An AI chatbot in an e-commerce store can handle simple questions like, "What's your return policy?" But what happens when a customer says, "I got the wrong item and want to exchange it for a different one?" Since this request is more complicated, the bot doesn’t know what to do and directs the customer to a human agent. That means longer wait times and frustration.
On the other hand, agentic artificial intelligence (AI) understands what the customer wants, pulls up order details, checks if the item is in stock, and processes the exchange without a human stepping in. Agentic AI builds on the capabilities of generative AI tools like ChatGPT to actively make decisions and optimize workflows.
That’s why experts predict that by 2028, 33% of enterprise software will have agentic AI built-in.
In this guide, we’ll walk you through what is agentic AI, its benefits, and real-life use cases so you can discern whether it’s a good fit for your business use cases.
Agentic AI vs. Gen AI
Agentic AI goes beyond simply responding to prompts. It actively perceives its environment, reasons through options, takes action, and learns from outputs.
In contrast, generative AI processes inputs, infers patterns, creates outputs, and adapts to new contexts. Traditional AI follows predefined rules.
Here's how they compare.
Now that we’ve covered how agentic AI differs from traditional and rule-based AI, let’s explore how it can benefit businesses.
Why agentic AI matters: Key benefits for businesses
AI is evolving, and its role in business is growing. Rather than just responding to commands, it can now analyze, adapt, and take action. This shift is transforming how businesses operate and compete.
Here’s how.
Increased workflow efficiency and productivity
Agentic AI handles complex, multi-step tasks, freeing employees to focus on higher-value work that requires human creativity and expertise.
For instance, ServiceNow's AI agents have reduced case resolution time for complex cases by 52%.
Similarly, when used in customer service, agentic AI can analyze a ticket, determine the root issue, draft a response, escalate complex cases, and even follow up with customers.
More strategic human-AI collaboration
Agentic AI enhances human decision-making through complex data analysis and actionable insights. It takes on tasks like risk assessment, fraud detection, and patient diagnostics, allowing professionals to concentrate on higher-level decisions.
In the travel industry, AI self-learning models manage bookings, optimize flight schedules, and handle customer queries in real-time. This reduces operational costs and frees up human resources for more personalized customer interactions.
Beyond automation, agentic AI also understands human intent and urgency.
For example, OpenAI’s Operator can handle entire tasks autonomously. So if a customer needs last-minute catering for an event, instead of just placing an order, an AI-like Operator could find a restaurant that meets dietary preferences, check availability, confirm the order, and even handle payment, creating a human-like customer experience.
Enhanced trustworthiness and specialization
Agentic AI systems analyze datasets to identify patterns, leading to more informed and trustworthy decisions. In the legal field, agentic AI can analyze legal documents, identify patterns in case law, and even assist in developing legal strategies.
The National Law Review says, "In the 20th century, mastering 'thinking like a lawyer' meant developing a rigorous, precedent-driven mindset. Today, we find ourselves on the cusp of yet another evolution in legal thinking — one driven by agentic AI models that can plan, deliberate, and solve problems in ways that rival and complement human expertise."
Let’s understand these benefits further through real-world use cases of agent-based AI models.
Use cases of Agentic AI
From improving customer experience to empowering financial decision-making, the use cases of agentic AI scatter across industries. Let’s take a closer look at how it’s making an impact.
Improve customer experience
To qualify as agentic AI, a system must perceive, reason, act autonomously, and learn from its actions.
Say you integrate Plivo's AI-powered voice agent to handle frequently asked queries. It can serve as a 24/7 customer support system, manage contextual voice interactions, and trigger workflows automatically.
You can build on this by creating another decision-making bot with an engine like GPT-4, Google's Gemini, etc., that analyzes customer intent, and adapts responses.
Now, when a caller says, "Please refund or exchange the product!" the AI assesses the request. One system checks refund eligibility, while another evaluates whether an exchange is possible.
Businesses are still in the early stages of adopting agentic AI for customer service, but the shift is accelerating. By 2029, Gartner predicts AI will autonomously resolve 80% of common customer service issues, significantly reducing response times and improving customer experiences.
Create personalized content
Agentic AI helps businesses create personalized content by making sense of vast amounts of customer data.
Yum Brands, the parent company of Taco Bell, KFC, and Pizza Hut, used AI-driven marketing to send personalized emails and notifications to their customers. They analyzed what customers typically order, what they prefer, and how their choices change over time to send hyper-personalized offers at the right moment.
As a customer interacts with the brand, agentic AI adapts the marketing message based on their evolving needs and behaviors. This ensures every conversation made with the customer is relevant and hyper-personalized.
Beyond messaging, you can even automatically adjust variables like bidding, ad placement, or audience targeting to optimize campaigns and conduct A/B testing at scale on multiple variables.
Improve patient care
Agentic AI systems can process vast datasets such as clinical notes, patient histories, lab results, medical guidelines, and even diagnostic imaging to extract actionable insights.
The National Health Service (NHS) introduced an AI physiotherapist named Kirsty to help patients with back pain. This AI agent offers same-day virtual appointments, personalized exercise plans, and real-time health advice, reducing wait times and improving accessibility.
Here’s how agentic AI improves healthcare workflows:
- Data coordination: When new clinical data is entered into an Electronic Medical Record (EMR), an AI-powered system pulls information from multiple sources and triggers workflows based on predefined logic.
- Specialized AI agents:
- A clinical data agent analyzes patient records using Natural Language Processing (NLP).
- A molecular test agent interprets genomic data from biopsy samples.
- An imaging analysis agent processes radiological scans and pathology reports.
- Coordinated decision-making: While specialized AI agents operate independently, a coordinating agent synthesizes their insights to recommend the most appropriate clinical decision.
Note: Although fully agentic AI systems are still evolving, healthcare providers are already leveraging AI-powered voice bots that automate patient interactions, appointment scheduling, and medication reminders. Solutions like Plivo can further personalize patient interactions and help reduce wait times.
Empower financial decision-making
The day when a trading AI agent analyzes market data, monitors market trends, adjusts strategies, and mitigates risks isn't far. Agentic AI will make this possible by integrating tools via application programming interface (APIs), sensors, and advanced reasoning.
Agentic AI could also autonomously assess micro-loans for smallholder farmers, using local data to evaluate risk without direct human involvement. Similarly, mobile banking powered by agentic AI could offer personalized micro-insurance products based on real-time weather data.
Optimize logistics and supply chain
When running an e-commerce business, the last thing you want is a customer placing an order for a high-demand product that’s actually out of stock. That’s where agentic AI steps in.
SAP has introduced two AI agents to tackle this issue: one for sales and another for supply chain management.
The sales AI agent determines the best price and product bundle for the customer while simultaneously checking inventory. Before making a sales commitment, the supply chain AI agent steps in to verify stock levels, assess delivery timelines, and adjust logistics accordingly.
Since these AI agents interact autonomously, they prevent sales teams from overpromising on orders that the supply chain can’t fulfill.
SAP CEO Christian Klein emphasized that contextualizing data is key to making agentic AI successful. “While 80% of businesses may not yet have the infrastructure to support AI-driven operations, SAP is bridging that gap by integrating predictive AI and automation directly into its software.”
Given the advancements in agentic AI, it’s only natural to wonder what the future holds.
What lies ahead: The future of agentic AI
Agentic AI systems provide the best of both worlds: LLMs handle tasks that benefit from dynamic responses, and these AI capabilities with conventional rule-based programming. So, the future of agentic AI consists of systems that fetch real-time information, retrieve updates, or pull specific data points important for decision-making.
However, as businesses integrate AI deeper into their operations, regulatory frameworks struggle to keep pace. A recent survey found that 93% of professionals recognize the need for clearer AI regulations to mitigate risks.
Ethical concerns, such as algorithmic bias, decision transparency, and compliance with evolving privacy laws, remain critical challenges. Companies must ensure AI-driven decisions are fair, explainable, and aligned with regulatory standards.
In industries where customer interactions matter, such as finance, healthcare, and e-commerce, solutions like Plivo help businesses use AI-powered voice and messaging tools to improve customer experiences while maintaining compliance.
Take the first secure step to agentic AI with Plivo
As agentic AI continues to evolve, businesses need AI-powered solutions that can learn, adapt, and improve over time. Plivo’s AI-powered voice agents make this transition seamless.
If you’re already using AI agents, you can take this a step further by building your own AI agent combining these two functions. You can launch the voice agents with any text-to-speech (TTS), speech-to-text (STT), and language model of your choice with Plivo’s APIs.
Whether you're looking to deploy autonomous AI agents, use AI for complex problem-solving, or build an entire AI-driven ecosystem, Plivo makes it easy.
Contact us today to explore the possibilities!
