McKinsey’s latest findings show that AI has progressed well beyond the experimentation phase, with 78% of surveyed respondents actively using it in at least one business function. These figures clearly illustrate that AI adoption is in full swing across businesses, which means many organizations are also stepping up their AI game and moving on to agentic AI automation. By 2032, the agentic AI market is projected to reach US$140.80 billion. This substantial figure underlines the profound influence agentic AI is exerting, not only on the way we work but also on the functioning of the world.
What is Agentic AI?
Two of the most iconic AI systems in pop culture, JARVIS and FRIDAY from the Iron Man series, offer a near-perfect example of what Agentic AI is all about. These intelligent assistants don’t just follow commands; they perceive, reason, and act autonomously, often making split-second decisions that have saved Tony Stark’s life more than once.
From reel to real. Advanced business process automation solutions are no longer in the realm of fiction, as illustrated by the mainstreaming of Agentic AI.
Agentic AI is more than just a buzzword, and this buzz is well-earned. Think of it as your digital workforce, which is an outcomes-focused blend of large language models (LLMs) and traditional programming. It operates autonomously, executing tasks on your behalf by following pre-defined workflows and utilizing the right tools for the job.
There is a reason why we said it can be very effective for your digital employee. This AI program is developed to make decisions and even take actions based on those decisions. It can solve complex problems and, more importantly, it continues to learn and improve over time, getting better without human intervention. The inherent characteristics of agentic AI enable orchestration of a step-by-step workflow that drives complex applications, a type of capability that goes beyond the scope of traditional AI. This makes this AI system ideally suited for deployment within enterprise-level automation strategies.
Let’s understand agentic AI through an example.
Suppose you want to plan a holiday in Canada and ask Perplexity or Gemini for the best options to stay in Vancouver. These LLM chatbots will provide a list of choices curated from the data they are fed, including customer reviews and other sources. However, Agentic AI can go further, taking several steps, booking hotels and creating detailed travel plans. When you give an input, multiple agents are activated, typically AI models that check your calendar, fetch flight data, prices, availability, and then book flights and hotels.
This means you have an LLM coordinating various tools and services to deliver outcomes, not just generate outputs.
How Does Agentic AI Differ from Traditional LLMs?
LLMs are designed to generate human-like responses and can answer a wide range of questions. However, they operate within certain boundaries, the kind of boundaries that agentic AI is designed to break free from.
Here’s how agentic AI goes further than traditional LLMs:
- Agentic AI combines the strengths of large language models (LLMs) and traditional programming to create systems that can handle rule-based tasks, respond to new inputs in real time, and continuously improve through feedback.
- LLMs rely on static data sets, which are comprehensive but have certain limitations. After your input, the LLM uses this data to generate output that may or may not be exhaustive. Agentic AI breaks free from the static constraints of LLMs by actively interacting with the internal and external environments. It can access data from APIs, query databases or spreadsheets, monitor live systems and logs, learn from user behavior and tap into a range of external tools to support dynamic decision-making.
- Agentic AI introduces autonomy to the automation framework by taking the next logical steps within a specific process without requiring human intervention. You can leverage agentic AI to manage digital campaigns, monitor cybersecurity threats, and guide employees through an onboarding process, among other applications.
- Today’s business systems are exceptionally user-friendly. Yet, business users often face steep learning curves to interpret dashboards, understand insights, and take appropriate action. With Agentic AI, all you need to do is ask questions and get outcomes.
What are the Use Cases of Agentic AI Automation Across Industries?
Manufacturing
When it comes to business process automation solutions in manufacturing, automating maintenance schedules or predictive modelling for maintenance is a key use case. This is for good reason, as the average unplanned downtime cost for industrial maintenance has increased by 30% primarily driven by rising costs of spare parts and logistics.
The deployment of Agent AI adds a new layer to predictive maintenance systems. Previously, automated maintenance systems would alert an operator when maintenance was due. Alerts were also triggered when specific health parameters, coded into the automation script, began falling outside standard patterns or ranges. However, with agentic AI, predictive maintenance evolves into autonomous maintenance execution. Agentic AI monitors data from various IoT sensors and, upon detecting a problem area, immediately opens a maintenance window and assigns it to the available technician on the roster. It can even go a step further, depending on the complexity of the issue, by identifying the necessary spare parts, preparing a list, and placing an order with an approved supplier.
Another very unique use case for the application of agentic AI in manufacturing is ESG. Manufacturers are under pressure to reduce their carbon footprint. Agentic AI can be pressed into use for granular analysis of the production environment, including scheduling, machine utilization, energy pricing and more. This data can be used to shift high-energy tasks to off-peak hours when energy is cheaper and cleaner, thereby reducing overall energy consumption. It can also minimize HVAC loads during periods of low demand. Agentic AI can seamlessly integrate with on-site management systems and energy dashboards, reducing energy consumption without compromising business continuity and efficiency.
Healthcare
Many chronic diseases require continuous care and management, including regular lab tests, doctor consultations, and prompt refills of prescription medications. In such cases, Agentic AIs act as care managers approved by a physician and coordinate different aspects of care management for the patient. Whether it is ordering lab work or generating reminders for a doctor consultation, agentic AI can take action on the patient’s behalf, with a healthcare provider’s oversight added to the management layer.
Another application of AI business automation in healthcare, using agentic AI, is reducing operating theatre (OT) bottlenecks. This helps optimize OT utilization by ensuring surgical slots are maximized based on availability, patient condition, and other relevant factors. Agentic AI can review surgeon calendars, patient availability, preoperative readiness, and other factors to suggest surgery slots once specific conditions are met.
Supply Chain
Remember the MV Ever Given, a ship that blocked the Suez Canal back in 2021. The resultant shipping disruption was costing US$9.6 bn worth of goods every day, which otherwise would have passed through the canal on time.
Global logistics can experience disruptions from various sources, such as a ship blocking a vital navigation canal, a Black Swan event like COVID, or even war. Imagine a ship caught in such a disruption; in these situations, Agentic AI automation keeps track of these issues in real time. In automation terminology, these disruptions are known as exceptions, which are considered when the agents suggest new transport routes. An agent informs the customer about the disruption, while another can notify the warehouse teams about the delay and updated ETA, allowing them to adjust their schedules accordingly.
Another use case is that of optimal fleet management. While you must have seen numerous trucks carrying goods on the highways, you might not have thought about the extensive management process that happens behind the scenes to ensure goods are transported on time. This involves addressing order changes, driver availability, truck availability, truck tracking, and more. Agentic AI can be your digital fleet and driver scheduler, ensuring fleet capacity is maximized and driver and trucker availability perfectly aligns with the quantity and nature of goods to be transported.
Retail
One of the most significant problem areas for retailers is inventory—the cost of improper inventory amounts to US$ 1.77 trillion worldwide. Agentic AI can ensure a retailer’s constant struggle to maintain optimal inventory comes to an end, and this is across both offline and online channels. Agentic AI can effectively draw data from various integrated systems and databases and enrich them with data on seasonal demand, sales velocity, and shelf-level stock. In such cases, it can not only reorder fast-moving products from suppliers based on predictive forecasting, but also recommend shifting (and initiate transport) of slow-moving products from one location to another, where they are flying off the shelves.
Optimal data usage to offer hyper-personalized retail experience is yet another Agentic AI automation use case for retail. Retailers generate vast amounts of data that helps them gain a better understanding of their customers. But what next? Making sense of, and acting upon, comprehensive customer intelligence is also a problem area. Agentic AI collates and gains insights from data gathered from diverse touchpoints to identify high-intent shoppers and trigger personalized promotions.
Agents can also make personalized recommendations and add items to carts based on their knowledge of the customer (items can be removed from the cart by the customer). This can reduce shopping cart abandonment rates, which are a significant challenge for retailers, and even improve customer retention and acquisition rates.
Conclusion
Yes, Agentic AI holds tremendous potential, but to truly realize it, you need the right environment. Your organization must first establish a well-defined governance model that helps manage the risks associated with operational AI, enabling informed decision-making and effective execution of those decisions. This includes setting clear ethical boundaries, defining escalation paths, and identifying areas where human oversight is absolutely necessary.
Moreover, you also need the proper infrastructure built to scale with synchronised platforms that can communicate with one another, sharing information through safe and secure data pipelines.
It is also essential that you do not overlook the human elements. Train your workforce to use AI-driven business automation and ensure they understand that Agentic AI will support decision-making and execution, but will not be used for strategic or high-value decisions.
To fully realize the potential of Agentic AI, partner with a business automation services provider that deeply understands these systems and brings cross-industry experience in transforming processes through agentic automation.
