Contact Center Voice AI: Where Most Businesses Go Wrong

Conversational IVR systems can interact with callers in a natural format, responding to their spoken queries instantly, and helping to guide them towards the right solutions. Intelligent IVR systems and chatbots enhance the customer experience, and speed up issue resolution times, also acting to reduce the number of conversations agents need to manage each day, improving operational efficiency. AI’s integration with predictive analytics is changing the way contact centers approach customer support, shifting from reactive to proactive service models. Instead of waiting for customers to reach out with problems, AI-powered systems can anticipate potential issues based on patterns in customer data, enabling businesses to address concerns before they escalate.

Organizations must recognize that this large group cannot be trained easily, may be unpredictable, and it is nearly impossible to make them adhere to the standards and processes embedded in the technology. Of course, this raises concerns around bias, hallucination, and the accuracy of bot-human interactions. As such, companies will continue to invest in data and AI governance to mitigate risks.

GenAI in Healthcare

A recent Verint report found that brands leveraging AI for self-service are up to twice as likely to improve self-service containment rates and first contact resolution rates across both digital and voice channels. Additionally, the report indicated that 76% of businesses that are not currently using AI for self-service plan to do so over the next year. AI’s ability to improve self service options, streamline operations, enhance personalization and reduce response times is transforming how businesses engage with their customers, making each interaction more efficient and effective.

With the advent of AI-backed IVR, however, these automated voice systems are lowering call center wait times, assisting with unique caller problems, and improving overall customer call center and contact center efficiency rates. AI analyzes past customer interactions and uses extrapolative analysis to predict the wants and desires of a customer. Additionally, AI integrated into an IVR system can tap into contact center agent training data to learn how to handle routine tasks and typical customer inquiries. AI can then direct callers to the information they require or the customer agent that can best handle their needs. Particularly through large language models (LLMs), generative AI augments the capabilities of virtual agents and chatbots, enabling them to interpret and respond to customer queries with greater accuracy and nuance. They use advanced AI technology to elevate call center interactions by providing a sophisticated analysis of voice tones.

Almost every day, new capabilities come to light, stretching far beyond helping school kids cheat on their homework and across into the customer experience space. Previously, they had to document these procedures step-by-step, script them, and create a slow, inflexible process flow. Now, AI can dynamically adapt and automate these workflows, improving efficiency significantly. In recent years, conversational AI vendors have brought various real-time translation models to market, with brands like Cognigy even making them available on the voice channel. “If GenAI helps create the very best self-service bots, this would inevitably create a situation where agents only receive the most complex cases,” he explains. In a recent interview with Aurélien Caye, Lead Solution Specialist at Sprinklr, we discussed the company’s innovative efforts and the impact of GenAI on customer service in 2024.

So how those conversations play out, plays a very, very important part of whether or not they will continue doing business with that brand. To conclude on this question, one of my favorite quotes, customer experience today isn’t just part of the business, it is the business. “Where I see the future evolving in terms of customer experiences, is being much more proactive with the convergence of data, these advancements of technology, and certainly generative AI,” says Traba. “Generative AI is revolutionizing customer experience by enabling personalized, data-driven interactions while dramatically enhancing contact center productivity,” he said. In parallel, interest will grow in a streamlined and unified orchestration engine that coordinates across AI models, systems of record, channels, and services used in multiple virtual agents to achieve their stated goals.

Boosting Customer Satisfaction

It can reduce operational costs, allowing agents to automate various tasks, and even provide insights into customer preferences and sentiment. Thanks to evolutions in artificial intelligence and automation, virtual agents can handle more requests for customers than ever before. However, there are still instances wherein the empathetic and creative support of a knowledgeable human agent is still essential. In these cases, AI solutions can help live agents work more efficiently, and resolve issues faster.

Somebody said they read a cool article, “We’ve got to use AI for that.” And yeah, you could use AI for that. But really you’re choosing a type of technology, or you’re choosing artificial intelligence, to solve a specific problem. VR in customer support, though less common than AR, offers a fully immersive environment where customers can interact with products or learn about services in a controlled virtual space. This can be particularly useful for product demonstrations, training or providing customers with a feel of a product before purchase. In addition, the integration of NLU and NLP with voice biometrics adds an additional layer of security and personalization, making voice recognition a powerful tool for customer identity verification.

What you can do is, instead of emailing a data analyst back and forth for a report, you could interact with generative AI. You could type a question to say, “Hey, who are my top 10 performing agents by sentiment, and what are their key skills that they are using in those interactions?” Then you can generate a report based off of that. So for typical goals in the contact center, these might be around measuring customer experience like CSAT, sentiment, first call resolution, average handle time, a digital resolution rate, digital containment rate.

These tools integrate with other platforms in the contact center tech stack and adhere to business leaders’ security and privacy standards. Advanced algorithms and machine learning techniques built into generative AI bots ensure these tools can evolve and adapt to new situations with minimum human intervention. Additionally, the ability to connect to CRM systems, customer data platforms, and business databases will lead to the development of more advanced, customized bots. And what this is doing is, it’s allowing decision-makers to focus more on their overall strategy and the overall experience that they’re delivering to customers. Rather than being very specific in emailing about a report, or even for agents to be able to type into a conversational AI interface that they can look for specific types of information, rather than searching everywhere for it. And what is really exciting that we’ve done is, we’ve used that type of technology to generate conversations and answers and information.

Many AI advocates claim that the introduction of better tools will improve other contact center challenges such as attrition. Individuals who project reduced attrition as a result of these types of tools should probably tamper some of their excitement…AI does not replace a poor supervisor or low pay. Looking ai use cases in contact center to the past, it is also difficult to determine whether the introduction of word processing and spreadsheets reduced attrition because they made individual jobs easier. A major challenge in effectively deploying voice technologies is the vast number of users interacting with IVRs, both outdated and modern.

The right Voice AI solution provider will help you to build and implement best-of-breed bots and systems with ease, and customize those tools to suit different requirements. They’ll give you the freedom to choose how you want to deploy your AI systems, and provide the back-end technology to ensure consistent quality. Look for a voice AI solution that supports a range of use cases, so you can scale your AI strategy over time, and unlock additional benefits as your business grows.

The company announced it is implementing an AI-based virtual assistant for the Dubai Police. The solution, done with AI provider Cognigy, aims to improve the citizen experience while removing much of the heavy lifting from the police department’s staff. Avaya bills the solution as “a significant step forward” for the business process outsourcing industry. It will enable Transcom to recruit agents based on what they know, not which languages they speak.

And I think that consistency plays a really powerful role in the overall customer experience of Starbucks’ brand. And when you think about the logistics of doing that at scale, it’s incredibly complex and challenging. If you have the data and you have the right tools and the AI, finding those gaps and offering more consistent experiences is incredibly powerful. Just consider how earlier ChatGPT releases of the LLM have undone the work of many analytics providers, which spent hundreds of hours engineering natural language processing (NLP) models to gauge intent, sentiment, and more. Looking forward, it will be interesting to see how internal AI tools such as speech transcription and agent assist transform and are utilized and accepted by individuals outside of the office.

This shortens wait times and increases the likelihood of first-contact resolution, which is a key differentiator for businesses in any industry. You’ve given your agents a measurement, a consistent measurement to deliver on your goal. And then three, you’re continuing to measure over time as you have more different interactions. So everything from how to ask good probing questions, to being empathetic, to taking ownership and resolving an issue efficiently. And I think that’s one of the pieces that helps in the day-to-day work for contact center agents.

AI is skilled at tapping into vast realms of data and tailoring it to a specific purpose—making it a highly customizable tool for combating misinformation. The company says the updated version responds to your emotions and tone of voice and allows you to interrupt it midsentence. However, as Trollope inferred, its successful integration depends on developing back-end integrations, alongside customizing the model to specific needs and ensuring accurate, context-aware responses. Yet, perhaps most pertinently, by opening up multimodal capabilities to a full audience of users, OpenAI takes us closer to real-time, cheaper AI in the enterprise. After all, one of OpenAI’s demos showed a GPT-powered voice changing styles on the fly.

In doing so, they collect information from various sources to inform and – across digital channels – draft agent responses to customer queries. Contact centers have leveraged tools for years to recommend next-best actions, proactively surface knowledge base content, and automate desktop processes. Then, GenAI will empower businesses to create more personal experiences at scale while improving reliability at the same time. Generative AI also surfaces more valuable insight into your CX performance and analytics. With the introduction of AI-powered contact summaries and Agent Scorecards, contact centers may access better insights into CX trends and agent behaviors.

From sentiment analysis to co-pilots to integration throughout the entire customer journey, the evolving era of AI is reducing friction and building better relationships between enterprises and both their employees and customers. Advancements in technology have been astounding, especially in relation to AI-powered tools. But, with these new technologies come more risk and a need to focus on AI ethics and transparency.

And then the third and final one just on this question is the really kind of rise of AI-driven journeys. Many, many years ago, you and I would call into a contact center, and the only channel we could use was voice. There’s social media, there’s messaging, there’s voice, there’s AI assistance that we can chat with. So being able to orchestrate or navigate a customer effectively through that journey and recommend the next best action or the next best channel for them to reduce that complexity is really in demand as well. Looking ahead, Traba foresees a shift to proactive and predictive customer experiences that blend both AI and augmented intelligence.

You can foun additiona information about ai customer service and artificial intelligence and NLP. They can use sentiment analysis to detect positive and negative feelings, and provide real-time insights to employees on how to de-escalate or improve a situation. They can even help with ensuring conversations are routed to the right employee, based on sentiment and intent analysis. Indeed, they’ll create a collaborative relationship between bots and agents, transforming employee and customer experiences at the same time while enabling organizations to drive improved agent-assisted and unassisted interactions. For example, businesses leveraging AI-driven platforms like Twilio Flex or HubSpot’s Service Hub are enhancing the way agents interact with customers. These systems use machine learning (ML) and predictive analytics to pull from customer data—such as previous purchases, interaction history, or browsing behavior—to deliver tailored solutions at every touchpoint. AI is at the forefront of helping businesses create highly tailored customer interactions by analyzing vast amounts of data in real time.

But among the chief dos and don’ts is, make sure you’re choosing AI that is specific to what your goals are. I would say very close second is making sure you’re choosing AI that is purpose-built for customer experience. Or purpose-built for, if you’re not in a contact center, whatever your specific type of organization does. So you can predict why a customer is calling, who and which agent they might best interact with. And you can use data kind of on both sides to understand the customer’s needs, and the agents, to direct the call so it has the best outcome.

It assesses how the call is going in real time and suggests the agents change or alter their tone while talking to the customer. One of the ways that organizations are doing this, they’re thinking about, we started with that IVR [interactive voice response]. By the time I get to item nine in the menu, I’ve usually forgotten what the previous items are. The integration of AR and VR into customer support signifies a shift toward more engaging, efficient and effective support experiences. In addition to improving customer satisfaction, self-service tools can lead to a reduction in support costs. They handle routine inquiries and issues that would otherwise require human intervention, allowing customer support teams to focus on more complex and high-priority tasks.

While generative AI might seem to be everywhere these days, it’s still a relatively new and complex concept that industry leaders are struggling to deploy and govern. As Gen AI and LLMs extend further into business landscapes, governments and institutions are taking action to protect users and customers. AI solutions can even leverage machine learning to make accurate predictions about call volumes and customer requirements. This helps businesses make more intelligent decisions about resource allocation and optimization over time. However, AI also comes with risks to consider, particularly in regard to ethics and security.

• They rely more heavily on algorithms for natural language processing (NLP), text to speech (TTS), and speech to text (STT).
• Agent after call work dropped by 35%, potentially enabling agents to handle more calls effectively.
• For instance, consider how many leading conversational AI vendors have augmented their solutions with image recognition (IR) to recognize entities within photos and make automated recommendations.
• Enterprises looking for best-of-breed solutions must be flexible to augment existing ecosystems rather than just rip and replace.
• Using advanced computer vision and voice analysis, AI systems have the capability to detect and analyze human emotions in real time.

Businesses under immense pressure to maximize their large investments in AI could be walking a thin line between improving the contact center experience and force-feeding the technology to customers. More than half of 5,728 consumers surveyed by Gartner indicated they would consider switching to a competitor if a customer service organization planned to use AI during interactions. The top concern acknowledged by 60% of consumers surveyed is it will be harder to reach a human, while 42% fear AI will provide them with the wrong answers. It plays a critical role in customizing experiences, retaining customers, building customer trust and securing brand loyalty. Many organizations now use virtual agents to answer routine customer queries, fulfill standard requests and handle simple problems over the phone or at company websites. More complex or unresolvable issues are usually handed off or escalated to a human agent to avoid a bad customer experience.

The innovation also inspires cooperation between quality assurance and coaching teams, who can create a connected learning strategy to bolster agent performance. With this, a QA leader can input simple prompts as to what a top-notch customer-agent interaction looks like on a specific channel. Generative AI solutions can now automate this process, shaving seconds from every contact center conversation and – therefore – saving the service operation significant resources. When a service agent ends a customer interaction, they must complete post-call processing. That typically involves uploading a contact summary and disposition code to the CRM system.

There’s a strategy that’s important to derive new information or derive new data from those unstructured data sets that often these contact centers and experience centers have. What actions did the agent take that either drove positive trends in that sentiment or negative trends? We’ve seen, certainly, a lot of recent news with the launch of Microsoft Copilot and other forms for copilots within the contact center and certainly helping ChatGPT App customer service agents. The reason driving that demand is the types of conversations that are getting to agents today are much more complex. How businesses integrate AI into their workflows will vary and depend on business needs. Perhaps you need conversational AI to understand the context of a user’s query, or generative AI to create unique, context-driven content within the structured business process Conversational AI is modeling.

What we’re seeing is that all of these solutions are not necessarily replacing people, but we’re seeing a lot of AI-adjacent or AI-augmented interactions in this contact center space that are coming into play. And that allows you to benefit from how those models and how that AI is built so that you can use something out of the box. You don’t have to build everything on your own, because that could be very time-consuming. And also creates some ethical dilemmas if you don’t have a large enough data set because your AI is only going to be as good as the data that it’s trained upon.