AI is transforming contact centers at an accelerating pace. Speech AI technologies are at the forefront of this revolution, enabling companies to provide better customer experiences through a combination of advanced agent-assist techniques and fully automated interactions that feel natural and human-like.
This article explores the cutting-edge speech AI capabilities being deployed by leading contact centers and cloud contact center as a service (CCaaS) providers in 2024. We'll examine how speech recognition (ASR), natural language processing (NLP), call routing automation, real-time sentiment analysis, and seamless CRM integration are allowing contact centers to increase operational efficiency and customer satisfaction. You'll gain insights into the key technical considerations and real-world business impacts of implementing speech AI.
Looking ahead, we'll peek around the corner at emerging innovations—from hyper-realistic text-to-speech (TTS) to voice cloning and LLM (large language models)—poised to further disrupt the contact center landscape in the years ahead. Whether you're a technical architect or business leader, this article will prepare you to capitalize on the future of speech AI.
Having gradually evolved from modest call centers into sophisticated customer experience hubs, customer support platforms are known for being innovation pioneers. They were among the earliest industries to implement automation technologies, and are in the process of embracing a new AI-powered paradigm, poised to transform the sector once more.
To better understand how they got here and the challenges they face today, let’s take a quick look at the technological evolution of the sector thus far.
The earliest call centers emerged in the 1960s, with notable implementations by the Birmingham Press and Mail, which used Private Automated Business Exchanges (PABX) to manage high volumes of calls efficiently.
In the beginning, contact centers typically consisted of physical call centers with banks of telephone operators working from clunky handset terminals. These human agents relied on paper-based knowledge bases and legacy on-premises software to handle inbound customer inquiries.
The drawbacks were obvious: legacy systems struggled with high call volumes which ultimately impacted customer experience owing to long waiting times. Furthermore, siloing agents into individual phone terminals made collaboration difficult and kept institutional knowledge fragmented across the workforce. On-premises hardware was costly to maintain and upgrade as requirements evolved.
PABX technology eventually paved the way for more automated systems, which could handle multiple lines and calls efficiently. This reduced the need for manual switchboard operators.
Automatic call distribution (ACD) systems, also called call routing automation, emerged to provide basic call routing capabilities. At their core, these systems use algorithms to direct incoming calls to the appropriate agent based on predefined rules, such as agent availability, skill set, and call volume. Unlike true AI systems, they do not independently think or learn over time – but have significantly reduced the need for manual switchboard operators.
Fast forward to the last few decades, the rise of the internet and mobile communications ushered in a new era. Contact centers had to support multiple channels beyond just voice, including email, webchat, SMS, and social media.
This omnichannel environment added complexity but allowed for more flexibility and customer convenience. They had to implement new technologies and processes to queue, route, and respond to all these disparate interactions across different platforms in a timely, integrated manner. However, even as contact centers evolved to support multi-channel communications, many still operated in silos with inefficient manual processes.
While omnichannel was gaining wider adoption on one hand, data was quickly moving to the cloud on another. The subsequent rise of cloud computing proved to be a pivotal catalyst, empowering the rise of CCaaS (contact center as a service) solutions delivered via the cloud. CCaaS providers like NICE, Five9, Genesys, and others introduced digitally transformative solutions with robust self-service, AI/analytics capabilities, open cloud platforms, and elegant administration portals.
Early AI experiments were deployed by the CCaaS pioneers, aimed at driving assistance through real-time conversational analysis, next-best-action recommendations, sentiment tracking, and integrated knowledge bases. Early robotic process automation (RPA) also helped to automate tedious after-call tasks, with predictive analytics being increasingly leveraged to provide a more customized, proactive customer experience.
While an increasing number of CCaaS providers are positioning themselves as AI-first, it would be premature to imply that latest developments in AI are having a truly meaningful impact on CCaaS deployments just yet. While GenAI is expected to see incremental adoption over the next years, the AI maturity index is still not what’d expect.
Here are some of the barriers preventing more widespread adoption and integration today.
One of the biggest obstacles is the technical debt and legacy infrastructure present at many large enterprises with established premise-based systems. After all, ripping and replacing entire tech stacks is extremely disruptive and costly. What’s more, security and compliance requirements add to the increasing complexity.
According to research by Metrigy, only 29.5% of major global contact centers have fully transitioned to cloud-native CCaaS platforms that enable easy integration with modern AI services. Among those, however, who have already transitioned to the cloud, 48.2% are planning to or already shifting to a different cloud platform by 2024, according to the same study.
Overhauling outmoded data architectures, re-tooling business processes, and the necessary organizational change management are huge undertakings. 40% of contact centers have faced similar challenges transitioning to a cloud environment, while 70% believe CCaaS can significantly elevate their business success.
On the human side, contact center employees often face high turnover, stress, and fatigue from the high-volume nature of their roles. Experts often emphasize the need for effective training and change management when rolling out new technologies. Conversational AI, while powerfully disruptive, raises key questions around authority and human-AI workforce management.
Customer reservations about being "put through the algos” and speaking with non-human agents can be another important impediment. While more personalization and handle time efficiency is expected, the primal emotional need for speaking to someone human often remains largely unmet.
The relative immaturity of speech ai is still perceived as an issue. While the technology has advanced considerably – especially with the democratization of Large Languages Models (LLMs) since the release of GPT by OpenAI – production-grade speech recognition, conversational AI, and related voice capabilities are not yet seamless. Low confidence scores, bias issues, contextual challenges, and suboptimal user experiences stifle adoption.
Customer reservations about being "put through the algos” and speaking with non-human agents can be another important impediment. While more personalization and handle time efficiency are expected, the primal emotional need for speaking to someone human often remains largely unmet.
Security, privacy, and compliance concerns with AI create additional regulatory hurdles. Many businesses also rightly worry about the "black box" nature of AI models and lack of transparency, especially in the absence of specialized AI staff in-house to oversee the adoption.
Cost optimization also remains an overarching challenge for contact centers and BPOs, who find themselves under constant pressure to do more with less through a combination of staffing efficiencies and greater automation.
AI investments can be perceived as significant, which makes proving hard ROI essential. Across all these multifaceted challenges lies the immense technical complexity of voice itself as an interface — from managing accents and languages to background noise, utterances, and more.
Today, AI in customer service platforms is being integrated at all steps of the customer journey. Speech AI technologies in particular – capable of understandings speech in multiple languages, analyzing the conversation, and responding in real-time – can directly address many of the key challenges contact centers face today.
The outcome of all of the above is a dramatic increase in operational efficiencies, agent satisfaction and improved customer experience due to reduced handle times.
While AI cannot fully replace humans for more complex issues, it is becoming a force multiplier that allows agents to focus on areas where they can maximize customer experience. Onboarding and change management are still required, but the ROI of speech AI is increasingly clear.
We’ll now delve deeper into the diverse technologies that AI makes available to contact centers and how they revolutionize operations in the local and global context.
AI voice agents and intelligent chatbots can act as the first line of interaction, handling incoming customer inquiries before ever needing to route to a human agent. Leveraging NLP speech-to-text (STT), LLM, Retrieval-augmented generation (RAG), and text-to-speech (TTS) models, these AI assistants can understand the context and intent behind simple, repetitive requests like checking an account balance, placing an order, getting shipping updates and more – all that in real-time, with virtually no delay.
They can then seamlessly self-serve those requests through direct data integrations and automated response flows, providing resolutions instantly without any human in the loop. This "deflects" those types of straightforward interactions from even needing to be fielded by contact center agents. As the speech-competent of such conversational agents is becoming more natural and human-like, their scope is predicted to increase significantly in the years to come.
For the subset of requests that are too complex for the conversational AI to handle autonomously, the virtual agent seamlessly escalates and hands off the interaction to a live agent. But by absorbing and self-serving that high volume of simpler tasks, it frees up human workforce capacity to focus on the more nuanced, high-value engagements that truly require real personnel.
This comprehensive call deflection driven by conversational AI translates into hugely improved operational efficiency metrics. With fewer agents needed to handle the same inquiry volumes, businesses maximize workforce productivity while keeping operating costs down. AI shoulders more of the front-line burden, while humans serve as an intelligent backstop.
For conversations that do require a live employee, real-time speech analytics leveraging NLP provide:
One of the biggest challenges contact centers face is having to juggle multiple disconnected back-end systems and databases where customer data resides. From CRM platforms to billing systems, order management tools, knowledge bases, and more — customer information exists in fragmented silos.
RPA (powered by AI) serves as the connective tissue that integrates and pulls data from these disparate sources into unified agent desktops. AI workflows can understand the context of a customer conversation, query the relevant systems in real time, and synthesize that information into a single coherent screen for the agent.
For example, if a customer calls with an issue about a recent order, the RPA bots will instantly retrieve and surface details like the order number, items purchased, shipping status, payment data, and related customer notes from wherever that data lives. Agents get a complete unified view presented on their desktop at their fingertips.
This automated data synchronization replaces immensely tedious and time-consuming processes where agents previously had to toggle between disparate apps and manually piece together information. With AI-driven RPA, the right data is dynamically pulled and presented at the exact right moment of the customer interaction.
This streamlined, integrated desktop experience allows agents to be more productive, resolve issues faster, and provide better overall experiences without the hassle of manually hunting for data across organizational silos.
The key to the power of AI is its ability to comprehend languages at very deep, almost human, levels. Speech recognition and NLP enable contact centers to understand and interpret spoken language, allowing for more natural and efficient interactions between customers and virtual agents. Contact centers can provide personalized responses and solutions, improving overall customer satisfaction (CSAT) by accurately transcribing and understanding customer queries – including with natural language IVR.
Modern speech analytics can automatically record and analyze 100% of calls, providing detailed data and visual reports. The technology involves recognizing keywords/phrases, scoring the data, and triggering actions. This helps to achieve compliance, resolve disputes, improve efficiency, reduce operational costs, improve productivity, enable omnichannel support, and enhance agent training.
NLP technologies also help with call record analysis, sentiment analysis, and voice-to-text. They benefit both customers and agents by providing faster, more accurate responses, intelligent routing, and personalized service.
No AI system is good enough if it runs in siloes, occasionally exchanging information. Integrating speech AI with Customer Relationship Management (CRM) systems allows contact centers to access and update customer information in real-time. This enables agents to provide personalized experiences based on past interactions, preferences, and purchase history. Owing to the centralization of customer data, contact centers can offer more efficient and effective service, leading to increased customer satisfaction and loyalty.
A unified omnichannel contact center solution that functions as a multimodal CCaaS platform is a feasible way for businesses to leverage AI capabilities in their day-to-day customer interactions without having to harness individual technologies in isolation. This particularly helps with complex large-volume interactions.
Multimodal capabilities enable seamless customer experiences across multiple channels. In providing web and mobile SDKs, many platforms allow businesses to embed support experiences directly into their apps and websites, ensuring a consistent experience for customers across voice (VoIP, PSTN), chat, and SMS.
This omnichannel approach eliminates the need for customers to switch between different touchpoints, improving satisfaction and reducing frustration. The platform's ability to unify customer interactions and preserve context across channels further enhances the overall support experience, enabling agents to provide more personalized and efficient assistance.
Now that we’ve seen the transformative power of speech AI technologies, let’s explore some practical implications for using them in the real world. Implementing AI in contact centers requires careful consideration of technical, operational, and regulatory factors to ensure successful integration and maximize its benefits. Here's an in-depth look at the key implementation considerations:
As you read this, speech ai technology is continuously evolving rapidly, driven by advancements in AI and ML. As we look to the future, several trends are emerging that are set to transform the contact center industry. Here are some key trends to watch out for:
In conclusion, the future of speech AI in contact centers is bright, with advancements in technology set to transform the industry. By embracing these trends and leveraging the power of speech AI, contact centers can enhance their customer experiences, improve operational efficiency, and stay ahead of the competition in an increasingly digital world.
At Gladia, we built an optimized version of Whisper ASR in the form of an API, optimized for customer support platforms and distinguished by exceptional accuracy, speed, extended multilingual capabilities, and state-of-the-art features, including speaker diarization, code-switching and sentiment analysis. Our latest model, Whisper-Zero, that removes hallucinations and improves accuracy across languages is available now.
Speech-To-Text
Over the last few years, Large Language Models (LLMs) have become accessible and transformative tools, powering everything from customer support and content generation to complex, industry-specific applications in healthcare, education, and finance.
Case Studies
In recent years, fuelled by advancements in LLMs, the numbers of AI note-takers has skyrocketed. These apps are increasingly tailored to meet the unique needs of specific user groups, such as doctors, sales teams and project managers.
Speech-To-Text
It happens all the time. A user submits a query to a large language model (LLM) and swiftly gets a response that is clear, comprehensive, and obviously incorrect.
