HomeBusiness NewsEnhancing Customer Experiences with AI Modeling 

Enhancing Customer Experiences with AI Modeling 

AI Modeling: A Transformative Force

The rapid ascent of generative AI, more specifically ChatGPT, into the mainstream is revolutionizing the business landscape at a pace that can leave even seasoned leaders bewildered. If you’re finding yourself daunted by the realm of generative AI, you’re not alone in this maze. Across various industries, individuals find themselves at diverse stages of their journey into an ever-evolving arena of AI and are struggling with integrating AI with their operations. Moreover, many are wrestling with where to initiate their exploration of AI modeling.

Crucially, it’s vital to grasp that there’s no one-size-fits-all approach to constructing AI-driven systems. Determining the right blend of technology and model types for a particular use case is pivotal from both an efficiency and cost-control standpoint. By harnessing the capabilities of generative AI, coupled with conversational and traditional AI models, alongside the automation prowess of robotic process automation (RPA), businesses can craft holistic solutions that supercharge the customer experience (CX).

Whether customer support with top BPO services providers, BPM companies, or American contact center services– customer-centric businesses are bound to align their operations toward more AI-driven customer journeys, and it starts with the right AI modeling and solutions for your customers.

Deciphering AI Models

Picture an AI model as the instruction manual accompanying a Lego set. Just as a Lego manual meticulously guides you through the assembly of a race car, an AI model functions as a set of instructions instructing a computer on how to analyze data and arrive at decisions.

AI models can be likened to possessing an extraordinarily intelligent Lego set—comprising cars, buildings, flowers, and more. Imagine prompting it to design a building resembling a flower. It uses its knowledge of building structures and flower appearances to generate a new Lego creation. It creates a flower-shaped building, an entity that doesn’t exist in reality.

On the other hand, traditional AI is akin to a Lego classifier. It’s trained to recognize individual Lego constructs. Show it a Lego creation, and it can accurately categorize it as a building, flower, and so forth. However, it struggles when confronted with unconventional amalgams, such as a building with floral attributes, something previously not seen by users. Both traditional and AI models for specific operations are used in CX management and can be utilized in offshore, onshore, and American contact center services to enhance processes and operations. The top BPO service providers are bundling AI applications with their outsourcing offerings and wide integration possibilities.

Edge of Conversational AI

Conversational AI can play a central role where the objective is to develop a system for users to find or craft their own model that suits their own functions or operations. Conversational AI is typically designed to analyze and understand human text and speech. Therefore, it can conduct meaningful interactive exchanges with humans and much more. For instance, if a user requests to see Lego cars, it can leverage traditional AI’s existing classifications to provide a list of extant car models for that user, but it stops there.

However, conversational AI can delve deeper. It asks questions like, “What is the age of the user who will be constructing the model?”. Furthermore, it utilizes this information to refine the results for its users further. A more advanced version with generative AI can create a unique creation like the aforementioned flower building. It can actualize the user’s vision. Moreover, it can make it more seamless by integrating other technologies. For example, when a buyer is prepared to make a purchase, you can introduce RPA bots to streamline backend processes. Moreover, this integration can help buyers, e-commerce employees, and contact center staff complete the purchase and dispatch the order efficiently and faster.

The Right AI Modeling Approach

In instances where generative AI holds sway, selecting the appropriate large language model (LLM) can substantially affect the cost-effectiveness of the solution. While larger and more advanced generative AI models like Bard and ChatGPT, boasting billions of parameters, might appear enticing, analyzing whether your use case necessitates all that training data is essential. Gathering and cleaning such an amount of data, implementing data annotation services, and training a model with such a large amount of data need resources and time.

Smaller, more budget-friendly models often yield comparable results, rendering them ideal for specific applications. For example, you can develop an AI application for analyzing the quality of calls and contacts and collecting customers and process insights for all interactions happening in your offshore or American contact center services to optimize them.

Evaluating and Selecting the Right AI Approach

Businesses must evaluate the unique requirements of each task to arrive at a judicious choice. Here are five key considerations when determining the suitable AI modeling approach:

  • Does the use case entail generating intricate and new content from existing data?

If so, generative AI is indispensable for this task.

  • Are precision and high accuracy critical in content generation to satisfy the needs of this specific use case?

Here, generative AI could be used. However, conversational and traditional AI can offer a more cost-effective and accurate solution for performing tasks in this use case.

  • Is advanced or predictive analytics essential for accomplishing a client’s tasks in a use case?

Therefore, for conducting the operation for this use case, traditional AI would be the more suitable and cost-effective choice.

  • Does the use case require accurate analysis of substantial amounts of structured or tabular data?

Here, traditional AI performs better when the inputs are clearly defined and explicit in nature. Moreover, data ideally should be devoid of any ambiguities and human nuances. Moreover, adopting data annotation services from an experienced provider can be a wise choice when training AI models more effectively with datasets.

  • Is a clear understanding of the decisions of the model required by the use case?

Here, it is possible that traditional AI techniques with their rule-based or interpretable AI are better suited than LLMs with their opaque models for accomplishing your tasks.

AI Modeling and CXM

By weighing these diverse variables, organizations may decide how best to match the AI model’s capabilities with their unique use cases and limitations that are influencing tasks and operations. Finally, it will enable them to reap the best out of their organizational AI journey. Therefore, utilize the right AI models and applications to uplift the experiences of all stakeholders and your CX services in your business and offshore, onshore, or American contact center services. Now, any progressive BPO company in the USA and worldwide is integrating AI into their operations and offerings for their clients.

Utilizing Several AI Models

Each of the three types of artificial intelligence—generative, conventional, and conversational—represents a different technology with its own special benefits, strengths, nuances, and best practices. Businesses may advance their CX by integrating these technologies with intelligent automation tools like RPA. Offshore, onshore, American contact center services are increasingly adopting intelligent and cognitive automation, fusing AI, RPA, and other technologies to level up their CXM and service quality.


Holistic Solutions for a Future with Greater Connectivity can be achieved by integrating these technologies with a comprehensive customer-centric CX strategy. In a nutshell, your customer experience may enter a new age by integrating generative AI, conversational AI, and RPA. However, increasing CX maturity requires careful model selection, a delicate balance between automation and human contact, and the design of all-encompassing solutions for BPM, BPOs, and offshore or American contact center services.