its output on, in conjunction with how LLMs will decide what to respond to an input with. In short, a "response" or "output" is generated to an input - creating tremendous productivity gains for organizations that properly set up their datasets and LLMs.  

Generative AI Concerns  

The main concerns of GAI are: 

Vulnerabilities for Businesses 

One of the largest concerns with Generative AI and any new technology is the vulnerabilities it introduces to businesses. For example, sensitive data such as personal health or identifying information could be generated outside normal controls and data governance if LLMs and applications are not configured properly.  

Privacy and Regulatory Issues 

Privacy and regulatory issues are also of major concern due to the large data sets GAI and LLMs leverage to produce responses. When you factor in various states, countries, and types of data all having varying laws, rights, protections, and so forth, it is critical to establish strong and transparent GAI policies and governance.  

Accuracy Concerns 

Finally, accuracy is a major concern because responses are only as good as the data sets used to produce them. Quality of development and a deep understanding of intended usage also come into play here. This means continued human monitoring and evaluation of GAI performance must exist to avoid fabrications, misuse, liability, and other mishaps, with the intent of continuously improving.  

As market product offerings for GAI mature, organizations will leverage third-party and in-product features built to implement immediate governance and compliant processes to safeguard the organization against the above concerns at a minimum. This accelerates the adoption of GAI and shortens the time to value realization. However, these features and controls are a launch point, and it is best to seek expert services to ensure a successful GAI journey.  

What Can Generative AI Do for My Business? 

Below are a few basic, industry-agnostic ways to rapidly leverage GAI to drive value in your organization, with future blogs covering industry-specific use cases and deep-diving into critical value areas per industry. For now, let's look at a few industry-agnostic start points for rapid value, piloting GAI, and adoption: 

Customer Service - Outreach, Complaints and Resolution 

In a world where 75% of transactions are conducted digitally, organizations are still evolving to meet the demands of today's digitally dominant marketplace. In the absence of modernized customer service solutions to support today's market, more and more customer service agents are being hired to address customer service inquiries, emails, support tickets, etc. 

A common use of GAI is integrating a knowledge base of common customer questions, issues, and resolutions with a virtual assistant to enable a more proactive customer service operation. This drives business value from a productivity perspective, reduces the time to respond to common issues around the clock, and provides agents with a "friend over their shoulder" to drive successful call resolution.  

Any issues not solvable by the application can be escalated for agent response, meaning a more efficient customer service operation at less cost. Many organizations pilot GAI in this area to build a foundation for more revenue and customer-focused solutions, as discussed in the two topics below.   

Sentiment Analysis for Customer Churn and Attrition 

In today's digitally dominant market, customers seek differentiated experiences and convenience, making brand loyalty and customer attrition more volatile than ever. In response, GAI can be leveraged to proactively address retention risks by leveraging key data points to generate applicable offers, payment plans, or other solutions to reduce.  

For example, if you are a cell phone service provider, and your customer is chatting with a bot upset because they cannot afford their phone insurance replacement cost of $300 but have no issue with their service, the system could proactively generate a payment plan over 6 months or an early upgrade option depending on their payment history and alleviate the issue in a way that does not cause the customer to leave the organization today.  

In this case, a customer is likely to switch carriers to take advantage of switch-and-save promotions to overcome this issue, often leading to organizations losing market share they never had a chance to retain due to a lack of proactive systems.   

AI-Powered Marketing and Sales 

Another common way to leverage GAI is by analyzing customer interactions with your organization, such as behavior, spending patterns, product selection, and what they are searching for, to generate more precise marketing campaigns and personalized experiences/offers to drive sales.  

Leveraging GAI in this manner also reduces the cost of securing new and expanding existing businesses due to increasing campaign conversion rates. This can also be leveraged in personalization to take recent searches, conversations, product views, and so forth to generate tailored offers and drive a differentiated experience.