Agentic AI: 6 Promising Business Use Cases

Originally reported by Grant Gross, Senior Writer at CIO.

Agentic AI is making waves in the tech world, standing out as a top emerging technology for 2025. It takes generative AI a step further, focusing on operational decision-making rather than just content creation. Companies like Salesforce, ServiceNow, and Microsoft are diving into this new frontier, exploring how autonomous AI agents can transform enterprise workflows and enhance business intelligence.

Agentic AI’s Growing Presence

Forrester highlighted Agentic AI as a leading technology to watch. Big names like Aflac, NASA’s Jet Propulsion Laboratory, and Atlantic Health System have already started exploring its potential. Salesforce’s latest strategy centers on Agentforce, an AI agent designed to streamline customer relationship management (CRM). ServiceNow has also integrated AI agents into its Now Platform, enhancing IT service management.

The buzz is clear: companies are eager to see how autonomous AI agents can improve efficiency. But where should businesses begin? According to industry experts, several promising use cases are rising to the top.

1. Software Development: Smarter Coding Assistants

Agentic AI is set to redefine software development. Coding assistants, which have received mixed reviews so far, are evolving into powerful tools capable of writing large sections of code independently. Gartner predicts that these advanced AI agents will handle most coding tasks within the next three years, prompting software engineers to reskill.

“AI agents can now reverse engineer specifications from code, create test cases, and approve artifacts based on quality thresholds,” says Sheldon Monteiro, EVP and Chief Product Officer at Publicis Sapient. The integration of Agentic AI into DevOps toolchains marks a natural evolution, increasing the level of automation and improving the development process.

2. Enhanced Robotic Process Automation (RPA)

RPA has been a game-changer for automating repetitive tasks, but Agentic AI takes it further. Instead of relying solely on predefined rules, AI agents adapt to more complex scenarios, making autonomous decisions in real-time. Monteiro describes it as “RPA on steroids,” allowing businesses to automate not just simple tasks but also nuanced processes that require exception handling.

“With Agentic AI, we can train agents to understand complex workflows, enhancing efficiency across business operations,” Monteiro explains. The technology’s adaptability is a significant leap from traditional RPA, making it a valuable asset for enterprises seeking to streamline their processes.

3. Customer Support Automation: Beyond Simple Chatbots

Companies have used basic chatbots for years, but Agentic AI promises to take customer service automation to the next level. According to Glenn Nethercutt, CTO at Genesys, these AI agents can handle complex, multi-step tasks that simple bots can’t manage.

Nethercutt explains, “Agentic AI’s strength lies in its ability to perform adaptive decision-making without human guidance.” In practical terms, this means AI agents can interpret requests with context, offering a more robust service. For instance, a bank customer might say, “Transfer money from the account with the highest balance to my checking account.” While a standard chatbot might struggle, an AI agent understands and executes the request seamlessly.

4. Streamlining Enterprise Workflows

Major software vendors like Salesforce and ServiceNow are embracing Agentic AI for improving enterprise workflows. These AI agents can automate routine tasks, such as turning meeting notes into actionable project tickets or triggering orders based on demand predictions.

Monteiro notes that companies using integrated IT solutions may have an edge. “Organizations that pool their data effectively and avoid information silos will see the most benefits,” he says. The key challenge for CIOs will be deciding who to trust with building their AI context store—a comprehensive knowledge base that informs AI decision-making across the enterprise.

5. Cybersecurity and Threat Detection

The role of Agentic AI in cybersecurity is growing, with several providers deploying AI agents to detect and respond to threats autonomously. “These AI agents can react to and mitigate security risks in near real-time, reducing response times and enhancing overall defense,” Monteiro says.

The adaptability of these agents allows them to tailor security protocols based on specific threats, providing a personalized and robust defense mechanism. Companies like Beam have highlighted the efficiency gains from using AI agents, noting their potential to automate routine security tasks and reduce costs.

6. Business Intelligence: Smarter Insights for Everyone

Agentic AI is poised to transform business intelligence (BI), making data insights more accessible. Ryan Janssen, CEO at Zenlytic, explains that while traditional BI tools require a data team’s involvement, AI agents can interpret ambiguous questions and generate actionable insights on the fly.

“An AI agent could advise a marketing team on budget allocation or even create visual reports based on a rough sketch,” Janssen says. Voice-enabled AI agents are also becoming more common, allowing users to ask complex questions like, “What are our top three marketing channels?” The agent can understand the context and provide clear, data-driven answers.

The Future of Agentic AI

Despite its promise, Agentic AI is still in the early stages of adoption. Most organizations are experimenting with pilot projects, exploring its capabilities and potential applications. “We’re just scratching the surface,” says Janssen. “In the coming years, we’ll discover new use cases and refine how these AI agents interact within businesses.”

The excitement around Agentic AI is building, but there’s still much to learn about its full impact. Companies are eager to see what happens when dozens of AI agents work together, creating new breakthroughs in automation and efficiency. As Janssen puts it, “We don’t know what the future organization will look like yet, but I have no doubt we’re going to figure it out soon.”

Source: Original reporting by Grant Gross, Senior Writer at CIO.