Agent Assist: Use Cases, Benefits, & Providers

While this type of AI can produce new content and analyze data effectively, it does not have the nuanced understanding of creativity of humans. Generative AI enables accurate budget forecasting by analyzing historical financial data, market conditions, and economic indicators. Using these information, GenAI models can design predictive scenarios so businesses can prepare for different financial outcomes. AI-generated forecasts give deeper insights into cash flow, profitability, and spending patterns, minimizing the risks of budgeting errors.

Regardless of the ease of use and effectiveness of these tools, some level of caution is still required. Typos and grammatical errors still exist in word processing documents (much fewer with spellcheck) and individuals still make errors in spreadsheets and therefore some level of review is required. Similarly, customer service agents should still review transcribed conversations for accuracy and clarity and organizations must make sure that information they provide to agents is accurate and relevant. IVR was promoted as a revolutionary technology with the benefits of providing a new service opportunity for customers and more importantly, requiring fewer customer service agents. Business cases primarily focused on the positive financial impact of IVR but did not effectively analyze what needed to be done to assure an outstanding customer experience (or even just an acceptable customer experience).

Benefits of Generative AI in Contact Centers

Over half of all contact centers leaders have already said they’re investing in the development of a specialized AI strategy. “The idea is not about replacing jobs, it’s about augmenting efficiency and effectiveness,” Yip said. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities (collectively, the “Deloitte organization”). DTTL (also referred to as “Deloitte Global”) and each of its member firms and related entities are legally separate and independent entities, which cannot obligate or bind each other in respect of third parties. DTTL and each DTTL member firm and related entity is liable only for its own acts and omissions, and not those of each other.

The Net Promoter Score (NPS) is a common customer experience metric, typically tracked in the contact center. If a contact center can continuously feed such a solution with knowledge sources, contact centers can continually monitor customer complaints and act fast to foil emerging issues. Like Nuance and Google, Cognigy has pushed the boundaries of generative AI innovation in customer service, as its “Conversation Simulation” tool exemplifies. Indeed, the bot detects the intent change and presents a message to refocus the customer, pull the conversation back on track, and improve containment rates. That will impact many aspects of customer service, and chatbot development offers an excellent early example.

When people were first introduced to GenAI tools such as ChatGPT, they unknowingly gave personal information, such as their name or date of birth. That digital footprint is permanently etched into the fabric of the AI and used to inform later generations of GenAI models. In addition, there’s always the risk that an AI model produces inaccurate suggested responses or summarization notes, so agents must play an active ChatGPT role in reviewing AI-generated content. Human-in-the-loop techniques and data aggregation –  which combines the output of the LLM across many conversations – help mitigate this risk. Many contact centers will even have multiple LLMs powering numerous use cases across their chosen platform, and – so they know which to use where – some vendors, including Salesforce, will benchmark LLMs against particular use cases.

We can anticipate refinement in its ability to generate more accurate and contextually-relevant content, as well as better creative and problem-solving capabilities. Generative AI is expected to remarkably impact more industries, but ethical considerations and human oversight will remain indispensable in guiding its development and use. In the race to make the most of generative AI, some companies are leading the charge and are not just adopting this technology but defining its future. Three of the top generative AI companies that push the boundaries of AI transformation include OpenAI, Microsoft, and Google.

Contact Center Voice AI: Where Most Businesses Go Wrong

Such strategies include implementations of self-service, conversational AI, and automation to address common demand drivers and drive the anticipated ROI. The following five use cases showcase their versatility and emphasize how service leaders can leverage the tech to bolster crucial customer, agent, and business outcomes. Instead of waiting in queue, customers have the option of receiving a call back when an agent becomes available. Agents could implement customer callback manually by keeping lists of customers to call back, but that approach is prone to risk and difficult to scale, making automated customer callback a valuable software feature. Contact centers can identify future bot topics and track key KPIs to continuously improve bots. It also helps reduce contact center costs by making it easier to deploy unified AI models tailored to specific industries — and scale them across use cases, channels, and functions to enhance contact center productivity.

That involves rearchitecting their initial solutions to ensure the best possible performance. Indeed, this list of generative AI use cases for customer service originally included 20 examples. That’s why evaluagent has launched a GenAI-powered solution that analyzes a customer’s contact center conversation before predicting what score they would have left if asked the NPS survey. From there, Sprinklr customers may harness the provider’s omnichannel capabilities to distribute these surveys, converge the data, and – again, using GenAI – analyze the feedback. Alongside spotting gaps in the knowledge base (as above), some GenAI solutions can create new articles to plug them.

This enables the service team to prioritize actions to improve contact center journeys. Such actions may include improving agent support content, solving upstream issues, or adding conversational AI. In the quest to deliver exceptional CX, embracing AI in customer experience offers more than just automation; it provides a canvas for innovation and differentiation. These three use cases demonstrate how creative applications of AI can transform customer interactions. This dynamic guidance encourages agents to engage in more empathetic and productive interactions.

From personalized content recommendations to better fraud detection, more and more organizations are integrating the technology into their operations. Generative AI has opened up new possibilities for creating media content in marketing and entertainment sectors, empowering businesses to make visually-appealing content without large production teams. GenAI tools can produce professional-grade visuals from text prompts, enabling marketers to build a promotional image or video with AI voiceovers, ready for social media or online ads. In the entertainment industry, the technology can compose music or scripts, develop animations, and generate short films. Even though businesses are investing in self-service technologies, a ServiceNow survey on customer service insights in the GenAI era reported “there’s nothing like the human touch for resolving customer service requests.” Personalization starts with gathering and analyzing relevant customer data to establish complete profiles of customer needs and preferences.

Calabrio offers the conversational intelligence platform for contact center leaders to run all these initiatives and many more. Importantly, the conversational intelligence solution is also able to provide a constant temperature check, informing contact centers as to whether or not the intervention(s) had the desired impact. You can foun additiona information about ai customer service and artificial intelligence and NLP. Other businesses have tried to track repeat contacts by identifying when an identical number makes contact multiple times Yet, this isn’t a true indicator of FCR either, as the customer may reach out about different issues. The only trouble is – without conversational intelligence – businesses can’t measure FCR accurately.

• On the one hand, its Enlighten Copilot technology supports agents in every step of their journey, guiding them through real-time interactions with contextual guidance to drive optimal outcomes.
• One of the most tedious parts of software development is creating documentation, but it is required for long-term maintainability.
• “But [contact centers] must scrub existing data to make sure the data is accurate and up to date. Otherwise, agents could be handing out bad information.”
• Looking ahead, generative AI will remain a major driver of innovation, efficiency, and competitive business advantage as it reshapes enterprise operations and strategies.

However, now contact centers can assess the performance of live and virtual agents on a much deeper level – and hone in on contacts that likely present the best learning opportunities. It’s time to transform your contact center from cost of doing business to revenue generating. Remember the days when quality assurance meant listening to a handful of random calls and hoping they were representative?

And I would say between the CSAT-type measurements and efficiency-type measurements, those make up the measurements for many of the voice types of interactions. So by putting everything, anchoring in on this interaction-centric piece and then converging everything on one type of a data platform. By delivering on one platform, you enable your organization to use the same data point in multiple places. Is that in real ai use cases in contact center time, that is not the first time agents are seeing this information about how they could become more empathetic, or how they can deliver on their coaching that they had with their supervisor in a previous interaction. “There are so many available artificial intelligence solutions right now, but it’s really critical to choose AI that is designed and built on data that is specific to your organization,” says Carlson.

Some of the more popular generative AI tools for customer interaction and support include HubSpot, Dialpad Ai, and RingCX. GenAI tools can automate repetitive tasks, such as writing post-call summaries, letting agents concentrate on delivering quality customer service. Artificial intelligence (AI) systems can also provide real-time assistance to agents during conversations, minimizing the time spent searching for relevant information. According to a report from McKinsey, generative AI could decrease the volume of human-serviced contacts by 50 percent. By understanding the tone and mood of the customer, service agents can tailor their responses to be more empathetic and effective, thereby improving the quality of customer interactions.

The modernized infrastructure allowed Boots to handle large sales events, such as Black Friday, and major product launches with ease. In addition, the transformation improved the site’s search function and personalized features to showcase products. That’s an excellent final point, and Bisley works alongside many Cirrus’ customers sharing such expert advice, diving deeper into the conversational AI blueprint, and boosting outcomes. So, they created a flow with an automated first response to the “hello”, with the query only passing through to the live agent when the customer responded.

AI-powered speech analytics is like having a super-smart assistant listening to every single call, picking up on things even the most attentive human might miss. It won’t be seen as a cost center, but a real driver of growth and better outcomes for patients and members. But I want to be clear that our mission with AI in contact centers shouldn’t just be to make things as fast and automated as possible.

Alongside the answer, the GenAI-powered bot cites the sources of information it leveraged, which the customer can access if they wish to dig deeper. Now part of Microsoft, Nuance was one of the first vendors to add ChatGPT to its conversational AI platform. When this happens, it may flag the knowledge base gap to the contact center management, which can then assess the contact reason and create a new knowledge article. Yet, sometimes, there is no knowledge article for the solution to leverage as the basis of its response. Because they leverage speech-to-text to create a transcript from the customer’s audio. It then passes through a translation engine to pass a written text translation through to the agent desktop.

This seamless blend of voice recognition with NLU and NLP technologies signifies a leap toward more intuitive, efficient and secure customer support systems. NLU and NLP are key components of AI that enable computers to interpret, understand, and generate human language in a way that is both meaningful and useful. NLP breaks down the language into its basic components, allowing the system to understand syntax and semantics. This means it can comprehend the structure of sentences, the meaning of words and the intentions behind customer queries. On the other hand, NLU takes this a step further by enabling the system to grasp context, nuance, and subtleties within the conversation, allowing for a more accurate and human-like interaction.

By deploying this tool to create Generative FAQs, companies may extract the key questions from their conversations and ensure FAQs are aligned with their customers’ issues. Integrating data and AI solutions throughout the customer experience journey can enable enterprises to become predictive and proactive, says vice president of product marketing at NICE, Andy Traba. While businesses once spent significant R&D resources building use cases like isolating key data points within a customer conversation, ChatGPT and other LLMs can do so instantaneously.

Towards the end of this year, an increased proliferation of fully automated dialogs in customer support will become much more normalized. As such, contact centers must ensure their systems only leverage data individuals already have permission to access based on that specific data source’s privacy and security rules. Still, this saves a lot of time for agents, thus producing a great ROI, but also minimizes the risk of hallucinations by involving human intelligence. When it comes to contact centers, attackers may attempt to manipulate voice recordings or generate synthetic voices to mimic legitimate customers and gain unauthorized access to systems protected by voice biometrics.

AI solutions give companies a powerful opportunity to enhance and optimize their customer support strategy. From bots that deliver 24/7 service, to solutions that enhance employee productivity, reduce operational costs, and deliver valuable insights, AI can play a role in every aspect of your CX strategy. The use of AI-based virtual agents will enable the Dubai Police to use chatbots and orchestrate journeys across all the various touchpoints citizens have with the agency. The second phase will include voice and digital channels supported by its contact center, designed to create a unified, AI-powered experience regardless of the channel. This level of personalization helps agents resolve issues faster and allows businesses to create more meaningful connections with their customers. With personalization becoming a key driver of customer loyalty, investing in AI to create these one-to-one interactions not only enhances the customer experience but also directly impacts retention and long-term customer value.

However, as generative AI trends and practices have evolved, many organizations have discovered limitations with these initial models. Not every large language model or bot can deliver exceptional experiences tailored to the needs of specific audience segments. There are so many available artificial intelligence solutions right now, but it’s really critical to choose AI that is designed and built on data that is specific to your organization.

In addition, AI-generated insights can recommend reliable fixes, helping maintenance teams address problems faster. Manufacturing companies can use generative AI to quickly create multiple prototypes based on particular goals, like costs and material constraints, optimizing the product design and development process. With several carefully-produced design options to choose from, manufacturers can start building innovative products speedily. Another significant generative AI use case in healthcare is the generation of synthetic medical data that mimic real patient details without compromising privacy.

When considering voice channels, the telephone comes to mind and is still among the most widely used and most personal forms of communication in the contact center. But with the advent of the internet and cloud, voice channels now include VoIP and virtual phone systems, which can offer some of the same features as the traditional phone. In a call center, inbound calls typically revolve around account inquiries and issues such as technical support, customer complaints and product-related questions. Outbound calls entail telemarketing, fundraising, lead generation, scheduling, customer retention and debt collection.

Generative AI, while still in its infancy, possesses unlimited potential for the contact center. At present, however, it can create problems that range from hallucinogenic responses to data privacy concerns. McKinsey estimates that applying GenAI and other technologies to customer service functions can potentially automate work that currently takes up 60% to 70% of a worker’s time.

In the contact center, this means business leaders will need to implement strong governance that combines advanced cybersecurity strategies with tools that protect against data breaches. Customers will need assurance that their data is being handled with care and respect. Offering ChatGPT App intuitive, intelligent support for everything from outreach automation to self-service, and employee assistance, Gen AI tools are becoming a must-have in the modern CX landscape. Here are the best practices businesses should follow when leveraging AI for customer support.

Mastercard is supercharging its fraud detection capabilities by deploying generative AI, which considerably quickens the discovery of compromised payment cards. This advancement enables the company to scan data across numerous cards and merchants at unprecedented speeds, doubling the detection rate for exposed cards before they can be exploited fraudulently. By applying GenAI, Mastercard strengthens the trust within the digital payment ecosystem. Generative AI speeds up the discovery of new treatments, complementing pharmaceutical research. It can create novel chemical compounds by analyzing biological data and molecular structures, expediting the identification of viable drug candidates.