Agentic AI is Revolutionizing Business and Daily Life

What Is Agentic AI?

Agentic AI systems combine natural language processing with decision-making engines. Dr. Vinesh Sukumar, vice president of generative AI and machine learning product management at Qualcomm, says that agentic AI’s autonomy is particularly impactful in areas such as healthcare and finance.

“In a perfect world, agentic AI improves decision-making and takes independent actions to achieve specific goals,” he says.

Ray Smith, vice president of AI agents for Microsoft, says agentic solutions are easier to build because they can connect to multiple systems, make sense of both the structured and unstructured data, and follow a business process through natural language instructions.

“This allows business users with few IT skills to simply describe the solution and connect to the required knowledge sources and systems to complete a business process,” he says.

What Are the Benefits of Agentic AI?

Agentic AI systems offer a transformative blend of efficiency, cost reduction and speed.

“The biggest benefit is time savings,” says Five9 CTO Jonathan Rosenberg. “Whether assisting with customer purchases or managing operational workflows, agentic AI is lightning-fast and highly efficient.”

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Businesses stand to gain from increased sales, improved lead qualification and enhanced customer satisfaction. These systems can also personalize interactions by remembering previous engagements, fostering long-term brand loyalty.

Sukumar adds that agentic AI also has the potential to redefine user experience (UX), particularly in consumer applications. “Imagine an agent arranging a movie night with friends, booking tickets and finding dinner reservations — all autonomously,” he says. “That’s the future we’re building.”

For enterprises, the potential is even greater: AI can analyze data, streamline processes and optimize resources, empowering employees to focus on strategic tasks.

What Are Some Agentic AI Use Cases?

Agentic AI has diverse applications across industries, but the supply chain is one area that is set to have rapid innovation, Smith says.

“Supply chains typically have many disparate systems and humans across specialized roles and manual processes,” he says. “Agents are always on, can bridge multiple systems, and can both reason and understand the data.”

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The latest deep reasoning models are also great for optimization tasks such as inventory allocation and supply chain planning. By connecting disparate systems and analyzing data in real time, these agents can improve efficiency and reduce costs, he adds.

Sukumar says that AI agents can streamline health insurance navigation and appointment scheduling. “From checking coverage to booking appointments, these systems can eliminate the frustration of lengthy customer service calls,” he explains.

What Are the Challenges of Agentic AI?

Despite its promise, agentic AI comes with significant challenges, particularly concerning ethics and security.

“Who is accountable for decisions made by autonomous systems?” Sukumar asks, noting the importance of transparency and trust.

IT leaders also need to add some accountability to the decision-making process, some oversight to prevent harmful actions resulting from incorrect reasoning or biased data.

Rosenberg warns of scenarios where AI agents make erroneous decisions, such as issuing unwarranted refunds or executing unauthorized transactions. As malicious actors exploit AI systems to manipulate data or disrupt operation, IT teams also face heightened security risks.

“Strong governance and robust data protection measures are critical to mitigating these risks,” he says.

How Can Organizations Prepare for Agentic AI?

To harness the potential of agentic AI, organizations must take a strategic approach. Sukumar says he recommends a focus on data accessibility and infrastructure.

For starters, modernize back-end systems and implement secure APIs. Organizations should also be sure that knowledge bases — such as policies, procedures and training materials — are accurate and up to date.

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Rosenberg notes that it’s important to start small and iterate. “Early wins and proven ROI can help align stakeholders and build confidence,” he says. 

He also recommends using generative AI analytics to identify promising use cases for agentic AI. Qualcomm’s approach has included building tools and software that enable continuous optimization. 

“If an AI agent makes a wrong decision, it should be able to self-correct,” Sukumar says.

This process of executing tasks and self-correcting is crucial for refining systems and ensuring they meet organizational needs.

“We’re working with a lot of enterprises to understand how we can scale agentic AI and optimize it for their workflows,” he says. “We are going to learn a lot.”