Sending millions of SMS messages? Scaling is easy—keeping them personal isn’t. Traditional automation rarely gets both right.

You could switch to channels offering more personalization, but with a higher open rate, SMS remains unmatched in reach.  Sticking with SMS, however, risks making messages feel robotic with 71% of customers expecting a personal touch.

AI SMS chatbots act as a strategic partner to bridge this gap. They manage high-volume interactions while ensuring messages stay personalized. From reminders to support and promotions, AI-powered SMS chatbots keep businesses responsive without overloading teams.

In this blog post, we’ll break down how AI chatbots for SMS work, their biggest business advantages, and how to set them up for maximum impact.

What is an SMS chatbot?

An SMS chatbot is a virtual assistant that interacts with customers through text messages. It works through standard messaging protocols and is accessible to anyone with a mobile phone without an internet connection or app downloads.

These bots analyze incoming messages, determine customer intent, and deliver appropriate responses. SMS chatbots for businesses have three categories:

• Rule-based SMS bots that follow specific instructions and recognize pre-set keywords. When a customer texts "DISCOUNT," the bot sends current promotion details. These bots work well for straightforward, repetitive questions but struggle with unexpected queries or variations in phrasing.
• AI-powered SMS bots which use artificial intelligence to create natural conversations. They understand context and intent, not just keywords. Whether someone asks, "Do you deliver to my area?" or "Can I get this shipped to my address?", an AI bot recognizes the underlying intent as delivery questions.
• Hybrid SMS bots that combine both approaches. They use quick, rule-based responses for standard questions while deploying AI for complex situations. For example, a retail hybrid bot might handle "TRACK" with an order status link but use AI to help a customer find the right product based on their specific needs.

Traditional SMS automation handles basic messaging, but an AI SMS chatbot takes it further. If you need to adapt to customer behavior, build smarter campaigns, and refine interactions in real-time, AI is the way to go.

Here’s how AI-powered techniques make it happen:

• Natural language processing (NLP) in SMS chatbots interprets customer messages regardless of how they're phrased
• Machine learning (ML) chatbots help the system improve from every conversation
• Context awareness allows bots to remember previous messages in a conversation for more natural interactions

Businesses are benefiting from an AI SMS chatbot like a pro

Businesses across the following industries are customizing AI SMS chatbots for their regular operations:

• E-commerce: To track orders, recommend products based on purchase history, and recover abandoned carts with automated reminders.
• Healthcare: To send appointment reminders, check initial symptoms, and answer common patient questions about procedures and policies.
• Real estate: To respond to property inquiries, schedule virtual tours, and provide mortgage calculation assistance to potential buyers.
• Transactional/operational: To deliver purchase confirmations, account access support, appointment updates, and shipping notifications automatically.
• Customer support: To answer FAQs, troubleshoot common issues, and collect information before human agent handoff when needed.
• Marketing: To distribute promotional offers, manage contest entries through keywords, and send personalized discount codes to drive sales.

Now, align it with your event-base requirements

With the benefits of AI SMS chatbots clear, let’s see how SMS messaging bots work to show tangible results. 

Automate appointment scheduling and reminders

Prevent appointment gaps in service-based businesses (say, clinics or salons) with automated reminders. Your SMS chatbot automation sends timely reminders, processes confirmations, and handles rescheduling requests without staff involvement.

Customers appreciate the convenience while your calendar stays full.

Deliver instant personalized offers and discounts

Send personalized offers to customers based on previous purchases. The AI analyzes shopping patterns and sends relevant promotions at optimal times.

For example, when a customer browses running shoes online but doesn't purchase them, the system texts a limited-time discount code for them to use.

Send real-time order and shipping updates

Keep customers informed about their orders through automatic alerts.

Your chatbot sends order confirmations, shipping notifications, and delivery estimates. If delays occur, it proactively explains the situation and offers solutions before customers need to ask.

Recover lost sales with cart reminders

Send recovery messages with direct checkout links. Your SMS chatbot reminds shoppers about items they left behind and offers simple ways to complete their purchases.

Sometimes, a limited-time discount offer provides the final push needed to complete the transaction, and you can automate that, too.

Get customer insights through automated surveys

Businesses often gather insights through post-purchase text surveys. You can request reviews at the right moment, especially when satisfaction is highest.

SMS chatbots will ask simple questions, measure satisfaction, and route negative responses to support teams for immediate resolution. The simplicity of responding to a question via text leads to higher participation rates than email surveys.

Answer customer queries with zero wait time

Train your chatbot on product manuals, FAQs, and support documentation to solve technical issues instantly. It will guide customers through troubleshooting steps and know when to escalate complex problems to human agents.

Choose the perfect vendor to make chatbots “work for you”

The market offers numerous chatbot solutions with varying capabilities, so you need clear evaluation criteria before making an investment.

What to look for when choosing a chatbot provider

Look for these four factors in your potential AI SMS chatbot vendor.

Setup time

Implementation speed impacts how quickly you'll see results. Some platforms offer no-code solutions that deploy in hours, while others require weeks of configuration and training.

So, ask vendors about their typical onboarding timeline and what resources you'll need to commit.

Consider whether the platform offers templates for your industry or if you'll build everything from scratch. The faster your chatbot becomes operational, the quicker you'll see returns on your investment.

Budget

SMS chatbot pricing structures vary widely. Most vendors charge based on message volume, number of active users, or features accessed.

Some charge separately for AI messaging automation, advanced analytics, or premium integrations. Look beyond the base subscription to understand all costs involved.

Choose platforms with winning case studies relevant to your industry, not just the cheapest pricing. Many vendors offer starter plans with limited features that you'll quickly outgrow, so understand upgrade paths before committing.

Calculate your expected monthly message volume and compare per-message rates across vendors. Check volume discounts if you expect more growth.

Integrations

Without proper integration, you'll create data silos that limit efficiency and need manual work to maintain. Look for vendors that offer native integrations to your platform.

Essential integrations are your customer relationship management (CRM) system for customer data access, an e-commerce platform for order information, and a support ticketing system for escalations.

Test these connections during your evaluation period to verify two-way data flow. The best chatbot solutions offer both pre-built connections and custom application programming interface (API) options for unique requirements.

Data security and compliance

SMS conversations contain sensitive customer information. Look for vendors offering encryption for data in transit and at rest. Ask about data retention policies and customer information storage locations.

For regulated industries, confirm the provider complies with security standards like the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR).

Understand how the vendor handles security updates and vulnerabilities. Many chatbot platforms use third-party AI models — know exactly where your conversation data goes and how it's protected throughout the process.

What to keep in mind while building a chatbot

Let’s look at the non-negotiables now.

Personalized messages

Generic AI text messages make interactions feel cold and impersonal. Use AI to personalize messages based on customer history, preferences, and previous interactions to make conversations more engaging.

Your chatbot should follow up with customers at the right frequency. The AI bot settings must allow you to customize follow-ups. Choose how often your bot reaches out if customers don't respond and set appropriate waiting periods between messages.

Multi-channel capabilities

Relying only on SMS while ignoring other customer touchpoints creates a disconnected experience. Choose a chatbot that integrates with your CRM, email, and social media messaging tools for a seamless platform experience.

For maximum reach, combine SMS chatbots with WhatsApp and Facebook Messenger options.

Use each platform for its strengths: SMS works best for appointment reminders and urgent notifications. At the same time, WhatsApp is great for order tracking with its ability to share links and product images.

Real conversations, not just quick replies

Complex menus, unnecessary steps, or wordy responses can frustrate your customers and lead to cart abandonment. Keep interactions short, clear, and intuitive with natural language responses rather than rigid structures.

Response speed matters, but so does conversation quality.

Your chatbot should mimic human chat patterns, for example, waiting for customers to finish typing before responding. Balance speed with thoughtful, relevant, and coherent responses so customers get the right information without feeling rushed.

Let Plivo AI SMS chatbots handle the heavy lifting

After selecting the right SMS chatbot platform, you need reliable messaging infrastructure to power it. Plivo comes with a complete package — powerful SMS capabilities + AI-driven automation for customer interactions.

With Plivo SMS API, your business gets frictionless, fraud-proof customer verification, plus:

✔ Direct carrier connections to eliminate third-party aggregators, ensuring faster delivery, minimal latency, and maximum campaign impact.

✔ Built-in fraud protection with FraudShield, an AI-driven system that detects and blocks SMS pumping and fake signups with a one-click setup — saving over 95% in costs.

✔ Seamless two-factor authentication (2FA) implementation for secure logins, financial transactions, and identity verification.

✔ Integration in minutes with intuitive APIs and comprehensive software development kits (SDKs) for PHP, Python, Ruby, .NET, Node.js, Java, and Go, getting you up and running with minimal development time.

✔ Global reach across 220+ countries and territories using compliant sender IDs, shortcodes, toll-free numbers, and long codes to maximize deliverability while meeting local regulations.

✔ Intelligent routing to better-performing carriers for sending alerts and reminders to 95% of recipients within three seconds.

Take Fastboy Marketing, for example. The Houston-based software company that sends millions of SMS messages monthly with Plivo. They don’t need customers to download an app; they simply direct messages to their mobile numbers. Plivo’s simple SMS API and easy integration have made their operations smoother.

Maya Tran, Product Manager at Fastboy, says, “What keeps us here is simple APIs, easy integration, a friendly interface, and an easy SDK. And every time we’ve needed support, Plivo has responded to our tickets in a timely manner.”

Automate SMS marketing with AI SMS chatbot

 Plivo’s AI SMS agents are virtual assistants that work 24/7 to:

• Acquire new customers with keyword-based opt-ins that trigger automated welcome sequences when prospects text specific terms (say, "JOIN" or "DEALS.")
• Recover lost sales using abandoned cart reminders that include direct checkout links and limited-time incentives. Convert hesitant shoppers without manual follow-up.
• Drive repeat purchases with personalized promotions based on purchase history, back-in-stock alerts for previously viewed items, and loyalty rewards that recognize customer value.

Automate engagement with SMS workflows that confirm orders, send shipping updates, request reviews after delivery, and recommend complementary products based on recent purchases.

Business leaders are already sharing their positive experiences with SMS automation across social media platforms like X:

Plivo: The AI SMS chatbot that works while you scale

With Plivo’s SMS infrastructure, businesses can deliver seamless, effective, and personalized customer interactions at scale.

Train AI agents to mirror your brand’s tone, pull real-time customer data for context-aware responses, and automate interactions across multiple platforms without losing the human touch.

Whether you’re integrating enterprise systems like Shopify, Salesforce, etc., feeding AI with knowledge bases and internal documentation, or deploying custom chatbots via APIs — Plivo helps you automate smarter right from where your business needs it.

Let’s build the future of AI-powered messaging that converts prospects and supports customers. Contact us to access Plivo's reliable, high-delivery SMS platform.

AI used to mean typing a prompt and watching it respond.

Now, it means your systems are taking the lead, spotting gaps, making decisions, and AI agents getting things done across tools, without you lifting a finger.

Sounds too good to be true?

The agentic AI framework behind it all makes this possible. It provides the foundation that AI agents need to function independently — an architecture that lets them interact, collaborate, and continuously improve without human input.

AI isn’t limited to executing tasks anymore; rather, it figures out the best way to accomplish things.

This guide breaks down what makes agentic frameworks work, which ones lead the market, and how to implement them in your business today.

What is an agentic AI framework?

Agentic AI frameworks provide the technical structure for building autonomous AI agents that act without constant human direction. These agents perceive inputs, process them using algorithms or large language models (LLMs), and execute actions like retrieving data, automating workflows, making API calls, or responding to users.

Businesses use agentic frameworks to automate routine operations through pre-built modules, simplifying development and ensuring scalability and efficiency.

The core components that make agentic AI intelligent and autonomous

Agentic AI frameworks rely on interconnected components to enable autonomous decision-making, action, and learning. These modules work together to process inputs, analyze data, execute tasks, and adapt without constant oversight. Here’s a closer look.

Perception module (data ingestion and signal processing)

The perception module collects and processes information from various sources, creating a foundation for all AI decisions. Here’s how it works:

• Data collection: Uses sensors like cameras, mics, or web scrapers to pull in text, images, or audio.
• Preprocessing: Normalizes and filters raw data to make it usable.
• Feature extraction: Extracts key details (like objects or voices) using machine learning (ML) or neural networks.
• Integration:  Sends processed data to other modules for decisions or complex tasks.

Cognitive module (decision engine and computational core)

The cognitive module processes information and makes decisions based on goals, constraints, and available data. This central component determines the AI's actions. It:

• Defines goals (say, optimizing workflows or resolving issues).
• Selects the best course of action to achieve the goals through reinforcement learning.
• Applies probabilistic reasoning to handle uncertainty and solves problems using logic.
• Uses meta-learning to adapt strategies over time.

Action Module (execution layer and task orchestration)

The action module turns decisions into results through direct task execution. It connects the AI system to external platforms, allowing it to interact with business systems and implement its decisions.

Here’s what it does:

• Automates repetitive tasks without human intervention.
• Controls physical devices when needed (like robotic systems).
• Monitors execution to track the progress of operations.
• Maintains control algorithms that direct specific operations.

Learning module (adaptive intelligence and model refinement)

The learning module enables AI to improve through experience and feedback. It examines what worked, what failed, and why, then applies these insights to future operations.

This adaptive capability reduces the need for constant reprogramming as business conditions change. See how it works:

• Uses reinforcement learning to identify successful approaches through trial and error.
• Analyzes historical performance data to refine decision-making across the board.
• Implements continuous optimization to increase precision and effectiveness.
• Adjusts to new situations and challenges based on past experiences.
• Self-corrects to improve outcomes over time.

Collaboration module (multi-agent coordination and system integration)

The collaboration module connects AI with humans and other systems to enable teamwork. It ensures that AI works effectively as part of a larger technology ecosystem rather than as an isolated tool. This module:

• Integrates with business tools like customer relationship management (CRM) systems and enterprise resource planning (ERP) software.
• Streamlines workflows that span multiple systems.
• Provides user-friendly interfaces for human interaction.
• Coordinates communication between different AI agents.
• Maintains consistency across connected platforms.

Security module (threat mitigation and data protection)

The security module safeguards operations and information from threats. Its protective barrier allows AI systems to access sensitive information without compromising privacy or compliance requirements.

This module:

• Detects threats like malware or unauthorized access in real time.
• Encrypts sensitive data to prevent breaches.
• Protects against cyber attacks.
• Ensures responsible AI system delivery.

The effectiveness of an agentic framework depends on how well these components integrate and communicate with each other.

Key features of a robust agentic AI framework

Here’s what separates basic AI tools from other powerful autonomous systems: seven core features that enable agentic AI systems to function independently.

Pre-built modules

These building blocks contain defined triggers and actions that control AI agent behavior, like workflow templates, data pattern recognition systems, and application integrations. Instead of programming every function from scratch, your teams can select and configure pre-built modules that handle routine operations, making setup easy.

Memory management

AI agents need to recall past interactions to handle extended tasks.

Memory systems store conversation histories, user preferences, and previous decisions to maintain context across sessions. It lets agents pick up where they left off and build an established knowledge base to create a consistent experience that doesn't require users to repeat information.

Multi-agent AI systems collaboration

Agentic frameworks support multiple AI agents working together on big projects. Instead of relying on a single agent to do everything, these systems use orchestrators to divide tasks among specialized agents.

For example, one agent might handle email account creation while another manages software licensing, improving accuracy since each agent has specific responsibilities to focus on.

Planning and reasoning

AI agent frameworks provide reasoning engines that enable AI to plan and adapt. Using LLMs and extensive datasets, these systems analyze situations, set goals, and develop strategies to achieve them. Unlike traditional AI that follows fixed instructions, these agents adjust to changing circumstances.

Customized decision-making

AI-driven decision-making frameworks tailor interactions by analyzing user preferences and behavior patterns. This personalization creates more relevant experiences for your customers, from product recommendations to support solutions.

Your developers can also adjust response styles, add features, and refine training data to match specific business requirements, helping convert first-time users into repeat customers.

Self-learning

These frameworks learn from results and feedback, constantly refining their performance. For example, your marketing AI agents can review metrics of your campaigns, identify what drove conversions, and apply those insights to future campaigns — all without manual updates from your team.

Contextual awareness

These AI automation frameworks understand context and respond appropriately. They process multiple information streams to grasp what's happening now and what matters most.

For example, agentic AI in healthcare uses this awareness to prioritize emergency cases over routine appointments. In customer service, it helps distinguish between minor inquiries and urgent problems that need immediate escalation.

Why agentic AI frameworks are important

According to Gartner, by 2029, agentic AI will autonomously handle 80% of customer service issues without human involvement, reducing operational costs by 30%.

This growth, you see, isn't just hype. Increases in both academic publications and public interest in agentic AI through 2024 show that this technology is a fundamental shift in how AI operates.

Without a proper framework, development teams would need to program each AI agent individually, defining capabilities, monitoring performance, and ensuring consistency across deployments. This process consumes significant resources and creates potential quality variations between agents.

Beyond that, these AI agent frameworks bring the following benefits to the table:

• They automate complex workflows from order processing to inventory management.
• AI agents make real-time decisions for fraud detection in finance and solution recommendations in customer service.
• Natural language interfaces let even non-technical staff operate advanced AI models through conversational commands.
• Companies can scale operations by quickly deploying additional agents using the same framework template.
• Agents integrate with existing business systems without disrupting current operations.

Top agentic AI frameworks you need to know about

Now that you know why these AI systems for autonomous agents matter, it’s time to pick the right framework. Here’s a rundown of the top agentic AI frameworks in 2025.

Agentic frameworks and AI agent builders: which is the best shot?

While we’ve discussed agentic frameworks extensively, you might still wonder how to start creating your productive AI agents, whether you really need a framework, or if an agent builder will suffice.

AI agent builders and agentic frameworks both create intelligent agents, but they fit different needs. Let’s break it down.

Agentic frameworks are like the blueprint for AI systems. They give developers the tools to craft complex setups where multiple agents chat and collaborate. You’ll need coding skills and know-how about system design, and knowledge about distributed system concepts.

But if you want to create and deploy AI agents easily to build and run fast, you need agent builders.

Plivo AI agents offer user-friendly platforms to whip up AI agents for specific tasks like AI chatbots (say, a customer service bot) without sweating code. From no-code options to pro-level platforms, they’re perfect for focused tasks and fast deployment.

Based on framework principles, these agent builders are for speedy, targeted tasks.

How Plivo handles your customer journey

Beyond the technical frameworks and builders lies the implementation question: What can these agents actually do for your business? Plivo's platform answers this with specialized AI agents that transform your entire customer journey.

• Always on: Digital agents working 24/7, handling anything from cart abandonment campaigns to closing sales deals at 3 a.m.
• Fully autonomous: Agents manage complete workflows without human intervention.
• Infinitely scalable: They can handle 1000+ simultaneous customer conversations without staffing concerns.
• Optimized resources: One Plivo AI agent delivers the output of 50 human support staff members.
• Relationship builders: Agents maintain personalized interactions across all communication channels.
• Specialized expertise: You can deploy specialized agents for marketing, sales, and support functions simultaneously.
• Complete memory: Agents access your entire customer interaction history for contextually perfect and consistent responses.
• End-to-end issue resolution: They eliminate 100% of support tickets by handling problems from identification to solution.

Configure your agentic AI with Plivo

Plivo CX puts you in control of how AI agents represent your brand and solve customer problems. These digital agents handle routine inquiries, process transactions, and build customer relationships while your staff focuses on complex business processes.

Within connected systems, your agents take immediate action, like processing returns, modifying orders, and updating customer records without transferring to human staff. When issues exceed AI capabilities, the platform’s smart escalation passes comprehensive case notes to your team.

Contact us to build an AI-driven ecosystem that works for you.

It's remarkable that a mere 5% improvement in customer retention can boost profits by 25% to 95%. Yet, the subtle shifts in customer behavior that point towards churn often go unnoticed by many organizations.

That’s where AI comes in. 

It lets you catch subtle behavioral shifts such as a drop in engagement, fewer purchases, or slower login activity. 

Take Chewy, for example. The brand uses AI to detect when customers stop reordering and sends timely, personalized reminders. You can do the same. 

This guide will explore how you can use AI to reduce churn, enhance customer satisfaction, and foster stronger customer relationships through real-life use cases.

Importance of AI for customer retention

AI-powered systems have shown a 31.5% increase in customer satisfaction scores and a 24.8% improvement in customer retention rates. 

Let’s break down how AI directly supports smarter, faster, and more effective customer retention strategies:

1. The shift from reactive to proactive customer engagement

From fixing issues after they happen to preventing them before they occur, AI is changing how you approach customer support. AI also enables teams to analyze patterns across users, helping prevent widespread issues and enhancing communication.

For example, SAP’s bi-directional support model uses AI to detect problems early and engage customers proactively. 

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During CyberWeek 2024, this model enabled SAP to maintain 100% uptime, despite a 23.42% increase in order volume and a 200% growth in mobile usage. 

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2. AI’s ability to analyze vast amounts of data in real time

AI in customer service gives you a clear view of what your customers want. It goes through large volumes of data to find patterns in behavior, preferences, and buying habits. 

You can use these insights to create targeted campaigns and improve your service. 

For example, Amazon uses generative AI to recommend products based on what you’ve viewed or bought, boosting sales and customer satisfaction. 

Additionally, with AI, you can quickly scale customer support without needing to hire additional resources. It also works around the clock, answering questions and guiding users in real time.

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3. Speed, scale, and precision

Think back to when you contacted customer support and got an immediate response. It felt good, right? 

That’s precisely what AI helps you deliver. 

It speeds up replies, handles common questions instantly, and frees your team to focus on complex issues. 

A recent study by Gartner found that by 2027, chatbots will become the primary customer service channel for 25% of organizations. AI also ensures consistent support across time zones, handling high volumes without delays, and scales as your business grows.

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4. Deeper customer insights and behavioral understanding

AI helps you dig deeper into what your customers actually want by analyzing their behavior, such as clicks, time spent on a feature, drop-off points, and more. 

Instead of guessing, you get hard data showing what drives engagement or causes churn.

For example, see how a Reddit user talks about how they used AI to personalize push notifications based on user behavior. 

The result? A 30% boost in retention in just one month!

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Core AI technologies used in customer retention

Here’s how  AI technologies help you understand, engage, and retain your customers more effectively:

Predictive analytics: Forecasting churn and customer behavior

Predictive analytics for customer retention uses historical and real-time data to forecast customer behavior. It helps you spot churn risks before they happen. 

For instance, if a user’s login frequency drops or they abandon carts repeatedly, the system can flag them as high-risk. You can then proactively re-engage them with special offers or personalized messages.

Natural Language Processing (NLP): Understanding customer sentiment from interactions

NLP analyzes customer conversations in emails, chats, reviews, or social media to detect tone, intent, and satisfaction levels. It helps you understand your customers' feelings, even when they don’t explicitly say it. 

As a case in point, a negative sentiment in repeated support tickets may signal dissatisfaction, allowing you to intervene early.

ML Algorithms: Identifying patterns in customer activity

ML continuously learns from customer behavior to detect trends that manual analysis might miss. It can uncover hidden patterns, such as which actions lead to churn or loyalty. 

Over time, it refines its predictions, making your retention strategies smarter and more targeted.

Automation tools: For customer communication and campaign delivery

AI-powered automation tools can send the right message at the right time. Whether it’s a reactivation email, loyalty reward, or post-purchase follow-up, these tools keep your brand top-of-mind. 

This reduces manual effort while ensuring consistency and timely communication.

Recommendation engines: For personalized product suggestions

AI-driven recommendation engines analyze browsing and purchase history to suggest products each customer is most likely to buy. This boosts engagement and repeat purchases.

For example, brands like Amazon and eBay keep users hooked by tailoring recommendations and suggestions to individual preferences.

AI-based retention strategies with a real-life use case

According to an Accenture report, the number of companies with fully AI-led operations nearly doubled in just one year, from 9% in 2023 to 16% in 2024. These organizations are achieving 2.4X higher productivity and building smarter retention strategies. 

Here are real-world examples of how e-commerce companies use AI to reduce customer churn:

Amazon’s predictive churn analysis

As an e-commerce giant, Amazon uses AI to analyze signals such as fewer purchases, cart abandonment, and longer gaps between visits. If a customer shows signs of disengagement, AI models flag them as being at risk.

Amazon then re-engages them with personalized actions, such as discounts, product recommendations, or reminders, based on their browsing history. 

If any customer abandons their cart, the company follows up with personalized emails and notifications offering discounts or suggesting similar produc

Hyper-personalized engagement with Sephora

Sephora has one of the best personalization strategies, using AI to nurture bonds and connections with customers. It offers tailored product recommendations through its website and app by analyzing customer behavior, skin type, and purchase history. 

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Shoppers can also create beauty profiles to receive personalized skincare and makeup suggestions. The brand features tools like Virtual Artist, which uses AR to let users try makeup virtually, leading to a 35% increase in skincare sales. 

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H&M’s Kik chatbot with round-the-clock support

H&M’s Kik chatbot shows how AI can make online shopping more personal and interactive. It chats with users to understand their style preferences and then suggests outfits that match their tastes. 

This creates a shopping experience similar to getting help from a personal stylist.

The chatbot also shares images of clothing and complete looks, helping users see how items might work together. This visual touch speeds up decision-making and keeps the experience engaging. 

Starbucks’ AI-powered loyalty programs

Nearly 80% of Americans belong to at least one loyalty program, and being a part of these programs can increase the chances of repeat purchases by 58%.

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One brand that does loyalty programs the right way is Starbucks. The Starbucks Rewards program reached 34.3 million active U.S. members in 2024. It operates on a points-based system, where customers earn Stars for their purchases, which can be redeemed for free items and exclusive deals. 

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Starbucks also uses customer data to deliver personalized offers. For instance, someone who regularly orders iced coffee may receive a tailored promotion for a new cold brew. 

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To add, the program is fully integrated with the Starbucks mobile app, making it easy for users to collect rewards, order ahead, pay, and locate nearby stores.  

eBay’s customer journey mapping 

eBay also relies on AI to personalize and optimize every step of the customer journey, ultimately boosting retention. It starts with tailored recommendations where AI analyzes browsing history, past purchases, and search behavior to suggest relevant products. 

The platform’s AI-powered search delivers more accurate results, helping buyers find what they need faster. Its AI system also helps improve the post-purchase experience by detecting delivery delays and sending proactive updates. 

Guided onboarding with Stitch Fix

Stitch Fix uses guided onboarding to create a highly personalized shopping experience from the start. When new users sign up, they complete a detailed style quiz about size, fit preferences, style inspirations, budget, and lifestyle. 

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This data feeds into Stitch Fix’s AI-driven recommendation engine, which pairs customers with human stylists. The more specific the input, the better the system can curate clothing selections that match the customer’s profile. 

Over time, the AI continuously refines recommendations based on feedback from each “Fix” (shipment), returns, and reviews.

Boost repeat business with Plivo‘s smart AI agents 

Evidently, leading brands like Amazon, Sephora, and Starbucks are excelling in AI-driven retention strategies.

If you’re ready to apply the same customer retention strategies to your business, Plivo can help. 

It is an all-in-one AI-powered customer engagement platform that helps your support team work smarter and faster by integrating all customer interactions, such as voice, SMS, MMS, email, and WhatsApp, into one place.

Here’s what you get with Plivo:

• Unified agent desktop: One interface to handle every channel, making it easy for agents to respond quickly and accurately while having the complete context
  
  
• Easy integration: Works with your CRM, billing, ticketing tools, and knowledge bases to ensure consistent, informed replies
  
  
• AI agents for key functions: Designed specifically for sales, retention, engagement, and support
  
  
• 24/7 chatbot support: Built on OpenAI, the chatbot handles up to 70% of common queries across channels, freeing up agents for complex issues
  
  
• Workflow automation: Sends follow-ups, routes tickets, and shares updates automatically to keep customers informed
  
  
• Smart escalation: When AI hits a limit, it hands off to a human agent with complete context, speeding up the resolution
  
  
• Enterprise-grade security: Complies with SOC 2 and GDPR for safe customer communication

Ready to increase repeat purchases and grow your revenue? 

Book a demo now.

AI agents are moving beyond isolated tasks and starting to work in coordination. Their potential becomes clearer when multiple agents are aligned toward a shared goal.

OpenAI’s new Agents SDK, which lets businesses coordinate agents, supports this shift. Unlike swarm-like architectures, where agents act independently and collectively emerge behaviors, OpenAI’s model gives developers structured control over how agents talk to each other.

For developers building AI-powered tools, implementing AI agent coordination ensures that they work synergistically than in silos. This helps optimize outcomes in customer support and operations.​

In this blog post, we’ll explain how AI agent orchestration works, why it matters, and its use cases across diverse industries.

What is AI agent orchestration?

AI agent orchestration is when multiple autonomous AI agents work together to achieve a specific business goal.

Each AI agent is designed for a specific role, like understanding customer queries, retrieving policies, or sending updates. The orchestrator defines the flow: which agent acts first, what triggers the next step, and how data moves between them. It keeps track of the entire process so that no step is skipped or repeated without reason.

Let’s say a customer sends a message asking for a refund.

Here’s how AI agent orchestration will work:

But what are the broader advantages of this kind of synchronized collaboration? Let’s see below.

Benefits of AI agent orchestration

AI agent orchestration simplifies complex workflows, benefiting industries across the board. Here’s how.

Self-improving workflows

AI agents have become integral to modern customer support, handling routine inquiries and providing quick responses. However, in most customer support systems, there’s no learning once an issue is resolved. The same questions pop up again, and agents keep repeating themselves.

With AI orchestration, AI agents can learn from every interaction. If the knowledge agent keeps surfacing the same policy or FAQ, the system can flag it, suggest updating the help docs, or even automate responses.

The direct benefit of this is AI agents power every step of your customer journey and significantly reduce the time taken to execute these workflows otherwise.

Increased operational efficiency

When every customer touchpoint is handled smartly, operational efficiency naturally improves. So when AI agents handle a customer refund request instead of sending it to a human, it saves hours. And 59% of customers are more likely to buy when their queries are resolved within a minute.

It’s not just about customers, either. Internal teams benefit too.

Instead of hopping between tools, digging through docs, or manually prioritizing tasks, AI agents can take care of it all behind the scenes, so staff can focus on work that really needs their attention.

Resource optimization

Are your cloud costs actually being put to good use? How much is going to waste?

You don’t need to worry about resource optimization, even in cloud environments with AI orchestration.

For instance, Netflix uses AWS's AI orchestration to dynamically scale computing resources for its machine learning tasks. When workloads are light, AWS scales down resources to save costs. For intensive tasks, like training recommendation models, it scales up to ensure performance. This allows Netflix to maintain high-quality streaming experiences consistently.

Improved customer experience

Without orchestrating AI agents, customer support is disjointed.

One agent might analyze the customer’s message, but that insight doesn’t flow to the next step, leading to responses that feel generic. With AI orchestration, the customer’s input, past behavior, and context are shared in real time, resulting in faster, more relevant answers.

Let’s expand on these benefits with specific use cases.

Use cases of AI agent orchestration

The benefits of AI agent orchestration span across industries. Here’s a closer look.

Customer service

No matter your industry, delivering exceptional customer service is non-negotiable.

While AI agents use cases like providing multilingual support, detecting fraud, and delivering personalized recommendations to improve customer service, these agents still operate in isolation.

What if these agents were well coordinated?

This provides customers with a consistent and fluid experience. For example, with Plivo, you can build a:

• Sales conversion agent to help customers complete their purchases.
• Shopping assistant to offer step-by-step guidance and help shoppers make confident buying decisions.
• Lead qualification agent to qualify leads and convert visitors into buyers.
• Recover abandoned cart agents or agents to welcome new subscribers.

When these agents work together, they create a fluid journey: a welcome agent greets the shopper, a shopping assistant offers personalized guidance, and the sales agent qualifies the lead and converts the sale.

Healthcare

Leading healthcare institutions like the Cleveland Clinic and Mayo Clinic have begun integrating AI-driven workflows to improve operational efficiency and patient care. While agentic AI in healthcare automates administrative duties, diagnostics, and patient engagement, AI orchestration takes this a step further by optimizing workflows and improving overall healthcare delivery.​

For example, the Mayo Clinic has developed a model called RadOnc-GPT that assists in creating treatment plans by analyzing patient records. They also utilize AI to automate routine clinical tasks, such as documenting nursing notes using ambient listening, and employ robots for non-clinical tasks like linen delivery.

Finance

AI-driven solutions in financial services can lower operational costs by up to 30% and enhance fraud detection by 50-80%. PayPal has already incorporated machine learning models to detect suspicious behavior in real time.

When the fraud agent detects an issue and alerts the service agent, the service agent proactively reaches out to the customer with the right context. The marketing agent pauses promotions until the issue is resolved.

On the customer-facing side, AI orchestration enables agents to collaborate and deliver personalized insights. So instead of generic advice, customers get financial suggestions that factor in their credit history, savings goals, and even recent transactions since all agents are coordinated.

For instance, incorporating Plivo streamlines the routine communication workflows that accompany these decisions, such as sending real-time alerts for credit score changes, automated voice updates for loan approvals, or follow-ups for investment opportunities.

E-commerce and retail

Amazon recently shared how connected retail systems can unlock new levels of intelligence using tools like Amazon SageMaker for machine learning-powered predictive analytics and automated workflows.

With these capabilities, retailers would be able to optimize inventory in real time using computer vision and Internet of Things (IoT) sensors, assign tasks smarter with AI-driven workforce management, predict equipment failures before they happen, automate pricing and promotions, and even enable frictionless checkout.

If you’re running an e-commerce or retail business, you can begin by introducing something as simple and powerful as an AI-powered shopping assistant. These assistants can help customers find products faster, recommend items based on behavior, and answer common questions in real time.

Plivo helps you take this a step further. Build your voice-based shopping assistant that talks to customers, guides them through product options, applies discounts, and even helps complete purchases over a phone call or chatbot.

Travel and tourism 

Planning a trip involves several moving parts. With AI orchestration:

• A natural language processing (NLP) agent interprets the traveler’s request (e.g., “Book me a flight to New York next Monday”).
• The predictive agent steps in to suggest the best travel routes and times based on preferences, price trends, and availability.
• A transactional agent handles bookings for flights, hotels, and car rentals.
• A document-check agent might verify passport and visa requirements, and a communication agent keeps the traveler updated through emails or SMS.

Here’s an example of OpenAI’s agents booking a flight for a user.

The future of AI agent orchestration

As UiPath CEO Daniel Dines mentions, “AI agents are not just tools anymore; they’re active participants in the automation ecosystem.”

This shift means AI agents, like NLP, predictive analytics, and decision-making agents, must work together in real time.

For example, in smart homes, AI agents orchestrate temperature control, security, and energy management. With IoT and 5G integration, industries like self-driving cars will rely on synchronized agents to manage navigation, safety, and vehicle health, creating smarter, more efficient systems across sectors.

A Reddit user shares an interesting insight on how the future may also hold an agent-to-agent conversation, where soon customers may also use agents to answer questions.

Despite the anticipated advancements, challenges persist. Ensuring alignment across agents, managing system complexity, and securing user data are all very legitimate concerns. With that in mind, platforms like Plivo enable businesses to orchestrate AI agents with real-time control, transparency, and human oversight, resulting in improved management.

How Plivo helps you orchestrate smarter AI conversations

Plivo helps businesses strike a balance between operational efficiency and customer satisfaction — the two core pillars that drive businesses. It lets you create AI agents tailored to your needs. From customer support and lead generation to workflow automation, you can build AI-powered agents with the flexibility to choose text-to-speech (TTS) and large language models (LLMs).

It integrates seamlessly with your existing tech stack, including e-commerce platforms like Shopify and WooCommerce, customer relationship management (CRM) systems such as Salesforce and HubSpot, customer support tools, and business management software.

Unified customer data and engagement analytics help you track the effectiveness of AI interactions and continuously optimize them.

Ready to automate tasks, improve response times, and offer a highly personalized customer experience across multiple touchpoints? Get started with Plivo for free!

When a pioneer like OpenAI invites users to create voice agents using its new audio models, it’s clear that voice agents are no longer futuristic.

But for growing businesses, the shift can be confusing.

How do you move beyond scripted bots? Which agent fits your use case? Do you need AI voice bots for customer support, appointment scheduling, or multilingual assistance? And most importantly, how do you ensure you're not overpaying for the best AI voice platforms giving bare-minimum performance? 

In this blog post, we break it down for you. We’ll explore the five best AI voice agents in the market, compare their pricing, highlight what makes each unique, and help you find the best fit for your business.

5 best AI voice agents: A quick overview 

Before exploring all five AI-powered voice assistants in detail, here’s a quick snapshot of their core strengths, pricing, and whether they’re built for real-time conversations.

Let’s closely examine each of these voice agents.

1. Plivo

Plivo’s AI Voice Agents do more than automate routine tasks. They help you build intelligent, customizable assistants. 

You can choose your preferred language model (LLM), text-to-speech (TTS) provider, and language to create voice agents customized to various communication needs.

From healthcare business requirements like sending real-time alerts and notifications or appointment reminders to offering 24/7 customer support, Plivo’s Voice AI for businesses handles varying touchpoints in the customer journey.

Plivo’s key features

• Real-time conversations: Plivo Voice Agents respond instantly due to ultra-low latency of under 30 milliseconds, so your customers don’t need to wait for a human agent and their queries are promptly answered.
• Custom-trained agents: You can personalize voice agents using your own knowledge base, FAQs, or business data to reflect your tone and customer needs.
• Reliable infrastructure: Since the bots are hosted on Plivo’s proven voice platform, these agents benefit from 99.99% uptime and scalable global coverage for high call quality. This makes it one of the best AI call center solutions.
• Plug-and-play integrations: Connecting to AI models and speech providers like OpenAI, Deepgram, and ElevenLabs is easy without a complex setup.
• Flexible use cases: You can use these agents to optimize any customer touchpoint of your customer’s journey, whether it’s making outbound calls, providing customer support, sending reminders, or converting a lead.

Plivo’s pros

• The API is well-documented and easy to use, enabling smooth integration. (Source)
• Plivo’s support team is highly responsive and well-informed. (Source)
• Pricing is competitive and designed to offer strong value for the features provided. (Source)
• Built on a global infrastructure with 99.99% uptime. (Source)

Plivo’s cons

• While functional, the dashboard could benefit from a more user-friendly interface. (Source)

Plivo’s pricing

$0.003 per min per stream

Note: You can save 40% with Plivo as other players charge > = $0.005 per min per stream.

Plivo is suitable for

Scaling businesses looking for a cost-friendly solution to optimize customer interactions and automate lead qualification and conversions through AI-powered voice bots.

2. Five9 Intelligent Virtual Agent (IVA)

Five9’s virtual agents use conversational AI to automate routine customer interactions, handle support calls, and assist live agents. So, if you want to boost efficiency, lower wait times, and scale support without increasing headcount, Five9 is a go-to option.

Five9’s key features

• Conversational AI: It employs advanced speech recognition and NLP in AI agents to understand and respond to customer inquiries across voice and digital channels.​
• Omnichannel support: Five9 provides consistent customer experiences across multiple channels, including voice, chat, SMS, and social media.​
• Integration capabilities: You can integrate it with popular CRM systems like Salesforce, ServiceNow, Microsoft, Oracle, and Zendesk to improve agent efficiency.

Five9’s pros

• Reliable platform with geographic failover for uninterrupted service. (Source)
• The outbound dialer is feature-rich, easy to set up, and simple to monitor. (Source)
• List Sync Plus automatically syncs records into the Five9 dialer for simplified list management. (Source)
• Accessible from anywhere, including mobile devices. (Source)

Five9’s cons

• Inconsistent call quality makes conversations difficult to hear. (Source)
• Its interface lacks appeal or text functionality. (Source)
• Initial tool deployment and setup can be challenging. (Source)

Five9’s pricing

$119/month for voice-only  

Five9 is suitable for

Enterprises aiming to improve customer support and automate lead qualification.

3. Tabbly.io

Tabbly.io is an AI-powered platform that helps businesses build custom voice agents for sales, support, and lead management without writing a single line of code. With multilingual support and CRM integration, the platform makes it easy for teams to automate conversations, qualify leads, and respond to customer queries in real time.

Tabbly’s key features

• Multilingual voice support: It offers over 50 language voices so businesses can interact with a diverse customer base in their preferred language. 
• No-code agent builder: Users can create custom AI voice agents without advanced coding skills by defining prompts, selecting voices, and setting output instructions.

Tabbly’s pros

• Engages customers naturally in over 50 languages
• Handles multilingual communication with regional accent support
• Works across use cases like sales, customer support, hiring, and collections

Tabbly’s cons

As a relatively new platform, businesses with large-scale operations may find scalability to be a concern.

Tabbly’s pricing

$0.08 per minute 

$0.06 per minute for large committed volumes

Tabbly is suitable for

Startups and SMBs looking for a quick way to scale communication through voice.

4. Talkdesk

​Talkdesk AI-driven call automation is built on the Talkdesk Ascend AI platform so that you can deploy them with a simple natural-language prompt. It also integrates with customer relationship management (CRM) and electronic health records (EHR) systems.

They analyze customer speech in real time with AI-driven speech recognition tools, adapt to nuances such as slang and idioms, and take appropriate actions without complex scripting or extensive training. ​

Talkdesk’s key features 

• Natural language deployment: You can create and deploy AI agents using simple prompts, eliminating the need for coding. 
• Real-time speech analysis: It understands and responds to customer conversations dynamically to handle variations in speech and context.
• Multi-language support: You can engage with customers in 59 languages without requiring translation, which lets you reach a global customer base.

Talkdesk’s pros

• Highly customizable through both native features and robust APIs. (Source)
• Flexible tools and customization options for both agent operations and admin management. (Source)
• Status settings like available or busy help control call flow, ensuring agents only receive calls when ready. (Source)

Talkdesk’s cons

• Customization options can make troubleshooting and support responses more challenging. (Source)
• The feature set may lack the depth and stability of specialized AI solutions. (Source)
• Occasional technical issues, including the inability to pause call recordings, may disrupt certain workflows. (Source)

Talkdesk’s pricing

$110/month per user for CX cloud voice services 

Talkdesk is suitable for 

Enterprises and global businesses needing scalable, multichannel, AI-powered voice automation.

5. Cognigy

Cognigy helps you automate and improve customer and employee interactions. Its flagship product, Cognigy.AI, empowers businesses to create virtual agents that deliver personalized, multilingual, and real-time customer experiences.

Cognigy’s key features 

• Multilingual capabilities: It supports over 100 languages with real-time translation, allowing businesses to engage with a global customer base.
• Low-code development: Cognigy.AI offers a user-friendly, low-code interface, making it accessible for technical and non-technical users to design and deploy conversational agents. 
• Real-time agent assistance: The tool provides real-time agent support and features like sentiment analysis, live translation, and automated recommendations, improving agent efficiency.

Cognigy’s pros

• Easy to use, web-based graphical user interface. (Source)
• The platform requires no coding expertise, making it an efficient option. (Source)
• Offers a robust library of out-of-the-box integrations for seamless connectivity with other tools. (Source)

Cognigy’s cons

• Building custom extensions can be difficult for non-technical persons. (Source)
• A few parts of the implementation documentation to implement new features are hard to find. (Source) 
• The reporting dashboard is a little outdated. (Source)

Cognigy’s pricing 

Custom pricing is available on reaching out to their sales team.

Cognigy is suitable for

Enterprises looking for advanced conversational AI to automate customer service, streamline support processes, and integrate with existing systems.

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Use cases of AI voice agents

Thinking about using AI phone agents for healthcare, finance, or just better customer service overall? Let’s explore how businesses across industries are already putting them to work.

1. Customer service

With 71% of Gen Z preferring live phone support, businesses can’t afford long wait times or robotic responses. 

Verizon tackled this by integrating Google AI voice agents trained on over 15,000 internal documents to assist human reps. This resulted in a 40% increase in sales. (An example of how you can use AI voice assistants for sales too!)

You can do the same (and more) with Plivo’s AI voice agents. Plug in your CRM or knowledge base and your agents will deliver personalized, context-rich conversations that convert.

Alt text: An image showing the Plivo AI voice agent assisting a customer

They can become your customers’ digital shopping assistant recommending products or a financial guide helping them invest wisely.

2. Finance

Finance is among the most highly-regulated industries. That’s why AI voice agents are increasingly being used not just for handling loan applications, eligibility checks, and routine queries but also for improving security.

JPMorgan Chase introduced AI voice assistants in 2024 to help verify callers and flag suspicious transactions, cutting down manual verification time and improving security during inbound calls.

If you’re a growing business without the budget or bandwidth of a bank, Plivo makes it easy to get started. You can build and launch without complex dev cycles and automate everything from loan eligibility flows to fraud detection triggers.

3. Healthcare

Agentic AI is shaping patient care by sending appointment reminders, monitoring patient health, and prescribing treatments. 

In 2024, MUSC Health launched “Emily,” an AI voice agent powered by SoundHound’s Amelia to handle appointment scheduling and patient inquiries. It reduced wait times and improved the experience for both patients and staff.

While many AI voice agents can streamline patient care and provider care routines, they may miss out on compliance. 

With Plivo, you can easily build voice agents that manage patient scheduling, send follow-ups, and securely access EMR data. Since Plivo supports HIPAA-compliant workflows, you get automation and peace of mind.

It also helps healthcare providers who need specific information. They can simply ask the AI voice agents, ”What medications is this patient taking?”, and the agent retrieves precise and relevant details.

4. Operations and logistics

In logistics, delays, last-minute changes, and miscommunication can disrupt entire workflows and drive up costs. 

Take Uber Freight, for example. They use AI-driven platforms to optimize truck routing by factoring in traffic, weather, and road conditions, leading to better delivery times and guaranteed pricing.

AI voice agents can bring similar efficiency to your operations. From automating delivery confirmations and driver check-ins to sharing real-time route updates and notifying customers, voice automation can address the operational hiccups that eat into your margins.

Just pinpoint the friction points before investing in AI voice agents.

5. Travel and tourism

With the AI travel market projected to reach $13.38 billion by 2030, AI voice agents are transforming how customers plan and manage travel.

One of OpenAI’s seventeen use cases includes helping users plan an entire weekend getaway, from booking flights to recommending local experiences. It’s like having a personal travel agent available 24/7.

You can also build voice agents to handle high call volumes, reduce wait times, and provide travelers with instant, personalized support without adding pressure on human agents.

Here’s why Plivo’s AI voice agents stand out on top 

Unlike AI agents that answer FAQs, route calls, or perform specific tasks, Plivo's voice AI agents engage, convert, and delight customers across industries.

Think of them as digital coworkers: one handling support tickets, another qualifying leads, and a third nudging customers toward a sale, all at once.

Suppose a lead calls your business asking about loan eligibility; the voice agent checks their details in your CRM, walks them through the application criteria, and even books a callback with a human advisor if needed. You can connect them with your internal systems to move conversations toward specific business outcomes.

Plus, getting started with Plivo AI voice agents is super easy:

• Sign up on Plivo.
• Create your AI Agent for Voice, SMS, WhatsApp, Chat, or RCS.
• Plug in your knowledge base (FAQs, product info, support docs).
• Connect your platforms like Shopify, HubSpot, and Salesforce.
• Go live and let the agent start engaging.

As more businesses demand personalized, outcome-driven communication, Plivo represents the future of AI voice agents. Whether your business falls under finance, healthcare, e-commerce, or travel, they will support you at every step of the customer journey.

Contact us to learn how Plivo’s AI voice agents can support your voice automation needs.

Picture this: a tool that answers customer questions at 2 a.m., nurtures leads while your team sleeps, and even predicts what buyers need before they ask. No, it’s not a sci-fi fantasy. It’s what AI sales agents are offering companies worldwide.

And here’s why adopting it makes sense:

• By 2026, 30% of your competitors will use AI to automate network activities.
• Buyers don’t like to wait. Nearly 40% expect a reply within an hour, and AI can respond in seconds.

Still, like any new technology, the real question is not just what it can do, but whether it truly works for your sales team.

That’s what this guide is here to talk about. Let’s dive right in!

What is an AI sales agent?

AI sales agents are self-operating tools that analyze your sales data and customer interactions. They utilize this information to automate tasks with minimal to no human intervention.

Here’s how they simplify your workflow:

• Tackle repetitive tasks first: Things like sending personalized emails, answering FAQs, and booking meetings get taken off your plate right away.
• Give your team more time to sell: With the routine stuff out of the way, your sales team can focus entirely on building relationships and closing deals.
• Step up to more complex jobs: Once they’re set up, AI agents can even run sales training role-plays or generate customized quotes for different customers.
• Fit right into your CRM: They plug directly into tools like Salesforce or HubSpot with barely any setup. You won’t need to tear down what you already use.
• Get smarter as they go: Over time, AI-powered sales agents spot customer patterns and help guide your team on the best next moves.

Types of AI sales agents

There are two main types of AI sales agents: autonomous and assistive. One takes over tasks completely, the other supports your sales team without replacing them. Here’s how they differ:

1. Autonomous AI sales agents

These AI tools handle repetitive sales tasks on their own. They analyze customer data to:

• Contact potential leads via email/chat.
• Answer basic questions about your product.
• Schedule meetings or demos only for interested leads.

This means your sales team skips cold outreach and focuses on closing deals with ready-to-buy customers.

Example: A lead fills out a form on your website. The AI immediately sends a customized product brochure, answers questions about pricing through a chatbot, and schedules a product demo — all without needing a sales rep to take action.

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2. Assistive AI sales agents

These agents partner with your sales team to make them more effective. They help by:

• Providing real-time support, like suggesting responses or pulling up pricing details during calls.
• Recommending products based on a customer’s past behavior or preferences.
• Spotting trends in sales data to highlight upsell opportunities or risks.

How does an AI sales agent work?

AI sales agents combine automation, real-time decision-making, and learning to simplify the sales process. Here’s how they work:

• Connecting with your tools: Links to CRM systems, email platforms, chat apps, and websites to gather customer data in real time.
• Analyzing behavior: Tracks actions like email opens, website visits, past purchases, and chat interactions to understand customer needs.
• Engaging leads automatically: Sends personalized emails, answers basic questions through chat, and provides relevant information without delay.
• Scoring and prioritizing prospects: Evaluates leads based on engagement and intent, helping sales teams focus on high-potential opportunities.
• Scheduling meetings or demos: Books appointments based on a lead’s interest level and availability.

Learning and improving continuously: Updates its approach based on new customer data, getting better at predicting needs and suggesting next actions.

To illustrate a real example, here is how Nicholas Puruczky, an expert on AI agents, built his system.

According to his video, the setup moves through several connected stages, each powered by a specific tool. It all begins when a lead opts in and shares their information, which immediately flows into the CRM system. 

An automated AI call then verifies those contact details. Next comes lead qualification. Targeted questions are asked, and demos are scheduled right away.

If a prospect does not respond, a follow-up sequence takes over: a voicemail is left, followed by a series of texts and emails. At every step, anyone who opts out is automatically removed from further outreach.

A phone-calling service runs the AI scripts and connects with scheduling tools. Meanwhile, a centralized database logs every interaction and displays key details on demand.

For inbound inquiries, AI-powered sales chatbots on websites, social channels, and SMS channels help capture incoming leads. These bots check against existing records and then either book appointments or pull answers from an internal knowledge base.

Nicholas also shared a summary of his AI agent and what it achieved in this X post. He listed the agent’s main functions:

But, as Nick pointed out, the really special thing was how the agent got better with each conversation.

This constant learning led to real improvements in results, which Nick shared as the real numbers:

These numbers clearly show how AI sales agents, when they learn and improve like this, can do much more than just basic tasks. They can boost sales, allowing humans to focus on the most important task: closing deals.

Use cases of AI sales agents

Here’s where you can use AI sales agents at different stages of the sales process:

Outreach

AI sales agents are useful for reaching out to potential customers, handling key tasks like:

• Ensuring that new inbound leads get a quick, good response right away.
• Managing sending out many personalized messages to nurture leads over time for outbound efforts.

These capabilities allow for targeted, automated follow-ups based on user behavior.

For example, an AI agent can track everyone who downloads your e-book and then launch a personalized email series. Plus, each message can be tailored to the reader’s specific interests and past interactions with your brand.

Sales team support

Behind the scenes, AI sales agents lighten your team’s load in two key areas:

• Training and coaching: Review call recordings to highlight strengths and suggest improvements.
• Onboarding new reps: Guide new hires through essential steps and resources.

These agents highlight what worked and where improvements are needed, helping sales reps learn faster. They also make sure new team members receive the right information exactly when they need it.

Administrative tasks

Administrative tasks often keep your sales team tied to paperwork. AI agents can handle these repetitive jobs, such as:

• Creating customer price quotes directly from CRM entries.
• Managing billing cycles.
• Preparing invoices for dispatch.

Automating these steps frees your team to focus on selling instead of manual work. For example, when a deal is marked “closed,” an AI agent can generate the final invoice instantly and queue it for approval or sending.

Lead management

Lead management is all about finding and prioritizing prospects. AI agents simplify this by handling tasks like:

• Prospecting new contacts or companies that match your ideal customer profile.
• Sorting incoming lists to flag the most promising leads first.

AI uses data analysis to find these opportunities efficiently. For example, an AI agent might scan public online information or look at who visited certain pages on your website. It finds potential leads showing high interest and automatically flags them in your system.

Customer interaction

AI sales agents improve customer interactions by taking over frontline support tasks, such as:

• Offering personalized product recommendations.
• Powering chatbots on your website or through e-commerce virtual assistants.
• Streamlining call center workflows.

These tools provide immediate, automated frontline support. Beyond chat, these agents are increasingly handling interactions using voice.

Platforms like Plivo enable businesses to create specialized AI Voice Agents tailored to various customer requests.

These agents can manage everything from qualifying leads and scheduling appointments to handling common customer support issues. What’s more? They can be built to sound natural and align with your brand's voice while offering 24/7 availability.

Even better? These AI agents are now context-aware and memory-driven. This means they can remember past talks and understand the current situation, giving more relevant responses.

For instance, an AI voice agent can automatically call a lead to remind them about a scheduled product demo. If the lead needs to reschedule, the agent can confirm a new time based on their earlier availability and update the sales team's calendar instantly.

Benefits of AI sales agents for businesses

Adding AI to your sales team can make a bigger impact than you might expect. Here’s what businesses gain when they put AI agents to work.

Increased efficiency

Sales reps spend just 28% of their time selling and the rest of it is dedicated to tasks like sorting leads, basic screening and scheduling. As a result, they have less time to close deals.

AI agents excel at handling these routine duties.

AI sales agents manage the early stages of the sales process, from connecting with leads to qualifying them for the next step. This ensures every lead gets a quick, personalized response. With the initial filtering done, sales teams can focus fully on meaningful conversations and closing deals, leading to higher productivity and stronger revenue growth.

A company's co-founder, Adam, faced this exact problem, as mentioned in this X post:

He was spending 20-30 hours each week just answering initial inbound sales calls, often tying up valuable time on conversations with leads who were not even qualified.

Their answer to this situation was to use an AI sales agent. This agent was designed to act like a digital version of Adam for those first calls.

The AI agent's primary task was to automate first contact and qualification. To do this, it handled the initial conversation and asked the right questions to understand what prospects needed and if they were a good fit. 

With AI handling this entire process, the co-founder drastically reduced the hours he spent on these calls. This reclaimed time could then be used for more important business growth activities.

Enhanced lead generation

AI sales agents analyze huge amounts of customer data (like website visits, email clicks, past purchases, or social media activity) to spot patterns that humans might miss. This helps businesses focus on leads that are most likely to buy instead of wasting time on people who aren’t ready. 

For example, suppose you’re a SaaS company selling a project management tool. 

Your AI sales agent will track the number of leads:

• Subscribed to a free trial of the tool.
• Opened onboarding emails or clicked on setup guides.
• Visited pricing or upgrade pages multiple times.
• Requested support or asked about paid features during their trial.
• Shared positive feedback or reviews on social media.

The AI agent notices that leads who used the trial more than three times a week and visited the pricing page are more likely to subscribe to a paid plan.

Instead of your sales team calling every lead, the AI sales agent automatically ranks these “hot” leads at the top of their list. Sales reps reach out to them first with personalized offers, cutting response time and boosting conversions.

Plivo’s lead qualification agent puts this intelligence to work across voice, chat, and WhatsApp. It gathers customer intent, checks your criteria, and hands off only high-value leads to your sales reps.

Significant cost reduction

Hiring, training, and managing a large sales team is a time-consuming and expensive endeavor. From recruitment costs and onboarding programs to salaries and software licenses, the numbers add up fast.

AI sales agents can make a real difference here.

Automating repetitive tasks like lead qualification, follow-ups, and proposal generation allows AI agents to reduce the need for a large sales team.

In fact, companies adopting AI have seen up to a 35% reduction in customer service costs and a 32% increase in revenue. This kind of efficiency directly impacts your bottom line.

A great example of this comes from an X post we came across:

It's a powerful case study on just how much time and money AI agents can save, even while scaling operations.

Predictive forecasting

Imagine knowing exactly when your next big sale will happen, which products will boom, or which clients are about to slow down. Predictive forecasting makes AI sales agents capable of this.

Instead of guessing or relying on gut feelings, these tools analyze your past sales data to predict what’s coming next.

For sales managers and revenue teams, this kind of visibility is a game-changer. You can adjust your strategy in real time, double down on what’s working, and spot risks early.

Limitations and challenges of AI sales agent implementation

While AI sales agents offer many benefits, they aren’t a cure‑all. Knowing their limitations helps you use them wisely and bring in a human when needed.

Here are the main challenges to keep in mind:

Customer interaction nuances

AI sales agents are great at handling repetitive tasks and guiding basic conversations. However, when it comes to understanding emotions, picking up on tone, or building genuine trust, they still have their limitations.

Emotional intelligence plays a big role in sales, especially in complex or high-value deals. A human rep can sense hesitation, respond with empathy, and build a connection. Most AI tools just aren’t there yet.

That’s why AI should support your team, not replace it.

Data privacy concerns

AI sales agents need access to personal customer data to work effectively. This introduces the risk of privacy issues and potential data breaches.

Customers trust you to keep their information safe. One slip-up can hurt that trust and your brand. That’s why strong security measures and transparency about data use are non-negotiable.

AI can boost sales, but it has to be built on a foundation of trust and responsibility.

Adaptability in complex situations

AI-powered sales agents are great with routine tasks and clear instructions. But when a conversation goes off-script or a deal gets messy, human reps still do a better job.

This is because humans can think on their feet, ask the right follow-up questions, and adjust their approach in real time. AI, on the other hand, can get stuck or give generic responses.

In high-stakes or unpredictable sales situations, a human touch is often what closes the deal. For now, AI is best used as support, not a complete replacement.

Cultural nuances and subtle cues

Selling in global markets goes beyond speaking the same language. It also means understanding local culture, tone, and context. A phrase that works well in one region might fall flat or even offend in another.

Human sales reps are often better at picking up on these subtle differences. They can read between the lines, adjust their tone, and connect more naturally with people from different backgrounds.

AI-powered sales agents are improving, but they still struggle with cultural cues and local context. This can lead to awkward interactions or missed opportunities.

Current trends and future trajectory of AI sales agent technology

AI sales agents are evolving quickly. Recent developments are pushing the boundaries of what these tools can do. Here’s what’s shaping the current landscape and where things are headed:

Advancements in core technologies

Recent advances in language-processing AI (like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers)) have made sales agents far better at understanding conversations, spotting patterns, and avoiding mistakes.

These tools pull from real-time data and past interactions to provide accurate, personalized responses. For example, they can remember a customer’s buying history, predict their needs, and even adjust their tone to sound more human.

What does this mean for sales teams? Let’s take a look:

• Fewer awkward chatbot fails.
• Faster, hyper-relevant customer interactions.
• Less time spent on fixing errors.

In short, AI-powered sales agents are shifting from “robotic helpers” to savvy teammates who learn, adapt, and actually improve your sales process.

Development of more autonomous agents

AI sales tools are evolving fast. They’re no longer just answering basic questions — they now handle complex tasks like qualifying leads, personalizing follow-ups, and booking meetings on their own. These systems analyze data, make decisions, and even fix issues without constant human input.

The market for these AI agents is booming. Experts say it could grow to $200+ billion by 2034, with companies racing to adopt them — 85% have already started using it in 2025. 

Big players like Microsoft and Salesforce are building tools (like Salesforce’s Agentforce) that let businesses create custom AI agents for sales and customer support. For example, SharkNinja uses these tools to answer customer questions and manage returns which helps them to reduce manual work.

By 2027, 50% of companies using AI will test systems that act fully independently. This is because these agents cut errors, handle more clients without extra staff, and let teams focus on closing deals.

Personalization at scale

Customers no longer want one-size-fits-all experiences. They expect every interaction to reflect their preferences, needs, and behavior. This is where hyper-personalized AI-powered sales agents are making a difference.

A recent study also shows that 70% of customers now expect companies to use AI for personalized interactions and offers. In fact, 61% prefer brands that provide quick, tailored journeys. These numbers make it clear: personalization is no longer optional.

AI sales agents are helping businesses meet this demand. They can study large volumes of customer data, including past purchases and browsing behavior, and use that information to predict what a customer might want next.

Embrace the AI revolution in sales with Plivo

AI is changing how businesses manage customer conversations, especially in sales. But with so many tools out there, knowing where to begin can be challenging.

Plivo CX simplifies the process. It brings AI-powered agents, smart automation, and proactive support into one platform that helps your team respond faster and sell more effectively.

Here’s what you get with Plivo CX:

• Prebuilt AI agents: Convert leads, qualify prospects, recover abandoned carts, and provide support across SMS, voice, chat, and WhatsApp.
• Omnichannel dashboard: Manage email, voice, chat, and messaging apps from a single view.
• Deep integration: Connect CRM, enterprise resource planning (ERP), and helpdesk systems in minutes.
• No-code workflows: Build triggers, assign skills, and set responses without needing developers.
• Knowledge-driven AI: Train the system using your FAQs and policies to deliver accurate answers.
• AI model choice: Choose between OpenAI, Google, Anthropic, or Meta models based on your needs.

Get started for free to see how Plivo delivers better conversations for more closed deals.

You’ve spent years building trust with your customers. That trust can unravel in days or even hours if their issues aren’t resolved quickly.

Let’s say a customer reports a billing error. Your team acknowledges the ticket, but the fix takes three days. 

By day two, frustration sets in.

By day three, they’ve already Googled your competitors.

This isn’t bad luck. It’s a direct result of a high time to resolution (TTR).

Reducing time to resolution starts with fixing broken workflows, unclear priorities, and slow tools. This blog post will break down time to resolution and discuss strategies to reduce it.

What is meant by time to resolution?

Time to resolution is the total time your customer support team takes to fix a customer’s problem completely. It starts when the customer first reaches out for help and ends when their issue is fully resolved.

This number shows how smoothly your support team works.

If the time to resolution is low, your team is solving problems fast and keeping customers happy. 

And if it’s high, it means something’s going wrong; maybe your team is stuck with slow tools, confusing steps, or too many back-and-forth messages.

The key is to use the metric of time to resolution to identify and resolve issues, not just to track them.

Why is time to resolution a key customer service metric?

Time to resolution is a key customer service metric that impacts efficiency, customer experience, and operations. Here's why it matters:

1. Saves time

No one likes to wait. Not your customers, not your team. Time to resolution matters because it’s the ultimate middle ground for valuing your customers’ time and your team’s effort.

Optimizing time to resolution means you’re building a business that runs smoother, retains more customers, and spends smarter.

2. Increases satisfaction

Time to resolution is the invisible thread connecting how fast you fix problems to how happy your customers feel.

When you close tickets quickly, you’re not just checking a box, you’re telling customers, “Your time matters to us.” This builds trust.

But speed isn’t the whole story. First response time (FRT), which refers to how quickly you acknowledge a customer’s request, sets the tone for the entire interaction. 

Imagine waiting hours just to hear, “We’re on it!” Even if you fix the problem later, that initial delay leaves customers feeling ignored. That’s why first call resolution (FCR) matters. 

Solving an issue on the first try without transfers or follow-ups:

• Saves customers from repeating themselves.
• Frees up your team to handle new issues.

Boosts satisfaction as every 1% improvement in FCR drives customer satisfaction score (CSAT) by 1-5%.

Why? Because fixing things fast and right the first time shows you’re reliable, which fosters loyalty.

3. Tracks performance

While other metrics might focus on quick replies like “We’re looking into it!”, time to resolution shows whether the solution actually worked. This makes teams responsible for solving problems completely, not just responding to them.

Customers care most about results, not promises. A fast reply might seem helpful, but if the fix is rushed or incomplete, trust disappears quickly. 

When issues are solved right the first time, ticket reopen rates drop, escalations become less frequent, and customers feel genuinely acknowledged.

4. Spots problems

Tracking time to resolution is a clear way to find out why your team struggles. For example, if time to resolution increases for a specific product, it could mean that agents don’t know how to resolve those issues quickly.

The solution? Better training and clearer instructions.

Now, let’s say your team has too many tickets to handle or new hires take 3-6 months to ramp up. A longer time to resolution reflects these slowdowns, giving you concrete data to justify hiring more people or accelerating training programs.

When you ignore these warnings, customers get annoyed. Slow fixes make them lose trust, and once that’s gone, it’s hard to win back. But by using time to resolution to find delays like repeating tasks or old workflows, you can fix processes before customers get upset.

5. Improves insights

Comparing time to resolution across teams, products, or support channels acts like a roadmap to your customer’s experience.

For instance, if one team consistently fixes issues faster than others, their methods, such as using templates or prioritizing urgent tickets, can become a blueprint for the rest. Meanwhile, teams with higher time to resolution might need extra training or tools to catch up.

These comparisons also expose hidden patterns.

Let’s say a specific product always has slower fixes. This could mean that the design confuses customers or the instructions are unclear. Updating product guides or simplifying its features could cut future support tickets and boost satisfaction.

How to calculate time to resolution

The formula for calculating time to resolution is:

Let’s break this down:

Total time spent resolving all issues: Add up all the minutes, hours, or days your team spent fixing customer problems in a specific period (like a day, week, or month).

For example, if a ticket took two hours to resolve, and another took four hours, your total time is six hours.

Number of issues solved: Count how many customer problems your team fully resolved in that same period.

If you resolve three tickets in a day, your number of solved issues is three.

To put it all together, divide the total time by the number of solved issues to obtain your average TTR.

This means, on average, your team takes 2.8 hours to fully resolve a customer issue. 

If this number feels too high, it’s a sign to dig deeper, maybe some tickets are getting stuck in endless back-and-forth, or your team needs better tools to speed things up.

6 Ways to leverage AI to reduce time to resolution

Support delays rarely stem from a lack of effort — they often result from noise: an excessive number of tickets, insufficient context, and tasks that should never be manual.

Artificial intelligence changes that by streamlining what gets in the way.

Here’s how to use AI to speed up support and cut resolution times:

1. AI-powered ticket routing

When a customer submits a request, AI uses natural language processing (NLP) to instantly “read” the problem, whether it’s a billing error, a technical glitch, or a shipping delay. It scans the keyword to understand intent and urgency.

This precision cuts through delays. That’s fewer frustrated customers, fewer follow-up emails, and a shorter TTR clock.

Platforms like Plivo CX use AI to automate this entire process. Its skill-based routing matches customer inquiries (calls, chats, or emails) to agents based on expertise, past performance, and even the customer’s history.

For instance, a loyalty program question goes to someone familiar with the customer’s account, while a bug report skips the queue and lands with IT.

Automated ticket resolution results in happier customers and teams freed from manual triage.

2. Automating initial support with AI chatbots

AI customer service chatbots can handle simple, repetitive questions like password resets, order updates, or billing details instantly at any time of day.

But the real magic happens when chatbots evolve into AI agents. 

Plivo takes this further by connecting its AI-powered self-service chatbot to your company’s knowledge base, policies, and brand guidelines.

These AI agents answer questions and take action by updating account info, processing returns, tracking orders, and solving multi-step issues without human help.

If they hit a roadblock, they hand off the conversation to your team with clear notes so agents don’t waste time asking, “What’s the issue?”

3. Anticipating customer needs

AI identifies patterns in customer data, past interactions, and usage behavior to detect potential issues early. It suggests solutions proactively, often before the customer even knows there is a problem.

For example, AI detects a shipping delay in a customer’s order. It can then automatically email a tracking update and discount code before they reach out.

This predictive power turns support from reactive to proactive. Customers feel understood, not frustrated. 

Teams spend less time firefighting repeat issues because AI flags trends, such as a surge in login errors after a system update. It also surfaces relevant help articles or troubleshooting guides in advance.

This results in fewer tickets, faster resolutions, and a low time to resolution because problems are solved before they escalate.

4. Providing self-service options to customers

78% of CRM leaders confirm that customers prefer to fix issues independently. That’s why self-service tools like interactive voice response (IVR) systems and AI-powered knowledge bases have become a game-changer.

IVR enables customers to resolve simple issues independently, such as checking order status or resetting a password, by using voice commands or pressing keys on their phone. For businesses, this means fewer calls piling up and faster fixes.

But self-service only works if it’s easy to use.

Clear, searchable knowledge bases filled with FAQs, how-to guides, and video tutorials let customers find answers in seconds. Combine this with IVR systems that guide them to the right resources, and you’ll significantly reduce resolution times.

Plivo offers customizable self-service IVR solutions that support voice commands, keypad inputs, and outbound surveys. The system is designed to streamline interactions and adapt to a variety of customer needs.

This lets customers vote, schedule appointments, or track orders without talking to an agent. Built with easy-to-use application programming interfaces (APIs), it adapts to your business needs, whether you’re a retailer handling returns or a healthcare provider booking patient appointments.

Consequently, customers get instant answers, agents focus on complex issues, and time to resolution drops because simple fixes happen in minutes, not hours.

5. Empowering agents through training and tools

Even the best AI tools can’t replace well-trained agents. They’re the backbone of fast resolutions. 

Agents need deep product knowledge, clear processes, and the right tools to solve issues quickly, be it troubleshooting a technical glitch or guiding a customer through a self-service portal.

In this context, efficiency means reducing friction. When agents switch between multiple tabs, channels, and tools, valuable time is lost on navigation instead of customer support.

A unified platform keeps everything in one place — voice, SMS, WhatsApp, and live chat — so agents don’t lose time switching between tabs or tools. Customer details, support content, and live metrics are available the moment they need them, keeping the workflow clear and uninterrupted.

Plivo’s unified agent desktop makes this possible, consolidating every channel and essential system together in a single, streamlined interface.

This helps agents work faster, leading to fewer errors and lower TTR since everything they need is just a click away.

6. Setting realistic expectations and communicating clearly

Clear communication starts the moment customers report an issue. 

For example, an AI chatbot might instantly reply, “We’re on it! Updates will come via SMS,” while a support agent shares a timeline, “Our team needs 24 hours to investigate. We’ll email you by noon tomorrow.”

This transparency prevents frustration by setting clear expectations, reducing the need for repeat follow-ups, and fostering trust from the outset. When customers know what to expect, they are far less likely to feel ignored or left in the dark.

If a billing error takes longer to resolve because of backend verification, explaining the delay upfront stops customers from flooding your queue with follow-ups.

Automated status updates and self-service portals let customers track progress independently, reducing “Where’s my fix?” calls.

Yet, accuracy is key when setting expectations. AI draws from past ticket data to predict resolution timelines that reflect real capacity. This helps teams communicate with confidence and prevents overpromising.

When customers trust the timeline they are given, they are less likely to escalate or follow up, giving agents more room to work efficiently and resolve issues faster.

Challenges to consider when reducing time to resolution

Reducing time to resolution helps improve efficiency and customer satisfaction, but it’s not always simple. Here are some challenges that businesses should consider in the process:

1. Balancing speed and quality

Faster fixes don’t always mean better fixes. If you rush to resolve issues without proper checks, you risk creating new problems. For example, a tech team might quickly "fix" a software bug only to break another feature because they skipped testing.

Customers end up more frustrated, and your team wastes time fixing the same issues repeatedly. The key is to find a middle ground and move quickly, but never sacrifice accuracy.

Set clear quality checkpoints. For instance, automate quick tests after every fix or assign senior agents to review complex tickets. This ensures speed doesn’t come at the cost of accuracy.

2. Integrating new technology

New tools like AI chatbots or ticket systems can clash with your existing software. Imagine buying a shiny new tool only to realize it can’t connect to your customer relationship management (CRM) system.

Agents would waste time manually copying customer data between systems, slowing everything down. Before adopting new tech, test how well it works with what you already have. If it doesn’t fit smoothly, it could cost you more time than it saves.

3. Training teams and managing change

Even the best tools fail if your team doesn’t use them.

For example, agents confined to old processes might ignore a new AI knowledge base unless they’re trained on why it helps. Studies show 70% of workplace changes fail because teams aren’t prepared.

To drive adoption, preparation needs to begin before the tool rolls out. Involve agents early by identifying where current workflows fall short. This context makes the new solution feel relevant, not imposed.

Once that foundation is in place, structure training around real tasks, such as using the AI knowledge base to resolve common ticket types or reduce handling time during peak hours.

Training should be ongoing, not a one-time session. Use short, scenario-based walkthroughs that agents can revisit and follow up with live support, whether through Q&A check-ins or peer-led refreshers. The goal is to build confidence gradually, not overwhelm on day one.

Future trends in time to resolution

Customer service is changing fast. New technology is helping companies solve problems quicker. Here are two trends that are making it easier for businesses to respond to customers and fix issues faster:

1. AI and automation to revolutionize resolution speeds

AI tools, especially newer types like generative AI, are transforming customer service by automating routine tasks and resolving issues more efficiently. Experts predict that by 2025, 80% of companies will use these tools, which could replace 20-30% of support roles.

AI chatbots and virtual assistants can handle common questions instantly, 24/7, slashing wait times. For example, a construction company cut resolution times from 125 minutes to seconds using AI, saving up to €300,000 daily by avoiding delays.

These tools include conversational user interfaces (CUIs), chat systems that understand natural language. CUIs learn from past interactions to answer questions more accurately over time, reducing the need for human agents. 

However, businesses must ensure that AI gives correct answers. If it makes mistakes or frustrates customers, it could slow things down instead of speeding them up.

2. Hyper-personalization

New AI tools are speeding up customer service by making interactions more personal. For example, hyper-personalization uses real-time data (like habits or preferences) to predict customer needs, solving issues before they become problems.

Why does this matter? 71% of customers expect personalized service, and 76% get annoyed when it’s lacking.

Studying past chats, purchase history, and other details helps AI adjust support to fit each customer’s needs. This makes customers feel heard and appreciated while avoiding frustration. Plus, when customers contact support, agents instantly see their history, which speeds up communication and resolves issues faster.

Lead the path to efficient customer support with Plivo

Time to resolution shows how much a company values its customers' time. Fast resolutions depend on intelligent systems, accurate AI-driven routing, and agents equipped with the right tools to act quickly. When support feels effortless, customers notice and they come back.

For teams ready to put these strategies into action, Plivo CX offers the tools to make it happen. With features like:

• Unified agent desktop: Consolidate voice, chat, SMS, and WhatsApp into one workspace, integrated with your CRM and knowledge base.
• AI chatbots and IVR: Resolve routine queries from customers instantly without human involvement. 
• Skill-based routing: Connect customers to the most qualified agent in seconds.
• Real-time analytics: Spot bottlenecks and optimize service performance on the fly.

Plivo helps teams resolve issues faster while making support smoother, more responsive, and easier to manage at scale. Over time, this shift improves team morale, lowers burnout, and creates space for support to evolve beyond ticket handling toward relationship building and long-term retention.

Ready to see how? Contact us to book a demo today and turn resolution times into your competitive advantage.

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Grok 3: The New Frontier in AI Intelligence

In the rapidly evolving world of artificial intelligence, the release of Grok 3 marks a significant leap forward. This groundbreaking model, trained on an unprecedented scale, sets new standards in AI performance and capabilities. As we explore Grok 3’s advancements, it becomes clear why this model defines a new frontier in AI intelligence.

Unprecedented Training Scale

Grok 3’s training process achieved monumental results. Utilizing 200,000 NVIDIA GPUs, the model vastly outpaces its predecessor, Grok 2, which was trained on just 20,000 H100s. This massive computational scale allowed Grok 3 to:

• Score above 1,400 on the Chatbot Arena LLM Leaderboard
• Outperform OpenAI’s top models

This training breakthrough cements Grok 3’s status as one of the most powerful AI models to date.

Overcoming Initial Challenges

Early testing revealed some hurdles, especially around overfitting. In its initial trials, Grok 3 underperformed compared to OpenAI’s o3-mini-high on the AIME 2025 benchmark. However, through iterative refinement, it overcame these obstacles and ultimately outperformed its competitors, demonstrating its adaptability and learning strength.

Creativity and Innovation

While Grok 3 is a powerhouse, it still faces competition in creativity. On Simon Willison’s “Pelicans on a bicycle” benchmark, it performed well but didn’t surpass Claude 3.5 Sonnet’s imaginative output.

That said, Grok 3 displayed extraordinary skill by replicating complex behaviors like the bouncing ball inside a tesseract—a feat on par with OpenAI's o3-mini—demonstrating its ability to handle abstract and technically demanding prompts.

Setting New Visual Benchmarks

Among Grok 3’s standout features is its prowess in visual processing. A prime example: the model successfully mapped a flight path from Earth to Mars, catching the attention of Elon Musk himself. This achievement sets new standards in:

• Spatial reasoning
• Visual interpretation
• Aerospace and navigation applications

Conclusion

Grok 3 stands as a milestone in the evolution of artificial intelligence. With its unmatched training scale, resilience, and innovative features, it’s helping redefine the future of AI. As these technologies advance, we’re left with a compelling question: How will AI like Grok 3 continue to reshape our world—and our role in it?

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Grok 3: Revolutionizing AI-Driven Game Development

The emergence of Grok 3 is causing a seismic shift in the world of AI-driven game development. Known previously as “that other AI on X”, Grok 3 now makes waves by setting a new benchmark in how artificial intelligence can enhance and transform the gaming industry. This article explores its profound impact and why Grok 3 stands out in the crowded AI landscape.

Grok 3: A New Perspective on AI

Grok 3 is not just another AI model—it truly changes the game. While many AI tools focus on language or data, Grok 3 specializes in game development. This niche focus allows it to push boundaries, giving developers access to tools and capabilities that once felt like science fiction.

Real-World Applications: Games Transformed by Grok 3

Several developers are already putting Grok 3 to work in compelling ways:

• Shaurya’s portal game demonstrates Grok 3's ability to help create intricate, interactive environments.
• Mickey Friedman's Bubble Trouble reboot integrates dynamic AI-driven mechanics that breathe new life into the classic.
• Banteg’s idle game uses Grok 3 to create more immersive, evolving gameplay experiences thanks to its real-time learning and adaptability.

These applications show how Grok 3 can elevate both player engagement and developer possibilities.

Aligning with Industry Giants

Grok 3's rise isn’t happening in a vacuum. Its development and adoption align with major industry trends:

• Tesla’s integration of games into vehicles
• Elon Musk’s public enthusiasm for gaming
• xAI’s push for photorealistic experiences

All these signals point to a future where AI-driven games are standard, not niche. Grok 3 is positioning itself at the center of that shift.

The Future of AI-Driven Game Development

As gaming continues to evolve, Grok 3 is emerging as a cornerstone of AI innovation in the space. Its dedicated focus on game development and ability to adapt in real-time make it an invaluable asset to developers eager to craft immersive, next-gen experiences.

Grok 3 is not just transforming the way we build games—it’s redefining what’s possible. The future of game development is AI-enhanced, and Grok 3 is leading the charge.

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