Agentic AI vs generative AI: why the futures not just smarter its bolder

Agentic AI is no longer just a concept; it’s quietly proving its worth across industries, paving the way for a future where technology doesn’t just assist but acts. If Generative AI is like a talented artist creating stunning works on command, think of agentic AI as a highly competent chief of staff. You give it a direction—“improve customer churn”—and it starts to act. It looks at retention data, cross-checks CRM logs, generates hypotheses, triggers outreach campaigns, and, crucially, updates its approach as new data rolls in.

Conversational AI targets two types of customer service buyers

This is especially true for those who often feel overlooked. Behind every AI assistant that “works” is a teacher who made it make sense. Educators now build these digital assistants into course plans. They feed them structured content, monitor how students interact with them, and fine-tune how they respond. There’s one little tidbit that doesn’t really have anything to do with the results, just the behavior of the companies.

• Agentic AI might sound like something from a sci-fi novel, but it’s not an abstract concept on the distant horizon.
• If your company is serious about scaling, they are the silver bullet your customer team needs.
• They provide the back-end for ambitious companies whose priority is growth.
• These systems aren’t just responding—they’re deciding.
• This means that it’s inaccurate to call most customer service chatbots “intelligent,” artificially or otherwise.

The “Why” Behind Customer Service Preferences

Customer service data is notoriously unstructured. Larger companies often have hundreds of different categories in which customer service tickets can be “tagged” for analysis purposes. This means that it’s inaccurate to call most customer service chatbots “intelligent,” artificially or otherwise. In short, chatbots replace people where people are asked simple questions and produce simple answers.

We Know Chatbots Are Falling Short, But Why?

For instance, AutoGPT has laid the foundation for this technology by combining tools like GPT-4 and vector databases. While not flawless, it showcases the skeletal structure of agency in action. Businesses adore Generative AI for its ability to complete routine tasks. Whether summarizing documents or creating social media visuals, it’s already transforming industries, with McKinsey reporting that 71% of organizations use it in at least one business function. Early adopters are already seeing impressive returns, delivering an average of $3.70 in value for every dollar invested. More profound (than current uses) is the use of bots as “flexible” interfaces, where any reasonably-worded query can be answered.

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I have not yet seen multiple bots talk to each other, but this is definitely on the horizon. This allows using human language as a loose specification. When students need help understanding or want to admit they’re confused, AI assistants offer a space that feels safe.

If your company is serious about scaling, they are the silver bullet your customer team needs. Now, machines can not only better understand the words being said, but the intent behind them, while also being more flexible with responses. “That means we can create much more sophisticated virtual assistants or customer care agents, whether they are text-based or voice-based,” Sutherland said. Nikola Mrkšić is the CEO and Co-founder of PolyAI, a leading provider of enterprise-ready voice assistants built for customer support.