What is Real-Time Agent Assist and How Does it Help Your Support and Sales Teams

One bad call can lose a customer or prospect.

Luckily, Real-Time Agent Assist makes sure your team never misses any moment that creates both revenue and value.

Why? Well, the ongoing reality is that training is expensive. But more importantly, it also takes a lot more time to show results.

Real-Time Agent Assist (RTAA) solves this issue instantly. And here’s how it helps teams get better results in their customer interactions.

Why Real-Time Agent Assist Matters for Enterprise Contact Centers and Sales Teams

These days, customer experience is a main point of competition for businesses. This means enterprise contact centers have turned into a main hub for customer engagement. 

• Real-Time Agent Assist moves away from reactive support to a proactive and intelligent model that uses data. At its center, RTAA is a type of software. Real-Time Agent Assist uses AI to give human agents live coaching and guidance during their interactions with customers.
  
  
• Real-Time Agent Assist support can happen over calls, chats, or emails. These AI tools act as an intelligent assistant for the agent. The system looks into conversations as they happen. Real-Time Agent Assist then presents relevant information, suggests the best responses, and checks for compliance with business rules.

How Does Real-Time Agent Assist Work? 

Overall, it’s important to point out the difference between RTAA and tools like chatbots. A chatbot, for example, is made to handle simple questions on its own, without any human help. 

RTAA, on the other hand, is built to assist human agents. These AI tools are used for more complex or high-value interactions. These are situations where human judgment and empathy are needed. This creates an effective human-AI partnership.

To pull this off, the system uses a set of advanced AI technologies. These AI tools get the job done in milliseconds:

• Real-Time Speech Recognition: During voice calls, a Speech-to-Text (STT) engine turns the live conversation into a text format. This format can then be read by a machine.
  
  
• Natural Language Processing (NLP): After that, AI models look at the text. They work to understand the customer's intent, figure out their feelings, and pull out key information like names or account numbers.
  
  
• Machine Learning: Finally, these models use past data to check the conversation against your knowledge bases and customer relationship management (CRM) system. Then, they generate the most relevant guidance and show it on the agent's screen.

Using Thunai for Real-Time Agent Assistance and ECC Automation

In the RTAA, Thunai’s main goal is to turn a company’s shared knowledge into a team of AI agents that automate and constantly help your company.

These Real-Time Agents can work on their own. In fact, they can carry out complete workflows from start to finish.

Aside from this, Thunai also automates Tier 1 tasks and assists with Tier 2 tasks. This direction matches the industry trend toward more advanced, agentic AI systems.

Instead of a single tool, Thunai has a suite of specialized AI agents:

• Voice Agent: This agent is designed to have very human-like conversations. Thunai AI voice agents can answer inbound calls, carry out live troubleshooting with screen-sharing, schedule meetings, and log data back to a CRM. Thunai lowers Average Handle Time (AHT) by 85% and improve First Call Resolution (FCR) by 92%.
  
  
• Chat Agent: This agent is made to answer many customer questions without fixed scripts. Thunai AI chat agents do this by accessing the central knowledge base to find answers. Thunai can also set up custom ticket workflows and sync with existing helpdesk tools.
  
  
• Email Agent: This agent automates how you manage your inbox. Thunai email agents organize messages, drafts personalized replies in a user's tone, and identify potential sales leads.
  
  
• Application Agent: This agent is for workflow automation and content generation. For example, it helps create social media posts, writes personalized sales outreach, and extracts client insights from data.

‍Voice Agent Integration for Real-Time Agent Assist Tools

Setting up RTAA successfully depends on a few things. This is especially true for voice channels. Voice agent integration in Real-Time Agent Assistants needs a dependable and advanced technical structure to work well.

The process of turning a spoken word into a useful insight on an agent's screen happens in just milliseconds. This happens through a set of stages.

In the grand scheme, this can be broken down into a quick, step-by-step process which starts with:

1. Transcription: First, a high-performance, low-latency Speech-to-Text (STT) engine captures the live audio. Your AI tool then turns it into text.
2. Understanding: Next, Natural Language Understanding (NLU) models read the text. Their job is to identify customer intent and pull out key details.
3. Analysis: At the same time, sentiment analysis measures the emotional state of the customer and agent. AI tools can flag rising frustration or satisfaction.
4. Recommendation: Then, the AI queries systems like the CRM and knowledge bases. AI tools do this to generate a relevant suggestion.
5. Delivery: Finally, the guidance is shown in the agent's existing user interface. This gets rid of the need to switch screens.

Common Set-Up Challenges With Real-Time AI Assistants

Actually setting up these systems can come with a wide range of technical problems. For example, most developers point out several main issues they run into like:

• Latency: This is mentioned as the most important problem. The entire process, from a word being spoken to a suggestion showing up, must take less than half a second to be useful. A good suggestion that arrives too late is not helpful at all.
• Accuracy: Sometimes, STT engines can struggle in noisy contact center environments. They may also have trouble with different accents and dialects. An inaccurate transcription can lead to suggestions that are not relevant.
• Agent Use: A system can fail if agents do not use it. For instance, a cluttered or disruptive user display can add to an agent's mental workload. This can lead to low usage instead of helping them out.‍

Security and Compliance When Using AI for Enterprise Contact Centers and Sales Teams

RTAA technology processes live customer conversations. During this, it handles sensitive personal information. Therefore, strong security and compliance become necessary requirements.

A failure in this area can, in many cases, lead to large financial penalties. Moreover, non-compliance also damages a company's reputation and leads to a loss of customer trust.

Managing Regulations With Real-Time Agent Assist

RTAA platforms must operate within a complex set of regulations. Furthermore, the main benefit of RTAA is its ability to change how compliance is handled. Real-Time Agent Assist shifts from a reactive review to a proactive, real-time action. 

How? Well, it does this by monitoring all interactions. Some of the main regulations it takes into account include:

• PCI-DSS: For businesses that handle credit card information, the system must make sure sensitive payment data is never captured or stored.
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  HIPAA: In healthcare, the solution must have strict controls in place. This is to protect sensitive patient health information.
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  GDPR and CCPA: These regulations give people rights over their personal data. For this reason, companies must strictly follow the rules for data processing, consent, and storage.

Necessary Security Features in Real-Time Agent Assist

To meet these requirements, company-level RTAA platforms must include a security design with multiple layers. They also need several key protocols in place:

• Real-Time Data Redaction: This is the most important security feature. The system must automatically find and mask sensitive personal information. It also does this for payment data from live transcriptions and logs. This makes sure it is never exposed or stored.
• End-to-End Encryption: In addition, all data must be protected with strong encryption. This applies when it is in transit over networks and when it is at rest in storage.
• Access Control and Governance: As AI becomes more autonomous, a "Zero Trust" model is needed. Each AI agent must have only the minimum permissions needed to do its tasks. This is set up to prevent unauthorized system access.
• Complete Auditability: Lastly, the system must keep detailed and unchangeable audit trails. These trails should log all AI recommendations and agent actions. This allows for transparency and accountability.

Thunai Integrations and Real-Time Agent Assist Ecosystem

An RTAA platform's effectiveness really depends on one thing. The fact is, Real-Time Agent Assist needs to smoothly connect with other company software to work effectively.

When technology is fragmented, AI cannot reach its full potential. Siloed data, for instance, prevents both human agents and AI from getting a complete picture of the customer. 

A well-connected RTAA solution, for example, acts as a central information hub. Thunai works by breaking down these information silos. This gives guidance that is specifically suited to the individual customer and their situation.

An effective RTAA platform must connect with several key company systems. This helps to add layers of context and value:

Main Features for Thunai AI in Real-Time Agent Assist:

• Customer Relationship Management (CRM) Systems: First off, this is the most important connection. Connecting to  CRM like Salesforce gives the agent rich customer data. This includes purchase history and past interactions. 
• Helpdesk Platforms: In addition, connecting with platforms like Zendesk makes sure sales and support teams are fully synchronized. A sales rep can look at recent support tickets before making a call. Meanwhile, a support agent can see the customer's account level from the CRM to prioritize service.‍
• Knowledge Bases: This is a fundamental connection. It allows the AI to analyze the conversation and automatically show the most relevant troubleshooting guide or article. This gets rid of manual search time for agents.
• CCaaS and Telephony Systems: Finally, a deep connection with the main contact center system is needed. This is necessary to get to the live audio and text streams. RTAA is also needed to show guidance within the agent's primary workspace.‍

Want to improve the performance of sales and customer support teams? Try Thunai to see just how it transforms teams.

FAQs on Real-Time Agent Assist

What is Real-Time Agent Assist?

Real-Time Agent Assist (RTAA) is a type of AI software. This tool gives live guidance to customer service and sales agents during conversations. Real-time agent assist listens to interactions to suggest responses, show information, and automate tasks like call summaries. This AI tool also helps agents follow compliance scripts.

How does it differ from a chatbot or a virtual agent?

The difference between chatbots and virtual agents is their usage. A chatbot is made to handle simple customer questions on its own, without a person's help. RTAA, in contrast, is designed to assist a human agent during more difficult or high-value conversations.

Will agents become over-reliant on the tool and stop thinking for themselves?

This is a real and common concern. For example, there is a risk that relying too much on the system could slow down an agent's own thinking. Overusing Real Time Agent Assist could also affect their problem-solving skills. To lessen this, experts advise that RTAA should be used more as a support and compliance tool.

How do you calculate the ROI for an RTAA investment?

A: A complete ROI calculation measures both cost savings and revenue gains against the total cost of ownership. Key metrics to look into are:

• Cost Savings: This includes lowered Average Handle Time (AHT) and shorter agent training time. It also includes savings from avoided compliance penalties and lower hiring costs due to less agent turnover.‍
• Revenue Gains: This is about increased sales conversion rates and higher success with upsell opportunities. It also includes improved customer retention from higher satisfaction scores.
• ‍Total Investment: This includes software licensing fees and setup costs. It also covers connections with existing systems, employee training, and ongoing maintenance.