The Power of Smart Automation with Agentic AI vs Conversational AI

Agentic AI vs Conversational AI: what’s the difference, and what kind of artificial intelligence solution should you be investing in to enhance team productivity, improve efficiency, or transform customer service strategies?

Conversational AI technologies, leveraging natural language processing algorithms, machine learning, and cutting-edge data analysis capabilities, have been revolutionizing business processes for some time now. Organizations are using conversational AI chatbots and virtual assistants to enhance data analysis strategies, deliver personalized customer service at scale, and even automate lead nurturing.

Agentic AI takes the potential of conversational AI solutions to the next level, allowing companies to create multi-functional systems that can handle a range of tasks, make decisions, set goals, and even learn from their experiences over time.

Here’s everything you need to know about the differences between agentic AI and conversational AI, and how you can use them synergistically to achieve endless business goals.

Agentic AI vs Conversational AI: Defining AI Solutions

One of the things that makes the Agentic AI vs Conversational AI debate so confusing, is that both technologies support similar use cases. Both agentic and conversational AI tools are excellent for boosting team productivity and efficiency, and transforming customer experiences.

However, there are some distinct differences between these types of artificial intelligence. Conversational AI excels at powering valuable interactions between humans and machines. This technology allows computer systems to understand human language, ensuring they can respond effectively to various requests and queries.

Agentic AI uses conversational AI technologies, automation, and integrations with other tools to power systems capable of completing a wider range of more complex, multi-stage tasks.

For example, an agentic AI bot used for customer service in the finance industry could proactively analyze customer segments, create content for marketing campaigns, and even send personalized text messages to customers at specific times to aid in their purchasing journey – all without human input.

Here’s a closer look at conversational AI and agentic AI, how they work, and the use cases they serve.

What is Conversational AI?

Conversational AI is the technology that allows machines to engage in natural conversations with human beings. By combining cutting-edge technologies like Natural Language Processing (NLP) and machine learning, conversational AI tools can understand, process, and respond to user input.

Conversational AI forms the foundation of many AI customer experience strategies, allowing companies to create bots that can help customers troubleshoot issues, discover relevant products, and complete basic tasks (like requesting a refund).

Unlike old-fashioned, rule-based chatbots, conversational AI can actually understand the meaning and intent behind customer queries, making it more effective at delivering valuable, 24/7 support to customers. (Check out our guide to chatbots vs conversational AI if you’re not sure about the differences).

Conversational AI tools can deliver consistent service across multiple channels, from SMS to social media platforms. They can also personalize interactions, drawing insights from integrations with Customer Relationship Management (CRM) tools and knowledge bases.

Beyond enhancing customer service, conversational AI tools have a number of other use cases. Some companies use these technologies to enhance lead follow up with AI, surface insights about customer preferences and sentiment from conversations, and amplify sales strategies with personalized product recommendations for every customer.

What is Agentic AI?

Agentic AI is essentially the proactive powerhouse of artificial intelligence. It builds on conversational AI, generative AI, and other intelligent technologies, empowering bots and virtual agents to complete a wider range of tasks autonomously.

Where conversational AI solutions focus on understanding and responding to user input, agentic AI is task oriented. It excels at identifying specific goals and required outcomes, developing “action plans,” and adapting to dynamic challenges rapidly.

Agentic AI solutions leverage many of the same technologies as conversational AI tools, such as machine learning and natural language processing algorithms. However, they also take advantage of rule-based algorithms and integrations with specific tools across business ecosystems.

Just as companies can use conversational AI in customer service tasks to improve customer experiences, they can use agentic AI to enhance various aspects of the customer journey. For instance, an agentic AI tool could streamline:

• Customer Onboarding: Agentic AI can automatically verify identities, collect necessary documents, and walk customers through the process of using new products.
• Transaction Handling: Agentic AI tools can handle various aspects of transactions, from processing payments to handling refund requests or making modifications to orders.
• Issue resolution: AI agents can troubleshoot common customer problems, resolve issues, and even identify how and when to escalate queries to human agents.

In the debate of agentic AI vs conversational AI, what makes agentic AI unique is its ability to accomplish more without human input. It can draw on workflows, meta-data, and predefined rules to automate various multi-step processes, make decisions, and constantly optimize its performance.

Conversational AI vs Agentic AI: Understanding the Synergy

Agentic AI and conversational AI aren’t really competing technologies. Ultimately, agentic AI simply gives companies an opportunity to take the benefits of conversational AI for sales, marketing, customer service, and other crucial tasks to the next level.

Though conversational AI and agentic AI have distinct purposes, they work better when they’re combined. Conversational AI gives agentic tools the ability to understand and respond to human input, while agentic AI gives conversational tools the ability to complete a wider range of tasks.

Think of it this way, in the customer service space, conversational AI handles human-like interactions – chatting, responding to questions, and gathering information. From there, agentic AI can step in to handle the heavy lifting, researching issues for customers, and completing tasks, like scheduling text appointment reminders or issuing refunds.

Agentic AI gives organizations a way to bypass the limitations of conversational AI tools. For instance, a company using conversational AI in insurance might have a bot that can help customers apply for a policy, sharing insights into available options, and collecting relevant details. However, that AI tool would still need to pass the process of setting up a policy over to a human agent.

An agentic AI solution, on the other hand, can take the information it collects from customers, verify data, run risk assessments, and process a purchase, all without human input.

The Benefits of Using Agentic AI and Conversational AI Together

Ultimately, using conversational AI and agentic AI together leads to:

• Faster resolutions: When agentic AI powers conversational bots, conversations between machines and customers don’t just end with an answer to a query. Agentic AI takes action, handling things like booking appointments, processing claims, or streamlining onboarding.
• Improved personalization: Conversational AI already excels at tailoring interactions through natural language processing and context awareness. When coupled with agentic AI, this personalization extends into action. For instance, in financial services, conversational AI tools can identify a person’s needs and risk levels, while agentic AI schedules an appointment with an advisor, checks eligibility for specific products, and submits purchase requests.
• Enhanced scalability: Conversational AI shines at engaging users at scale, but when paired with agentic AI, scalability extends to resolution. Hundreds—or even thousands—of complex tasks can be processed simultaneously without sacrificing accuracy or efficiency, whether in customer service, logistics, or finance.
• Dynamic problem-solving: Unlike deterministic systems that follow rigid scripts, agentic AI adapts to unexpected scenarios. It identifies constraints (like inventory shortages or regulatory rules), finds alternatives, and adjusts strategies in real time.
• Human-in-the-loop efficiency: When situations require human intervention, agentic AI ensures seamless handovers by passing all relevant context from the conversational interaction. This eliminates repetitive back-and-forth, empowering human agents to focus on resolution rather than information gathering.

Aligning Agentic and Conversational AI: Implementation Tips

The question today’s companies need to consider isn’t: “Agentic AI vs conversational AI: which option is better?” It’s actually “How do we enhance the potential of conversational AI with agentic capabilities?” Here’s how you can take advantage of the synergy between conversational AI technologies and agentic AI systems.

Step 1: Identify Your Use Cases

The first step is understanding how conversational and agentic AI can address specific challenges within your industry. Use cases vary by sector, but the goal remains the same: to align engagement (conversational AI) with execution (agentic AI). For instance, if your overall goal is to improve customer experiences, here are some of the ways companies in different industries can build on conversational AI with agentic AI tools:

• Finance: In finance, conversational AI tools could handle routine inquiries (like checking account balances or branch locations). Agentic AI, on the other hand, can take on more complex processes, like verifying loan applications, and escalating high-value cases to advisors or relationship managers.
• Healthcare: In the healthcare space, conversational AI tools could provide 24/7 access to information about symptoms, medication options or appointment availability. Agentic AI tools, on the other hand, can process insurance claims, schedule appointments, and help with AI SMS based follow-up strategies.
• Retail: Conversational AI bots for retail and ecommerce companies, can engage customers with product recommendations, insights into stock availability and order tracking updates. Agentic AI can handle operational tasks like processing returns, exchanges, and refunds, or issuing personalized recommendations based on purchase history.

Step 2: Explore Platform Options

To align conversational AI and agentic AI, companies need technologies that support advanced integration capabilities. Intuitive platforms need to offer access to intuitive AI models, task-oriented AI systems (for agentic AI), and natural language processing algorithms.

These platforms should empower business leaders to create comprehensive workflows for agentic systems to follow. Most importantly, they need to ensure that AI systems can easily leverage existing tools, data, and software to complete tasks. Advanced agentic AI systems support everything from texting integrations to enable proactive and personalized customer service, to integration with CRM platforms, transaction processing tools, and more.

These integrations are what allow agentic AI systems to act on specific requests, whether they need to draw data from a database to answer a question, assign a task to a human team member, schedule appointments, or handle refunds.

Step 3: Keep the Human in the Loop

While AI technologies bring incredible efficiencies, keeping humans involved is critical. AI systems excel at handling repetitive tasks and managing data-driven processes, and agentic AI tools can automate a wide range of tasks, but keeping the human in the loop is still crucial.

Businesses should ensure they’re still leveraging strategies for:

• Human Escalation: In complex or emotionally sensitive situations, systems should automatically escalate to a human representative with full context of the interaction.
• Continuous improvements: AI systems require regular training, fine-tuning, and maintenance. Make sure you have a strategy for monitoring AI performance, gathering feedback, and refining workflows to meet evolving customer needs.
• Balancing automation with empathy: Avoid using AI to automate every task. Implement policies to ensure you maintain a human touch in situations that require human creativity, empathy, and emotional intelligence.

Agentic AI and Conversational AI: Better Together

Hopefully, this guide to Agentic AI vs Conversational AI has shown you that these technologies aren’t actually competing systems, even if they focus on different things. Conversational AI excels at understanding and engaging users, while Agentic AI streamlines task automation and helps companies to more proactively solve a range of problems.

Together, both of these tools work to enhance communication between companies and their customers, transform customer service experiences, and boost operational efficiency.

If you’re looking for a way to take customer service, sales, and marketing strategies to the next level in your organization, Clerk Chat offers the perfect solution. Combining conversational AI technologies with workflow automation, our all-in-one platform can help you differentiate yourself from the competition, increase conversions, boost customer satisfaction rates and more.

Step into the new era of AI optimization and discover the benefits conversational and agentic AI can offer your business with Clerk Chat today.