Accenture’s 2024 report indicates that 74% of organizations say their investments in generative AI and automation have met or exceeded expectations. Even more telling, 63% plan to increase that investment by 2026.

This sends a clear signal: AI is paying off.

Now the question is, are you using it to its full potential? Or are outdated systems slowing your team down?

To help you get a full overview of the current landscape, we have handpicked top customer service stats from McKinsey, PwC, Gartner, and other credible sources. You’ll see exactly where AI agents deliver results and how Plivo helps you act on them.

Top AI agent statistics 

AI agents are changing how you deliver customer service. But how much impact are they really making? 

To help you make smarter decisions, we’ve broken down the latest statistics into seven key categories. Each one highlights where AI agents impact, from adoption and performance to ROI, customer preferences, and challenges.

Growth of AI agents in customer service

More companies are deploying AI agents to reduce wait times, cut support costs, and scale faster. Let’s look at the numbers showing how fast AI adoption is accelerating:

1. The AI agents market was valued at $3.7 billion in 2023 and is projected to reach $103.6 billion by 2032, growing at a CAGR of 44.9% from 2024 to 2032 (S&S Insider)
2. 63% of top-performing companies plan to increase cloud budgets by 6% or more (PwC)
3. 85% of enterprises are using AI in 2025 (Wiz)
4. By 2025, 80% of companies will have adopted or plan to adopt AI-powered chatbots to support their customer service operations (Gartner)
5. As of early 2025, 78% of organizations are using AI in at least one business function, up from 72% in early 2024, showing steady growth in AI adoption across industries (McKinsey)
6. 78% of respondents report using AI in at least one business function, a rise from 72% in early 2024 and 55% the year before, showing rapid growth in AI adoption(McKinsey)

7.  The share of companies running fully AI-led operations jumped from 9% in 2023 to 16% in 2024. These businesses are seeing 2.4 times higher productivity and  developing more effective retention strategies (Accenture)
8.  By 2029, AI agents will autonomously resolve 80% of common customer service issues, eliminating the need for human intervention in most routine cases (Gartner)
9.  63% of retail organizations report using generative AI to enhance their existing customer service efforts (Capgemini)
10.  Organizations are turning to AI to drive top-line growth and stand out from competitors. 42% aim to improve product or service quality, while 39% are focused on  boosting revenue. At the same time, they’re using AI to enhance operations—40% to increase workforce productivity, 41% to improve IT efficiency, and 39% to  speed up innovation (Weka)

Use cases: where AI agents are creating impact

If you think AI has limited use, these stats will shift your perspective:

11. Automating just up to 20% of support tickets can lead to an 8-point increase in repeat purchase rates within 28 days (Gorgias)
12. Automated responses resolve some tickets instantly and help teams respond faster overall. On average, merchants using automation reply 37% faster than those who don’t (Gorgias)
13. Merchants who automate customer tickets resolve them 52% faster than those who don’t, showing how AI significantly speeds up support (Gorgias)
14. AI agents will cut the time needed to exploit account exposures by 50%, emphasizing the importance of AI in both defense and risk response by 2027(Gartner)
15. Organizations using Gen AI–enabled customer service agents saw a 14% increase in issue resolution per hour and a 9% reduction in time spent handling issues (McKinsey)

Performance metrics that matter

AI agents now match or exceed human benchmarks in speed, accuracy, and resolution rates. But how do they actually perform under pressure? These numbers tell you what to expect and where to set your goals:

‍

16. 72 % of top-performing companies have seen increased productivity by implementing AI (PwC)
17. Using generative AI tools boosts user performance by 66% on average. The biggest gains come from complex tasks, with less-skilled workers seeing the most improvement (Nelisen Norman Group)
18. Conversational AI in contact centers will cut agent customer service operations costs by $80 billion by 2026. In 2022, global end-user spending on these solutions reached $1.99 billion, showing substantial investment in automation (Gartner)
19. Among leading generative AI adopters, the majority report a high or very high impact on key business areas: 79% say it boosts innovation, 76% say it supports new product launches, and 76% see faster time to market—all contributing to stronger competitive positioning  (Weka)
20. Customer support agents using a generative AI assistant boosted their productivity by 14% on average (NBER)
21. Workers are, on average, 33% more productive during each hour they use generative AI (Federal Reserve Bank of St. Louis) 

AI agent ROI and cost efficiency

Businesses are saving millions in overhead while improving support outcomes. Want to know how much ROI you could unlock with AI agents? These stats break it down:

22. Support agents using AI tools can manage 13.8% more customer inquiries per hour, boosting productivity without the need to hire more staff (Nielsen Norman Group)
23. Generative AI and related technologies could automate tasks that currently take up 60 to 70% of employees’ time, significantly changing how work gets done (McKinsey)
24. AI-powered systems have led to a 31.5% boost in customer satisfaction scores and a 24.8% increase in customer retention, showing clear gains in both experience and loyalty (ResearchGate)
25. Companies using generative AI are 35% less likely to report that human agents feel overwhelmed by the amount of information during customer calls (Deloitte)
26. 9 in 10 of organizations using AI report saving both time and money. Service operations professionals are especially optimistic, nearly all believe generative AI will improve their company’s customer service (Salesforce)
27. 1 in 3 companies with omnichannel integration tools resulted in 9% lower cost per assisted contact (Deloitte)

AI agents + human agents: The hybrid advantage

The best support teams blend human empathy with AI efficiency. AI agents handle volume. Human agents step in for nuance. These figures show what’s working and where companies get it wrong:

28. 71% of customers believe it is important for a human to validate the output of AI (Salesforce)
29. Today, 54% of workers worldwide trust humans and AI to work together on most tasks, highlighting the need for businesses to implement AI responsibly and in ways that earn user trust(Salesforce)
30. Among organizations using generative AI, 27% say their employees review all AI-generated content before it’s used, whether it’s a chatbot response for a customer or an image for marketing (McKinsey)
31. While 68% of people have used automated customer service chatbots, 88% still prefer speaking with a human when they need support, highlighting the ongoing importance of human interaction (Ipsos)
32. 87% of executives believe generative AI will augment jobs rather than replace them, suggesting most roles will evolve with AI support instead of being automated away (IBM)
33. Executives can drive AI adoption by showing employees that the goal is to add value, not just cut costs. Companies that foster a culture of experimentation and don’t penalize failure see a 10% boost in revenue growth during tech adoption. Among AI users, those with this open mindset achieve a 22% higher revenue growth rate than those who don’t (IBM)

AI Agents and Customer Preferences

Customers care about fast, helpful answers, not who gives them. But preferences still matter. You’ll want to see what real users are saying before making your next CX decision:

34. 28% of respondents have used AI-powered visual search to find products that look like what they’re interested in purchasing (BCG)
35. Nearly 24% of consumers are already comfortable with AI agents making purchases on their behalf, and that number rises to 32% among Gen Z shoppers (Salesforce)
36. About one in three consumers prefer to buy products through automated or digital channels, such as AI agents, rather than interacting with a person (Salesforce)
37. Some of the top reasons for customers interacting with AI include better availability (41%), faster issue resolution (37%), and more accurate information (30%) (SurveyMonkey)
38. 52% of consumers are interested in using AI to guide them through a product, website, or feature. 47% want personalized deals based on their purchase history, and 42% are interested in AI-driven product recommendations (SurveyMonkey)
39. 89% of customers say it’s important to know whether they’re interacting with a human or an AI, highlighting the need for transparency in customer support (Salesforce)

Challenges & considerations

If you’re thinking about scaling AI, you need to know what could go wrong and how others navigate it. These stats show the friction points worth planning for: 

40. 92% of analytics and IT leaders say that the demand for trustworthy data is at an all-time high, especially as AI becomes more central to decision-making (Salesforce)
41. 61% of workers are open to using generative AI, but many still lack access to reliable data and the security skills needed to use it effectively (Salesforce)
42. Nearly 64% of organizations express concern about how AI and machine learning projects affect their energy consumption and carbon footprint. 25% say they are very concerned (Weka)

43. 35% of organizations cite storage and data management as the top infrastructure barriers to AI adoption. However, those with widespread AI implementation report fewer issues in these areas (Weka)
44. 37% of the U.S. IT leaders see data quality as a major obstacle to AI success (Hitachi Vantara)
45. 40% of employees will need retraining due to AI adoption, underscoring the need for ongoing learning and strong human oversight (IBM)

Experience next-gen AI-driven engagement with Plivo 

The data speaks for itself—AI agents are no longer optional. They mean faster resolutions, lower costs, and higher satisfaction. But success depends on choosing the right partner.

Plivo is an all-in-one omnichannel customer service platform that  helps you deploy open AI-powered AI agents that actually perform. No overpromises. Just faster responses, fewer escalations, and better CX.

With Plivo, you get:

• AI agents for instant support: Handle FAQs, status checks, and call routing automatically, reducing agent load and response times 24/7
• Metrics & reporting: Get live insights into call volume, handle time, and team performance
• Smart, context-rich escalations: Transfer complex issues to human agents with full conversation history. No repeated questions, no friction
• Easy integrations: Connect Plivo to your CRM, helpdesk, and other tools without heavy dev work
• Flexible scalability: Whether you’re supporting 10 users or 10,000, Plivo adapts to your scale and needs
• Workflow automation: Automate follow-ups, ticket routing, and updates to keep customers informed without manual effort
• Enterprise-grade security: Built with SOC 2 and GDPR compliance to keep your data safe and your business trustworthy

Want to see how AI agents transform your support strategy? Book a demo now.

Organizations are struggling to do more with less when it comes to customer success. Many organizations are facing tight budgets. As TSIA’s The State of Customer Success 2025 webinar showcases, in this money crunch, they are moving towards AI in customer success to automate repetitive tasks. 

In fact, 40% of organizations have already deployed AI in customer success management operations. AI-powered customer success agents are a promising solution in this area. 

In this post, we will look at what a customer success AI agent is, its top use cases, and how to implement one. 

What is a customer success AI agent?

A customer success AI agent uses machine learning and artificial intelligence to automate the routine work of customer success managers (CSMs) such as upselling, cross-selling, promotions, engagement, etc. It operates like a virtual assistant to CSMs but is an automated one. 

How do customer success AI agents differ from AI-powered chatbots?

AI-powered chatbots typically answer customers' queries and help with customer service. On the other hand, customer success AI agents are capable of doing more advanced functionalities in marketing, customer engagement, and customer service.

How does an AI customer success agent work?

‍

An AI-powered customer success agent typically works in four steps:

1. Data collection: It connects with your existing CRM platforms, support portals, and marketing platforms to create a complete database of customers (website activity, support queries, orders, etc.).

2. Data analysis: Once the data is collected, AI agents analyze it using natural language processing and machine learning. These interpretations can be stored as customer profiles, which can then be used to personalize any further customer interactions.

3. Task automation: AI agents can automate specific tasks in custom engagement/service and use the interpreted data to personalize communication. 

4. Learning and adaptation: AI agents also have self-learning capabilities, which means they can continuously improve by analyzing past interactions and responses.

Top AI as customer success agent use cases

Some top tasks of CSMs where AI customer success agents can assist are: 

1. Personalized communication

AI customer success agents analyze past customer interactions (mail, chat, calls, and support tickets) and send personalized communication. This communication could be related to onboarding, predictive alerts, automated check-ins, nudges, follow-up messages, feedback surveys, etc.

2. Customer engagement

AI customer success agents can further run customer campaigns for engagement, such as welcome series, feedback surveys, and more. These campaigns help improve customer retention/loyalty and increase production adoption.

3. Upselling and cross-selling

AI customer success agents analyze past customer data (activity, orders, etc) and run:

• Upselling campaigns: Recommending higher-tier products
• Cross-selling campaigns: Suggesting complementary products. 

4. Customer support

AI customer success agents can also take away some manual customer support tasks off the CSM's task list, such as:

• Ticket routing: It analyzes the ticket, categorizes it, and assigns it to the relevant department for resolution.
• Call routing: It analyzes the customer’s query and automatically routes calls to the next best executive available for the customer call.
• Customer support: It acts as a virtual support agent and handles some repetitive customer queries.  

AI agents transform customer support with automation.

5. Customer health scoring

AI customer success agents analyze data from existing systems (CRM, marketing, support tickets, etc) and build customer profiles with a score. These profiles can help identify which customers are more likely to come back and which are more likely to churn. You can further build marketing campaigns using these scores.

6. Call quality monitoring

AI customer success agents automatically analyze customer call recordings to assess the following:

• Call quality: Is the audio clear, volume consistent ,and connection stable?
• Conversation quality: Does the tone of voice and choice of words by customers show they were happy with the solution?

This information further helps you improve the customer experience and quality of customer calls.

7. Customer success agent training 

AI customer success agents provide live cues to CSMs in case of any issues, like a real-time virtual assistant. It can also simulate customer interactions to train new CSMs who have just been onboarded. Overall, it significantly reduces the team's training efforts.

How to implement an AI customer success agent

You can integrate and implement an AI customer success agent successfully in five simple steps:

1. Understand your use cases

A simple strategy to identify the right use cases for AI agents in customer success is to go into your CSM’s task list and identify tasks that are:

• Repetitive with the scope of automation
• Manual with the scope of error

You can further look into business priorities and look for any automation scope (example: upselling, cross selling, etc) that leads to long-term benefits. 

2. Identify current data sources

The next step is to identify where your data currently is, such as:

• CRM records
• Support tickets
• Emails
• Call recordings
• Survey responses

3. Choose the right technology

Once you have the base information ready, you can choose a provider that can:

• Integrate with your existing data sources
• Support the required use cases
• Support all your communication channels
• Handle scale as your business advances
• Meet your budget with growth

4. Create customer workflows

After taking a subscription of an AI customer success agent, identify all the touchpoints where these agents can handle the interactions and where human intervention is required. 

For example, an AI customer success agent can handle level-1 and level-2 queries and need CSM help for level-3 queries. 

5. Continuously improve workflows

Using AI customer success agents requires regular monitoring and experimentation. You can identify new use cases that can be implemented in your AI customer success platform and opportunities for further workflow enhancements. 

The reporting and analytics modules of the AI customer success platform also provide necessary KPIs to track implementation.

Use Plivo for customer success

Plivo is an advanced AI agent helping you to provide:

• 24/7 support: Provides round-the-clock assistance on SMS, Voice, and WhatsApp.

Conversational AI: Use company data (by connecting with CRM, billing, and support systems) and provide precise answers to customers.

• Omnichannel support: Engages customers via voice, Email, SMS, WhatsApp, live chat, and more.
• Sales & engagement boost: Sends AI-driven cart reminders, offers, upsell, cross-sell, and proactive messages
• Real-time insights: Monitors resolution rates, pain points, and customer satisfaction.

Start building better customer experiences with AI. Book a demo today.

Short message service (SMS) might not make headlines in 2025, but it continues to outperform flashier channels where it matters most — reach and response. With 90% of texts read within three minutes and returns hitting $8.11 per message, it remains one of the most dependable tools for customer communication.

What’s changed is the intelligence behind the message.

AI-powered SMS bots can now understand intent, personalize responses, and hold real conversations. Whether it’s appointment reminders or sales follow-ups, these bots handle it with speed, context, and clarity.

In this blog post, you’ll learn how to build a powerful SMS chatbot using the latest artificial intelligence (AI) tools and messaging platforms.

What is an SMS chatbot?

An SMS chatbot is an automated software application that simulates human conversation over SMS using text-based interactions. Instead of requiring human agents to respond manually to every message, an SMS chatbot uses pre-programmed rules or AI to interpret and respond to incoming text messages in real time.

These chatbots are commonly used in customer service, marketing, appointment scheduling, lead generation, and other business functions where quick, scalable, and consistent communication is needed.

How does an SMS chatbot work?

At its core, an SMS text bot follows a structured workflow that enables two-way communication between users and businesses. Here's a breakdown of the process:

The user sends a message

The interaction begins when a user sends a text message to a designated phone number, typically a short code or a virtual number. For example, a customer might text “Check alance” or “Book Appointment.”

The SMS gateway forwards the message to the chatbot

The incoming SMS is routed through a gateway or messaging platform (e.g., Plivo), which bridges mobile networks and the chatbot’s backend. The gateway receives the user’s text and passes it to the chatbot server or engine for processing.

The chatbot processes the input

Once the chatbot receives the message, it evaluates the content using one of the following methods:

• Keyword recognition: Looks for specific predefined keywords or phrases. For example, “balance,” “schedule,” or “cancel.” This is a rule-based system suitable for straightforward use cases.
• Predefined conversation flows: Follows a set of scripted responses and branching logic based on the user’s input. These flows simulate a conversation and can include multiple decision points and replies.
• Natural language processing (NLP): Uses NLP and machine learning (ML) to understand user intent beyond keywords. This allows bots to interpret variations in sentence structure, spelling, or phrasing (e.g., "I'd like to check my account balance" vs. "balance").

The processing logic maps the input to an appropriate intent (i.e., what the user wants) and retrieves information from a database, customer relationship management (CRM) system, or knowledge base to form a relevant response.

The bot sends a response

Once the input is processed, the chatbot composes a response, either static (predefined text) or dynamic (based on real-time data, such as account status or appointment slots).

It sends it back through the SMS gateway to the user’s mobile phone. This entire interaction typically occurs within a few seconds, providing a seamless user experience.

Step-by-step guide to creating an SMS bot

Here’s a straightforward guide to help you build an SMS bot from scratch.

Step #1: Choose an SMS platform

Start by picking a platform that can reliably send and receive SMS. It’s what enables your bot to respond in real time, manage message delivery, and scale conversations without manual effort.

Plivo provides a scalable infrastructure, global reach, and features such as message delivery reports, two-way messaging, and more. To get started with Plivo:

1. Create an account: Head to Plivo's website and sign up for an account.
2. Get your API credentials: After logging in, access the "Dashboard" to retrieve your Auth ID and Auth Token. These credentials will be necessary for authenticating your API requests.
3. Purchase a phone number: You will need a phone number capable of sending and receiving SMS messages. Plivo offers a variety of numbers to choose from based on your region.
4. Configure your number: Finally, set up the number for SMS functionality in the Plivo Console to ensure it's ready to receive incoming messages.

With Plivo set up, you’re ready to begin designing the backend of your SMS bot.

Step #2: Set up your development environment

Next, you must set up your development environment to integrate the Plivo SMS API and build the bot. Here’s how:

1. Choose a programming language: Select a programming language that you're comfortable with that also supports Plivo's SDK. Common choices include Python, Node.js, or Ruby.
2. Install the necessary dependencies:

1. For Python: Install Python if it has not already been installed. Use pip to install Plivo's SDK by running pip install plivo.
2. For Node.js: Install Node.js from the official website, then Plivo’s SDK using npm: npm install plivo.

3. Set up your IDE: Choose your integrated development environment (IDE) for writing code. Popular options include VSCode, PyCharm, or any editor of your choice. Ensure that you have version control to manage your codebase.
4. Create a basic API request: Before diving into actual bot development, test to make sure you can send an SMS via Plivo’s API.

Step #3: Design the bot flow

A well-structured bot flow helps your SMS bot respond clearly and stay on track during a conversation. Below is a simple way to plan:

1. Understand the text bot's purpose: Determine what the bot is supposed to do. Is it for customer support, order tracking, or information retrieval? Clearly define the use case.
2. Map out user interactions: Create a flowchart or wireframe of the conversation. Identify key touchpoints, such as greetings, user queries, responses, and potential follow-up actions.

For example, a simple order tracking bot might start with a welcome message, ask for an order number, and then return the order status.

Define response logic: Think through how the bot will respond at each stage. Consider the different types of responses: simple text, questions, or actions (like triggering a function or fetching data from a database).

The bot flow will serve as a blueprint for development, ensuring a smooth user experience.

Step #4: Develop the text bot

Now, it's time to write the code that brings your AI SMS chatbot to life. Let’s see how:

1. Authenticate with Plivo: At the start of your bot script, import the Plivo library and authenticate using your API credentials:

2. Create messaging logic: Write the logic for sending and receiving messages. For example, to send a message, use the following code:

3. Handle incoming messages: Set up a web server (with Flask or Django, for example) to receive and respond to SMS messages. You'll need to design an endpoint that Plivo can call to notify you of incoming SMS messages.

Example using Flask:

4. Add bot logic: Based on the user’s incoming message, define a series of if-else conditions or use more advanced techniques like ML or AI to interpret and respond to the user's queries. For instance:

Step #5: Test your SMS bot

Once the bot is built, thorough testing is key to making sure it works as intended. It helps catch bugs, validate message formatting, and confirm that conversations play out smoothly across different scenarios. You must test:

• Response accuracy: Simulate various user inputs to see how the bot responds. Test both expected and unexpected messages. For example, test valid order numbers, invalid inputs, and edge cases, such as what happens if the user sends an empty message.
• Edge cases: Ensure the bot can handle edge cases, such as special characters, long messages, or incorrect inputs, in a graceful manner. You may need to implement input validation and error handling to manage these cases.
• Different scenarios: Run through all possible conversation paths based on your bot flow. Ensure the bot can handle various user scenarios and provide helpful feedback.

Step #6: Deploy and monitor

Once your SMS bot is working as intended, it’s time to deploy it and start using it in a live environment. Deployment involves making the bot available to real users and monitoring its performance to ensure it functions correctly.

Below are a few key areas to focus on during and after deployment to keep things running smoothly:

• Host the web server that processes incoming messages on a cloud platform like Amazon Web Services (AWS), Google Cloud, or Heroku. Ensure your server is publicly accessible so Plivo can reach it. Set the webhook URL in Plivo to point to your server’s endpoint (e.g., https://yourdomain.com/receive_sms/).
• Use Plivo’s built-in analytics to monitor your bot’s performance. Keep an eye on crucial metrics like message delivery rates, response times, and error rates.
• After deployment, continually analyze user interactions to identify opportunities for enhancing the bot’s performance. Collect user feedback to refine your chatbot’s SMS responses and add new features over time.

How AI enhances SMS bot performance

AI significantly enhances SMS bot performance by making conversations more intelligent, personalized, and efficient.

Traditional SMS bots rely on predefined scripts, but AI-powered bots apply NLP to understand user intent, even with typos or slang. This results in faster, more accurate responses and increased customer satisfaction.

Let’s understand this better.

In fact, AI SMS text bots are expected to save businesses over $11 billion annually through reduced costs and improved customer service efficiency.

The technology also lets bots analyze past interactions and personalize messages, improving engagement rates. For example, an AI SMS chatbot can recommend products based on previous purchases or browsing behavior. Furthermore, machine learning empowers continuous improvement as the bot learns from every interaction.

Challenges when building an AI SMS bot

AI makes SMS bots smarter, but it also raises the bar. Here’s what can trip you up and what to plan for.

Ensuring data privacy and regulatory compliance

When your SMS bot handles user data — even something as simple as a name and phone number — you're entering regulated territory.

Frameworks like the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA) apply strict rules on how that data is collected, stored, and used. SMS interactions are no exception.

Compliance shapes how your bot is designed. That means building in consent prompts, limiting data collection to what’s absolutely necessary, and giving users a clear way to opt out or request data deletion. It also means being thoughtful about integrations — if your bot connects to third-party CRMs, analytics tools, or cloud storage, you're still accountable for how that data is handled downstream.

Don’t treat privacy as an afterthought because regulators won’t either.

Balancing automation with human support

Automation can improve efficiency, but relying too heavily on it can alienate users, especially when the conversation requires empathy or complex reasoning.

Program your bot to recognize its limitations and offer seamless escalation to human agents when needed. Be transparent with users about when they’re chatting with a bot and when a human is taking over.

It’s also important to define clear handoff points within your bot flow. For example, if a user repeats the same question twice, expresses frustration, or types something your NLP model can’t categorize, that’s a signal to escalate. You don’t need complex sentiment analysis to make this work — just a few well-placed triggers can keep conversations from going off the rails.

On the backend, make sure your support team has context when they step in. Passing along the full conversation history, user metadata, or selected intents can help human agents respond faster and more accurately.

Training AI with low-quality message data

An AI SMS bot is only as good as what it’s trained on, and most teams get this part wrong. They either feed the model with perfect, internally written scripts (“What are your store hours?”), or synthetic training data generated by people who don’t text like actual users.

The result? A bot that looks smart in testing but crumbles when it meets real messages like:

“u open today?”
“store open sat??”
“hey r u guys taking returns rn”

SMS language is messy. It’s informal, typo-heavy, and full of shorthand or implied meaning. If your AI model hasn't seen this kind of input before, it won't know what to do with it, or worse, it will respond with confidence to the wrong intent.

To avoid this, train on anonymized real user messages whenever possible. Include edge cases, abbreviations, slang, and even common customer frustrations. Augment your dataset with diverse tones, sentence structures, and intents.

And if you're fine-tuning large models, don’t overtrain: it’s better to build fallback logic for unclear queries than to have a bot that responds confidently to something it clearly didn’t understand.

How Plivo helps build an AI-powered SMS bot

Plivo is a leading Communications Platform as a Service (CPaaS) that enables businesses to build intelligent SMS bots easily. Its robust APIs and SDKs allow seamless integration with AI models, making it simple to automate conversations and streamline customer interactions.

The platform supports advanced features, including message scheduling, interactive SMS, and multi-language capabilities. Businesses can utilize AI-driven conversation management tools to deliver more intelligent responses, while built-in analytics and reporting tools provide in-depth insights into message performance.

Plivo tool also prioritizes security and compliance, providing encryption and data privacy controls to protect customer information.

Talk to us about launching an AI-powered SMS bot today.

‍

Manus AI emerges as a groundbreaking solution for autonomous task execution in a world where efficiency and automation drive innovation. Unlike traditional chatbots that merely assist, Manus AI independently executes tasks, offering a new paradigm in AI technology.

The Autonomous Edge of Manus AI

Manus AI stands out by performing real-world functions autonomously, setting it apart from conventional AI tools like DeepSeek. While many AI systems require human intervention to complete tasks, Manus AI initiates and executes them from start to finish. Imagine an AI that screens resumes, organizes complex data sets, conducts in-depth research, and even builds digital assets—all without human oversight.

Real-World Applications

Manus AI's capabilities extend across various domains, making it a versatile tool for businesses and individuals alike. For instance, Manus AI handles property research, providing comprehensive market analysis and valuation insights. In the financial sector, it performs stock analysis, offering timely and accurate data-driven decisions. Moreover, Manus AI develops websites and generates entire courses or presentations, showcasing its broad utility.

Seamless Task Management

Users assign tasks to Manus AI and receive notifications upon completion, ensuring a seamless workflow. This feature benefits entrepreneurs and small businesses that need to maximize productivity without expanding their workforce. By accessing data through APIs and writing code to solve complex problems, Manus AI automates processes that traditionally required significant human resources.

Setting New Benchmarks in AI

Manus AI's capabilities underwent rigorous testing on the GAIA benchmark, where it achieved state-of-the-art results. This benchmark evaluates AI systems on reasoning and tool use, areas where Manus AI excelled. Its ability to autonomously navigate complex tasks and provide solutions demonstrates its advanced reasoning skills and practical utility.

A Competitive Edge

In a market crowded with AI agents like Anthropic Computer Use and OpenAI Operator, Manus AI offers a compelling alternative. These competing systems often prove costly or complex, limiting their accessibility to large corporations. Manus AI aims to democratize AI-powered task execution by offering a more affordable solution. This approach broadens access to advanced AI technology and encourages innovation across different sectors.

Open-Source Innovation

The developers behind Manus AI plan to open-source parts of the model, further enhancing AI accessibility. By doing so, they invite developers and researchers to contribute to its evolution, fostering a collaborative environment for AI advancement. This initiative could lead to new applications and improvements, benefiting a wider audience and accelerating the pace of technological progress.

Redefining AI-Assisted Automation

Manus AI has the potential to redefine AI-assisted agentic automation, making advanced task execution widely available. By bridging the gap between AI potential and practical application, Manus AI sets a new standard for what autonomous systems can achieve. Its ability to perform tasks independently not only enhances productivity but also frees up human resources for more strategic and creative endeavors.

Manus AI represents a significant leap forward in AI technology, offering unprecedented capabilities in autonomous task execution. As it continues to evolve and expand its reach, Manus AI could transform how we approach work and productivity. How do you envision AI like Manus AI changing the landscape of your industry?

Are you measuring the right set of call center metrics that actually improve performance?

This is important because a 2023 survey by TCN revealed that 73% of U.S. consumers would abandon a brand after a single negative customer service interaction. It also marks a significant increase from 42% in 2021.

In this context, monitoring metrics like Average Handle Time (AHT) can reveal inefficiencies that cost both time and money. 

However, all seasoned CX leaders know that not every metric deserves your attention.In fact, focusing on the wrong numbers wastes resources and can lead to adverse decisions.

In this blog post, you’ll learn about 12 expert-vetted call center metrics you should track. Each includes a clear definition, why it matters, and actionable tips to help you perform better.

Why is tracking call center metrics important?

Metrics defeat their purpose if you don’t act on them, or promptly use them to spot delays, fix weak spots, and improve how your team works. However, when you do, customers definitely notice. They get faster answers and better support. As a result, they come back, and they tell other prospects about your brand.

As Maya Angelou once put it,

According to a 2024 Salesforce report, 88% of customers say good customer service makes them more likely to purchase again, and 80% consider the company's experience as necessary as its products and services.  

To meet these rising expectations, businesses must ensure their call centers aren't just functional but truly effective. Here is how measuring call center success can help you:

Boost customer satisfaction

Widely used metrics like Customer Satisfaction Score (CSAT) and First Call Resolution (FCR) tell you if you meet customer needs. Regularly tracking these metrics ensures alignment with customer expectations and helps reduce churn.

Enhance agent performance

Metrics such as Average Handle Time (AHT) and Agent Utilization Rate provide insights into agent efficiency. Monitoring these can help optimize schedules and identify areas for targeted training.

Optimize operational costs

Efficiency directly impacts operational expenses. Metrics like average handle time and call abandonment rate can highlight process inefficiencies, allowing businesses to optimize labor budgets and improve overall cost-effectiveness.

Top 12 call center metrics 

When hold times exceed 2 minutes, customer satisfaction takes a hit. To maintain high-quality service and stay ahead of customer expectations, it’s crucial to monitor performance closely. That’s where these 12 key call center metrics come in:

Customer-focused metrics

Let us go over the key call center metrics that show how well you’re meeting customer expectations. You’ll learn which numbers reflect satisfaction, loyalty, and effort so you can act fast when things slip. 

1. First Call Resolution (FCR)

FCR shows how often your team solves customer issues on the first try. It’s a strong indicator of customer satisfaction and loyalty. If customers need to follow up, frustration and costs build up.

‍

A reasonable FCR rate is 70–79%. World-class call centers aim for 80% or more. According to SQM Group, improving your FCR by just 1% can save you over $280,000 annually.

Tips to improve FCR:

• Define what counts as a repeat or escalated call
• Set a clear time window to track follow-ups
• Exclude customer errors (like calling the wrong department) when evaluating FCR
• Give agents quick access to knowledge bases and internal tools
• Run regular call audits to spot common resolution gaps
• Train agents to ask the right questions early to avoid callbacks
• Use call summaries or confirmations to reduce confusion

2. Customer Satisfaction Score (CSAT)

How do your customers feel after they interact with your team? CSAT measures their satisfaction and shows whether you’re meeting their expectations.

How to measure CSAT:

Ask customers a simple post-interaction question: “How satisfied were you with your experience?” Provide a scale (e.g., 1 to 5 or 1 to 10).

CSAT Best Practices:

• Set clear, realistic goals based on your current score and industry benchmark
• Personalize every customer interaction using their history and preferences
• Tackle negative feedback head-on. Spot trends and offer tailored fixes
• Build a QA checklist to monitor support quality regularly
• Train agents with internal resources and provide omnichannel support
• Speed up replies with AI chatbots that collect info before routing to agents
• Offer in-app customer service, FAQs, and video guides for self-service

3. Net Promoter Score (NPS)

NPS measures how likely your customers are to recommend your business. It’s a quick way to gauge loyalty and identify issues that hurt your customer experience.

How to calculate NPS:

Ask your customers: “How likely are you to recommend us to a friend or colleague?” They respond on a scale of 0 to 10.

• Scores 9–10 = Promoters
• Scores 7–8 = Passives
• Scores 0–6 = Detractors

Formula:

NPS = % of Promoters – % of Detractors

Example: If 60% are Promoters and 20% are Detractors, your NPS is +40

How to improve operations with NPS:

• Reach out to Detractors promptly. Ask what went wrong. Be empathetic and offer specific solutions
• Share it with support, sales, and product teams. Recognize employees who move the score up
• Let teams review real customer feedback. Talk through common complaints. Spot trends early
• Use actual NPS responses in training. Help agents learn what creates Promoters and what doesn’t
• Are poor scores tied to specific agents, products, or workflows? Investigate. Then fix it
• After you make updates, monitor NPS again. Did it rise? If yes, build on it. If not, dig deeper
  

4. Average Handle Time (AHT)

AHT measures the total time it takes to resolve a customer issue, from the start of contact to the end of it. It’s a core KPI for call centers that want to monitor efficiency without sacrificing service quality.

How to calculate AHT:

The formula differs slightly by channel. Here’s how to break it down:

• For Calls:
  AHT = (Talk Time + Hold Time + Follow-Up Time) / Total Number of Calls
  
  
• For Email:
  AHT = (Total Time Spent + Customer Wait Time) / Total Number of Emails
  
  
• For Live Chat:
  AHT = Total Handle Time / Total Number of Chats
  
  

AHT varies by industry and complexity, but for many contact centers, a solid benchmark is around six minutes per interaction.

How to reduce AHT (without hurting CX):

• Host ongoing training, call reviews, and performance feedback to keep skills sharp and reduce unnecessary delays
• Build an easy-to-navigate help center or self-service chatbot. Let customers solve simple issues on their own and reduce ticket volume
• Send alerts, how-to guides, and status updates before customers reach out. Fewer incoming issues mean faster response times for complex ones
• Use skill-based routing to match customers with the right agent the first time
• Deploy chatbots or virtual assistants to handle FAQs, triage issues, or collect context before the agent steps in

Operational Efficiency Metrics

Managing time, reducing hold times, and resolving issues quickly all come down to how efficiently your team operates. These contact center KPIs show how well your team manages time, call volume, and resources.

5. Call Abandonment Rate

This metric highlights how efficiently calls are managed, especially regarding wait times. Call abandonment refers to the percentage of inbound calls that disconnect before a caller speaks to a live agent. It’s often a sign of long hold times, confusing IVR menus, or poor customer experiences.

Formula:

Call Abandonment Rate = (Total Calls - Completed Calls) / Total Calls

Example:

Suppose your call center received 1,000 calls daily, but only 850 were completed (i.e., connected to an agent).

Call Abandonment Rate = (1,000 - 850) / 1,000 = 150 / 1,000 = 0.15

Abandonment Rate = 15%

This means 15% of your callers hung up before getting assistance.

How to reduce abandonment rates:

• Give agents caller context to resolve issues faster 
• Use data to identify when and why callers hang up
• Deploy AI voice agents for fast, automated help
• Announce real-time wait times to set expectations
• Optimize IVR flows for clarity and quick routing
• Offer a self-service help center for common queries
• Analyze abandonment points across the caller journey
• Train agents on soft skills and product knowledge

6. Service Level

Service levels measure how quickly and effectively your support team responds to customer inquiries. They reflect your ability to meet agreed expectations and keep customers satisfied.

Main types of service level agreements (SLAs):

• Customer-based SLA: Tailored for one customer with terms unique to their needs
• Service-based SLA: Applies the same terms for one service across all customers
• Operational SLA: Tracks internal performance metrics like uptime and maintenance
• Multi-level SLA: Combines elements of the above, covering company-wide, customer-specific, and service-based aspects
  
  

How to monitor and maintain service levels:

• Set realistic, data-backed targets
• Predict peak times using historical trends
• Staff agents based on skill, not just availability
• Cross-train agents for flexible coverage
• Track real-time performance and adjust instantly
• Cut handle time without sacrificing quality
• Route simple queries to self-service tools
• Offer call-backs during high-volume periods

7. Average Speed of Answer (ASA)

ASA tracks how long, on average, it takes for a customer to reach a live agent after entering the call queue. It doesn’t include time spent navigating IVR menus but only the wait time once the caller is in line to speak to someone. 

It is a direct indicator of your team’s responsiveness. A high ASA often signals understaffing or inefficient routing, damaging your brand reputation and hurting CSAT scores.

How to improve ASA:

• Set a benchmark ASA target using historical call data
• Staff appropriately based on forecasted peak volumes
• Use skill-based routing to get calls to the right agent faster
• Monitor queue lengths in real time and reallocate agents as needed
• Encourage the use of IVR or self-service for simple requests
• Offer call-back options when wait times spike

8. Call Volume

This refers to the total number of incoming and outgoing calls handled by your support team over a specific period. It gives a clear picture of demand and helps measure your contact center’s workload.

Monitoring call volume trends reveals valuable patterns, such as peak hours, seasonal spikes, or campaign-driven surges. This insight helps managers plan and avoid service bottlenecks.

Common triggers for spikes:

• Seasonal demand: Holidays like Christmas or sales events like Black Friday lead to a surge in customer inquiries
• Promotions and launches: Marketing campaigns or new product releases generate more interest and questions
• Service issues: Outages or disruptions push customers to call for updates or support
  
  

Smart ways to handle the load:

• Use call data to predict peaks, hire part-timers if needed, and prevent agent burnout
• Direct callers to the right agent or department automatically to reduce wait time and improve handling
• Provide updated FAQs, knowledge bases, and AI chatbots so customers can resolve issues on their own
• Integrate CRM and call systems so agents can access caller history instantly and resolve issues faster
• Track metrics to identify patterns, plan better, and continuously optimize your capacity and staffing
  
  

Agent Performance Metrics

These metrics help you assess how effectively your agents resolve customer issues, manage time, and contribute to overall service quality. 

9. Agent Utilization Rate

Are you making the most of your agents’ time on the clock? AUR measures how much of an agent’s paid time is spent handling calls or doing related work. It’s a direct indicator of how efficiently your team is being used.

Tracking this metric helps you balance productivity and burnout. Underutilized agents waste the budget, while overutilized agents burn out and make mistakes. 

Formula:

Agent Utilization Rate = (Total Handle Time / Total Logged-in Time) × 100

Example:

If an agent spends 6 hours handling calls during an 8-hour shift:

Utilization Rate = (6 / 8) × 100 = 75%

This means the agent was actively engaged in work 75% of their shift.

How to  improve agent utilization without overloading your team:

• Use call volume forecasts to align staffing levels with demand
• Automate repetitive tasks so agents focus on high-value conversations
• Cross-train agents to cover multiple roles or channels when needed
• Monitor in real time and adjust breaks or shift lengths on the fly
  
  

10. Agent Occupancy Rate

A key efficiency metric, agent occupancy rate, measures how much time agents spend handling calls or after-call work compared to their total available time.

Occupancy vs. Utilization:

While utilization includes all logged-in activities (including breaks and training), occupancy focuses strictly on time spent on customer-related tasks.

Formula:

Occupancy Rate = (Talk Time + After-Call Work) / (Available Time) × 100

Example:

• Talk Time: 5 hours
• After-Call Work: 1 hour
• Logged-In Time: 8 hours

Occupancy Rate = (5 + 1) / 8 = 0.75 or 75%

This means the agent was actively engaged with work 75% of their shift.

Practical tips to improve Agent Occupancy Rate without burning out your team:

• Match staffing levels to peak and low demand hours using historical data
• Mix inbound, outbound, and non-call tasks to keep agents engaged without overloading
• Automate repetitive wrap-up tasks like call tagging or follow-up email templates
• Send the right calls to the right agents to reduce handling time and increase occupancy
• Let supervisors and agents track occupancy levels and make quick adjustments

11. Quality Scores (QA Scorecards)

Quality Scores (QA Scorecards) measure how well your agents handle customer interactions based on pre-set criteria.

These evaluations cover tone of voice, product knowledge, script adherence, resolution accuracy, and compliance. For instance, you might score a call out of 100 based on these areas, each weighted according to importance.

Tracking quality scores helps you spot coaching opportunities, reward top performers, and ensure consistent service. When used with regular feedback and training, QA scorecards become a tool for continuous performance improvement.

12. Adherence to Schedule

Sticking to assigned shifts is critical to maintaining service levels. Adherence to Schedule measures how closely agents follow their assigned work schedules, such as logins, breaks, and logout times.

Even a few agents going off-schedule can lead to longer wait times and missed SLAs. For example, if 5 out of 20 agents take unplanned breaks during peak hours, your call queue could double.

Tips to improve adherence to the schedule:

• Communicate start times, breaks, and end-of-shift rules upfront
• Track adherence live and address deviations immediately
• Allow short flexibility between tasks to reduce lateness from call overruns
• Use system alerts to notify agents before shift or break changes
• Review adherence reports during one-on-ones and offer support when patterns emerge
• Incentivize punctuality with public praise or small rewards

Improve your call center metrics with Plivo 

If you want faster resolutions, lower wait times, and better visibility into performance, you need more than just basic reporting. You need a solution built for modern support teams, one that works as hard as your agents do.

As an all-in-one platform for omnichannel customer service, Plivo gives you that edge. It comes with OpenAI-powered AI agents and specific actionable insights on its console. As a result, you can run a smarter, faster, and more efficient call center.

Here’s how Plivo improves your operational efficiency with its tailored features:

• AI agents for instant resolutions: Automate routine queries and reduce agent workload. Let AI handle FAQs, status updates, and call routing 24/7
• Context-aware escalation: Automatically create tickets, escalate complex issues from AI agents to expert agents with complete context, reducing transfer friction and improving resolution times
• Easy integrations: Plug Plivo into your CRM, helpdesk, or other tools with minimal effort
• Scalable and flexible: Whether you’re a team of 10 or 1,000, Plivo grows with you
• Workflow automation: Manages follow-ups, ticket routing, and status updates without manual input, keeping customers informed at every step
• Real-time analytics: Track call volume, handle time, and agent performance as it happens. No more guessing
• Enterprise-grade security: Complies with SOC 2 and GDPR requirements to protect customer data and maintain trust.

Ready to reduce wait times, boost agent productivity, and delight your customers at every touchpoint? 

See how Plivo can transform your call center. Book a demo now.

Workflows used to mean fixed paths: Click A, then B happens. One step led to another, like clockwork — predictable but inflexible.

Now, AI agentic workflows plan their own work, select tools, learn from mistakes, and adapt to changing conditions.

Andrew Ng, founder of Deeplearning.AI, finds this game-changing. He says, “I think AI agent workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models.”

The business adoption rate is on the rise, too. With Gartner predicting that AI agents will be part of 33% of enterprise software apps, leading to 15% of day-to-day work done autonomously without human intervention, the question isn't if you'll use this technology, but when.

In this guide, we’ll discuss everything you need to know about AI-driven workflow automation.

What is an AI agentic workflow?

AI agentic workflow is a sequential process that uses large language models (LLMs) to perform complex tasks with the help of AI agents. At their core, these agents combine generative AI's cognitive abilities, natural language processing (NLP), and machine learning (ML). They make decisions based on context, learn from available data, communicate through plain language, and take specific actions to complete defined objectives.

Unlike standard automation, these workflows adapt as they run. They plan, assess progress, and change course when needed to complete tasks.

A quick look at how workflows have evolved

The concept traces back to IBM's MAPE control loop from the 1990s: monitoring, analysis, planning, and execution. Modern agentic workflows build on this foundation but with far more capability. Over the past few decades, workflows have undergone significant evolution. But here’s how it all began.

Traditional workflows operated like assembly lines. Each step happens in a fixed order with clear rules. Think of an expense report that moves from submission to manager approval to accounting in the exact same way every time. These systems can't handle exceptions well and break when faced with unexpected situations.

AI workflows added intelligence to the process.

Instead of just following rules, these workflows use machine learning models to handle certain steps. A text summarization workflow, for example, just takes in content and gives summaries.

Agentic workflows represent the next step forward. Beyond using AI for specific tasks, these systems let AI run the show. The agents decide what needs to happen next, choose which tools to use, and adjust plans based on results.

Today's most advanced systems use multi-agent workflows for more complex tasks with greater efficiency and reliability. These workflows distribute responsibilities across specialized agents working together rather than relying on a single AI agent for everything.

What makes a workflow agentic?

For an AI workflow to be truly agentic, you’ll need these four capabilities.

1. Task decomposition and planning

Agentic workflows first use the agents to divide larger tasks into smaller, manageable components. When faced with a challenging goal, the AI agent:

• Analyzes the overall task.
• Identifies logical subtasks.
• Map dependencies between steps.
• Creates a sequential priority list.

For example, when you perform tasks like processing insurance claims, an agentic system identifies the necessary steps: validating customer information, reviewing policy details, checking for fraud indicators, and calculating payouts.

It then creates an execution plan that accounts for dependencies between these steps.

2. Tool use and integration

At execution time, agentic workflows pull data from many sources (sensors, databases, APIs) and decide what to do next.

The concept was originally developed to help with computer vision challenges. Early language models couldn't process images, so developers created functions that linked them to visual APIs. This approach expanded as models like Generative Pre-trained Transformer (GPT) evolved.

Modern agentic workflows connect with external resources like:

• Web search engines to find current information.
• Code interpreters to run computations.
• APIs to interact with other services.
• Data stores to retrieve specialized knowledge.

The selection of tools can be predetermined or left to the agent's discretion. For complex tasks, allowing the agent to choose appropriate tools works best, while simpler workflows benefit from predefined tool selection.

3. Reflect and iterate

If you think the job is done after task execution, this is where it gets interesting. Agentic workflows improve through self-evaluation. Rather than delivering single-attempt outputs, they review their work, identify problems, and make refinements.

The workflows store context and feedback across interactions. This memory capability comes in two forms.

First, short-term memory keeps track of recent conversation history and current task progress, helping the agent maintain context and determine next steps. And second, long-term memory stores information across multiple sessions, enabling personalization and performance improvements over time.

Without memory, AI systems would restart from scratch with each interaction. Memory turns one-off interactions into ongoing, evolving relationships.

4. Distribute responsibilities

Complex tasks often require multiple types of expertise. Agentic workflows can distribute work across specialized AI agents, each handling different aspects of the work, much like how human teams collaborate on complex projects.

For example, this multi-agent collaboration in customer service automation would look like:

• One agent interprets customer requests.
• Another searches knowledge bases for relevant information.
• A third crafts personalized responses.
• A supervisor agent coordinates the entire process.

This division of labor enhances overall performance by leveraging each agent's strengths. It works particularly well for tasks requiring diverse skills or parallel processing.

Key components of agentic workflows

Under the hood, agentic workflows combine different technologies. When properly integrated, they create something far more powerful than any single component could achieve on its own.

AI agents

AI agents form the core intelligence within agentic workflows. Built on LLMs, these agents provide reasoning, planning, and reflection capabilities. The semantic engine (typically the LLM) provides both reasoning capabilities and a conversational interface. This allows agents to seek clarification or approval when needed while still working autonomously on routine tasks.

While traditional AI requires constant guidance, agentic AI evolves and adapts to new situations without much human guidance and training.

Robotic process automation (RPA)

RPA enables AI agents to handle rule-based, repetitive tasks across different applications. This technology creates software robots that mimic human actions like data entry, transaction processing, and report generation.

In an agentic workflow, RPA serves as the hands that perform structured work. For example, after an AI agent extracts information from unstructured invoice documents, RPA automatically enters that data into accounting systems, eliminating tedious manual work.

Natural language processing

NLP enables agentic workflows to understand and respond to human language. Here are a few critical functions that it takes care of:

• Interpreting user requests and questions.
• Understanding context and intent.
• Generating human-like responses.
• Processing text documents for information.

This component eliminates the need for specialized training or programming knowledge when working with AI agents. Users can simply express their needs in everyday language.

Machine learning algorithms

ML algorithms help agentic workflows learn from experience and improve over time. These algorithms identify patterns in data, make predictions, and optimize processes without explicit programming.

This self-improvement capability means workflows get better with use. They learn which approaches work best in specific situations and adapt their strategies accordingly.

Prompt engineering

The performance of agentic workflows depends heavily on how agents are instructed. Prompt engineering shapes agent behavior through carefully designed instructions and examples.

Some advanced prompt engineering techniques include:

• Chain of thought: Guiding the agent through logical reasoning steps.
• Planning: Breaking complex tasks into manageable steps.
• Self-reflection: Enabling the agent to review and improve its work.

These techniques help LLMs understand complex requirements and produce more accurate, relevant outputs consistently.

Workflow orchestration

This coordinates all components within an agentic system. It defines execution sequences, manages dependencies, and ensures tasks happen at the right time.

Orchestration tools provide interfaces for designing, monitoring, and troubleshooting complex workflows. They connect multiple technologies and handle scheduling, error management, and resource allocation to keep processes running smoothly.

Integrations

These connect agentic workflows with existing business systems and data sources. Integrations ensure agents can access required information and take meaningful actions within your environment. They can be of any type, such as:

• Data integrations that consolidate information into accessible formats.
• Agent frameworks like LangChain, LangGraph, and crewAI that expand capabilities.

Tool integrations that give agents access to specialized functions.

Practical use cases for agentic workflows

We’ve covered enough about the mechanics behind autonomous AI workflows. Now, let’s look at them in action and how they benefit industries in the real world.

Supply chain

Managers in supply chains now implement agentic workflows for various use cases, one of which is to resolve backorder problems.

Traditional backorder handling involves multiple steps: A system notifies customer service about unavailable items, an employee checks the customer relationship management (CRM) and enterprise resource planning (ERP) system to find alternatives, and then manually coordinates with shipping teams.

Even during disruptions like natural disasters, the workflow helps agents identify safe shipping routes and alternative suppliers without requiring manual decisions at each step.

Finance

Financial institutions use agentic workflows to combat fraud.

In traditional financial fraud detection, a system flags suspicious transactions first. Analysts then step in to manually review account history, cross-check databases, and contact customers, often delaying action.

However, in an agentic workflow:

1. A monitoring agent scans transactions, flagging anomalies.
2. An analysis agent examines patterns, cross-references historical data, and assesses behavior.
3. A response agent alerts customers and freezes suspicious activity.

Microsoft has launched Agent Flows, where users can simply define their intent in everyday language to create flows. It uses multiple agents and aims to improve process management.

Marketing

Agentic workflows autonomously lead marketing tasks, a function that previously required multiple employees to participate. For example, sending a personalized marketing campaign in an AI-powered workflow involves:

1. A segmentation agent grouping customers by demographics, behavior, and purchase history.
2. An analysis agent predicting future actions using data patterns.
3. A delivery agent sending tailored emails and social media recommendations.

Plivo CX's Audiences feature manages your customer data across touchpoints. You can import contacts from e-commerce platforms, segment them based on behaviors, and create targeted workflows.

This integration lets your AI agents access comprehensive customer profiles, enabling more personalized interactions based on purchase history and engagement patterns.

Healthcare

Agentic workflows improve healthcare by monitoring and adapting patient care. And it’s not just the clerical tasks like documentation, insurance compliance checks, or form submissions that agentic AI in healthcare can automate.

AI workflow optimization also helps with real-time patient care and ensures timely expert intervention. Here’s a quick look:

1. A diagnostic agent analyzes medical images to identify anomalies, such as early-stage cancer.
2. A care agent evaluates sensor data and recommends adjustments to personalized plans.
3. A monitoring agent tracks condition changes, alerting doctors to bigger issues.

Coding assistance

Agentic coding assistants execute codes, debug errors, refine outputs, and even create commits and pull requests with minimal input. For example, tools like Claude Code can automate software development by autonomously submitting code changes.

Here’s how it looks:

1. An AI agent generates code based on user input, using LLMs to create the required functionality.
2. A second agent reviews the code, checking for errors, style issues, and adherence to best practices.
3. The original agent refines the code based on feedback, iterating until the code meets the desired quality.

Unlike early coding assistants that only generated snippets, agentic workflows continually test and improve their work.

Customer support

AI agents streamline customer support by handling routine tasks and escalating complex issues. A typical workflow looks like this:

1. An inquiry agent receives and categorizes the customer’s query, using NLP to identify the issue.
2. The response agent generates an appropriate response based on past interactions and customer data, offering a personalized solution.
3. If unresolved, the escalation agent escalates the query to a human agent, providing context and previous interaction history.

This multi-agent approach ensures faster resolutions, improves customer satisfaction, and optimizes the workload for human agents.

Steps for Implementing AI Agentic Workflows

Now, let’s start building agentic workflows that think, adapt, and improve on their own. Here are the steps for effective implementation.

Step #1: Set specific, actionable goals

Check your existing infrastructure, available budget, and your team's technical expertise. Make sure everyone, from employees to executives and investors, understands why you're adopting AI agentic workflows.

For AI agents to deliver results, they need precise directions.

Generic goals like "improve operational efficiency" won't work. You must define exactly what you want to accomplish with measurable outcomes.

For example, if you want faster customer service, specify "reduce response time from 10 minutes to 2 minutes" or "increase first-contact resolution by 25%." This will give the workflow and the agents the direction needed.

Step #2: Build teams of specialized AI agents

AI agents work best when they focus on specific tasks. And just like skilled employees, each agent should handle what it does best.

For healthcare workflows, this means having one agent analyze medical data while another manages appointment scheduling. In financial systems, one agent might detect fraud patterns while another communicates with customers.

Identify what each step in your workflow needs, then assign the right agent with the right tools for each job.

Step #3: Ensure strict data governance

As agentic workflows become more prominent across industries, it’s important to ensure strict data governance and security policies. Apply metadata to build audit trails that track data from its origin through every access and transformation, ensuring accountability and compliance with privacy regulations.

Then, develop clear policies on how data moves through your workflow system. Define who can access what information and how it's used to avoid data breaches.

Regular audits also ensure your workflows maintain data integrity and stay within legal boundaries, even as regulations evolve.

Step #4: Start small with test runs

Test your AI workflow on contained projects before going all-in. Select a specific process with clear success metrics that allow you to see results quickly.

In this limited rollout, you’ll spot unexpected issues, so adjust your approach based on real feedback and calculate the actual return on investment (ROI) before making a larger investment.

Once your pilot confirms the workflow delivers value, use those lessons to scale gradually across more departments and processes.

Step #5: Prepare your team for AI collaboration

Get your employees ready to work alongside AI agents. Provide training that focuses on effective prompting, when to trust agent outputs and when to verify them, and understanding the workflow boundaries between human and AI tasks.

This knowledge helps staff see AI as a productivity tool rather than a threat.

After setting up your workflow structure, deploy Plivo's AI agents to handle specific customer interactions across all communication channels.

Choose from prebuilt agents that support the entire customer journey:

• Convert: Sales conversion and shopping assistant agents help customers complete purchases.

• Engage: Loyalty, upsell, and retention agents deliver personalized offers at the right moment.

• Delight: Support, order tracking, and appointment scheduling agents provide instant service.

How Plivo streamlines your workflows with AI agents

With Plivo’s AI agents, you can implement agentic workflows with zero technical complexities.

The system connects with your existing tools (CRMs, helpdesks, payment processors) to create workflows that take action. Agents access your knowledge base to deliver accurate, consistent responses in your customers' preferred languages.

Implementation is simple and easy:

• Select prebuilt agents designed for different customer journey stages (convert, engage, and delight).
• Connect to your existing business tools (say, Shopify, Stripe, or any CRM) without developer help.
• Import your knowledge base for accurate responses (no prompt engineering required).
• Launch your agents to respond, resolve, or convert.

Plivo supports all major AI models (OpenAI, Google, Anthropic, Meta), letting you choose what works best for your specific needs.

Request a trial to access Plivo’s features before you dive in.

Every day, billions of messages fly across WhatsApp.

For businesses, it’s a powerful channel where customers are already chatting, asking questions, and making decisions. But keeping up with messages, responding on time, and adding a personal touch? That’s tough.

This is where WhatsApp automation changes the game.

Imagine recovering lost sales with a simple follow-up or instantly answering common customer questions without lifting a finger. Automation makes this possible.

With 200 million businesses already using WhatsApp Business, staying ahead means adapting.

In this guide, we’ll break down WhatsApp automation, its benefits, and how to put it to work for your business.

Understanding WhatsApp automation

WhatsApp automation is the practice of using software to manage messages and client interactions automatically. It sends messages at set times, replies to customer questions, and shows you how your messages are performing.

This makes it easier to stay in touch with customers, improve their experience, and promote your business effectively. Plus, it saves you time so you can focus on more urgent queries and tasks.

How does WhatsApp automation work?

WhatsApp automation works by using third-party tools like Plivo. It connects to WhatsApp’s API (application programming interface), which acts as a digital bridge, allowing businesses to automate messages, set up bots, and integrate WhatsApp with other systems for seamless communication.

Let’s break this down.

Chatbots

Think of these as virtual helpers that chat with your customers for you. They’re automated programs that can answer everyday questions like “What are your business hours?” or “How do I track my order?”

Plivo's powerful self-service bots can direct common questions to automated responses, ensuring your customers get quick replies 24/7 without you typing the same answers repeatedly.

Plus, Plivo allows you to use customer data to enhance these chatbot conversations, making interactions feel more personalized and relevant.

Notifications

These are automatic messages that keep your customers updated without additional effort on your end. For example, you can send a reminder about an upcoming appointment or a friendly note to confirm an order has been shipped.

Plivo makes it easy to set up these notifications, ensuring they go out at the right time or after a key event like a purchase. This keeps your customers in the loop effortlessly.

Workflow automation

This feature allows you to create smart processes for WhatsApp. For instance, if a customer types “Menu,” they instantly receive your menu with options to choose from. You can also set messages to send at optimal times.

With Plivo's unified channels, you can even switch between voice, chat, SMS, and WhatsApp or use them together for a seamless experience.

It’s a hassle-free way to organize tasks and keep conversations flowing.

WhatsApp Business API vs. WhatsApp Business app

WhatsApp automation can be set up in two ways: through the WhatsApp Business app or the WhatsApp Business API. The app is great for small teams that don’t need much automation since it’s simple and caters well to basic needs.

On the other hand, the API is best for enterprises seeking more advanced features as it facilitates connections with other helpful tools.

Here’s a quick look at what each option offers.

Basic automation with the WhatsApp Business app

The app provides simple automation to keep customers engaged when your team is busy or unavailable. You can set up two types of WhatsApp auto-reply messages.

Greeting messages provide a warm welcome when someone contacts your business for the first time, creating a positive first impression. Away messages let customers know when to expect a response, ensuring they’re not left waiting without clarity.

While these are handy, they’re pretty limited.

What if you want to automatically answer common questions, like “How do I return an item?” or “What’s your pricing?” Or maybe you’d like to qualify leads or send reminders for demos?

The app doesn’t have the tools for that, which can be a challenge for growing businesses.

How the WhatsApp Business API enhances automation

WhatsApp Business API’s automation makes it easier to manage customer interactions as your business grows.

Unlike the app’s basic features, the API lets you connect with powerful tools like chatbots, customer relationship management (CRM) systems, and scheduling apps to enhance communication and efficiency.

WhatsApp chatbots can handle conversations, answer common questions, collect customer details, and even guide users through processes like placing an order — all without human involvement.

WhatsApp Flows takes this a step further, allowing businesses to create seamless, step-by-step interactions within the chat. Customers can book appointments, make purchases, or complete other actions without ever leaving WhatsApp, keeping the experience smooth and efficient.

WhatsApp automation use cases

WhatsApp automation improves customer interactions, streamlines operations, and ensures timely updates. Here are three key areas where it excels:

Automated customer support

There’s an increasing demand for quick and dependable support. In fact, 57% of consumers expect live chat replies within minutes.

WhatsApp chatbots can address common questions immediately, ensuring assistance is available beyond regular office hours.

For example, a traveler checking their flight status can skip navigating the booking platform or calling support. Instead, they can message the platform on WhatsApp. Within seconds, a chatbot asks for their booking ID to retrieve the required details. 

This is exactly how EaseMyTrip, the travel booking platform, works:

Collecting feedback is simple too, with short surveys sent post-conversation to improve service. When paired with tools like ticketing systems, every inquiry stays organized and gets resolved efficiently.

If you’re ready to tap into these benefits, Plivo’s WhatsApp AI chatbot is worth a look. This no-code, ChatGPT-powered tool handles inquiries instantly, lightens your team’s load with routine tasks, and makes support available 24/7.

It can manage everything from exchanges to record updates, and if something tricky comes up, it effortlessly connects customers to a human agent. Best of all, keeping it updated with new information is quick and easy.

Marketing and sales automation

WhatsApp automation revolutionizes sales teams with a faster and more personalized approach towards customers. Chatbots play a key role by asking straightforward questions like “What’s your budget?” or “Which product interests you?” to find serious buyers.

For example, a car dealership can use a chatbot to ask about a customer’s favorite model and price range. Then, it sends the best leads to a salesperson.

Follow-ups, an important part of any sales strategy, can also become effortless with automation. Businesses can send reminders for appointments or messages after a sale. So, a software company might remind someone about scheduling a demo and later suggest options that fit their needs.

Automation lets teams handle more leads without extra staff. It’s an easy way to grow.

Plivo’s WhatsApp marketing automation platform is a good pick for businesses seeking these capabilities.

It sends timely reminders for appointments or payments, personalizes messages based on customer behavior, and uses audience segmentation to target the right people.

You can also schedule messages and track performance with built-in analytics. These features save time, keep customers happy, and boost WhatsApp marketing outcomes.

Transactional notifications and alerts

Transactional notifications and alerts keep us informed about key moments. They confirm purchases, remind us of appointments, and verify payments.

With WhatsApp automation, these messages can reach us in real time, directly on our phones.

Let’s suppose you just made an online purchase. Moments later, a WhatsApp message arrives: “Your order is confirmed! Get ready to unbox your new gadget soon.” When it’s on the move, you receive a message saying, “Your package is on its way! Track it here.”

E-commerce shipping alerts like these make the wait feel exciting. Amazon follows a similar approach:

In healthcare, it’s just as helpful. Have you ever forgotten an appointment? With WhatsApp automation, your clinic can send a friendly reminder: “Don’t forget, your check-up is scheduled for tomorrow at 10 AM. See you then!” It’s a thoughtful nudge that keeps your schedule and your health on track.

For banking, security is paramount. Receiving an instant alert, “Just a heads-up, a payment of $50 was made from your account,” is reassurance delivered straight to your pocket. This keeps you confident and in control.

Benefits of WhatsApp automation

Here are four key benefits that make WhatsApp automation a must-have tool in business communications.

Enhanced customer engagement

Imagine reaching your customers right where they are, with messages they’re almost certain to see.

WhatsApp automation makes this a reality, boasting an impressive 98% open rate, far surpassing the 20% typical of email marketing. This means your updates, offers, or reminders don’t just land in an inbox; they get noticed.

This level of visibility can be a game-changer. Whether it’s a quick order confirmation or a personalized promotion, WhatsApp automation ensures your messages arrive at the perfect moment.

And with 80% of messages being read in the first five minutes rather than hours, you’re meeting customers’ expectations for swift, meaningful interactions. The result? Stronger relationships built on trust and relevance, keeping your brand top of mind.

Increased efficiency and productivity

Every business knows the weight of repetitive tasks; answering the same questions or sending routine updates can drain time and energy.

Automation lifts that load effortlessly. It cuts first response times by 37% and resolution times by 52%, ensuring customers get answers faster while your team breathes easier.

With automation handling routine chats, your staff can focus on complex issues, client relationships, and innovation. It’s a shift that boosts productivity across the board.

Improved lead generation and conversions

Capturing a prospect’s interest is one thing; turning it into a sale is another. WhatsApp automation bridges that gap with finesse. With a standout open rate, your messages grab attention in a way emails often can’t.

Add in the ability to respond instantly, think chatbots or quick replies, and you’ve got a recipe for keeping prospects engaged.

Whether it's providing product details, sending follow-ups, or guiding customers toward a purchase, automation ensures no lead goes cold. Delivering timely, relevant messages helps turn prospects into customers, boosting sales with every conversation.

Cost savings and scalability

Growth is exciting, but it often comes with a catch: more customers mean more messages, and a strain on resources.

WhatsApp automation flips the script. It manages rising message volumes without demanding a similar increase in staff or budget. How? By taking on the repetitive workload, like sending confirmations or fielding common queries, to keep your team lean and focused.

The logic is straightforward: every automated message is one less task for a human agent.

As your business expands, this efficiency compounds, sparing you the expense of hiring more people or scaling up infrastructure. It’s a scalable solution that grows with you, keeping costs steady while your reach soars.

Best practices for implementing WhatsApp automation

WhatsApp automation improves customer service, marketing, and sales with efficiency and scale. But to get the most out of it, planning is key.

Here are some best practices to ensure it aligns with business goals and delivers a consistent user experience.

Set clear objectives and key performance indicators (KPIs) 

Before you jump into WhatsApp automation, it’s a good idea to figure out what you’re aiming for. Think of it like picking a target in a game; you’ve got to know what “winning” looks like.

Are you trying to speed up replies to customers, boost happiness, or maybe get more people to buy after chatting with you? These are your objectives.

To check if you’re nailing those goals, you’ll want to establish KPIs. These are scorecards that show how well you’re doing. Here are a couple of examples:

• Response time: How fast you get back to customers.
• Conversion rate: The percentage of chats that lead to sales.
• Customer satisfaction score (CSAT): How happy customers are with their interactions.
• Engagement rate: How often customers interact with automated messages.
• Retention rate: How many customers return after their initial interaction?

An efficient way to set these up is with SMART goals. That means your goals must be:

• Specific: Clearly defined with no ambiguity.
• Measurable: Quantifiable or trackable.
• Achievable: Realistic and within reach.
• Relevant: Aligned with business objectives.
• Time-bound: Set with a clear deadline, such as “within the next month.”

For example, “We’ll reply to customer messages within 5 minutes, 90% of the time, by the end of next month.” The goal is simple, measurable, and attainable.

Design intuitive conversational flows

A conversational flow serves as a structured guide that enables automated chats to interact with customers naturally and seamlessly. It defines the step-by-step path a conversation follows, ensuring customers receive quick and clear responses without confusion.

Having a good flow keeps customers happy and stops them from getting frustrated.

That’s it! It’s short, clear, and gets the job done. To make it more efficient, plan the steps ahead of time and test it with a friend to make sure it’s easy to follow.

Personalize messages to avoid seeming robotic

Personalizing messages makes interactions feel genuine rather than robotic, creating a stronger connection with customers. Adding a personal touch — like using their name, referencing past purchases, or writing in a warm, conversational tone — helps make each message feel more natural and engaging.

For instance, the robotic version of a message would be, “Your order is confirmed.”

The personalized version will look like, “Hey Sarah, your order’s all set! Your new blue sneakers are on their way. Hope you love them!”

The difference is clear: the second message feels more like a friendly check-in, making the interaction more engaging and personal.

Monitor performance and iterate based on analytics

The work doesn’t end once automation is live. Regularly track performance using WhatsApp Business API analytics to monitor metrics such as response times, engagement rates, and customer feedback.

Use these insights to refine your strategy, making adjustments as needed to boost efficiency and satisfaction.

Remember, automation is an ongoing cycle of learning and optimizing, ensuring your communications stay effective.

Automate business communication with Plivo

Incorporating WhatsApp automation into your business growth strategy can transform how you connect with customers and manage daily tasks. It’s a simple yet powerful way to make interactions feel personal while keeping your team efficient.

Plivo offers WhatsApp automation tools to simplify this process. They fit into your existing setup and provide features to improve customer service:

• Unified channels: Handle WhatsApp and other channels from a centralized platform.
• Quick integrations: Link to your preferred business tools for a complete picture.
• Tailored workflows: Create custom steps for how customers interact with you.
• Live analytics: Review conversations as they happen to enhance your approach.
• Team assistance: Equip your agents with tools like call recording and whisper for better support.

These features are designed to help your business grow while ensuring customer interactions stay smooth and pleasant.

To discover how Plivo can improve your communication, contact us to schedule a demo and see the difference it can make.

Excessive paperwork, endless communications, and unclear processes. This was the reality for insurance customers until artificial intelligence (AI) chatbots showed up.

Now, these virtual assistants for insurance handle claims, guide policy selections, and answer questions 24/7 without the usual runaround. In fact, the global AI in insurance market size reached $10.82 billion in 2025 and is projected to exceed $141.44 billion by 2034.

Insurance chatbots use conversational and generative AI to manage entire processes from marketing to customer support, not just answer basic FAQs. They create and share answers through natural, human-like interactions.

This blog post will discuss how AI in insurance automation works and offers customers value through a conversational experience.

What are insurance chatbots?

Insurance chatbots are virtual assistants that automate customer interactions across multiple channels. They are the first touchpoint for customer processes, be it answering basic policy questions or guiding complex claim submissions.

These tools work across websites, apps, and messaging platforms to provide 24/7 support for improved customer satisfaction.

But not all chatbots function in the traditional sense.

For instance, rule-based chatbots follow preset scripts and decision trees. They operate on simple if-this-then-that logic, answer FAQs, guide customers through basic processes, and handle routine tasks with preset responses. These work well for standard questions but struggle with complex requests.

On the other hand, AI-powered chatbots use natural language processing (NLP) and machine learning (ML) to understand customer intent. They can handle more complex conversations, learn from interactions, and provide personalized responses.

The most advanced systems can analyze customer history, suggest personalized coverage options, and complete transactions across channels.

AI chatbot for insurance — putting conversational experience in the limelight

Conversational AI in insurance chatbots uses NLP and machine learning. It understands queries, provides accurate policy information, and guides users through processes like claims, renewals, and payments.

From answering simple questions like "What does my policy cover?" to assisting with complex tasks like "How do I file a claim for my damaged car?", an AI chatbot ensures quick, seamless customer support. It also maintains contextual understanding throughout conversations and allows customers to switch topics from policy coverage to claims without losing the conversational flow.

Unlike basic rule-based bots, AI-powered insurance chatbots can analyze customer history, suggest personalized coverage options, and even complete transactions. Plus, they work across websites, apps, and messaging platforms, providing 24/7 assistance wherever customers need it.

You can configure your AI agents with Plivo to automate your chatbot experience in various use cases, such as:

• Pre-sales inquiry agents explain policy coverages, deductibles, and exclusion clauses using your underwriting guidelines and product database.
• Sales conversion agents turn leads into policyholders with personalized plan recommendations based on coverage needs and budget.
• Campaign orchestrator agents trigger policy renewal campaigns, launch seasonal coverage promotions, and upsell messages to the policy lifecycle.
• Personalized offer agents build custom policy packages through risk factors, claim history, and demographic data analysis.
• Customer retention agents prevent policy lapses with timely reminders and loyalty incentives before renewal deadlines.
• Billing and subscription agents handle premium payment questions and explain coverage adjustments with clear, helpful answers.
• Customer support agents resolve policy questions and claim status inquiries 24/7.

The platform also offers specialized agents that analyze shopping behavior to study customer preferences and deliver product updates to target audiences.

Here’s how a conversation with Plivo’s AI-powered agent looks like in action:

👤 Customer: Hi, I need details about your health insurance plans.
🤖 Plivo AI Agent: Sure! Are you looking for individual or family coverage?

👤 Customer: Family. Also, what is the eligibility criteria?
🤖 Plivo AI Agent: Got it. I’ll need some basic details to check eligibility. How many family members are included, and what’s the age of the primary policyholder?

👤 Customer: Four members; the primary holder is 35 years old.
🤖 Plivo AI Agent: Perfect! Based on this, you qualify for our Premium and Standard plans. Would you like a quick comparison of their benefits?

👤 Customer: Yes, please.
🤖 Plivo AI Agent: Sure! The Premium Plan includes OPD, maternity, and dental coverage, while the Standard Plan focuses on hospitalization and critical illness. Would you like a quote for both?

👤 Customer: Sounds good. Also, what is the renewal period?
🤖 Plivo AI Agent: Policies renew annually. Right now, we’re offering a 10% renewal discount for early sign-ups.

👤 Customer: Nice! Let’s do it. Can I pay here?
🤖 Plivo AI Agent: Absolutely. Here’s a secure payment link: [link]. Let me know if you need any assistance.

👤 Customer: This was super easy. Thanks!
🤖 Plivo AI Agent: Happy to help! Let me know if you have any other questions. 😊

This seamless experience is just the beginning. You can also train the chatbot based on your custom dataset. Simply feed your company policies and knowledge base for accurate and relevant responses. Try Plivo CX’s self-service AI chatbot, developed and powered by OpenAI, for businesses of all sizes.

Top companies using the platform are seeing results.

"Our support team was drowning in basic queries before we found Plivo's AI Agents. We simply fed our support documents to the Customer Support AI Agent, and within days, the Agent was handling routine cases like a pro! The best part? It works around the clock and speaks multiple languages. Plivo made implementation surprisingly straightforward.” - Uber

AI chatbots’ impact across various use cases

AI chatbots are transforming customer interactions, with 80% of users reporting positive experiences. In the insurance industry, where companies handle countless queries daily, chatbots help provide faster responses, reduce wait times, and improve customer satisfaction.

Here are some key ways insurance chatbots can enhance efficiency and engagement.

Claims processing and settlement

Insurance customers always find the claims process long and frustrating. However, with an insurance claims chatbot, customers can report incidents, upload documentation, and track status through a single interface.

The chatbots collect claim information through conversational exchanges, rule out suspicious actions, request supporting evidence like accident photos, and guide users through each step.

Here’s a look at the benefits:

• Reduced processing time 
• Collecting First Notice of Loss (FNOL) information through guided conversations
• 24/7 claim submission and status tracking
• Automated fraud detection through data analysis and image verification
• Consistent updates on claim progress and expected settlement dates
• Decreased workload for human agents

Policy management

Insurance chatbots handle the entire policy lifecycle (from application to renewal) without human intervention. Your agents can skip all the lengthy phone calls and paperwork to give customers direct control over their experience.

Customers can use policy management chatbots to:

• Update personal information
• Review policy documents
• Make coverage adjustments
• Complete policy renewals
• Order insurance cards

Customer onboarding

Traditional insurance onboarding requires manual data entry and verification. However, a chatbot makes onboarding faster, more efficient, and less stressful for customers. It also automates follow-up tasks so customers can complete all required steps to finalize their policy without feeling overwhelmed.

These chatbots for policyholders simplify onboarding by:

• Answering product and coverage questions
• Guiding users step-by-step through the purchase process
• Collecting payment data
• Directing customers to relevant resources (FAQs, knowledge base, and documentation)
• Assisting with initial account setup

Risk assessment and underwriting

Insurance chatbots pre-screen applications and provide underwriters with customer data through guided conversations. This speeds up underwriting with accurate assessments.

Some AI-driven chatbots also analyze data and offer risk recommendations to help insurers make informed decisions faster.

Chatbots offer an added layer of security through secure, sensitive customer data handling. Since data processing tasks are automated, there’s minimal human intervention and a lower risk of data breaches.

Fraud detection

AI-powered chatbots can analyze large volumes of data faster than humans and identify hidden threats that might otherwise go unnoticed. Here’s how these automated systems prevent fraud:

• Flag suspicious claims based on inconsistent information
• Detect unusual patterns during the application process
• Request additional documentation when fraud indicators appear
• Alert human investigators to potential problems

Payment collection

Customers no longer wait on hold to make payments over the phone.

Chatbots allow policyholders to make one-time payments or set up recurring payment schedules within the same conversation flow. With these tools, you can:

• Send timely reminders for upcoming premium payments
• Auto-fill customer payment details to save time
• Process payments through secure digital channels
• Provide instant payment confirmations and receipts

Advertising and promotion

Marketing teams use chatbots to replace static website forms with interactive conversations that engage visitors. The outcome is valuable data about customer preferences and pain points. Using this, chatbots can:

• Capture lead information through conversational interactions
• Distribute relevant content like guides and blog posts
• Share information about seasonal promotions and loyalty discounts

For example, when data shows many customers asking about specific coverage types, insurers can adjust their promotion strategies to highlight those policies or develop new offerings to meet emerging customer needs.

Cross-selling and upselling

Chatbots help you analyze customer profiles, policy information and claims history to identify sales opportunities at the right time. They can:

• Suggest additional coverage options based on life events
• Recommend policy upgrades when customer needs change
• Offer bundled products with special discounts
• Present relevant add-ons during policy renewals

For example, when a customer adds a teenage driver to their auto policy, the chatbot might recommend an umbrella policy for extra liability protection. Or when a homeowner updates their property value, the chatbot can suggest adjusting coverage limits.

Chatbots support multiple languages, so diverse customer bases can benefit from your services.

Feedback and loyalty

After claim processing or support resolution, you can collect customer feedback directly through the chat interface. The chatbots:

• Gather responses immediately after claim processing
• Present simple button options for quick ratings
• Send automated surveys via email or chat after conversations end
• Track Net Promoter Score (NPS) data over time

The best part? Customers don’t need to leave their preferred communication channel, leading to better response rates.

Why an AI insurance chatbot makes business sense (beyond just conversations)?

Leading insurance providers have already proven that AI chatbots deliver measurable results. Beyond basic customer interactions, their implementation shows clear return on investment (ROI).

Lemonade, a renter’s insurance company, improved customer experiences with three specialized chatbots: Maya, Jim, and Cooper.

Users rely on Maya for seamless navigation through insurance processes, while Jim specializes in managing claims and detecting suspicious activity. Cooper streamlines internal workflows between teams.

Maya can process new policies in just 90 seconds and even made headlines for approving and paying a claim in under 3 seconds. Meanwhile, Jim handles over 20,000 claims annually without human intervention.

Aetna's chatbot, Ann, provides 24-hour support on their website. She understands natural language queries and delivers immediate written and spoken responses.

Since implementation, phone calls to Aetna's call center have decreased 29%. Members receive the same responsive service from Ann that they would get when calling customer service, all without leaving the website.

Tokio Marine Insurance deployed its chatbot "Tokio" to serve UAE customers across the web, WhatsApp, and Messenger. The bot handles quotes, claim tracking, and policy renewals with zero human intervention.

It manages 70% of their inbound queries and will expand to support both Arabic and English to serve their diverse customer base.

The next decade for AI insurance chatbots looks strong

CB Insights recently shared these key predictions on X for the insurance industry in 2025:

In the next few years, AI will change the insurance industry (for good) in the following ways:

• Policy purchasing will become instantaneous. AI algorithms will create risk profiles in seconds, while telematics and Internet of Things (IoT) devices enable carriers to issue immediate policies. Life insurance will expand into mass-market instant products through AI-refined risk identification.
• Claims automation will handle half of all processing activities. IoT sensors, drones, and video footage will replace manual assessments. Connected home devices will alert both residents and insurers before damage occurs.
• Underwriting for most personal and small-business products will change completely. Machine and deep learning models will compress the process to seconds. These systems will combine internal and external data to generate tailored bindable quotes.

Adding to the list, blockchain and augmented intelligence will improve security, claims processing, and fraud prevention. As chatbots continue to advance, a scalable solution is key.

The right vendor will help you stay competitive, adapt to industry shifts, and deliver faster, smarter customer experiences.

Implement an end-to-end conversational chatbot using Plivo

Plivo’s AI-powered self-service chatbot handles complex support and sales scenarios with 90-95% query resolution.

With a single platform for customer engagement across acquisition, engagement, and service, Plivo centralizes customer data, eliminates the need for ETL (Extract, Transform, Load) processes, and automates workflows using AI.

It delivers a personalized experience with a custom voice and identity by supporting 30 different languages with global coverage in 220+ countries and territories.

As these AI agents take over routine conversations, your team can focus on innovation, strategy, and growth — doing 50x more with the same resources. Contact us to get started today.

Online shopping should be effortless, but too often, customers encounter confusing menus, slow support, and impersonal interactions. Frustration sets in, carts are abandoned, and businesses miss out on sales.

Retail chatbots are changing this.

Designed to simplify e-commerce, these AI tools act like 24/7 digital assistants. They resolve queries instantly, guide shoppers to relevant products, and personalize experiences at scale.

With the chatbot market expected to grow from $8.71 billion to $25.88 billion by 2030, adopting this technology is a necessity.

In this article, you’ll learn how using a chatbot for retail turns fleeting transactions into lasting customer relationships and why your brand’s survival depends on quick adoption.

What are retail chatbots?

Retail chatbots are AI-driven virtual assistants designed to mimic human conversations while solving real customer problems.

Think of them as your 24/7 sales and support team, powered by advanced natural language processing (NLP) and conversational AI. These tools anticipate customer needs and drive action at every stage of their shopping journey.

When deployed strategically, chatbots:

• Engage shoppers with instant, round-the-clock support.
• Boost conversions by guiding customers to the right products.
• Build loyalty through personalized interactions that feel human.

For example, if a customer hesitates at checkout, a chatbot can intervene: “Need help? Use the code CHAT10 for 10% off your first order!” This seamless blend of service and sales turns friction into revenue.

How do retail chatbots work? 

Chatbots for e-commerce may seem like magic, but their power comes from two key technologies working behind the scenes:

Artificial intelligence (AI) and machine learning (ML)

Imagine a chatbot for retail that learns from every customer interaction. That’s AI and ML in action.

These systems analyze what customers bought in the past, products they browsed but didn’t buy, and how long they spent on specific pages.

Over time, the chatbot spots patterns. For example, if a shopper keeps eyeing running shoes, it might say, “You’ve viewed these sneakers 3 times this week! They’re back in stock — want to grab them before they sell out?”

Here, the chatbot uses data to predict what the customer wants and nudges them toward making a purchase.

NLP

NLP is what lets chatbots “get” human language. It helps them understand slang, typos, or questions like, “Yo, got any summer dresses under $50?” or “Is this jacket waterproof?"

Here’s how it works:

• The chatbot breaks down sentences to grasp the intent (e.g., “Find a dress” or “Check product features”).
• It pulls key details (price range, product type, etc.) to craft a helpful reply.

For instance, if a customer asks, “Does this come in red?” The chatbot says, “Yes! Red is available in sizes S–L. Want me to set one aside for you?”

Types of retail chatbots

While all retail chatbots aim to improve shopping experiences, their approaches vary. Let’s break down the two most common types.

Rule-based chatbots

These chatbots follow a strict script. Think of them as a friendly FAQ section that talks back. They’re programmed with pre-set rules and responses, like a flowchart guiding customers to answers.

What they’re great at:

• Answering routine questions (e.g., “What’s your return policy?” or “Are you open on Sundays?”).
• Providing basic product details (price, size availability, etc.).
• Handling simple tasks like tracking or cancelling orders.

Why businesses love them:

• They work 24/7, reducing customer wait times to zero.
• They handle the majority of repetitive questions, freeing human agents for more complex issues.
• They’re easy to set up with basic tools; no tech knowledge needed.

AI-powered chatbots

AI customer service chatbots use machine learning to understand natural language, learning and adapting over time. The more they chat, the smarter they get.

What they’re great at:

• Giving tailored recommendations (e.g., “You liked moisturizers, try this vitamin C serum for glowing skin!”).
• Answering open-ended questions (e.g., “What foundation works for oily skin?”).
• Creating interactive experiences, like virtual styling sessions.

Why businesses love them:

• They mimic human conversations, making shoppers feel understood.
• They turn casual browsers into buyers by suggesting relevant products.
• They handle complex tasks, like troubleshooting or giving advice.

Real use cases of retail chatbots

Chatbots are reshaping retail by streamlining processes, enhancing customer experience, and boosting sales.

Here are five key use cases representing that impact.

Personalized product recommendations

Retail chatbots excel at curating suggestions that align with individual customer preferences.

Sephora’s Virtual Artist chatbot is one of the best examples of this.

It suggests makeup products based on a user’s past purchases and offers virtual try-ons using augmented reality (AR).

This blend of data-driven recommendations and interactive tools keeps customers engaged while boosting sales.

AI-driven retail customer service

Modern retail customers want quick answers and relevant suggestions. Retail chatbots meet these needs by providing instant help and personalized interactions, all while reducing pressure on human teams.

Take Plivo’s self-service chatbot as an example.

Integrated with WhatsApp, it can handle your business routine inquiries, such as customer service, store hours, or product availability.

But this chatbot does more than answer basic questions. It uses customer data to personalize conversations.

For instance, if someone asks about a product, the chatbot might say, “You recently browsed winter accessories, would you like to see matching gloves?”

This approach solves two problems at once. Shoppers get fast answers to routine questions, while the chatbot for retail uses their purchase history to suggest products they might like.

The outcome is smoother support, higher customer satisfaction, and more sales without overwhelming human agents.

Order tracking and updates

In retail, uncertainty about delivery status is a top cause of customer anxiety. Shoppers want to know exactly when their orders will arrive, and a chatbot for retail stores solves this by providing instant, real-time updates.

These tools integrate with inventory and logistics systems to track every stage of fulfillment — from warehouse processing to last-mile delivery.

Suppose a customer asks, “Where’s my order?” The chatbot instantly retrieves data and replies with precise details, “Your package left our warehouse today and is en route to your city. Estimated delivery: Thursday by 7 PM. Track it here: [link].”

For example, Amazon’s chatbot helps customers track packages easily.

If someone asks, “Where is my order?” The chatbot checks the shipping system and replies: “Your package left our Dallas warehouse yesterday and will arrive today. Track delivery here: [link].”

Cart abandonment recovery

Many shoppers add items to their carts but leave without buying. Retail chatbots help recover these lost sales by gently nudging customers to complete their purchases.

When someone abandons their cart, the chatbot sends a friendly reminder, like “Your cart is waiting! Need help finishing checkout?” It can also offer incentives, such as free shipping or a discount code, to encourage action.

For instance, Wellbeing Nutrition’s chatbot targets users who abandon carts by sending urgent, personalized prompts like:

This strategy works because it combines urgency with a clear benefit. Reminding customers of limited-time offers or low stock helps address the fear of missing out (FOMO) that drives quick decisions.

For businesses, this means recovering lost revenue. And for shoppers, it’s a helpful nudge to complete purchases they might have forgotten.

Inventory and store locator assistance

Shoppers often struggle to find products online or locate them in nearby stores. Retail chatbots simplify this process by instantly checking real-time inventory data and guiding customers to the closest store with the item in stock.

Here’s how it works in practice:

A customer searches for a specific power drill online but sees it’s out of stock.  Instead of leaving the site, they ask the chatbot, “Is this drill available anywhere nearby?”

The chatbot scans inventory across local stores and responds, “This model is available at your nearest store, just 3 miles away. Store hours: 8 AM–9 PM. Would you like directions or to reserve it for pickup?”

If the item is unavailable everywhere, the chatbot offers alternatives.

“This drill is out of stock, but a similar model with the same features is available. Would you like details?”

Bridging the gap between online browsing and in-store shopping turns potential frustration into a seamless experience. Customers find what they need faster, and businesses keep sales from slipping away.

Benefits of retail chatbots

Today, businesses are seeking innovative ways to enhance customer experience, boost sales, and streamline operations.

Let’s look at four key ways AI-powered chatbots are transforming the retail industry.

Enhanced customer satisfaction

Unlike traditional support channels with long wait times, chatbots provide immediate assistance, whether resolving order issues, recommending products, or answering FAQs 24/7.

This speed and personalization pay off. Studies show that 80% of customers who interact with chatbots report positive experiences.

For example, a shopper asking, “Do you have this dress in red?” doesn’t just get a yes/no reply. The chatbot checks inventory, suggests styling tips, and even shares a discount code for similar items.

Higher conversion rates

Customers often leave sites due to confusion, indecision, or hidden costs.

Chatbots simplify this journey by acting as real-time guides. They answer questions, recommend products, and nudge shoppers toward checkout with gentle reminders or incentives.

The impact is undeniable. In fact, a study found that 99% of B2B marketers saw higher conversion rates with chatbots.

For instance, a business buyer researching software might ask, “Which plan supports 50 users?” The chatbot responds with a tailored comparison, offers a demo signup, and follows up with a time-sensitive discount: “Get 15% off if you purchase today.”

Cost efficiency

Hiring and training support teams is expensive, especially for businesses handling thousands of daily queries. Chatbots slash these costs by automating repetitive tasks like order tracking, returns, and stock checks.

Take seasonal sales as an example.

Instead of hiring temporary staff for holiday rushes, chatbots handle spikes in questions like “What’s the delivery cutoff for Christmas?” or “Is this sweater in stock?” This frees human agents to tackle complex tasks, like resolving delivery disputes or handling custom orders.

Lower costs, happier teams, and faster service? That’s efficiency done right.

Actionable insights

Every chatbot conversation generates data on what customers ask, what they buy, and where they struggle. Retailers use these insights to:

• Spot trends (e.g., rising demand for eco-friendly products).
• Fix pain points (e.g., improving unclear return policies).
• Personalize marketing (e.g., targeting discounts to frequent buyers).

For instance, if chatbot data shows many shoppers abandon carts due to high shipping costs, a retailer might introduce free shipping thresholds.

Challenges of retail chatbots

Chatbots for retail are growing in popularity, but challenges remain. Here are three key challenges they face.

Answering tricky questions

While chatbots excel at handling routine queries, they often stumble with complex or multi-part questions. For example, a customer might ask: “Can I return these shoes if I bought them online but exchange them in-store for a different size and color?”

Chatbots may misinterpret the request, provide incomplete answers, or direct users to irrelevant links. This confusion frustrates customers, forcing them to repeat their questions to human agents.

Even with advanced NLP, chatbots struggle with nuanced language, slang, or sarcasm. The result? Misinformation, wasted time, and damaged trust.

Having trouble working with other systems

Chatbots rely on real-time data from inventory databases, order management systems, and customer profiles to function accurately. Without these seamless integrations, they risk sharing outdated or incorrect information.

For instance, a chatbot might tell a customer, “This jacket is in stock!” only for the shopper to discover it’s sold out when they try to buy it. This happens when the chatbot isn’t integrated with live inventory updates.

Similarly, outdated order data can lead to wrong delivery estimates or failed discount applications.

Fixing these issues requires technical expertise and investments in an application programming interface (API) or system upgrades. This is a hurdle for smaller retailers with limited IT resources.

Making the chatbot feel like it knows the customer

Personalization is key to winning shoppers, but chatbots need vast amounts of data like purchase history, browsing habits, and preferences to mimic human-like understanding.

Collecting and analyzing this data is technically challenging and raises privacy concerns.

For example, a customer who frequently buys organic skincare products expects the chatbot to remember their preferences. But if the chatbot asks about their skin type every time they return, the shopping experience begins to feel generic.

Smaller businesses face additional hurdles. They may lack the budget for AI tools that analyze data or the infrastructure to store it securely. Without these, chatbots feel robotic, failing to build the emotional connections that drive loyalty.

Future trends in retail chatbots

Retail chatbots are advancing with AI and shifting consumer expectations. Here are three key trends shaping their future.

Hyper-personalization

71% of consumers expect brands to tailor interactions to their preferences, and 61% of marketing leaders say personalization is critical for building loyalty.

Retail chatbots are rising to this challenge. Using advanced AI, they analyze browsing history, past purchases, and even real-time behavior to offer instant customization.

For example, if a customer lingers on winter coats, the chatbot might suggest: “Love this style? Here’s a matching scarf others bought with it.”

But there’s a gap.

While businesses prioritize personalization in their strategies, 57% struggle to deliver it effectively during the pre-purchase phase. Chatbots often default to generic replies like “How can I help?” instead of proactive suggestions.

The future lies in bridging this gap. Retailers investing in AI that learns from every interaction will turn chatbots into intuitive shopping companions, ones that feel less like robots and more like trusted advisors.

Voice-enabled chatbots

Voice technology is reshaping retail. By 2029, the voice assistant market is projected to reach $50 billion, with 40.2% of U.S. internet users already relying on tools like Alexa or Google Assistant monthly.

Retail chatbots are adapting to this shift. Imagine asking your smart speaker: “Alexa, reorder my favorite protein powder.”

The chatbot confirms your preference (“Optimum Nutrition Vanilla, 5 lbs?”), checks inventory, and places the order — all through a voice conversation.

For businesses, this trend means meeting customers where they are with hands-free convenience.

Omnichannel experiences

Shoppers today switch seamlessly between WhatsApp, Instagram, and websites. They expect brands to keep up.

Retailers that deliver consistent chatbot experiences across these channels reap big rewards: omnichannel shoppers spend 1.5x more than those using a single channel.

For example, a customer might start a chat on Instagram asking, “Is this dress in stock?” Later, they switch to WhatsApp to confirm delivery details. A unified chatbot remembers the conversation, avoiding repetitive questions like “What’s your order number?”

This seamless experience builds trust. Customers feel understood, whether they’re on social media, email, or a website.

Transform your retail operations with Plivo

Adding retail chatbots to your business can boost sales by automating tasks, engaging customers faster, and delivering personalized shopping chatbot experiences. These AI tools handle repetitive work, allowing your team to focus on strategic growth.

Plivo’s AI chatbot is a ready-to-use solution that integrates smoothly with your current tools and systems.

It simplifies customer interactions with features like:

• Omnichannel support: Manage customer conversations across WhatsApp, SMS, websites, and social media from one platform.
• Quick integrations: Connect the chatbot to your existing customer relationship management (CRM) system, payment apps, or inventory databases without delays.
• Automated workflows: Create custom paths for customers, like sending discounts to shoppers who abandon their shopping carts or reminding them about restocked items.
• Real-time analytics: Track customer interactions to identify trends, such as popular products or common support issues.
• Agent coaching tools: Improve team performance with call recordings and live monitoring to guide agents during complex queries.

Contact us to book a demo and see how Plivo’s chatbot transforms your retail operations.