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.

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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.

Mathverse recently launched an artificial intelligence (AI) agent that allows users to create unique cards and sell them through a blockchain-powered system. On the other hand, Shopify’s AI assistant, Sidekick, helps merchants analyze sales trends and automate tasks.

Clearly, AI agents are changing how businesses operate across industries.

A recent McKinsey report also shows that 78% of companies now use AI in at least one function, up from 72% earlier in 2024.

Despite learning about how AI agents benefit businesses, implementing them can feel like a steep and complicated gamble (not to mention a technical nightmare). You may be eager to improve your business's efficiency and still wonder: “How to build an AI agent that truly fulfills my business needs?”

In this blog post, we’ve addressed this question thoroughly so that you can build AI agents that cater to your needs.

What is an AI agent?

At its core, an AI agent is a smart software system that works on its own to complete tasks — whether that's answering FAQs, analyzing data, or handling transactions. It processes information, makes decisions, and helps businesses run smoothly.

However, not all AI agents work the same way. Some assist humans, while others take full control. Let’s break them down:

1. Assistive agents: These agents are like a co-pilot for your business tools. They help humans be more productive but don’t replace them. AI virtual assistants like Siri and Alexa are classic examples as they understand user queries and respond while keeping humans in the loop.
2. Autonomous agents: They operate without human intervention. Self-driving cars, warehouse robots, and AI agents in customer service that handle support without needing a human touch, all work on autonomous AI agents.
3. Hybrid agents: These agents assist when needed and take complete control when possible. For example, Shopify’s Sidekick suggests marketing strategies (assistive) but can also generate sales reports on its own (autonomous).

No matter the type of AI agent, they all rely on the same building blocks that make them function.

The building blocks of AI agents

An AI agent architecture consists of six building blocks. To see these building blocks in action, let’s walk through a real use case.

Use case: You want to build an AI-powered voice agent that handles tasks like answering FAQs, processing orders, or routing calls.

Collecting data: Listen to the caller

Before the AI agent can respond, it needs to collect relevant information.

In this case, automatic speech recognition (ASR) technology accurately transcribes voice inputs into text in real time and ensures the AI agent gets structured, usable data. It might also pull past interactions or customer relationship management (CRM) data to personalize responses.

So when a customer calls to check their order status, the AI agent identifies the caller using their phone number and retrieves their order details from the CRM database.

Reasoning: Figure out what the caller wants

Now that the AI agent has the data, it understands what the customer is asking. Using natural language processing (NLP) and decision-making models, it deciphers the intent and chooses the best response.

If the caller asks, “Where’s my order?” the AI agent quickly analyzes their request and retrieves the latest tracking details, providing an instant update without needing a human agent.

Action: Respond to the query

After understanding the intent, the AI agent takes action based on a predefined AI agent workflow. This could involve pulling information from a system, updating records, or escalating to a human agent.

For instance, if an order is delayed, the AI agent automatically notifies the customer and provides an estimated delivery time. If the customer wants to cancel, it can even process the request.

Learning: Improve responses over time

Here’s when deep learning for AI agents comes into the picture. A well-trained AI agent gets better with each interaction by learning from previous conversations and customer feedback. They can use call logs and machine learning models to refine responses.

For example, if many customers ask, “Why is my order late?” and tend to request human support afterward, the AI can learn to proactively offer solutions before escalating the call.

Communication interface: Learn from previous interactions

A great AI agent is also accessible on every platform so that businesses can ensure real-time conversations across multiple communication channels.

If a customer calls about their order status, the voice agent answers over voice. But if they later send a WhatsApp or SMS inquiry, the AI will remember the conversation history and continue to offer support without asking customers for details again.

Memory and profiling: Personalize customer experiences

When an AI agent remembers past interactions and adapts to user preferences, it automatically becomes more powerful. For example, Plivo’s AI-powered voice agents can store caller history, making future conversations smoother.

Let’s suppose the same customer calls about their order again. The AI agent recognizes them and starts with: “Hi [Name], I see you called earlier about your order. Do you need more details on the delivery timeline?”

Now that we know the building blocks, let’s understand how to build an AI agent.

Build and train AI agents in 6 steps

Building an AI agent may seem complex, but breaking it down into six clear steps makes the process straightforward. Let’s go through these steps in detail.

Step #1: Define your business goal and purpose of the AI agent

To build an effective AI agent, define its purpose and business goal.

Are you looking for:

• A customer support AI assistant that answers FAQs?
• A fully autonomous agent that operates without human input?
• A marketing tool to analyze trends and offer insights?
• A virtual shopping assistant to recommend products and help close sales?
• An AI financial advisor for personalized recommendations?

For example, if you run an e-commerce store, a virtual shopping assistant such as Plivo's AI-powered voice bot can guide customers, recommend products based on their browsing history, and even help close sales, all without human intervention.

Customers can get real-time assistance while businesses increase engagement and conversions.

It's also important to consider the specific use cases and industry constraints. For example, a small clinic with only a few daily appointments may not require an AI agent, while a mid-sized hospital with high call volumes can benefit from one to manage patient scheduling.

Understanding your domain and challenges will help you build an AI agent that truly adds value to your business.

Step #2: Collect data to train the agent

Training autonomous AI systems requires high-quality data so they learn and improve their performance. Depending on their purpose, this data could include text, images, audio, call logs, transcripts, and more.

For example:

• A chatbot requires a vast dataset of conversations to understand human communication patterns.
• A recommendation engine analyzes user behavior data to make personalized suggestions.
• An AI voice agent needs call logs and transcripts to process speech patterns, detect intent, and improve response accuracy.

Once you have the data, it should be prepared for training. This includes fixing typos in text transcripts, filtering out background noise in voice recordings, etc. Plivo goes the extra mile as its profanity filters detect and mask inappropriate content in transcriptions.

Step #3: Choose the right machine learning model

The development of AI agents relies heavily on selecting the right machine learning (ML) model based on task complexity. Common ones include rule-based models, supervised learning models, and deep learning architectures like neural networks.

Choose models as per their respective use cases:

• A rule-based model works well for simple tasks like FAQ bots.
• A supervised learning model is ideal for AI agents who need to classify data or predict outcomes based on labeled datasets.
• A deep learning model is best for complex tasks like NLP and speech recognition.

You can also pick pre-trained models like a Generative Pre-trained Transformer (GPT) for AI agent development. They could be a great starting point as they've already been trained in human interactions.

Step #4: Train the AI agent

Training autonomous AI systems is where the agents learn from prepared data to perform their intended tasks.

Here are the key steps involved:

• Set up the training environment: Establish the necessary software libraries and the best frameworks for AI agents. For instance, you can integrate Plivo with Deepgram, OpenAI, and ElevenLabs to empower context-aware customer conversations.
• Split data: Import the cleaned and labeled data, then divide it into training and testing sets. The training set teaches the model, while the testing set evaluates its learning.
• Model training: Use the training data to teach the model, adjusting parameters to minimize errors and improve accuracy.
• Decide the parameters: Set values for batch size, learning rate, and other factors that influence how the model learns and adapts.

Step #5: Test and validate the AI agent

Before deployment, you need to ensure the AI agent functions correctly and meets performance standards. You can choose from the following testing methods:

• Unit testing: Evaluate individual components of the AI agent to ensure each part functions as intended.
• User testing: Have real users interact with the AI agent to gather feedback on its performance and user experience.
• A/B testing: Experiment with different versions of the AI agent to determine which performs better in terms of user satisfaction and task completion.

Additionally, consider setting up mechanisms to collect user feedback, such as surveys, feedback forms, or direct interviews. Use the feedback to continuously improve the AI agent.

If the AI agent doesn’t perform as per your expectations, revisit the training phase.

Step #6: Deploy and monitor the AI agent

Once the AI agent is trained, the next step is to deploy it and ensure it performs effectively. This involves:

• Deployment: Integrate the AI agent with the intended platforms, such as websites, apps, or customer service channels.
• Performance monitoring: Track key performance indicators (KPIs) like response accuracy, user engagement, and error rates to identify areas for improvement. If a voice agent frequently escalates calls, you may need to refine its intent recognition.
• Continuous improvement: Use real-time data and user feedback to retrain and fine-tune the AI agent so it adapts to evolving user needs and consistently delivers high performance.

You can understand AI tools better through real-world use cases. Let’s go through a few to fulfill specific goals.

Real-world use cases of AI agents

From finance and healthcare to inventory management, AI agents are transforming how businesses operate. Here are some business use cases to explore.

Streamline routine financial operations

With real-time transactions reaching $5.3 trillion globally, the demand for instantaneous financial solutions is higher than ever. AI agents help businesses meet this demand.

Depending on your existing business gaps, decide whether you need an AI agent that analyzes large datasets and helps with stock analysis or a bot that provides instant support.

For instance, Plivo’s AI-powered voice bot simplifies financial services by providing instant account updates, processing transactions, and offering personalized financial advice, anytime, anywhere.

Optimize inventory management

Traditional inventory tracking methods often fail to provide real-time insights, causing stockouts that frustrate customers or surplus stock that ties up capital and increases storage costs.

Walmart has effectively used AI agents to optimize stock levels, reduce waste, and improve customer satisfaction by preventing understocking.

Improve patient communication in healthcare

AI agents streamline healthcare operations by automating appointment scheduling, sending reminders to reduce no-shows, and managing patient inquiries 24/7. They can also assist with prescription refills, route urgent cases to human staff, and provide multilingual support for better patient communication.

This helps healthcare providers improve patient engagement and scheduling efficiency, freeing up staff to focus on critical care.

Offer 24/7 customer support

Businesses aim to provide 24/7 assistance to meet growing consumer expectations. AI voice agents can facilitate this by handling inbound and outbound calls without human intervention, offering immediate responses, and resolving common inquiries.

Plivo, for example, significantly improves customer support operations by automating routine tasks, reducing wait times, and freeing human agents to address more complex issues.

Improve language learning with a virtual tutor

Over 16 million people in the U.S. speak English “less than very well.” Clearly, there is a substantial demand for effective language learning solutions.

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AI agents can provide personalized tutoring experiences by offering real-time translations and clarifying complex terms in learners' preferred languages. This technology supports inclusive learning environments, allowing students to overcome language barriers and engage more fully with educational content.

Build and train your AI agent with Plivo

When a customer calls your support line for an order update, they expect a quick, natural-sounding response, just like talking to a real person. That’s exactly what you can build with Plivo’s AI-powered voice agents.

The moment a call comes in, Plivo’s AI agent transcribes the customer’s query using speech-to-text (STT). That message is then sent to ChatGPT (or a large language model (LLM) of your choice), which crafts a relevant response. Once the response is ready, Plivo converts it back into speech using text-to-speech (TTS) and plays it back to the caller.

No long wait times, no robotic scripts.

Whether you run an e-commerce store, a healthcare practice, or a financial service, Plivo lets you create an agent that suits your needs. And if you’re not ready to switch to voice, start by automating text-based communication for a smoother transition.

Ready to build AI agents without the hassle of coding or complex integrations? Contact us today!

ByteDance has unveiled a groundbreaking innovation that promises to redefine how we perceive and interact with video content. Enter Goku AI, an advanced video generation model that is set to transform advertising, media, and content creation by making AI-generated videos indistinguishable from real ones. Let's explore the fascinating capabilities of this new technology and its potential impact on various industries.

The Power of Goku AI

ByteDance's Goku AI stands out as a revolutionary tool in the realm of AI-driven video generation. At its core, Goku is a flow-based video generative foundation model capable of creating highly realistic videos. Goku truly shines with its ability to generate videos of people interacting with products using nothing more than text descriptions. Imagine typing a few sentences and watching as a lifelike video emerges, showcasing your product in action.

This capability is not just a novelty; it significantly streamlines the content creation process for brands. By converting still product images into dynamic videos, Goku AI simplifies the task of producing engaging visual content, thereby opening new avenues for creativity and efficiency.

Goku+: A Game Changer for Advertising

ByteDance has introduced Goku+, a specialized video ads foundation model, alongside the standard Goku model. Goku+ is designed to revolutionize the advertising industry by reducing costs by an astonishing 100 times compared to traditional advertising methods. This cost-effectiveness is achieved without compromising on quality, as Goku+ produces videos that are virtually indistinguishable from those featuring human actors.

The implications of this technology are profound. Brands can now create compelling video ads with minimal resources, allowing even small businesses to compete on a level playing field with larger corporations. The democratization of high-quality video content creation could lead to a more diverse and innovative advertising landscape.

Realism and Beyond: Animating the Impossible

Goku AI impressively animates characters with natural movements, facial expressions, and gestures. Sophisticated algorithms achieve this level of realism by mimicking the subtleties of human behavior. As a result, AI-generated characters appear lifelike and engaging, enhancing the viewer's experience.

The potential applications of this technology extend beyond advertising. In the entertainment industry, for instance, Goku AI could lead to movies and digital content that do not require human actors. This shift could inspire new forms of storytelling and creative expression, as filmmakers explore the possibilities of AI-generated narratives.

The Future of AI in Content Creation

As advancements from Goku AI suggest a world where AI plays a central role in content creation, we can focus more on innovation and storytelling due to the ability to produce realistic videos at a fraction of the cost and effort. The technology also raises important questions about the nature of creativity and the role of human input in the digital age.

ByteDance's Goku AI represents a significant leap forward in the field of AI-driven video generation. Its potential to transform advertising, media, and content creation is immense, offering new opportunities for efficiency and creativity. As we embrace this technological evolution, we must consider the broader implications and possibilities it presents.

What do you think the future holds for AI-generated content, and how might it reshape the way we tell stories and connect with audiences?

An AI chatbot in an e-commerce store can handle simple questions like, "What's your return policy?" But what happens when a customer says, "I got the wrong item and want to exchange it for a different one?" Since this request is more complicated, the bot doesn’t know what to do and directs the customer to a human agent. That means longer wait times and frustration.

On the other hand, agentic artificial intelligence (AI) understands what the customer wants, pulls up order details, checks if the item is in stock, and processes the exchange without a human stepping in. Agentic AI builds on the capabilities of generative AI tools like ChatGPT to actively make decisions and optimize workflows.

That’s why experts predict that by 2028, 33% of enterprise software will have agentic AI built-in.

In this guide, we’ll walk you through what is agentic AI, its benefits, and real-life use cases so you can discern whether it’s a good fit for your business use cases. 

Agentic AI vs. Gen AI

Agentic AI goes beyond simply responding to prompts. It actively perceives its environment, reasons through options, takes action, and learns from outputs.

In contrast, generative AI processes inputs, infers patterns, creates outputs, and adapts to new contexts. Traditional AI follows predefined rules.

Here's how they compare.

Now that we’ve covered how agentic AI differs from traditional and rule-based AI, let’s explore how it can benefit businesses.

Why agentic AI matters: Key benefits for businesses

AI is evolving, and its role in business is growing. Rather than just responding to commands, it can now analyze, adapt, and take action. This shift is transforming how businesses operate and compete.

Here’s how.

Increased workflow efficiency and productivity

Agentic AI handles complex, multi-step tasks, freeing employees to focus on higher-value work that requires human creativity and expertise.

For instance, ServiceNow's AI agents have reduced case resolution time for complex cases by 52%.

Similarly, when used in customer service, agentic AI can analyze a ticket, determine the root issue, draft a response, escalate complex cases, and even follow up with customers.

More strategic human-AI collaboration

Agentic AI enhances human decision-making through complex data analysis and actionable insights. It takes on tasks like risk assessment, fraud detection, and patient diagnostics, allowing professionals to concentrate on higher-level decisions.

In the travel industry, AI self-learning models manage bookings, optimize flight schedules, and handle customer queries in real-time. This reduces operational costs and frees up human resources for more personalized customer interactions.

Beyond automation, agentic AI also understands human intent and urgency.

For example, OpenAI’s Operator can handle entire tasks autonomously. So if a customer needs last-minute catering for an event, instead of just placing an order, an AI-like Operator could find a restaurant that meets dietary preferences, check availability, confirm the order, and even handle payment, creating a human-like customer experience.

Enhanced trustworthiness and specialization

Agentic AI systems analyze datasets to identify patterns, leading to more informed and trustworthy decisions. In the legal field, agentic AI can analyze legal documents, identify patterns in case law, and even assist in developing legal strategies.

The National Law Review says, "In the 20th century, mastering 'thinking like a lawyer' meant developing a rigorous, precedent-driven mindset. Today, we find ourselves on the cusp of yet another evolution in legal thinking — one driven by agentic AI models that can plan, deliberate, and solve problems in ways that rival and complement human expertise."

Let’s understand these benefits further through real-world use cases of agent-based AI models.

Use cases of Agentic AI

From improving customer experience to empowering financial decision-making, the use cases of agentic AI scatter across industries. Let’s take a closer look at how it’s making an impact.

Improve customer experience 

To qualify as agentic AI, a system must perceive, reason, act autonomously, and learn from its actions.

Say you integrate Plivo's AI-powered voice agent to handle frequently asked queries. It can serve as a 24/7 customer support system, manage contextual voice interactions, and trigger workflows automatically.

You can build on this by creating another decision-making bot with an engine like GPT-4, Google's Gemini, etc., that analyzes customer intent, and adapts responses.

Now, when a caller says, "Please refund or exchange the product!" the AI assesses the request. One system checks refund eligibility, while another evaluates whether an exchange is possible.

Businesses are still in the early stages of adopting agentic AI for customer service, but the shift is accelerating. By 2029, Gartner predicts AI will autonomously resolve 80% of common customer service issues, significantly reducing response times and improving customer experiences.

Create personalized content

Agentic AI helps businesses create personalized content by making sense of vast amounts of customer data.

Yum Brands, the parent company of Taco Bell, KFC, and Pizza Hut, used AI-driven marketing to send personalized emails and notifications to their customers. They analyzed what customers typically order, what they prefer, and how their choices change over time to send hyper-personalized offers at the right moment.

As a customer interacts with the brand, agentic AI adapts the marketing message based on their evolving needs and behaviors. This ensures every conversation made with the customer is relevant and hyper-personalized.

Beyond messaging, you can even automatically adjust variables like bidding, ad placement, or audience targeting to optimize campaigns and conduct A/B testing at scale on multiple variables.

Improve patient care

Agentic AI systems can process vast datasets such as clinical notes, patient histories, lab results, medical guidelines, and even diagnostic imaging to extract actionable insights.

The National Health Service (NHS) introduced an AI physiotherapist named Kirsty to help patients with back pain. This AI agent offers same-day virtual appointments, personalized exercise plans, and real-time health advice, reducing wait times and improving accessibility.

Here’s how agentic AI improves healthcare workflows:

• Data coordination: When new clinical data is entered into an Electronic Medical Record (EMR), an AI-powered system pulls information from multiple sources and triggers workflows based on predefined logic.
• Specialized AI agents:
  
  • A clinical data agent analyzes patient records using Natural Language Processing (NLP).
  • A molecular test agent interprets genomic data from biopsy samples.
  • An imaging analysis agent processes radiological scans and pathology reports.
• Coordinated decision-making: While specialized AI agents operate independently, a coordinating agent synthesizes their insights to recommend the most appropriate clinical decision.

Note: Although fully agentic AI systems are still evolving, healthcare providers are already leveraging AI-powered voice bots that automate patient interactions, appointment scheduling, and medication reminders. Solutions like Plivo can further personalize patient interactions and help reduce wait times.

Empower financial decision-making

The day when a trading AI agent analyzes market data, monitors market trends, adjusts strategies, and mitigates risks isn't far. Agentic AI will make this possible by integrating tools via application programming interface (APIs), sensors, and advanced reasoning.

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Agentic AI could also autonomously assess micro-loans for smallholder farmers, using local data to evaluate risk without direct human involvement. Similarly, mobile banking powered by agentic AI could offer personalized micro-insurance products based on real-time weather data.

Optimize logistics and supply chain

When running an e-commerce business, the last thing you want is a customer placing an order for a high-demand product that’s actually out of stock. That’s where agentic AI steps in.

SAP has introduced two AI agents to tackle this issue: one for sales and another for supply chain management.

The sales AI agent determines the best price and product bundle for the customer while simultaneously checking inventory. Before making a sales commitment, the supply chain AI agent steps in to verify stock levels, assess delivery timelines, and adjust logistics accordingly.

Since these AI agents interact autonomously, they prevent sales teams from overpromising on orders that the supply chain can’t fulfill.

SAP CEO Christian Klein emphasized that contextualizing data is key to making agentic AI successful. “While 80% of businesses may not yet have the infrastructure to support AI-driven operations, SAP is bridging that gap by integrating predictive AI and automation directly into its software.”

Given the advancements in agentic AI, it’s only natural to wonder what the future holds.

What lies ahead: The future of agentic AI

Agentic AI systems provide the best of both worlds: LLMs handle tasks that benefit from dynamic responses, and these AI capabilities with conventional rule-based programming. So, the future of agentic AI consists of systems that fetch real-time information, retrieve updates, or pull specific data points important for decision-making.

However, as businesses integrate AI deeper into their operations, regulatory frameworks struggle to keep pace. A recent survey found that 93% of professionals recognize the need for clearer AI regulations to mitigate risks.

Ethical concerns, such as algorithmic bias, decision transparency, and compliance with evolving privacy laws, remain critical challenges. Companies must ensure AI-driven decisions are fair, explainable, and aligned with regulatory standards.

In industries where customer interactions matter, such as finance, healthcare, and e-commerce, solutions like Plivo help businesses use AI-powered voice and messaging tools to improve customer experiences while maintaining compliance.

Take the first secure step to agentic AI with Plivo

As agentic AI continues to evolve, businesses need AI-powered solutions that can learn, adapt, and improve over time. Plivo’s AI-powered voice agents make this transition seamless.

If you’re already using AI agents, you can take this a step further by building your own AI agent combining these two functions. You can launch the voice agents with any text-to-speech (TTS), speech-to-text (STT), and language model of your choice with Plivo’s APIs.

Whether you're looking to deploy autonomous AI agents, use AI for complex problem-solving, or build an entire AI-driven ecosystem, Plivo makes it easy.

Contact us today to explore the possibilities!

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We’ve all experienced single AI models working independently, whether it's a bot answering questions or an algorithm making recommendations. But what happens when a single AI system isn't enough for a business?

When your call center deals with complex, multi-step customer queries, or you need a broader perspective on stock marketing trading and risk management, single AI agents can fall short. It’s no surprise that AI agent applications in customer service and virtual assistants are expected to account for approximately 78.65% of the market share by 2035.

If you’re considering multi-agent AI systems for your business, it’s only natural to wonder: what is a multi-agent system? What are some multi-agent system examples, and applications? We’ll cover all this and more in this blog post.

Let’s get started.

What is a multi-agent system?

A multi-agent system (MAS) is a network of single AI agents that work together to achieve business goals.

For instance, if your online store deploys a single AI chatbot, it can handle answering FAQs, giving order status updates, etc. But if a customer raises multiple queries to track an order and request a refund, a single AI agent struggles to juggle both.

In this case, introducing multiple AI agents to assist customers with refund requests, return policies, sentiment analysis, and more, reduces response time and increases customer satisfaction. In fact, 57.7% of AI-driven customer support agents now operate using a MAS to handle complex interactions. 

It operates on three core principles:

1. Autonomy

Each agent in a MAS operates independently, without waiting for a response or instruction from a central system. For instance, in an AI-driven customer support system, a refund policy handling agent retrieves the policies without waiting for instructions from another agent.

2. Collaboration

While agents in an MAS work independently, all these distributed AI systems communicate and coordinate to solve problems. For queries like, "Where's my order, I want to cancel it." The ‘order tracking’ agent and the ‘cancel orders’ agent collaborate to provide a response that addresses both queries.

3. Distributed control

Multi-AI agents distribute tasks that self-organize based on real-time inputs. For example, Google Maps doesn’t have a central system dictating traffic patterns, road closures, and delays.

Now that we’ve covered what MAS is, let’s break down how it operates in real-world scenarios.

How multi-agent systems work

Multi-agent systems work as a well-coordinated team, where each agent has a role, and follows certain rules. Here's a quick breakdown of the key components:

• Agents: These are independent decision-makers. In customer support, one might handle FAQs, another may tackle refund requests, and so on.
• Environment: This is a space where agents operate, like a website, within transaction data, or customer interactions.
• Perception and data collection: This is when agents sense surroundings (customer tone, transaction data).
• Communication protocols: Agents need structured ways to talk to each other. For example, how drivers (agents) communicate in ride-sharing apps or when riders request a ride.
• Decision-making mechanisms: Each agent must then decide how to act based on the situation. For instance, sending a cancellation confirmation or rerouting calls.
• Coordination strategies: Agents must collaborate efficiently, just as robots coordinate to pick and ship items without colliding.
• Learning and adaptation: Smart agents learn from experience. If multiple customers have order or refund-related inquiries, AI-driven agent-based modeling analyzes these patterns to help you make informed decisions.

Control architecture: Multi-agent systems can be centralized, where a single entity directs all agents (like an air traffic control tower managing flights), or decentralized, where agents operate independently while coordinating with each other (like multiple drones adjusting their paths in real-time).

Real-world applications of multi-agent systems

Multi-agent systems are working behind the scenes in many industries. They help manage traffic, improve customer support, and even assist in public health efforts.

Here’s how they make a difference in the real world.

Manage transportation systems

Traffic congestion during peak hours or roadblocks during accidents can bring an entire city to a standstill. Multi-AI agents tackle this with real-time voice updates and intelligent coordination.

In Pittsburgh, the deployment of the Scalable Urban Traffic Control (SURTRAC) system led to a 25% reduction in travel times and a 40% decrease in vehicle wait times at intersections. They used multi-AI agents that communicate to adapt traffic signals based on real-time conditions, effectively easing congestion and improving traffic flow.

So if a major highway causes a traffic jam, one AI agent can instantly analyze traffic while another delivers voice updates via navigation apps, guiding routers to take alternative routes.

Prediction and prevention in healthcare and public health

Missed appointments and no-shows in the U.S. healthcare system are leading causes of healthcare inefficiency. Even charging full fees for no-shows or missed appointments will cost you patient frustration and retention issues.

MAS bridges this gap by improving patient engagement and ensuring timely care.

For instance, Plivo's smart healthcare interactive voice response (IVR) system helps providers automate patient communication and optimize call handling.

When a patient calls a clinic, Plivo’s smart IVR can screen symptoms using AI-driven voice interactions and direct them to the appropriate specialist based on urgency. Instead of long wait times or misrouted calls, the system efficiently prioritizes critical cases while handling routine inquiries without human intervention.

Automate customer support

93% of customers are more likely to stay loyal to businesses with great service but achieving that at scale can be a challenge. In fact, long wait times and misrouted calls frustrate customers and overwhelm support teams.

AI-powered multi-voice agents improve customer service efficiency since businesses automate routine queries, ensuring customers get instant responses without waiting on hold. Smart call routing directs calls to the right department, reducing frustration, while speech recognition and customer relationship management (CRM) integration can send personalized follow-ups — whether it’s confirming appointments or providing order updates.

Streamline routine financial tasks

Managing routine financial tasks shouldn't feel like a constant back-and-forth. Multi-AI systems work together to streamline everything from payment confirmations to loan processing.

For instance, if you use one of Plivo's AI-powered voice agents to automate reminders, others can take care of fraud alerts, and give account updates in real-time. Depending on your customer base and their queries, you can customize these AI agents to provide contextual responses.

Connect components of supply chain management

Supply chain inefficiencies cost businesses an average of $1.3 trillion annually, representing nearly 10% of the global gross domestic product (GDP).

The power of MAS is that it makes every part of the supply chain, from warehouses to delivery trucks, streamlined. You can use one agent for supply management, another for inventory management, and one for demand forecasting.

It’s also convenient to send voice notifications for order confirmation, delays, and even emergency alerts in case of weather delays, stock shortages, etc.

Benefits of multi-agent systems

Multi-agent systems bring a lot to the table, making complex processes more efficient and intelligent. Let’s take a look at what makes them so useful.

Flexible

AI is already making 81% of companies respond faster to market shifts. MAS adds to this benefit as it adapts to changing business needs and automates processes like logistics, customer support, and compliance.

Scalable

Multi-agent systems are inherently scalable. As the complexity or scope of a problem increases, additional agents can be introduced to handle new tasks or responsibilities. This scalability makes these systems suitable for an array of applications, and dynamic environments.

Businesses can handle fluctuating demand without adding human resources, which reduces operational costs.

Robust

Multi-agent systems improve fault tolerance so if one AI component fails, another takes over. This ensures continuity, especially critical for industries like healthcare and finance.

Efficient

One of the primary benefits of multi-agent systems is the separate modeling-based AI agents. As each agent focuses on a singular task, agents can perform more efficiently and reduce manual errors.

Make multi-agent systems smarter with Plivo’s AI-powered voice agents

Autonomous AI agents often hit a dead-end as businesses scale, frustrating customers with repetitive, or worse, generic responses. Plivo's AI-powered voice agents understand your existing business gaps and fill those with adaptable solutions across industries.

These agents assess customer intent in real time so that callers don't waste time navigating endless IVR menus. Moreover, they sense customer sentiment, redirect them to the right department, and help gauge honest feedback to make informed decisions across tech support, appointment scheduling, or billing.

Plivo also offers data-driven analytics, where you can gain business insights and optimize customer interactions. Whether you're in healthcare, finance, or retail, Plivo's AI-powered voice agents ensure every conversation is efficient, personalized, and frustration-free.

You can use AI agents to speak your brand's language, and personality, get a 360-degree customer snapshot to make context-aware conversations, or resolve queries by utilizing internal business documents like FAQs, SOPs, and blog posts.

Contact us to deploy AI voice agents using Plivo, convert leads, resolve queries instantly, and send timely promotions in over 220 countries and territories.