In the rapidly evolving world of business technology, staying ahead of the curve is crucial. Enter Manus AI, a groundbreaking tool that's reshaping how B2B companies understand their customers. Its autonomous agents do not just serve as a tool—Manus AI acts as a game-changer in customer profiling and data visualization.

The Power of Autonomous Agents

Autonomous AI agents represent the future of business intelligence, and Manus AI leads this revolution. These agents operate independently, unlike traditional AI systems that require constant human intervention, making them ideal for dynamic business environments. Manus AI's autonomous agents seamlessly identify and categorize customer profiles, offering B2B companies insights that were previously difficult to attain.

Identifying Customer Profiles

In the B2B sector, understanding customer needs and behaviors is paramount. Manus AI excels in this domain by autonomously generating detailed customer profiles. This capability particularly benefits American companies in the pre-Series B development stage, where precise customer insights can significantly influence growth trajectories. Manus AI enables businesses to better tailor their offerings to meet the specific needs of their clients, enhancing both satisfaction and retention.

Creating Functional Dashboards

Data holds value only when presented well. Manus AI takes raw data and transforms it into visually appealing and functional dashboards. These dashboards provide a clear, concise view of critical information, enabling companies to make informed decisions quickly. For instance, tech consulting firms use specific prompts within Manus AI to generate dashboards tailored to their unique requirements, showcasing the versatility and adaptability of this powerful tool.

Target Audience and Application

B2B American companies, especially those in the pre-Series B stage, primarily benefit from Manus AI's capabilities. These companies often face the challenge of scaling operations while maintaining a deep understanding of their customer base. By leveraging Manus AI, they streamline their customer profiling processes and enhance their data visualization strategies, positioning themselves for sustained success.

Manus AI offers more than just a tool; it acts as a catalyst for innovation in the B2B sector. Its autonomous agents provide businesses with the agility and insight needed to thrive in today's competitive landscape.

Conclusion

Manus AI transforms the way B2B companies approach customer profiling and data visualization. By harnessing the power of autonomous agents, businesses gain unprecedented insights into their customers, leading to more informed decisions and better outcomes. As companies continue to navigate the complexities of modern markets, tools like Manus AI become indispensable in driving innovation and growth. How will your company leverage AI technology to stay ahead in the competitive B2B landscape?

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OpenAI continues to push the boundaries with its latest release, the o1 Pro model, in artificial intelligence. This advanced reasoning model promises enhanced computational power and consistency in tackling complex problems. But with a price tag that surpasses its predecessors, the question remains: Is the investment in o1 Pro truly justified?

The Cost of Advancement

OpenAI prices the o1 Pro at $150 per million input tokens and $600 per million output tokens, positioning it as a premium option compared to the base o1 model and even the renowned GPT-4.5. This pricing structure reflects the model's increased compute resources, which are designed to deliver consistent answers on intricate tasks. However, the elevated cost has sparked debate among developers and businesses alike.

Performance vs. Price: A Delicate Balance

The o1 Pro model allows developers to adjust the reasoning effort, a feature that directly influences both the cost and latency of operations. While this flexibility is advantageous, user feedback has been mixed. Some users report that the model incurs high costs even for relatively simple tasks, while others have noted occasional inaccuracies in its outputs.

Benchmarking the o1 Pro

In a recent test, the o1 Pro model faltered when predicting the outcome of a visual test, a task where another model, Claude 3.7 Sonnet, excelled. This example highlights the potential limitations of the o1 Pro, particularly in areas that require precise visual comprehension. Internal benchmarks suggest that improvements over the base model are modest, especially in fields like coding and mathematics.

Is the o1 Pro Worth It?

The o1 Pro's value proposition hinges on its ability to consistently deliver high-quality results in complex scenarios. For organizations that require robust reasoning capabilities, the model's cost may be justified. However, for tasks that do not demand such intensive computational power, the base models or alternatives like GPT-4.5 may suffice.

Conclusion

OpenAI's o1 Pro represents a significant step forward in AI reasoning models, offering enhanced capabilities at a premium price. While it may not be the best fit for every use case, its potential to tackle complex problems cannot be overlooked. As the AI landscape continues to evolve, the decision to invest in such technology should be weighed carefully against the specific needs and goals of the user.

Are the benefits of the o1 Pro enough to justify its cost, or do the limitations outweigh the potential advantages?

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Artificial intelligence in image editing has reached new heights. Grok 3 and Gemini 2.0 stand out as notable contenders. Both AI systems promise to revolutionize image editing, but how do they compare? This comparative analysis explores their strengths and weaknesses through a series of tests.

Elon Musk's Quiff: A Case Study in Hairstyle Modification

One of the first tests involved modifying an image of Elon Musk by adding a quiff hairstyle. Grok 3 creatively altered the image with a stylish quiff. On the other hand, Gemini 2.0 initially struggled but eventually delivered a satisfactory result. This scenario highlights Grok's outside-the-box thinking, whereas Gemini excels in perseverance and precision.

Product Creativity: Imagination vs. Realism

In a test of product creativity, Grok's outputs were imaginative yet awkward, reminiscent of a surreal art piece. Conversely, Gemini 2.0 produced a coherent and realistic advertisement on its first attempt, showcasing its ability to deliver professional-grade results with minimal iterations. This difference underscores Grok's freeform creativity and Gemini's focus on precision and usability.

Colorizing History: The JFK Photograph

When tasked with colorizing a black-and-white photograph of JFK, Grok created a remarkably realistic version, capturing the essence of the era. In contrast, Gemini produced an oversaturated result, demonstrating a tendency to overemphasize color. This test reveals Grok's nuanced approach to historical accuracy, while Gemini's vivid style offers a modern twist.

Wardrobe Transformation: The Suit Swap

In a final test, both AI systems replaced a shirt with a three-piece suit in a photo. Grok altered not just the clothing but also the subject's face and posture, offering a completely transformed image. Gemini, however, maintained the original pose and facial features, ensuring a more consistent representation. This highlights Grok's transformative capabilities versus Gemini's commitment to preserving the original essence.

Conclusion

Overall, Grok 3 and Gemini 2.0 each bring unique strengths to the table. Grok excels in creativity and offers less restrictive editing, which can be ideal for artistic endeavors. Meanwhile, Gemini shines with its consistency and precision, making it a reliable choice for professional editing tasks. As AI technology continues to advance, the possibilities for image editing are boundless. Which approach do you think will shape the future of digital artistry?

Customer expectations have changed, no doubt. People want instant, personalized, and effortless support without long wait times or scripted chatbot responses. To keep up, businesses are moving from AI-assisted to AI-first customer service.

Why make this shift? The answer is efficiency and scale. AI-first customer service reduces response times, cuts support costs, and delivers proactive assistance. 

For example, AI agents can detect customer frustration, suggest solutions before they are needed, and analyze interactions to improve future experiences. Businesses using AI-first models like chatbots have reported a 30% drop in support costs while maintaining high customer satisfaction.

Are you still relying on AI as just an assistant? It might be time to rethink your strategy. Let’s explore how AI-first customer service can transform the way you support your customers.

What is AI-first customer service?

AI-first customer service is  AI taking the lead in handling customer support rather than just assisting human agents. 

For example, if you message a company asking, “Where’s my order?” an AI agent can track it and give you an update in seconds, instead of waiting for a human agent.

AI-first models can also detect frustration, adapt to customer needs, and improve with every interaction. Human agents still play a role, but AI handles most routine tasks, freeing up humans for more complex support issues.

How AI-first customer service differs from traditional chatbots 

If you’ve ever used a website’s live chat to track an order or reset a password, you’ve interacted with a traditional chatbot. These bots are great for simple, rule-based tasks but often fail to understand context with complex queries.

On the other hand, AI-first customer service utilizes state-of-the-art AI models, such as AI agents, to interact with customers. These agents are more like personal digital assistants who can recommend products, schedule appointments, and even offer beauty advice.

Benefits of AI-first customer service

72% of business leaders want AI agents to be an extension of the brand’s identity, while 51% of consumers prefer interacting with bots over humans when they want immediate service.

Here are some benefits of AI-first customer service:

Speed

AI agents can significantly reduce average handling time (AHT) by decreasing the time it takes to address customer inquiries. AI-first customer service can also scale on demand, enabling businesses to manage huge volumes of customer interactions in minutes, without compromising on service quality.

Personalization

AI models can create tailored product recommendations and targeted promotions by analyzing past behavior, preferences and purchase history. This personalization is vital as 64% of customers prefer to interact with companies that understand their wants and needs. 

For example, Marks & Spencer uses AI to advise shoppers on outfit choices based on their body shape and style preferences, boosting online sales. 

Scalability

With AI agents skillfully handling countless conversations simultaneously, you don’t need to hire extra staff. This also gives the existing agents more time to focus on complex and high-value interactions without feeling pressured. 

This ensures fast, efficient support while preserving the human touch where it matters most.

Data & insights

AI-first customer service tools can analyze large volumes of customer data using machine learning and natural language processing algorithms. This unlocks valuable insights into customers’ browsing habits, feedback, and social media interactions. 

This data is especially helpful in recognizing behavior patterns and trends for customer profiling, assessing customer satisfaction levels, and anticipating problems.

Self-service support

Studies show that 81% of customers want to solve issues independently before reaching out to a live representative. AI-first customer service makes it possible by facilitating self-service tools to serve multiple customers with no downtime simultaneously. 

For example, knowledge bases are always live, offering prompt troubleshooting information at scale beyond what humans can handle.

Key features of AI-first customer service

AI has been part of customer service for years, but AI-first customer service takes it to an entirely new level. Here are some of its set-apart features:

AI model orchestration

Just like the conductor of an orchestra, AI orchestration harmonizes all the elements of an AI system, enabling each component to contribute to creating a successful outcome. 

The different AI models come together to optimize various tasks, like understanding customer intent, analyzing sentiment, and suggesting the next best actions.

Let’s say a customer contacts you through multiple channels (voice, chat, or email). Gen AI, like an LLM, can immediately analyze the customer’s input and determine their need and intent. An adaptive learning model uses this information in real-time to predict the most effective way to respond based on the customer’s past behavior and preferences. 

Voice AI and sentiment analysis for phone support

Voice AI creates natural-sounding voices that can be customized based on age, gender, accent, and emotions. Modern AI voice tools can also analyze sentiment and speech context and understand user intent to generate appropriate responses without human intervention.

For example, E-commerce giant Amazon utilizes voice AI through Alexa to activate voice shopping on its platform. It allows users to search for products, add items to their cart and even place orders using voice commands.

This modern technology uses the IVR system for phone support to understand NLP, route calls based on intent, and resolve common issues without human intervention. These systems collect customer data and transfer complex queries to live agents while maintaining context.

Predictive analytics for proactive customer service

In contrast to traditional setups primarily designed to react to customer inquiries, AI-first customer service can anticipate issues and opportunities. Businesses can use AI to alert customers before any issues even arise, maintaining trust and confidence.

For example, the world’s leading beauty retailer, Sephora, uses predictive analytics to recommend products to their customers personally. It also creates exclusive offers for each customer by analyzing their browsing behavior, purchase history and preferences.

It also deploys predictive models to forecast demand and manage inventory levels for extensive beauty product selection.

Unified interaction across channels

Customers often switch between email, chat, social media, and phone calls. AI-first customer service ensures they get consistent support across all platforms without repeating themselves.

For example, a customer might start a conversation on your website’s chat, follow up via WhatsApp, and later call your support team. An AI agent keeps track of these interactions, so the customer doesn’t have to explain their issue again, saving time and reducing frustration.

Brands like H&M use AI to manage customer inquiries across chat, social media, and messaging apps. Their AI assistant answers common questions and hands off complex issues to human agents when needed. This improves response times and keeps support teams efficient.

Agent augmentation

AI handles routine questions, suggests responses, and provides real-time insights so your team can focus on complex issues needing personal touch.

For example, AI-powered tools like Shopify analyze customer sentiment during live chats. If frustration levels rise, AI suggests personalized solutions or flags the conversation for human intervention. This helps agents step in at the right moment with the right information.

By handling repetitive inquiries, AI-first systems allow agents to focus on more meaningful interactions. Instead of spending time on password resets or shipping updates, your team can assist VIP customers, resolve escalations, and build stronger relationships.

How AI Agents work

AI agents handle customer interactions intelligently by gathering data, making decisions, taking action, and continuously improving. Here’s how:

1. Gathering Data

AI agents collect information from multiple sources, including customer chats, purchase history, and social media. This helps them understand context and intent. 

Advanced AI agents process data in real-time, ensuring they always have the latest information to respond accurately.

2. Making Decisions

AI agents use deep learning models to analyze past interactions and current inquiries to determine the best response. 

For example, if a customer asks about an order delay, the AI can check shipping records and provide an update instantly. Over time, it learns from each interaction to improve accuracy.

3. Taking Action

Once the AI decides on a response, it executes the task that may include answering a question, updating an account, or escalating a complex issue to a human agent. This ensures fast and relevant support without unnecessary delays.

4. Learning and Improving

AI agents don’t just follow scripts; they evolve. They analyze feedback, update their responses, and refine their decision-making. If customers frequently ask the same question, the AI can adjust to provide a better response, reducing the need for human intervention.

Future trends in AI-first customer service

The global AI market size is projected to grow at a compound annual growth rate (CAGR) of 27.67% from 2025 to 2030, reaching a total value of $826.73 billion by 2030.

New advancements will make AI agents smarter, faster, and more intuitive in the coming years. Here’s what you can expect.

Multimodal AI

The multimodal AI market is expected to expand significantly, growing from $1.0 billion in 2023 to $4.5 billion by 2028, with a projected annual growth rate of 35.0%. This surge shows how businesses are investing in AI that can process text, voice, images, and even emotions at the same time.

How businesses are using it today:

• Retail brands offer virtual try-ons, helping customers make confident purchases
• E-commerce platforms let users describe what they need through voice and text
• Tech support teams analyze screenshots and messages together for faster fixes

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In the future, AI agents will go beyond text-based chat. They will understand tone, facial expressions, and product images for better support. Imagine an AI assistant that instantly detects frustration in a customer’s voice and adjusts its response. Or an AI that analyzes a product photo to suggest better alternatives.

Agentic AI

Agentic AI is  AI, but smarter and more autonomous. Gartner predicts that by 2029, it will have handled 80% of common customer issues without human help. This means faster resolutions, lower costs, and more efficient support.

How businesses are using it today:

• AI-powered assistants resolve routine inquiries without human intervention
• E-commerce platforms use AI agents to recommend products and process refunds automatically
• Contact centers deploy AI to handle support tickets, escalate complex cases, and optimize workflows

In the future, agentic AI will not only follow rule-based automation, but make independent decisions, predict customer needs, and proactively resolve issues before they escalate. 

Offer proactive support with Plivo CX’s AI agents

Plivo CX transforms customer service by turning your business knowledge into an AI-powered support agent. It delivers instant, accurate responses, ensuring customers always get help. With advanced learning capabilities and smart integrations, it enhances efficiency while maintaining your brand’s voice.

Here is how it can help you offer exceptional customer service: 

• 24/7 availability: Ensures uninterrupted customer support without downtime
• Smart integrations: Syncs with CRM, billing, ticketing, and knowledge systems for accurate responses
• AI-driven agent roles: Handles sales, retention, engagement, and support with specialized AI agents
• Human-like conversations: Uses verified company content to deliver precise and reliable answers
• Omnichannel support: Engages customers via voice, WhatsApp, webchat, and more for a unified experience
• Boosts sales & engagement: Sends AI-driven cart reminders, personalized offers, and proactive messages
• Reduces workload: Helps fill workforce gaps and minimizes the impact of agent turnover
• Actionable insights: Tracks resolution rates, customer pain points, and satisfaction in real-time
• Enterprise-Ready Security: Offers bank-grade security, GDPR compliance, and 99.99% uptime

Book a demo to learn more.

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AI chatbots and AI agents—have increasingly become instrumental in reducing ticket queues, streamlining support delivery, and enhancing overall customer experience. 

That’s unsurprising, given that AI chatbots can resolve as much as 80% of your routine queries, and 47% of CS specialists believe AI agents can bolster operational workflow. However, it’s quite easy to mix up both solutions and assign them to the wrong roles. 

While AI chatbots handle basic requests such as “where is my order?” or “how do I reset my password?”, AI agents are designed to manage more nuanced interactions. 

This includes making critical situation analysis based on provided data, handling multi-step processes, and adapting to new contexts.

In this article, we’ll explore their differences and when to use each to modernize your customer support.

What is an AI Chatbot?

An AI chatbot is a program designed to simulate human-like interactions with customers and provide helpful answers to questions. It uses NLP to understand and respond to customers’ queries. 

However, AI chatbots are primarily trained on structured datasets and hooked on each end by a stock of rules that define how they respond to user inputs. This makes their responses static, preprogrammed, and lacking in depth of autonomy or self-thinking.

Of course, these restrictions do not make chatbots any inferior to AI agents in helping brands achieve their customer support goals. According to Hubspot’s State of Customer Service report, 91% of CS leaders consider chatbots effective, and 36% consider them essential for making customer service available 24/7.

You can find cases of AI chatbots used in customer service for basic IT support, sales assistance, learning and development, etc.

• Basic IT support: AT&T uses an openAI-powered chatbot to help its employees find quick predefined answers based on the company’s database. The chatbot also simplifies and translates brand documents for easy reading. 
• Sales assistance: Sephora uses its chatbot to provide product recommendations, which has helped drive sales by 35% while reducing response time by 40%.
• Learning and development: Duolingo uses AI-powered chatbots to simulate conversations with users in order to enhance interactive language learning and provide real-time feedback.

Amazon and H&M also implement some of the best AI chatbots to reduce ticket load and improve customer service experience.

What is an AI Agent?

An AI agent is a more sophisticated software program designed to execute complex tasks and achieve set goals with minimal help from your support team. Like AI chatbots, AI agents leverage NLP to understand the context of customer input. 

However, they also use ML to process requests, engage in complex interactions, and autonomously make accurate decisions like processing refunds.

The cherry on top is that AI agents can utilize reinforcement learning and other adaptive algorithms to grow continuously. 

That means they learn from previous interactions to become better at handling tasks and helping your support team scale with increasing levels of difficulty.

According to 8x8’s State of Conversational AI, 54% of organizations say this AI solution has helped streamline their internal workflow and 39% say it has decreased repetitive tasks that are done manually.

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AI agents are useful for triaging tickets, escalating complex issues, analyzing customer sentiment, personalizing responses, and streamlining workflow automatically.

• Intelligent ticketing: AI-powered ticketing agents can understand the urgency and difficulty level of each customer complaint. This is useful for setting ticket priority, assigning tickets to the right service rep, or escalating them when necessary.
• Predictive AI: Predictive AI agents can autonomously analyze customer data for patterns and deduce future behaviors, preferences, and potential issues. This enables proactive support delivery.

Outside of customer support, AI agents from brands like Please help users make restaurant reservations by accessing booking websites in real time and confirming their availability, costs, and menu.

AI Agent vs. AI Chatbot: 6 key differences 

Let’s see how AI agents and AI chatbots differ.

It’s easy to think of AI chatbots like ChatGPT as AI agents; however, that might not be true. 

ChatGPT is semi-agentic; that is, it is an advanced AI chatbot capable of multitasking and performing complex interactions. However, it is not fully autonomous since it cannot make decisions without explicit prompts from users—and that’s the takeaway difference between these two solutions.

When to use AI Chatbots

87% of contact center and IT leaders believe chatbots increase productivity and 63% somewhat agree it will help boost their business’s revenue. But how do you know when to use it to achieve these results?

Scaling first-line support

Most L1 queries are routine and monotonous—and you’ve likely gotten them more than a dozen times. You can use AI chatbots to manage these requests while assigning your service reps to higher-level (L2 and L3) complaints.

Cost efficiency in handling high volumes

The amount of tickets you log increases as your business grows. And that means hiring more hands to sustain delivery, which might be cost ineffective. In this case, you can employ AI chatbots to do the heavy-lifting and save your business over 30% in recruitment costs.

Enhancing response time & customer satisfaction

Consumers now want an immediate response. According to a valuable report from Hubspot, “immediate” can be 10 minutes or even less. If you’re struggling to achieve that, then it’s time to implement an AI chatbot.

Ensuring 24/7 automated support

The clock strikes 9 pm. Your customers are having issues initiating a product refund. And your service reps are off to bed, leaving them with no one to talk to. That’s one of the fastest ways to lose your customers.

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If this sounds familiar, getting an AI chatbot may be your next go-to to provide round the clock support.

When to use AI Agents

When you need a self-thinking AI solution that can autonomously handle nuanced tasks rather than a bot that solely depends on prompts, AI agents are your best bet.

Ticket categorization

Logging, sorting, and labeling each ticket manually is draining. Assessing the priority level of each complaint before routing it to the appropriate support tier and rep can also be quite difficult.

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If this is you, you might want to use an AI-powered ticketing agent. These agents can autonomously log, categorize, and prioritize incoming tickets using NLP and sentiment analysis without human prompt or intervention. 

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They also learn from past interactions, refine classification accuracy, and route issues to the right support tier based on urgency, expertise, and workload.

Handling personalized and context-aware interactions

A customer logs a ticket and is frustrated about transferring funds to the wrong account. Your chatbot replies, “We’re sorry this happened. Please check out these articleson how to transfer funds to the right account.”

For an already frustrated customer, things could turn ugly.

Thanks to sentiment analysis, reinforcement learning, and adaptive algorithms, you can use AI agents to avert such situations. AI agents can understand the customers' emotions, dig up the necessary data, and walk them through personalized assistance. 

In fact, 76% of consumers say personalization is essential in deciding whether to repurchase from a brand or not. 

Automating end-to-end customer workflows

Customer workflows for escalating hard-to-resolve queries and providing proactive customer service are often disconnected. They’re usually a bunch of triggers and branched flows. This results in siloed lapses which can delay support delivery and cap your productivity.

In this case, it is crucial to use AI agents to unify and automate these systems. AI agents can autonomously utilize APIs to fetch customers’ real-time data, analyze ticket status and priority, synchronize with existing workflows, and use the resulting analysis to trigger escalation or predictive and proactive support.

AI Agents vs. AI Chatbots: Which one should you use?

Both! You can use AI chatbots to resolve the majority of your routine requests and give back time to your human agents so they can focus on core tasks. 

At the same time, you can integrate AI agents to automate your workflows, make real-time decisions, and integrate seamlessly with backend systems for complex issue resolution, escalations, and proactive support.

Combining both solutions ensures no gap is left in your customer support.

It’s also important to note that advanced AI chatbots, not simple rule-based and scripted models, can function as an AI agent. 

For instance, Plivo CX’s openAI-powered AI chatbot can leverage your database in real-time to make decisions, autonomously handle refund requests, modify orders, and make personalized recommendations. 

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Modernizing your customer support with Plivo CX’s AI agents

AI is at the heart of every scalable customer support. But finding the right AI agents to use can be daunting.

And that’s where Plivo CX comes in. This all-in-one omnichannel communication platform offers openAI-powered agentic chatbots, automated workflow builders for escalations, and proactive support solutions—seamlessly integrating AI agents to optimize every customer interaction.

We also offer: 

• Unified communication: Manage all customer conversations across channels like email, voice, SMS, WhatsApp, and live chat from a single, agent-friendly platform.
• Seamless integrations: Connect Plivo CX with your existing tools, including CRM, ERP, helpdesk, and more.
• Data-driven optimization: Track real-time analytics and generate custom reports to gain insights into customer interactions and agent performance.
• AI-enhanced efficiency: Prioritize urgent tickets, ensure brand consistency, and adapt quickly to changes with AI-powered support.
• Rich communication: Enhance interactions with multimedia support, including images, videos, and documents.
• Increased productivity: Streamline workflows with message templates, internal notes, and skills-based routing.
• Comprehensive features: Leverage call recording, IVR, multi-language support, and robust security features for a complete solution.

Book a demo now!

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Your customers expect you to be there every step of the way, but that’s difficult to do as your business scales. More customers also mean more tickets—which increases your agent workload and decreases the efficiency of your support delivery. 

This results in longer response times, unsatisfied customers, and a declining ROI.

To avoid such an outcome, you need to scale your support along with your growth. And that’s where AI comes in. 

Businesses using AI have reported a significant 30% drop in customer support volume and a 45% cut in customer service operations cost. Interestingly, 52% of consumers now want AI to help improve their experiences and 61% prefer faster responses from AI than waiting to talk to an agent.

AI adoption to scale customer support is therefore a necessity if you want to maintain an edge over your competitors. In this article, we’ll explore a few ways to do that.

5 ways to scale customer support with AI

Utilizing AI to scale your customer support enhances customer experience and satisfaction while reducing operational cost, agent workload, and burnout.

To get started, here are some expert-recommended strategies you can try:

1. Implement AI-powered chatbots to resolve routine queries

A typical payment system, say Payoneer, will likely face a barrage of routine queries such as transaction status updates, account verification issues, delayed payments, withdrawal failures, currency conversion rates, refund processing, and fee clarifications.

Imagine assigning an agent to constantly retrieve transaction status updates for customers when they could be handling more complex technical issues instead.

For a small business with only a couple of queries daily, that might not be any issue. But it’s a game changer for larger brands or those scaling rapidly. The consequences are far reaching:

• Routine and monotonous queries can cause agent burnout, which in turn, affects delivery.
• Customers with technical complaints receive diffused attention and eventually a poor experience. 
• Unsatisfied customers are more likely to switch to competitors, reducing your customer retention rate and increasing acquisition costs.

You can proactively avoid these issues by transferring your routines to Chatbots. These bots are capable of addressing repetitive requests by leveraging pre-trained data and access to your knowledge base. 

According to a recent report from Comm100, AI-chatbots can reduce routine queries by up to 80%, thus freeing up your agents to do other core tasks.

Sephora is an example of a brand using chatbots to reduce the queue and provide excellent support. The beauty retailer integrated AI-driven chatbots into its social media platforms such as Facebook’s Messenger. 

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• It not only handles repetitive questions, but also provides product recommendations and helps customers try out their choice before making a purchase.
• Sephora also introduced smart bots on its website to handle common queries. 
• According to Renascence, this implementation has helped it reduce response time by 40% and boost customer satisfaction even at peak hours.

As an all-in-one, omnichannel customer service platform, Plivo CX offers an OpenAI-powered chatbot to help deliver exceptional customer support around the clock and take the load off your agents.

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2. Empower agents with generative AI for real-time assistance

We get it—it’s quite unavoidable to get stuck choosing the appropriate words to respond with. And sometimes, peak hours won’t give your agents the luxury of time to decide on one. This is where GenAI is a perfect fit as a solution. 

GenAI primarily uses ML and NLP to analyze customer queries and data. The insights from this analysis are then used to provide real-time suggestions, context-aware responses, and automated knowledge retrieval during interactions. 

That means your agents can copy and paste an auto-generated response template tailored to the customer they’re helping.

Besides harshing out templates, GenAI can also auto-complete your sentences, especially when the complaints are so common. This helps to improve response time, reduce the workload on your agents, and clear ticket logs faster, thus highlighting why 85% of customer service leaders hope to integrate the technology in 2025.

3. Use an AI-optimized knowledge base to reduce ticket load

Empowering your agents is a good customer support strategy, but empowering your customers is even better. According to Gartner, approximately 73% of customers use self-service options such as knowledge bases and FAQs to find answers at some point in their customer journey. 

This effectively reduces the number of queries logged and helps your agents focus on issues customers can’t resolve by themselves.

However, only 14% of customer support issues are fully resolved via self-service, leaving the remaining 86% to agents and chatbots. Additionally, 45% of customers who engage self-help feel companies don’t understand their problems. In 43% of failed self-service cases, the reason for failure is that customers can’t find relevant content to their queries.

Not finding a useful solution frustrates your customers. Frustration weakens customer trust in your brand and damages your relationship.

To avoid this, you can employ AI to create a rich knowledge base through automated tagging. AI-tagging systems analyze your common queries, especially those not likely to require specialized intervention, and high-volume complaints to reveal where your customers are struggling the most.

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This allows you to create targeted content and populate your knowledge base with the right articles, ensuring your customers find the content they need at the right time. Your agents can also use this content as a reference when resolving unfamiliar complaints. 

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Plivo CX’s Unified Agent Desktop solution helps you integrate AI-optimized custom knowledge base with your communication channels and enables easy data retrieval in real time.

4. Implement smart routing to direct tickets to the right agents

Smart routing utilizes AI algorithms to automatically assign incoming support tickets to the most suitable agents based on various factors such as skills, availability, workload, and priority. 

This ensures tickets reach the right experts faster while streamlining escalations to prevent agents from being overwhelmed with issues beyond their expertise.

When customers lose contact with an agent, advanced AI systems can also reconnect them rather than assigning a new agent when they reach out again. For example, Plivo CX’s IVR solution uses sticky routing to ensure repeat callers are directed to the same agent, enhancing the experience for both customers and agents.

Most importantly, AI-powered routing minimizes ticket collision, which occurs when two agents work on the same ticket unknowingly.

5. Meet customers where they are with scalable omnichannel support

35% of customers expect to be able to contact the same customer service representative on any channel. But this can be cumbersome since your agents have to switch from one channel’s tab to another as required. Needless to say, this is time-consuming and may lead to disconnected customer experiences if you fail to meet the expectation.

Using AI-driven omnichannel support solutions resolves this impasse. Omnichannel platforms integrate all communication channels to give your agents a single point of view when addressing queries, eliminating tab switching.

Plivo CX’s Unified Agent Desktop is designed to help you seamlessly switch between voice, chat, text/SMS, and WhatsApp or use multiple channels simultaneously. It also enables integration with your CRMs, ERPs, and other home-grown systems, utilizing customer data to deliver personalized experiences.

Scaling customer support with Plivo CX

You need the right AI-driven tools to scale your customer support effectively. Plivo CX offers an OpenAI-powered self-service Chatbot that you can customize to autonomously address routine customer queries or escalate when necessary. 

You can also embed this Chatbot into your app to provide in-app customer service and reduce agent workload. In addition to that, Plivo CX offers several features, such as:

• Unified communication: As noted before, you can manage all customer conversations across channels like email, voice, SMS, WhatsApp, and live chat from a single, agent-friendly platform
• Seamless integrations: Connect Plivo CX with your existing tools, including CRM, ERP, helpdesk, home-grown systems, and more
• Data-driven optimization: Track real-time analytics and generate custom reports to gain insights into customer interactions and agent performance
• Communication with media support: Enhance interactions with multimedia support, including images, videos, and documents
• Increased productivity: Streamline workflows with message templates, internal notes, and skills-based routing
• Comprehensive features: Leverage call recording, IVR, multi-language support, and robust security features for a complete solution

To learn more, book a demo!

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The clock strikes 8 am. You mount your desk only to be greeted with dozens of unresolved tickets from the previous day and a row of new ones. This has been the norm over the past few months. 

It used to be manageable, but now, as your business scales, your ticket queue is gradually spiralling out of control. 

Believe us, you’re not alone; many customer support managers face the same challenge. Ticket overload can result in team burnout, decrease the quality of support to customers, and dent your brand reputation if not well managed. 

Unsurprisingly, 65% of customers will switch to another brand after a bad experience with your support team.

And that’s why you need AI agents. Let’s see how these models can reduce your support tickets without raising costs or compromising quality.

How do AI agents deflect support tickets

AI agents are advanced AI models equipped with ML, NLP, and reinforcement learning algorithms to accurately analyze, interpret, and execute tasks. Most importantly, they are autonomous—meaning AI agents are capable of self-reasoning instead of constantly depending on user inputs.

To deflect support tickets, AI agents employ four automated approaches: ticket analysis, categorization, routing, and sentiment analysis. Let’s take a detailed look:

1. AI-powered ticket analysis

When a customer logs a complaint, your AI agent instantly analyzes the ticket using ML and NLP to understand its context. It assesses priority, urgency, and complexity while identifying whether:

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• You’re dealing with a first-time complaint from a new user
• It’s a follow-up on an unresolved issue
• It’s a new concern from a previously satisfied customer

2. Smart categorization

What comes next is categorization based on a combination of the metrics above. By urgency, tickets can be grouped into:

• High urgency needing immediate response, such as in a product security crisis or VIP complaints
• Standard resolution time following the SLA, such as in general troubleshooting
• Low urgency in cases of feedback requests and suggestions

By priority, we have:

• High priority when it affects the customer’s business operations, like in service outages
• Medium priority when it affects users but not business critical. Example is a feature malfunction
• Low priority if it’s just general requests and non-urgent inquiries

Lastly, based on complexity:

• Basic L1 requests such as repetitive inquiries
• L2 complaints, such as product bugs, and usually require technical interventions
• L3 tickets featuring highly technical issues like API failures

Another salient categorization method is based on the ticket’s relevant department. Complaints about subscriptions fall under billing and payments, while logins and password resets fall under account management.

3. Intelligent routing

Following the categorization above, an L1 ticket with low urgency and low priority will be automatically routed to AI chatbots. If it’s L1 with low urgency and high priority, it will still likely be routed to an AI chatbot but will be addressed before other L1 tickets of lower priority.

On the other hand, all L2 and L3 tickets are automatically assigned to relevant service reps or senior support agents. The urgency and priority of the tickets determine which ones are assigned first.

Your AI agent also considers the status of your support agents before assigning them a ticket. This includes their availability, ticket load, previous tickets successfully closed, categories of ticket topics they find easier to resolve, and how fast they complete the task.

4. Post-support sentiment analysis

Traditionally, you can collect post-support feedback through surveys or thumbs up/down options. However, AI agents take a more efficient route which involves using NLP to analyze the tone, phrases, or keywords used by customers during support resolution.

If there’s a negative sentiment, it triggers follow-up actions or even escalations. For neutral sentiments, AI agents consider what could have been done better.

• For instance, if the negative sentiment is because of agent-customer mismatch, your AI agent uses available data to refine its routing approach.
• If a ticket was successfully resolved but slowly, resulting in neutral sentiment, the AI model decides whether to increase the urgency of such issues in the future.

Also, compiled reports from these analyses can help your service reps refine their strategies and customer resolution approach.

Benefits of AI agents on ticket resolution

According to Hubspot’s 2024 State of Service report, teams adopting AI have reported up to a 30% reduction in their support volume. 92% say it has helped improve their response time. This is as a result of the following:

Faster categorization and routing

Manual ticket sorting is a repetitively monotonous task and there are two problems with it. 

First, it’s slow—imagine having to categorize over 100 tickets in a day and still expecting to deliver a response to each complaint in less than 10 minutes. That’s virtually impossible.

Second, it consumes your service reps’ time and prevents them from focusing on the majors.

AI agents eliminate these outcomes and handle all the categorization processes in seconds, freeing your team to handle core tasks. This optimizes time to first response and time to resolution, which, in turn, enables faster response times to your customers.

Minimized ticket collision

Ticket collision is when two service reps unknowingly work on the same ticket, whether in parallel time or one after the other. This is usually as a result of errors in manual routing, delayed update of ticket status, and or lack of a centralized agent dashboard to track backend activities.

AI agents minimize the incidence of these errors and ensure each ticket gets updated—pending, ongoing, active, or closed—in real-time so as to avoid collision. 

As an omnichannel platform designed to scale your support team, Plivo CX’s Unified Agent Desktop also helps your reps visualize ticket statuses and eliminate collision.

Reduced service operational cost

A surge in tickets either means your brand is in high demand or you’re turning off your customers really bad. Whichever one it is, you’re going to need more hands on the desk to manage your tickets. And that’s cost-ineffective, especially if you have a limited budget.

AI agents reduce costs by handling the bulk of ticket management, no matter the volume. This minimizes the need for additional hires, allowing you to allocate resources toward more advanced AI solutions that further optimize customer support.

Also, faster response time as a result of a more efficient categorization and routing helps you retain customers. In fact, according to Bain & Company, increasing retention by 5% can boost profits by as much as 95%.

Less employee burnout and more efficiency

Ticket overload can result in customer service burnout. AI agents in customer service help prevent this through efficient distribution, notifications for tickets exceeding SLAs, and integration with AI chatbots for automated ticket resolution. 

This ensures that ticket backlogs do not build up, optimizes your team’s workflow so they can function at maximum efficiency, focuses on complex tasks, and increases their productivity by almost 66%.

How to reduce support tickets with AI agents

77% of teams use AI to manage key CS tasks, including reducing support tickets, and 79% find it effective. Here’s how to do it:

1. Use ticket categorization agents

Traditional ticketing systems mainly follow pre-defined workflows. That means they lack some of the optimized features we’ve discussed such as NLP sorting and AI-powered post-support analysis. 

So, you can either use a full-stack AI categorization agent capable of handling ticketing hands down or integrate your existing ticketing system with an AI-powered customer support platform. 

An example of such a platform is Plivo CX’s unified agent desktop which enables integration with ticketing programs like Salesforce to automate your tickets, route to appropriate reps, and initiate proactive services.

2. Utilize AI-powered insights to optimize knowledge base

Remember we said AI agents can analyze ongoing conversations—tone, phrase, and keywords—using NLP, in order to figure out customer sentiments? They can also help you identify common or routine questions that do not require routing to your service reps.

Use this insight to optimize your knowledge base and fill in missing gaps. This will improve the effectiveness of your service options, reduce inbound requests, and free up your backlogs.

3. Integrate AI self-service chatbots

The giveaway in CS is that most of the requests your customers log in a day are routine queries—which AI chatbots can effectively handle. 

So, integrate an AI-powered chatbot directly with your AI categorization agent to cut your ticket load and help your reps focus on complex complaints.

‍Plivo CX’sOpenAI-powered self-service agentic chatbot is designed to relieve your team's pressure, escalate difficult queries to service reps, and reduce the time to resolution.

Reduce support tickets with Plivo CX’s AI agents

Ticket overload can result in customer service burnout, reduce support delivery, cause customer dissatisfaction, and slash your retention rates. 

Plivo CX offers openAI-powered agentic chatbots, automated workflow builders for escalations, proactive service, and other AI-powered solutions to help you avoid these outcomes and reduce your ticket volume.

Our award-winning, omnichannel support platform also provides:

• Unified communication: Manage all customer conversations across channels like email, voice, SMS, WhatsApp, and live chat from a single, agent-friendly platform.
• Seamless integrations: Connect Plivo CX with your existing tools, including CRM, ticketing systems, ERP, helpdesk, and more.
• Data-driven optimization: Track real-time analytics and generate custom reports to gain insights into customer interactions and agent performance.
• AI-enhanced efficiency: Prioritize urgent tickets, ensure brand consistency, and adapt quickly to changes with AI-powered support.
• Rich communication: Enhance interactions with multimedia support, including images, videos, and documents.
• Increased productivity: Streamline workflows with message templates, internal notes, and skills-based routing.
• Comprehensive features: Leverage call recording, IVR, voice support, multi-language support, and robust security features for a complete solution.

Book a demo now!

“AI will not make us obsolete, but it will make us superhuman.” 

– Max Tegmark, Swedish-American physicist

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AI can answer thousands of customer queries in seconds. But can it understand frustration, build trust, or solve complex problems as well as humans?

Businesses today are caught between efficiency and personal touch. AI offers speed and cost savings, while human agents provide emotional intelligence and problem-solving skills. The real challenge isn’t choosing one over the other but knowing when to use each.

So, how can companies effectively leverage both? This article explores the pros and cons of AI and human agents helping you create a balanced, customer-focused approach.

AI vs. human agents: A comparison

A Goldman Sachs report cited by the BBC estimates that AI could potentially replace 300 million full-time jobs. Customer service is among the industries impacted, but does this mean human agents are becoming obsolete?

The short answer: No.

While AI enhances efficiency and automates repetitive tasks, customer service relies heavily on human connection and meaningful interactions. AI may assist, but it can’t fully replace the empathy and critical thinking that human agents provide.

Both AI and human agents have distinct strengths and limitations. Here’s a closer look at how they compare:

Availability:

AI: Works around the clock, handling a high volume of requests and providing instant responses

Human agents: Work in shifts limited to business hours. 

Response time:

AI: Provide instant responses for straightforward questions, ideal for FAQs, troubleshooting, or any high-speed tasks and computations

Human agents: Comparatively slower responses, takes time to gather and collate information, and then switch between different systems.
However, they are more flexible and adaptable, excelling in tasks that require empathy, creativity and complex decision-making

Complexity of problem-solving:

AI: Perfect for routine or structured questions, struggles with complex or unique situations since they rely on pre-programmed data and algorithms

Human agents: Great for complex or emotionally sensitive situations by assessing context errors and temperament, handling unexpected challenges better than AI

Learning process:

AI: Learns through data and algorithms and requires large amounts of data and training to improve performance and acquire knowledge

Human agents: Learn through education, experience and exposure

Creativity:

AI: Lacks creativity and generates content based on patterns in data and lacks genuine creativity or imagination

Human agents: Inherently creative, produces original ideas and innovative solutions to problems

Emotional intelligence:

AI: Cannot experience emotions to comprehend human temperament without external input

Human agents: Emotionally intelligent and capable of empathizing with others. They can also interpret non-verbal cues from social connections

Adaptability:

AI: Cannot adapt to new, unforeseen situations without reprogramming or extensive training

Human agents: Highly adaptable and can apply knowledge and skills across diverse situations

Error handling:

AI: Precise and consistent but can make errors when faced with unexpected or complex situations

Human agents: Prone to cognitive biases and errors but can handle contingencies better

Now, let’s take a look at the flip side of the coin:

AI vs. customer service agents: What do customers think? 

Customers have mixed feelings about AI in customer service. A study by Salesforce found that 75% of consumers expect AI to improve response times, but 43% say AI-driven interactions feel impersonal. 

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These numbers highlight a key challenge—businesses must balance efficiency with human connection. Now, let’s dive deeper into what customers really want:

Consumer sentiments for AI

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Younger Generations: Gen Z and millennials are more open to using AI agents for shopping or customer service, with 43% believing AI improves brand interactions

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Convenience: Over a third of consumers prefer AI to avoid repeating themselves during interactions. Additionally, 44% are more likely to use AI if its logic is explained clearly

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Comfort with AI: About 24% of consumers are comfortable with AI agents shopping for them, and 39% are okay with AI scheduling appointments.

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Speed and Efficiency: 54% of consumers prioritize quick problem resolution over the medium of interaction, showing that efficiency is a top concern

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Self-service: 81% of customers attempt to solve issues independently through self-service options before reaching out to a human agent

Consumer preferences for human agents

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• A survey of 4,000 individuals across the U.S. and U.K., conducted by Five9 and Team Lewis, revealed that 75%  of consumers favor interacting with live customer service agents over other options

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• According to a study conducted by SurveyMonkey, the majority (90%) of Americans favor human customer service agents over AI.

Reasons for favoring human agents:

• Better understanding of needs (61%)
• More thorough explanations (53%)
• Less likely to cause frustration (52%)
• Offer more options (50%)

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Striking the right balance: AI-human collaboration

The decision to use AI agents, human agents, or a combination of both depends on several factors—your business goals, customer preferences, and industry demands. 

Here’s a quick guide to help you determine the best approach for your business:

When to use AI

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Routine inquiries: Ideal for handling frequently asked questions, simple requests, and basic tasks like checking order status or providing operating hours. This allows for quick responses and reduces wait times for customers

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High-volume tasks: AI can efficiently manage a large volume of interactions simultaneously, making it suitable for businesses with high inquiry rates

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Data analysis: It can analyze customer data and sentiment to provide insights and enhance service delivery

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When to use human agents

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Complex issues: Humans can address intricate problems that require critical thinking, empathy, and personalized solutions. This includes emotionally charged situations where customers expect understanding and reassurance

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High-value interactions: For premium clients or sensitive cases, human interaction is crucial for building trust and providing tailored support

‍Creative problem-solving: Situations that demand creativity or unique solutions benefit from human insight, which AI cannot replicate

When to use a hybrid approach

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Seamless transitions: A hybrid model allows AI to handle initial inquiries and gather relevant information before escalating to a human agent for complex issues. This ensures customers receive timely assistance while still having access to personalized care when needed

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Enhanced efficiency: Businesses can improve response times and overall customer satisfaction by combining AI's speed with human empathy. This model helps reduce operational costs while maintaining high service quality

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Continuous Improvement: Regularly monitoring interactions between AI and human agents can help refine processes and enhance both systems’ effectiveness. Training agents to work alongside AI tools maximizes the benefits of both

AI-human collaboration made easy with Plivo CX

After everything we explored and discussed, it is safe to say that AI is here to enhance human capabilities, not replace them. The smartest businesses leverage AI for efficiency while keeping human agents at the forefront of complex, high-value interactions.

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Plivo’s AI agents help you achieve this balance by creating a seamless customer support experience that blends automation with human expertise. 

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Here’s how it enhances customer support:

• Always-on support: Provides instant responses to customer queries, day or night
• Brand-consistent communication: Uses your support docs, FAQs, and policies to deliver accurate, on-brand answers
• Smart escalation: Knows when to escalate complex cases to human agents for a seamless customer experience
• Multilingual capabilities: Communicates fluently in multiple languages to serve a global customer base
• Continuous learning: Gets smarter with every interaction, improving response accuracy over time
• Easy integration: Works with popular platforms like Salesforce, Zendesk, and more—no coding required
• Enterprise-ready: Offers 99.99% uptime, bank-grade security, and GDPR compliance to meet business needs

Book a demo now to learn more.

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Customer input: “Check the status of my refund.”

Automated response in the 2000s: “Your refund has been processed. It will appear in your account within 5–7 business days. For more help, contact support.”

Gen AI response in 2024: “Hi Sally, your refund for Dyson Airwrap was processed on November 20th and should reach your account by November 28th. Let me know if you don’t see it by then or need further help!”

Gartner predicts that by 2025, more than three-quarters of customer service and support organizations will deploy generative AI in some form to boost agent productivity and customer experience.

Unlike its predecessors, which relied on decision trees and pre-scripted responses, Generative AI in 2024 doesn’t blindly follow predefined rules but constantly evolves.  

The modern Gen AI is about personalization, empathy and providing actionable support to refine customer experiences.

This blog post will explore the concept of generative AI, how it differs from conversational AI and the 12 most compelling use cases of generative AI in customer service. 

What is generative AI in customer service?

Generative AI applies artificial intelligence to generate human-like responses and interact seamlessly with customers. It can create new content, including text, images, and music decoding patterns, making customer interactions more personalized and dynamic.

This is done by learning from patterns in existing data and using that information to create contextually relevant answers on the spot.

How does generative AI differ from conversational AI?

To efficiently leverage the power of conversational and generative AI, you first have to understand their unique qualities.

Conversational AI, as the name suggests, works best for on-the-spot talks, while generative AI’s specializes in generating original content from prompts given by humans.

12 use cases of generative AI in customer service 

When we think of generative AI for customer service, we generally think of chatbots and instant answers. But in reality, the scope is much more enormous. In fact, McKinsey estimates that generative AI could increase productivity by 30% to 45% of current function costs. This means more productivity, less time, effort, and money.

To understand more about how Gen AI can revolutionize the service industry, let’s have a look at some of the impactful use cases:

1.Live support

Gen AI-powered chatbots can engage with customers and provide instant assistance. They can analyze customer queries in real time to provide accurate and relevant answers, reducing wait times and ensuring customers get the information they need without delays.

For example, Delta features “Ask Delta”— a Gen AI-powered chatbot that helps customers check in, track bags, and find flights. It provides detailed responses instantly, making it easy for customers to find answers.

2.Automated helpdesk

The idea of an automated helpdesk is to reduce the burden on human agents and give customers autonomy by answering common questions. Customers type their queries into the portal, and Gen AI retrieves relevant information to provide an instant solution.

Slack’s Gen-AI-enabled help desk displays a prominent search bar and links to common troubleshooting topics, empowering users to find solutions independently.

3.Customer sentiment analysis

Gen AI in customer service can detect the tone of a customer’s message— positive, neutral or negative. This helps AI or the human agent understand the emotional state of the customer and tailor responses accordingly.

For example, if the customer sounds frustrated or annoyed in a conversation, the AI can immediately display empathy in its reply to calm the customer.

Customer: “This is the second time I am contacting you, and my issue remains unresolved. I am running out of patience.”

Gen AI: “I am truly sorry for the inconvenience. This must have been frustrating for you. Let me look into the details and resolve this as quickly as possible.

Thank you for your patience.”

4.Customized recommendations

Gen AI for customer support can analyze user data such as purchase history, browsing behavior, and past interactions to understand customer needs and preferences. It then creates real-time tailored suggestions to make them feel more important.

Music giant Spotify does it best by creating personalized playlists based on your musical preferences. Here’s how:

• The Gen AI takes note of your listening history, including plays, skips, rewinds and likes
• The algorithm picks your favorite genre or artists by recognizing patterns
• Gen AI dives deep into the science of sound by analyzing tempo, mood, and instrumentation to tailor a playlist for you

5.Personalized marketing campaigns

Much like the previous point, Gen AI for customer service can create personalized marketing campaigns by processing vast amounts of data, including:

• Purchase history
• Behavioral data such as browsing patterns, clicks, and time spent on specific pages
• Demographics, including age, location and other profile information
• Feedback and preferences

Funko, a famous toy brand best known for its bobblehead figures of pop cultural icons, launched a “Pop! Yourself” line to allow customers to make Funko figures of themselves and their loved ones. This is a unique way of using Gen AI to promote a connection with the brand by creating something personal.

6.Multilanguage support

Gen AI can handle multiple inquiries around the clock while identifying the language a customer is communicating with. It can translate conversations or even respond in multiple languages without needing pre-scripted answers for each language. The bot can then solve customer issues independently or transfer the conversation to a support agent who speaks the language.

Airbnb uses Gen AI to translate texts from hosts and guests, ensuring clear communication across all languages. They also have a team of support executives who can speak multiple languages to escalate conversations whenever necessary.

7.Automated transcription

Call transcripts are necessary for review, training, and quality assurance. Gen AI leverages advanced NLP and ML techniques to convert speech to text accurately.

This is how it works:

• Process audio data to produce transcriptions even in noisy environments or varied accents
• Differentiate between multiple speakers in a conversation, assigning text to the correct person
• Use contextual understanding to transcribe industry-specific jargon, acronyms, and technical terms
• Generate summaries or highlight important information

8.Complaint resolution

Gen AI can make complaint resolution more efficient through automation and generate more accurate responses and personalized solutions. It can quickly analyze and categorize customer complaints (e.g., delivery issues, product defects, service problems).

Once a customer raises a complaint, Gen AI can instantly generate tailored responses and provide immediate acknowledgement about the issue being addressed. It can then suggest personalized resolution suggestions or escalate to a human agent by analyzing the severity of the issue.

9.Immersive training

Businesses can incorporate Gen AI to refine training programs and offer instant and accurate customer service. It creates realistic scenarios and simulates customer interactions to provide employees with hands-on practice.

Gen AI-driven training programs analyze historical customer data to identify regular customer problems and develop tailored training modules. This ensures customer reps gain the specific skills and knowledge required to deliver outstanding service.

10.Automated billing support

Gen AI-powered automated billing systems can efficiently address the pressing pain areas in billing. It can:

• Detect fraudulent invoices by analyzing patterns and detecting irregularities in invoice records
• Check and confirm customer information, payment details, and other relevant information before initiating payment
• Create visually appealing invoices with Gen AI-generated suggestions for text, images and design elements

For example, Mastercard uses Gen AI, which features an automatic billing updater that can provide insight into account balances, review transaction histories and make personalized recommendations. It also automatically updates card information in real-time to reduce declined transactions.

11.Automated survey

Gen AI supports businesses in creating tailored survey invitations and reminders. It can effectively generate close-ended questions and analyze open-ended responses, streamlining the survey process while facilitating better data analysis.

It can even summarize reviews and categorize insights without manual effort. For example, if a user writes, “The website is slow on mobile.” Gen AI immediately tags it as a “mobile usability issue.”

12.Appointment scheduling

Traditional appointment scheduling is a time-consuming process that often requires customers to deal with long hold times and booking errors. Gen AI can address this issue by:

• Scheduling according to availability by integrating with calendars
• Allowing users to schedule, reschedule, or cancel appointments anytime without human intervention
• Recommending personalized schedules by analyzing preferences, habits and historical data
• Sending automated reminders and notifications to improve customer engagement
• Rescheduling by identifying new slots

Generative AI meets Plivo CX: A new era in customer experience

The 2020s mark a significant turning point in customer support as AI shifts from being an add-on to becoming its backbone. It’s a giant leap toward a future where customer service, powered by data-driven insights, becomes the core of business success.

As an AI-powered omnichannel customer service platform, Plivo CX can be your ally in this journey, built to integrate effortlessly into your business and support mechanisms. It brings frequently-used communication channels, such as voice, SMS, WhatsApp, and live chat, under a single window pane.

Additionally, you can train OpenAI-powered chatbots using your company’s unique datasets, ensuring customer queries are answered with the most relevant and precise information.

Here are some of its top features:

• AI-powered chatbots: Interact seamlessly with customers by providing them accurate responses at speed
• Omnichannel support: Seamlessly monitor, manage, and track customer interactions across multiple channels on a single platform.
• Unified agent desktop: Empower agents with intelligent customer context and insights into a single, easy-to-use application
• Round-the-clock availability: Provide continuous, global customer support 24/7 without interruptions.
• Simplified escalations with notes: Effortlessly summarize conversations and pass them to human agents whenever needed.
• Detailed customer insights: Examine live or recorded interactions to uncover opportunities for growth and improvement.
• Agent training tools: Enhance agent performance with features like call recording, whispering, and barge-in coaching.
• Automated ticket management: Automatically organize and prioritize incoming tickets, ensuring critical issues are addressed quickly.
• Interactive voice response (IVR): Set up automated menus to direct callers to the appropriate team or agent efficiently.
• CRM integration: Access customer details from your existing CRM and tech stack for a more personalized experience.

Book a demo with us today!