Agentic AI is having a moment, as proponents see the benefits of using autonomous AI agents to automate manual tasks across organizations.
Agentic AI, which Forrester named a top emerging technology for 2025 in June, takes generative AI a step further by emphasizing operational decision-making rather than content generation. The promise the approach has for impacting business workflows has organizations such as Aflac, Atlantic Health System, Legendary Entertainment, and NASA’s Jet Propulsion Laboratory already pursuing the technology.
CRM leader Salesforce has since centered its strategy around agentic AI, with the announcement of Agentforce. IT service management giant ServiceNow has also added AI agents to its Now Platform. Microsoft and others are also joining the fray.
Agentic AI will integrate smoothly with ERP, CRM, and business intelligence systems to automate workflows, manage data analysis, and generate valuable reports, says Rodrigo Madanes, global innovation AI officer at EY, a consulting and tax services provider. AI agents, unlike some past automation technologies, can make decisions in real-time, making process automation a primary use case.
“AI agents can automate repetitive tasks that previously required human intervention, such as customer service, supply chain management, and IT operations,” Madanes says. “What sets the technology apart is its ability to adapt to changing conditions and handle unexpected inputs without manual oversight.”
Software development
Agentic AI promises to transform AI coding assistants, or copilots, into smarter software development tools that write large pieces of code. While coding assistants have so far received mixed reviews, analyst firm Gartner predicts that smarter AI agents will write the majority of code within three years, leading to a need for most software engineers to reskill.
Coding agents will not only write the code, but separate agents will review code for errors, says Sheldon Monteiro, executive vice president and chief product officer at Publicis Sapient, a digital transformation advisory firm.
RPA on ‘steroids’
Many organizations are already using robotic process automation to automate simple and repetitive tasks in many areas. Agentic AI can also automate tasks, but it can take on more complex problems that require higher-level decision-making functionality, Monteiro says.
“With AI, RPA moves beyond rule-based actions to adaptable, autonomous processes, significantly enhancing efficiency across business operations,” he says. “The new tools give us the ability to train agents to not just do the simplest of those tasks that RPA was doing, but actually to be able to understand some of the nuances of when exception logic also works.”
Customer support automation
Organizations have long used simple chatbots and voice bots to handle simple customer service requests, but agentic AI will allow customer service automation to evolve into a more robust service that doesn’t just answer a few frequently asked questions, says Glenn Nethercutt, CTO at Genesys, provider of AI-based customer experience solutions.
“The way I tend to define agentic AI is an autonomous ability to perform reason-based, multistep tasks that are nondeterministic,” Nethercutt says. “It’s an ability to handle really complex and adaptive decision-making processes without having human guidance.”
These customer service agents will cover a variety of industries and functions, including retail, financial services, and IT service desk help, he says. Instead of a highly curated bot that answers a limited number of questions, AI agents will be able to understand and provide contextual answers for a wide range of customer needs.
For example, a bank customer will be able to say, “Take money from my account that has the most money in it and move it to my checking account.” A simple chatbot typically can’t understand what “the account with the most money in it” means, Nethercutt says.
“The idea that presents itself is having this kind of catalog of the actions that can be done, and having an AI that is intelligent enough,” he says. “Here’s the panoply of options I have in front of me, and what I can choose to use, and guardrails will become increasingly complex.”
Enterprise workflows
With ServiceNow, Salesforce, and other vendors embracing agentic AI, enterprise workflows will be a sweet spot for the technology, experts say, enabling businesses to streamline processes by automating routine tasks.
Organizations deploying IT tools from a large vendor across the business should have an advantage over companies using a variety of solutions that may need to be linked by APIs, he adds. It will be important for enterprises to pool all their data and avoid information silos.
“The question that is materializing for CIOs is, ‘Who are you going to entrust with building your context store, which is your deep knowledge of how your enterprise works?’” he adds. “Think about all of the knowledge you have of your enterprise. What if your LLM actually knew the entirety of how your enterprise works?”
Cybersecurity and threat detection
Several cybersecurity providers have deployed AI agents to detect and respond to threats. “Agentic AI in cybersecurity can autonomously detect, react to, and even mitigate security and fraud threats in near real-time, reducing response times to potential attacks and enhancing overall security,” Monteiro says.
AI agents can also drive efficiency and cost savings by automating routine tasks and security responses, according to Beam.
Business intelligence
Another area where AI agents will have a large impact is business intelligence. While BI dashboards are relatively simple to use, gaining insights that go beyond the standard categories has often taken the work of a data team to extract, says Ryan Janssen, co-founder and CEO at Zenlytic, an AI-powered BI vendor.
Agentic AI paired with a BI solution could give more employees access to useful analytics, he says. For example, an AI agent for BI could advise a marketing team about where to spend its budget or create a chart based on an example drawn on a napkin, Janssen says.
AI agents that understand voice inputs can generate business data insights based on spoken questions such as, “What are our top three marketing channels?”
Many organizations are just at the start of their agentic AI journeys, and there are hundreds of uses yet to be discovered, Janssen adds. Coding agents are an early use case because programming is detail-driven and time consuming, but now coding hobbyists are building apps using coding assistants.
“The way that they are best applied is when you have work that is grindy, takes a lot of work, or requires a lot of attention to detail,” Janssen says.
“We haven’t even scratched the surface yet with what agents can do,” he says. “We don’t know what an organization looks like yet, how they’re supposed to interact, and how it is governed. But I have no doubt that over the next couple of years, we’re going to figure that out.”
FUENTE: Gross, Grant. ''Agentic AI: 6 promising use cases for business'' Cio.com. 14/11/2024. (https://www.cio.com/article/3603856/agentic-ai-promising-use-cases-for-business.html).
