ChatGPT’s rapid rise to household name is reshaping business faster than many business leaders can keep up with. If you’re feeling overwhelmed by generative AI, you’re not alone. Across industries and sectors, people are at varying stages of maturity in their broader AI journey, and many don’t know where to start with AI and AI modeling.
What’s important to understand is that building AI-powered systems is not one size fits all. Determining the right kind technology and type of models for the use case is crucial from both an effectiveness and cost control perspective. By harnessing the power of generative AI, alongside conversational and traditional AI models and robotic process automation (RPA), businesses can create comprehensive solutions that supercharge the customer experience (CX).
What Is an AI Model
Think of an AI model as an instruction manual for Legos. Just as a Lego manual provides step-by-step instructions to build an F1 race car, for example, an AI model is like a set of instructions that a computer follows to analyze data and make decisions.
Generative AI models are the equivalent of having a super-intelligent Lego builder who has studied and learned from every Lego instruction manual ever created—cars, buildings, flowers, etc. So if you prompt it to create a building in the shape of a flower, it uses this knowledge of what a building looks like, how it’s constructed, and what flowers look like to generate a new Lego set of a flower building that doesn’t exist today.
Traditional AI on the other hand is more like a Lego classifier who has been trained on individual Lego creations. If you show it a creation, it can accurately determine if it’s a building, flower, etc., because it has been trained on what those creations look like. However, it would struggle to accurately classify something more unconventional, like a building shaped like a flower, since it has elements of both and is something unseen before.
If you wanted to create a system for users to find or design their own Lego models, you could also leverage conversational AI to facilitate a conversation with the user. Conversational AI is designed to understand human text and speech in order to hold interactive conversations with humans. If the user asks to show them Lego cars, it can leverage the existing classifications done by traditional AI to bring back a list of existing car models.
Conversational AI can also ask additional questions like, “How old is the user who will be building the model?” and use those results to further narrow the results. If the user asks to develop a custom creation like the flower building above, the system can leverage generative AI to make that creation. Once a user is ready to make a purchase, you can automate back-end processes by using RPA bots to make the purchase and send the order.
Selecting the Right AI Modeling Approach
When developing AI-powered systems, measuring outcomes compared to cost becomes extremely important. Generative AI is powerful, but not always the best solution especially when considering how expensive it can be to build and maintain. In many cases, traditional or conversational AI is more than sufficient for the desired outcomes.
In cases where generative AI is the right approach, selecting the appropriate large language model (LLM) can make a massive difference in the cost to run the solution. While larger and more advanced generative AI models, like Bard and ChatGPT that have billions of parameters, may seem appealing, does your use case really need all that training data? Often, smaller and more affordable models can deliver comparable results, making them ideal for specific use cases.
By evaluating the requirements of each task, businesses can select the technology that strikes the right balance between outcomes and cost. Here are the five things you need to consider when it comes to determining which type of AI modeling to use:
- Does the use case require generating complex new content from existing information? If so, generative AI is necessary.
- If generating content, does it need to be precise and highly accurate? Generative AI may be applicable, but conversational and traditional AI may be more appropriate and cost effective (and more accurate).
- Does the use case require advanced or predictive analytics? Traditional AI would be more appropriate and cost effective.
- Does the use case involve analyzing large amounts of tabular or structured data? If those inputs are well defined, traditional AI is more appropriate.
- Does the use case require clear interpretability of the model’s decisions? Rule based or interpretable AI, like those found in traditional AI approaches, may be more appropriate than the black box models of LLMs.
By evaluating these factors, you can make an informed choice that aligns the capabilities of the AI model with your specific use cases and constraints.
Leveraging Multiple AI Models
Generative AI, traditional AI, and conversational AI are distinct technologies, each with their unique focus, benefits, technology, and best practices. By combining these technologies with intelligent automation solutions like RPA, businesses can evolve their customer experience for the future.
Here is just one example of how these technologies can be used together to achieve a more impactful CX.
Holistic Solutions for a More Connected Future
Integrating generative AI, conversational AI, and RPA holds the key to taking your customer experience to the next level. But to reach a greater maturity in your customer experience, you’re going to have to select the right AI models, strike a balance between automation and human interaction, and create holistic solutions.
Generative AI can help jumpstart improved customer service, bridging the gap between current service levels and ever-rising customer expectations. Embracing AI technologies will become ever more critical for businesses seeking to remain competitive and thrive in the digital landscape. So, you better make sure you get that AI model right.
Start connecting to your AI future with one of our generative AI solutions.
Luke Moss
Senior Director, Text Analytics
