AI use cases and limitations in customer support: brief overview

There are several ways of implementing AI in customer support, including automating responses, personalizing interactions, and deriving business insights.

However, it's important to recognize AI's limitations, such as the need for specialized training and human supervision.

Tasks that AI can handle in customer support

AI applications for customer support encompass a variety of scenarios and approaches, including the following use cases and solutions.

Response automation and semi-automation. The solutions: AI assistants

AI can handle routine customer queries through completely automated responses. This frees up human agents to focus on more complex issues. CoSupport Customer is a good example of this with its AI-based autonomous solution for handling customer service interactions on any channel, including email, social media, and chat.

Semi-automated solutions assist agents by providing suggested replies as the CoSupport Agent does. You integrate the solutions with Zendesk or any other CRM to speed up responses and enhance your agents’ efficiency.

Such solutions reduce operational costs and allow agents to maintain higher levels of customer satisfaction.

AI-powered actions. The solutions: AI assistants

AI-powered actions in customer support enable businesses to automate various account and order-related tasks. These include:

• Managing account and subscription changes: updating email addresses, payment information, and account preferences; adjusting subscription plans, pausing services, or changing billing cycles;

• Automating orders and payments: cancelling or modifying orders based on customer requests; processing refunds, exchanges, and resolving payment issues automatically.

By implementing these AI-powered actions, businesses can improve operational efficiency, reduce manual workloads, and deliver faster, more accurate customer support services.

Customer service personalization. The solutions: recommendation engines

AI-powered recommendation engines analyze customer behavior, preferences, and past interactions to offer personalized solutions or product suggestions. This personalization improves the customer experience, making interactions feel more relevant and tailored to individual needs. By leveraging AI, businesses can boost sales, improve engagement, and build stronger customer loyalty.

Business analysis and insights derivation. The solutions: BI assistants

AI can analyze vast amounts of customer data to extract meaningful insights about customer preferences, pain points, and emerging trends. These insights help businesses:

• Improve their products;

• Optimize support strategies;

• Enhance operational efficiency.

By leveraging AI’s ability to process data efficiently, companies can make informed decisions that enhance overall business performance.

AI’s limitations in customer support

The question “Will AI be able to create a better version of itself?” is debatable. However, at the moment, AI can only realize its full potential with humans’ skills and supervision. Understanding this fact is key to avoiding pitfalls with AI in customer support.

Limitation 1. An ability to self-tune and be focused on a particular domain

Basic (foundational) models like Open AI’s ChatGPT 4o or Google’s Gemini 1.5 Pro need extra training to become intelligent in a particular domain. Without additional “knowledge,” AI provides responses that are too generic and don’t involve specific context, terminology, or nuances needed to accurately address customer queries.

The additional training provides the following solution’s abilities:

• Improved accuracy in responses;
• Adaptation to brand tone and style;
• Handling specialized terminology;
• Contextual understanding in customer interactions;
• Enhanced ability to handle multi-step conversations;
• Increased response relevance to customer queries.

A representative example of an AI assistant proficient in customer service is CoSupport Customer, an AI-powered solution tailored for customer support tasks, capable of working autonomously with high response accuracy and relevance.

Limitation 2. The ability to self-assess and improve

AI isn’t aware of whether it acts correctly or not. An AI assistant can’t assess its own responses' relevancy and accuracy, and it won’t recognize when improvements to its responses are required.

That’s the point when human supervision is needed. It includes the following tasks:

• Reinforcement learning with human feedback (RLHF) during training - enhancement of response accuracy based on human feedback;
• The evaluation of the quality of extra-training and its outcome — response accuracy and alignment with a request’s context — during set-up and testing stages;
• The evaluation of AI performance after changes — for example, after adding new AI assistant functions, the implementation of innovations in AI architecture, or changes in algorithms;
• Regular monitoring of the AI solution’s performance to ensure accuracy, and relevancy, and maintain high efficiency.

By incorporating these human oversight tasks, businesses can ensure that their AI assistants continue to evolve, and deliver high-quality customer interactions over time.

Limitation 3. Limited decision-making and independent action capabilities

AI is a dynamic system, meaning it doesn't rely solely on fixed rules and algorithms, but instead learns and adapts over time based on data and interactions. However, AI operates within the limits of the algorithms and instructions provided by humans.

Algorithms are defined by the following elements:

• System prompts — pre-configured instructions embedded within the AI to set the scope and boundaries of its responses;
• Response templates — predefined response formats or templates that shape how AI structures its outputs;
• Action triggers — predefined conditions or events that initiate a specific action by the AI or system, such as escalating the issue to a human agent.

Sometimes, ad-hoc or unexpected situations can occur, where the predefined rules don’t account for certain variables. In such cases, AI may behave incorrectly or fail to make optimal decisions.

To avoid potential negative impacts in these ad-hoc cases, it’s important to have human oversight in place to step in when needed, ensuring the AI’s actions are reviewed and corrected when necessary.

Possible AI implementation mistakes to avoid

As we’ve found out, AI is a powerful yet controversial technology in the sense that if its capabilities are overrated while the limitation is underestimated it could result in inefficiencies.

So now, let’s take a straightforward look at the common pitfalls with AI in customer service focusing on the ways to avoid potential bottlenecks.

We’ll elaborate on two groups of AI implementation mistakes — strategic and technical — since they require different approaches to deal with.

We’ll also focus on the ways to avoid these pitfalls.

Strategic pitfalls

AI is built by humans. Thus, certain pitfalls are related to how management approaches AI implementation that isn’t strong enough.

Too general or poorly defined goals

“I need an AI solution for my customer support” is an example of a goal that is too general. The more specified the goal is, the better the AI solution’s efficiency will be. Moreover, it’s crucial to recognize that the company’s internal expertise is invaluable in creating a clear AI implementation strategy.

How to avoid: set goals and define use cases for AI implementation clearly.

While outlining goals with precision, the answers to the questions listed below are vital.

Which solutions do you need? For which use cases will you use AI?

The irrelevant choice of use cases can cost you valuable time, resources, and hinder the overall effectiveness of your AI solution.

Let’s look at the two examples:

• The choice of the solution. Let’s say you’re considering providing your customer support with an AI assistant. What level of automation would you like to set? For up to 100% automation, your solution is a chatbot like CoSupport Customer. If you need assistance for your agents, the optimal option is an AI assistant like CoSupport Agent.
• The choice of the use cases. For instance, you may decide whether AI should fully automate order cancellations or collect information and pass it to the support team for manual processing. Another example is determining if AI will only provide responses based on your company’s knowledge base or if it will engage leads by encouraging them to book calls or leave contact information.

While full automation and extended AI functions might seem ideal, it's essential to evaluate each use case carefully. For example, full response automation saves time; however, semi-automation might provide more nuanced customer care, especially for complex cases.

Similarly, extending AI’s capabilities doesn’t always lead to an improvement in customer service quality. Engaging leads and encouraging actions like booking calls, in particular, may not always be beneficial, if a lead isn't at the right stage of the buyer’s journey or a customer prefers self-service options or asynchronous communication like email.

In conclusion, the success of AI in customer support depends on selecting the most relevant use cases. By carefully aligning AI capabilities with your business needs, you ensure that the implementation delivers real value, improves efficiency, and enhances customer satisfaction.

Which channels will you use AI in (website/application chat, email, social media, communication tools like Slack)?

Identifying the communication channels ensures you tailor the AI to your customer support unique interaction style. It then won’t matter if it’s real-time chats or asynchronous email responses, the style will match. This is important because each channel requires different configurations for response structure, tone, and escalation processes.

Which type of communication do you need: textual, voice, or visual?

The communication type defines the technological requirements for the AI, such as:

• Natural language processing for text;

• Speech recognition for voice;

• Image recognition for visual inputs.

Different communication types also require distinct development approaches, and if you don’t specify this, it could lead to a poorly optimized or incomplete solution.

The vision of AI as a self-sufficient entity

As we've come to understand, AI cannot build and improve itself. Overestimating its dynamic and self-managed nature can actually diminish the performance of your AI solution, leading to unmet expectations and ineffective results.

How to avoid: effectively manage the setup of the AI solution and continuously monitor its performance.

The more concise the company's vision of real AI capabilities, the higher the chances they can build a solution that meets or exceeds expectations.

There are several points in which the company and the team of ML engineers should have a clear vision to build an efficient solution:

• The expected AI capabilities (is an AI assistant expected to answer FAQ-like, complex queries, or both types)?
• What data should be used for generating responses (e.g., customer records, knowledge base, or external data sources)?
• What is the optimal response structure (for different types of queries, at different stages of a conversation)?
• What are the algorithms of AI assistant’s actions for different types of queries?
• How should the AI respond to ad-hoc situations, such as unexpected queries or issues that fall outside of standard procedures?
• Which issue types should be escalated?
• Which systems should the AI integrate with, and how should these integrations be utilized (e.g., CRMs, ERPs, or third-party APIs)?

Clients typically do not have immediate answers to these questions, so the solutions should be developed in close collaboration with the CoSupport AI ML engineering team.

Usually, companies do not have immediate answers to these questions. The answers can be found in collaboration with the ML engineering team — as is the case when CoSupport AI develops the solution for a company.

Human participation is important not only during the AI solution setup phase.

In cases of significant changes to the algorithms, monitoring the solution's performance is essential. Examples of such situations are as follows:

• The need arises to modify the length or structure of responses;
• The list of tasks for the AI assistant expands (it now needs to check order status based on its number or customer ID);
• A data conflict occurs due to the expansion of the internal or public knowledge base.

In summary, building an effective AI solution requires a clear understanding of its capabilities, continuous collaboration with ML engineers, and ongoing human oversight to adapt to changing needs and ensure optimal performance.

Technical pitfalls

Technical pitfalls are related to the technical requirements dictated by the AI nature.

The lack of data or its inconsistency

The response quality depends heavily on data quality — it’s a core principle of AI model training.

Let’s consider the development of an AI assistant for response automation as an example.

To train the model, the following sources of data are used:

• Correspondence with customers;

• The public knowledge base (help center with help articles, FAQs, product manuals, etc.);

• The internal knowledge base (internal instructions, including SOPs for agents).

The AI response quality decreases if your data is:

• Poorly structured (disorganized articles in the knowledge base or blog posts):
• Not exhaustive (main issues aren’t described in the instructions):
• Ambiguous (two solutions for the same issues are described differently in the knowledge base and an SOP):
• Outdated (changes that took place aren’t reflected in the data sources).

How to avoid: Provide the AI development company with data that meets the ML engineers’ recommendations mentioned above. It's also vital to continuously monitor the data used to ensure it remains consistent and up-to-date.

Regular updates to the data sources, such as knowledge bases and customer records, ensure that AI responses remain accurate and relevant over time.

The lack of focus on customer support

A solution based on a popular commercial LLM like ChatGPT 4o needs fine-tuning to be able to provide responses deeply oriented to the company’s products/services and policies. Moreover, you have to use prompt engineering to improve AI’s ability to grasp context and provide accurate and company-related responses.

The pitfall is to rely on off-the-shelf solutions exclusively and underestimate the need to adjust the solution to the company’s needs.

How to avoid: Carefully choose the solution that meets your business needs to avoid AI implementation errors. The key to success lies in developing a custom solution. AI performs best when it undergoes proper initial setup, including model fine-tuning and prompt engineering by skilled ML engineers. It’s crucial to work with vendors who have proven experience in creating AI solutions tailored to customer support needs.

Conclusion

Successfully implementing AI into customer support requires more than just adopting the latest technology — it demands a clear strategy, solid data, and a thorough understanding of your business needs.

By addressing both strategic and technical pitfalls early in the process, you can avoid common pitfalls with AI implementation and ensure that AI enhances customer experience and drives tangible business outcomes.

CoSupport AI is here to help you navigate this complex journey, tailoring solutions that are fine-tuned to your company’s unique goals and challenges. With the right approach, AI can unlock unprecedented value for your customer support operations.