Why a Smart IVR Is Harder to Make Work Than Most Realize

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If AI could get tired of being talked about, it probably would by now. Instead, it’s getting shoehorned into just about every facet of everyday life, and people are even starting to expect AI features to come standard in most consumer-facing products and digital services. 

Among those services are smart (or intelligent) IVRs, which are traditional interactive voice response phone systems that have incorporated AI to improve the user experience whenever a customer calls a company’s customer support line. 

Smart IVR systems primarily leverage AI to support advanced, automatic call routing and a long list of self-service options. Additionally, smart IVRs employ natural language processing (NLP) so that callers can speak to the system more naturally and receive personalized assistance. In most cases, this is already a huge step up from chatbots that provide a limited number of canned responses.

However, despite the benefits, many companies still cling to traditional IVRs because of how much hard work it can take to implement a more intelligent one. Not only do you have to train your smart IVR model according to your company’s offerings, products, and resources, but you also have to set up the educational components of the system so that callers will understand how to navigate and use it.

Reasons Why A Smart IVR is Difficult to Launch and Maintain

A smart IVR doesn’t come with perfect decision-making and accurate responses on its own. Instead, you must build, cultivate, engineer, and combine its various components into a single sophisticated entity before it can react with human-like intelligence.

Nevertheless, the steps necessary to design a system that responds like a person are wicked hard—and there are many reasons why a smart IVR is difficult to implement and sustain. 

A smart IVR must be trained

For your smart IVR to carry out intelligent, human-like functions, you must train the AI model on which it is built. This often requires the use of multiple datasets in order to perform its automated alchemy of smart decisions. 

In general, training an AI model is a relatively advanced process, especially when it involves language modeling and machine translation—of which both are needed for IVR question-and-answer procedures. Moreover, building an innovative system like a smart IVR requires acquiring enough data to teach its AI to behave in ways that resemble how a human (who works at your company) would respond.

1. The data must be harvested

You never know what kind of random questions a customer will ask your system. In order for your smart IVR to be able to respond to them, a training process must first feed it with many words, paragraphs, and sentences of things people are likely to say—especially in the context of your specific company’s products and services. 

Therefore, training your model involves gathering and labeling the proper training data. As the common adage in computer science goes, “Garbage in, garbage out.” Consequently, it is necessary to acquire high-quality data if you hope to get high-value, reliable results. 

If you start out with a small sample size of data, you can expect the results to be fairly inefficient or incomplete. Similarly, improving the performance of your system’s deep learning algorithm requires drastically increasing the size of your training data. 

Admittedly, just finding labeled data for AI can be hard enough, but when it involves NLP, you must also grapple with building datasets that contain various languages, dialects, and topics. Therefore, not only can you be hampered by insufficient training data, but you can also be bogged down by all of the resources and effort it takes just to make sure your AI can parse various inputs. 

At the end of the day, the size of the data set needed to train an AI model to perform above a given threshold can be substantial, and the larger the dataset, the more costs you can incur.

2. The AI model must be trained

Since customers will be asking your smart IVR for answers, it should already have accurate responses in its arsenal. This means you’ll have to curate the data first—including collecting, creating, cleaning, indexing, normalizing, organizing, and maintaining it—to ensure the system can adequately process customer requests. 

Additionally, you must also annotate the data before you train it, which involves labeling the data with relevant tags so your training model can understand and interpret everything. Once again, the larger the data size, the more labor-intensive the process will be. 

As for the actual process of training an AI model, this can be done with supervised or unsupervised learning models and algorithms. Supervised training uses labeled targets in its data, while unsupervised training learns patterns and clusters unlabeled datasets. 

Fortunately, once your smart IVR has been trained via datasets, it can continue learning as more customers use it. This is called reinforcement learning. Until you’ve reached this point, however, your system may not be very effective at using its AI features such as predictive call handling.

For example, there are many ways that someone can say, “I need to check my account balance.” Regardless of how they say it, what you need your smart IVR to do is understand their request, ask them authentication questions, and then take them to their account without human intervention.

3. Assembling the right technical team

Understanding your data and the problems you are solving with it is paramount in guiding your system to handle its automated tasks. This knowledge requires both machine learning and deep learning expertise. 

Your engineers and development team must understand how to architect a natural language processing and deep learning system. This includes performing operations like fine-tuning hyperparameters and applying regularization to prevent overfitting. 

For example, if a customer says, “I was trying to check my account balance and I realized I had a more important issue,” you need your system to be sophisticated enough not to jump ahead and start the authentication process for dealing with account balance inquiries. 

Naturally, working professionals with such expertise are highly sought after in today’s market, so you must be prepared to pay a premium price to hire them.

The challenge of understanding context

While your objective is to add a much-needed human touch to your IVR system, understanding context can pose an additional challenge. This is because human language is rife with complexity and ambiguity, meaning the same words can signify different things depending on the context.

A smart IVR’s conversational AI relies on both natural language processing (NLP) and natural language understanding (NLU) to operate. While NLP is more widely known, NLU is also vital to the natural communication between computers and humans. In AI models, NLU intersects with NLP as a subset that enables the AI model to draw inferences from given information.

One of the challenges you’ll face when designing a smart IVR system is to understand and balance the needs of NLP and NLU. While NLP focuses more on machine learning and deep learning techniques, NLU makes sense of natural sentences by extracting context and meaning through semantic and syntactic analysis. 

In other words, NLP is primed for text analysis and language translation, while NLU’s expertise lies in speech recognition and sentiment analysis.

Keep in mind that the globalized environment in which most businesses operate is bound to encounter a wide variety of colloquialisms and foreign languages. Therefore, since the internet is a global community, NLU must be able to deal with challenging multilingual situations and the code-switching instances they bring. 

The challenge of rooting out bias in AI datasets

Bias is a significant problem in AI, both in its underlying data and interpretation, so you’ll have to confront it if you choose to implement smart IVR. In addition to your data, bias can also creep into the algorithms used to process your data.

In practice, bias can undermine customer confidence in your smart IVR. Therefore, you must understand the strategies for mitigating biases in your AI deployments, whether through data selection, modeling, or other forms of curation. 

Fighting bias can be challenging since algorithms have grown more complex, and the vast amount of data typically required to train a deep learning model accurately is vast. Meanwhile, one of the main problems with bias is that it is often considered too late in the process. 

You can try to combat this by rigorously ensuring bias doesn’t creep in during your data-gathering process from the very beginning. For example, remove any data samples that can be flawed by underrepresentation or overrepresentation of certain demographic groups. A conspicuous red flag is when your training data triggers higher error rates for particular groups. 

It is also vital to note that while following ethical processes is commendable for reducing unfairness, your AI models may still pick up on biases unintentionally. Consequently, it is critical to address bias even though you think you may have followed the right protocols in establishing your smart IVR system. 

A Smart IVR’s Success Depends On People’s Ability to Use It

Even when you manage to overcome the initial barriers of creating a smart IVR, your problems aren’t over. Ultimately, the maxim that claims the human element to be the weakest link in a chain also applies to smart IVRs.  

Customers must carefully construct and adequately tailor their inquiries 

While AI and NLP add a human touch to an otherwise formulaic system, it can be a double-edged sword. Just because you have natural language processing doesn’t mean callers can say whatever they want and expect it to understand them. 

There are several reasons why your smart IVR may not understand customers, such as the following:

  • The caller doesn’t know how to use a smart IVR or how it operates. If your system isn’t user-friendly enough, it may not overcome this obstacle. 
  • The caller has an accent or dialect the system isn’t familiar with. Even if your product targets local customers, there’s still a chance your IVR system encounters non-local dialect speakers it can’t understand.
  • The caller’s speech pattern or cadence is non-standard. Humans aren’t monoliths, so even if they come from the same geographic area and share similar cultural and linguistic influences, their speech may not be uniform enough for the system to react accordingly. 
  • The caller has an advanced vocabulary. If your natural language processing model has been trained on data with a certain threshold, such as intermediate-level speakers, it may not be able to handle a more advanced vocabulary level.
  • The caller doesn’t understand the product well enough to communicate their issue. Without a working knowledge of your product or services, they may not be able to provide enough details for the system to extract a proper response. 
  • The customer speaks freely with multiple intentions. When you allow customers to talk openly, they may ask for several things simultaneously, hampering the ability of the IVR to figure out the best thing to do first.  
  • Frustrated callers acting irrationally. If a customer interacts with a conversational IVR system without patience and level-headedness, the system may not be able to understand their requests. 

Customer education and training

For your smart IVR to work effectively, you must teach your customers how to use it—especially if it’s a new system. This requires extra effort to guide and assist customers at crucial touch points throughout the system, showing them what they can say and how to communicate with the IVR to get the desired results. 

If necessary, you may need to create training videos and write detailed FAQ pages to demonstrate the variety of ways callers can educate themselves on the nuances of your smart IVR system. 

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