The Conversational AI Blueprint: A Cautious Approach to Contact Center Bots

As that trend continues and additional AI use cases bubble to the surface, it’s fascinating to consider what the CCaaS platforms of tomorrow will look like. CCaaS is a crowded market, with vendors announcing massive release waves, moving into adjacent markets, and rolling up competitors to differentiate and grow. “It’s going to be hard to completely let go of on-premises agents. They are still very necessary. I’m very optimistic about AI being a game changer but don’t expect it to displace physical contact centers next week. That’s not happening.” Yet, Five9 must inspire the regional partners – beyond the channel – to embrace Genius AI. Our AI is built on a shared infrastructure, giving consistency across all AI components and making the integration smooth for users.

Given the speed of cloud-based innovation, augmenting experiences with “low risk” GenAI will help achieve value-based and personalized engagement ambitions. The adoption of voice driven technologies such as Alexa, Siri, etc., by households will help train individuals to successfully interact with voice technology and drive acceptance of advanced voice technologies by consumers. After all, with a deeper understanding of customer needs and preferences, businesses can tailor their offerings, optimize sales strategies, and cultivate lasting customer relationships. Consequently, efficiency increased, and performance evaluations became more accurate across all customer calls. Additionally, automated quality management streamlines the evaluation process, reducing manual effort and increasing efficiency.

Hopefully, the introduction of speech transcription and agent assist, along with other AI based productivity enhancers, in contact centers will have a similar impact. Rather than attempting to replace the agent’s role entirely, generative AI, automation, and – of course – conversational intelligence will most effectively supplement existing workflows. Around $1.6 trillion is lost every year just in the U.S. due to customers receiving poor customer service and switching brands as a result. Long call times, angry customers and inefficient agents all contribute to this lost revenue.

Its GPT models and DALL-E technologies have revolutionized applications in content creation, customer service, and creative industries. With a strong focus on ethical AI development and substantial backing from partners like Microsoft, OpenAI is influencing the future of generative AI. These agents might also follow various communication scripts when speaking to a customer, identify customer needs, build sustainable customer relationships, upsell products and services, and organize all records of conversations. To handle these tasks, agents must possess several skills and qualities, including being detail-oriented, knowledgeable about products, empathic and friendly, calm under pressure and an effective communicator. Although the terms call center and contact center are sometimes used interchangeably, there are important distinctions between the two entities. AI holds the promise of eliminating many barriers that have prevented contact centers from turning a profit.

The rapid transformation of one-dimensional, phone-based call centers into multifunctional contact centers was propelled by advanced technologies. AI, machine learning, the cloud and CRM ushered in new approaches to engaging customers over multiple channels of communication, including the phone, text messaging, email, web chats, social media and video. Many contact centers are exploring the possibilities of implementing true omnichannel in their operation, but few have implemented a fully working system — and for good reason.

It Supports the Development of Live and Virtual Agents

Embracing the advent of large language models (LLMs), Zendesk built a customer service version of this – on steroids. Such knowledge sources likely include web links, the knowledge base, CRM, and various other customer databases – which may also allow for personalization. That capability sits at the core of many new customer service use cases for the technology – such as auto-generating customer replies. However, the ability of a large language model (LLM) – like ChatGPT – to extract context and entities from customer conversations on the fly has removed the requirement to spend hundreds of hours engineering those NLP solutions.

Identified by some analysts as critical to the future of contact centers, conversational analytics extracts data from text and voice conversations between customers and human agents or chatbots. Customer interactions are evaluated in real time so agents can discover behavioral patterns instantly. Agents acquire a more in-depth understanding of each customer through past data and journey insights. Using AI-powered analytics and optimization features, managers and supervisors can proactively identify issues with customer experiences, agent performance, and operations in the contact center. This empowers businesses to make intelligent decisions about everything from which customer service channels to use, to how to manage their workforce, and deliver training.

The prospect of expensive, out-of-the-box AI solutions often disillusions small businesses. For example, CCaaS providers are making all these big Salesforce announcements to bring this big corpus of CX data together. But, CCaaS can’t just be about delivering new software capabilities, especially for contact center managers who aren’t accustomed to constant updates. The future of generative AI promises greater sophistication and broader application across various fields.

Conversational analytics in real time

You can foun additiona information about ai customer service and artificial intelligence and NLP. Customer satisfaction and loyalty are vital to the long-term success and health of any business. As a main point of contact post-sale between businesses and customers, contact centers are important connection points to building, maintaining and improving this relationship. By doing so, service leaders can isolate the specific queries customers often have to recontact customer service regarding. They also shed light on broken processes, contact center demand drivers, customer sentiment, and much more. We also use genAI to help our support agents do their jobs better and more efficiently. Our contact center platform can instantly gather and organize EHR information about the caller, along with customer interaction records across channels and notes from those interactions.

These 24/7 solutions enhance customer experiences, reduce strain on employees, and minimize operating costs. Platforms such as Zendesk and Genesys Cloud AI are using predictive analytics to forecast customer needs by analyzing historical data, behavioral patterns, and even sentiment analysis. As customer expectations rise and the demand for seamless, personalized interactions increases, businesses are turning to AI-driven solutions ChatGPT App to enhance their contact center strategies. Recent investments in contact center AI are reshaping the industry, enabling faster response times, more accurate issue resolution, and enhanced customer service experiences. Cogito AI applies advanced natural language capabilities, machine learning and behavioral science principles to analyze human emotions and assist contact center agents in improving their conversations with customers.

Yet, alongside this greater ease in configuration, use, and deployment, the third generation has enabled new AI use cases to reimagine cradle-to-grave contact center experiences – from pre-call insights to post-call automation. Even the regulations created by the EU and US require companies to ethically implement AI in a way that augments human employees, rather than replacing them entirely. We can expect is that organizations, nations, and individual customers will look to the regulations created by the EU and US for inspiration. We saw a similar process taking place when the EU introduced their General Data Protection Regulation (GDPR) guidelines a few years ago.

So I think there’s a clear distinction then between artificial intelligence, really those machines taking on the human capabilities 100% versus augmented, not replacing humans, but lifting them up, allowing them to do more. And where there’s overlap, and I think we’re going to see this trend really start accelerating in the years to come in customer experiences is the blend between those two as we’re interacting with a brand. And what I mean by that is maybe starting out by having a conversation with an intelligent virtual agent, a chatbot, and then seamlessly blending into a human live customer representative to play a specialized role. So maybe as I’m researching a new product to buy such as a cell phone online, I can be able to ask the chatbot some questions and it’s referring to its knowledge base and its past interactions to answer those. And I think we’re going to get to a point where very soon we might not even know is it a human on the other end of that digital interaction or just a machine chatting back and forth? But I think those two concepts, artificial intelligence and augmented intelligence are certainly here to stay and driving improvements in customer experience at scale with brands.

Predictive analytics is enhancing customer support by enabling businesses to anticipate customer needs, preferences and potential issues before they arise. This proactive approach uses historical data, machine learning (ML), and statistical algorithms to predict future customer behavior and trends. In customer support, this is particularly valuable as it helps in understanding the customer’s experience and satisfaction levels. Sentiment analysis is becoming a crucial tool in customer support, offering deep insights into how customers feel about their interactions with a brand. “New large language models have dramatically changed the ease with which people can now actually interact with systems,” says Krishnan. These innovations, once the hallmarks of businesses at the cutting edge of technology, are now setting new standards for personalized, efficient and insightful customer interactions within the customer service industry and beyond.

If Alexa or Siri work as expected 90% of the time (because of user error or a technology gap) that may be OK. But from a customer service perspective technology must approximate 100% accuracy and effectiveness or else customers lose trust in the technology. A major area of opportunity where AI can support customer needs is related to self-service, ChatGPT which includes the use of conversational IVR, chatbots, etc. As speakers at conferences and authors in articles tout the benefits of AI in contact centers, it often seems pitched as the “silver bullet” for enhancing every aspect of the operation. This includes improving customer experience, reducing costs and decreasing attrition, among others.

With it, Five9 will reduce – if not completely eliminate – the complex process of building and training bespoke AI models. Additionally, it’s equally important for organizations to choose purpose-built, domain-specific AI for CX. This type of AI was built specifically for contact centers, including all proper guardrails. Since 1982, RCR Wireless News has been providing wireless and mobile industry news, insights, and analysis to mobile and wireless industry professionals, decision makers, policy makers, analysts and investors. It is necessary to follow a set of best practices to successfully integrate generative AI into business processes and maximize its benefits. By adhering to these guidelines, contact centers can seamlessly incorporate GenAI into their operations.

They don’t just automate; they integrate seamlessly into any contact center, offering multilingual support, real-time agent assistance, and pre-trained industry knowledge. In doing so, the contact center’s virtual agent will create data-driven value and eliminate the need to guess what customers are most likely reaching out about. Starbucks uses AI to “amplify the human connection.”1 Through its Deep Brew initiative, Starbucks built a set of AI tools to elevate the coffee business and in-store customer experience. Major businesses have started to harness the power of AI in customer experience and are starting to see its ROI.

Without these technologies, contact centers wouldn’t have evolved into the multifunctional juggernauts they are today. Automation facilitates fast and efficient responses to customer contacts and agent workflows, while AI provides valuable customer intelligence and insights. Contact center agents, whether human or virtual, are the frontline representatives of the business and thus shape a customer’s first, and perhaps last, impression of the company.

Integrating your voice bot solutions with systems that allow you to track important metrics about issue resolution times, customer satisfaction and more, will ensure you can guarantee your bots are always delivering the right results. The more data you collect over time, the more you’ll be able to train and tweak your models to deliver better results. Additionally, make sure your agents know how to take full advantage of the AI solutions available to them. Show them how they can use AI tools to streamline processes, automate routine tasks, and achieve their professional goals. This will help to strike a better balance between the AI tools and human employees in your ecosystem.

“An increasing number of companies are not implementing AI for AI’s sake,” Lazar reported. AudioCodes VoiceAI Connect service is an excellent example of a solution that can help companies overcome common mistakes. The unique solution facilitates the voice enablement of conversational AI solutions for a range of use cases, with comprehensive flexibility and support. Finally, adding voice AI solutions to your contact center shouldn’t be a set-and-forget process. Even if your AI bots have the capacity to improve automatically over time, with machine learning, you still need to ensure you’re actively reviewing their performance and looking for opportunities to improve. The complexity of implementing a voicebot into your system may prompt you to use the same framework for every region, country and target audience.

Contact center owners can leverage AI (particularly AI agents) to overcome many of the barriers to both providing great customer service and reducing costs via increased efficiency. Post-contact surveys enable customers to provide feedback about their contact center experience. They provide an opportunity for businesses to identify and rectify customer interactions that didn’t go smoothly. Post-call survey data can also help companies track customer satisfaction trends over time.

During the interaction, the chatbot will explain the steps being taken to resolve the issue promptly and reassure the customer that the company is committed to excellent service. The IBM Institute for Business Value has identified three things every leader needs to know about AI and customer service. Deliver smarter experiences across your customer journey and drive transformation across the customer lifecycle. AI is likely to play a bigger role in customer experience as more advancements arise.

Similarly, CRM vendors shouldn’t attempt to handle global voice networks, which would significantly impact their licensing margins. The broader lesson here is the importance of realistic, strategic partnerships that complement rather than compete. Pureplay CCaaS providers face increasing competition from CRM and Customer Engagement Center (CEC) vendors, which can provide a deep and rich experience across all kinds of use cases.

Generative AI can simplify this step by automatically composing detailed, accurate documentation based on the code itself. GenAI tools can draft technical documentation, including usage instructions and response formats, ensuring that it is always aligned with the actual codebase. “Unlike competitors relying on third-party integrations or bolt-ons, Five9’s unified solution provides an end-to-end AI experience with a cohesive user interface,” said John. In this step, business ground AI models – including third-party LLMs from OpenAI, Google, Meta, etc. – to ensure optimal performance within the contact center environments and guard against risks, such as AI hallucinations. Here, contact centers can assess where their pain points lie, using tools like large language models (LLMs) to reduce each interaction down to the core contact driver.

While many may look at Google’s latest innovations and raise concerns over the time and resources it may take to implement these tools, thanks to its CCAI Platform, all the GenAI and LLMs discussed are available on day one. Not only does this tool save time, but it is also better for compliance, and improves the spelling and syntax of the information being added to the system. But, the tech giant has gone even further to enhance contact center outcomes with GenAI, as the following three use cases – available within its CCaaS platform – demonstrate.

Additionally, unlike point solutions, Genesys Cloud AI is optimized for CX and ready to deploy on day one, enabling faster time to value. With its end-to-end architecture, agents on the Genesys Cloud have support from start to finish in their workspace. That tech-savviness is crucial as agents must be able to use and adapt to continuously changing technologies beyond what’s immediately available on their desktops. Yet, agents will also need to become more sales- and tech-savvy, which requires significant training. These instructions can pop up automatically during an interaction to guide an agent on how to help a customer, enhancing case management.

These technologies deliver businesses rapid ROI and actionable insights that can streamline processes and improve operational efficiency. However, organizations must be aware of the challenges that come with adopting generative AI, such as potential biases and the need for human oversight. Adhering to best practices in GenAI usage and deployment will ensure that the technology will be an effective support for human agents. Looking ahead, generative AI holds promise for further deeper customer communications—and by embracing this technology, contact centers can better meet the requirements of their customers.

• We designed Talkdesk Autopilot to perform tasks patients request, but also to seamlessly bring in human agents when necessary.
• Moreover, they offer embedded AI to help guide and automate elements of these experiences.
• DTTL and each DTTL member firm and related entity is liable only for its own acts and omissions, and not those of each other.
• So everything from how to ask good probing questions, to being empathetic, to taking ownership and resolving an issue efficiently.

By automatically synthesizes incoming calls into summaries within 10 seconds of hang-up, reducing the after-call workload for the agent significantly. This enables the agent to focus on their primary task – engaging the customer in a meaningful and productive conversation. Flow Modelling by Cresta offers such a solution, determining this path based on its impact on various customer experience and business outcomes.

In addition, they share their favorite success stories and predictions for the future of the conversational intelligence market below. Recognizing this success, more businesses are implementing such solutions and trialing many new use cases – from tracking new metrics ai use cases in contact center to pinpointing customer journey pain points. Customer satisfaction increases the faster their issues are resolved and particularly when solved in the first interaction. Simple changes or requests can be taken care of by AI agents and routed to a human as needed.

Organizations often use legacy systems and modern software together, which may not be compatible with new AI technologies. Successful integration requires an in-depth assessment of the current infrastructure and strategic planning. The efficient rollout to over 2000 end-users across six business functions was achieved through active collaboration between our tech and business teams. By managing the project, developing the technology and integrating data all in sync, we were able to create a robust and expedient solution. Alongside that ability to attach a chosen LLM, some providers – like Five9 – allow customers to customize the prompt that powers the GenAI use case.