From Hype to Impact: Implementing Agentic AI in Your Enterprise – Part 1

Can Agentic AI live up to the hype that lately started around it? Is now the time for agents to finally hit the road? This article, the first in a three-part series, will explore the fundamentals of Agentic AI, its significance for enterprises, and key application scenarios. Read this if you want to know what agentic AI actually means and how you can use it to create value in your organisation. 

What is Agentic AI?

An AI agent is a system that reasons, acts, and operates autonomously using tools and rules. Agents are action-oriented, reason-driven, and autonomous, meaning they pursue goals, plan steps, and operate with minimal human input. They function as a software layer around LLMs that enable systems to reason, plan, act, and learn:

• Reason – understand complex instructions, context, and objectives
• Plan – break down tasks and choose the most efficient execution path to achieve the objective
• Act – autonomously interact with enterprise systems and execute multi-step problem solving
• Learn  – adapt and improve over time through experience and stored information

The emergence of Agentic AI only marks the latest step in the long, ongoing evolution of automation. This evolution hasn’t been a single leap, but a series of transformative stages, each building upon the last: 

Beginning in the late 20th century, companies started automating processes and routine tasks using Rule-based Automation. By implementing pre-defined logic of if-then-else statements, systems were designed to execute defined, repetitive processes. 

With the rise of AI and machine learning, automation evolved. Intelligent Automation introduced the ability to learn from past patterns, moving beyond rigid constraints of rule-based logic. 

The current state where most enterprises stand today can be described as Agentic Workflows. Large Language Models (LLMs) orchestrate workflows that enable complex automated interactions. While the sequence of tasks is still predefined, the LLM can intelligently interact with different tools and APIs to complete each step. 

This evolution to autonomous goal achievement unlocks unprecedented potential for efficiency and innovation.

Finally, we are slowly entering the next frontier: Autonomous AI Agents. Unlike the structured workflows of the previous stage, an autonomous agent is given a high-level goal, not a set of instructions. It can independently reason, plan, and execute a dynamic series of actions to achieve that goal, learning and adapting along the way. 

So, Agentic AI unlocks a new class of applications that entail proactive & multi-step problem solving in an orchestrated environment. Let’s explore what this technology means for enterprises and the concrete possibilities it opens. For this purpose, we will guide through key application scenarios.

Why it matters for enterprises – Application Scenarios

Most enterprises are already experimenting with GenAI, but only a few are truly extracting significant value. While the adoption of copilots and chatbots is pretty high, with over 80% of the enterprises having piloted them and 40% reporting productive deployment, measurable gains beyond individual productivity have been hard to come by 1. At the same time, we see efforts of deploying function-specific use cases, out of which only 10% make it to production 1. Most fail due to fragile workflows, lack of contextual learning, and misalignment with day-to-day operations. This is where Agentic AI comes into place, unlocking the true potential of the foundation laid by GenAI. It transforms AI applications from reactive, isolated single-turn answers to proactive, multi-step problem-solving workflows that are deeply integrated into business processes. 

Let’s explore what this means for concrete application scenarios across key business functions.

Operational Efficiency: The Engine of Productivity

Agentic AI unlocks operational efficiency by automating end-to-end workflows that were previously too complex for traditional systems. This isn’t about simple task automation; it’s about delegating entire, multi-step processes to autonomous agents.

Enhanced Decision-Making & Risk Mitigation

Unlike traditional systems that rely on pre-programmed rules, agents can continuously monitor real-time data from various sources to identify emerging risks or opportunities. They can simulate different scenarios and recommend the optimal course of action, helping to mitigate financial, operational, or reputational risks before they become critical.

Flexible Data Leveraging & Employee Enablement

Agents can tap into the potential of unstructured and imperfect data, which was previously unusable. By freeing employees from repetitive tasks, agentic automation allows them to focus on innovation and value creation.

While many companies are still struggling to move from GenAI hype to real business value, the transformative use cases outlined above reveal that Agentic AI is the strategic answer, poised to unlock the next wave of enterprise-wide efficiency and innovation.

Why act now?

By 2028, agentic AI is expected to make 15% of daily work decisions and be embedded in 33% of enterprise software according to Gartner Agentic AI Forecasting. This shift bridges current inefficiencies with intelligent automation, addressing complex, high-friction workflows to unlock significant business outcomes.

While Agentic AI might still sound like a buzzword, and it may seem prudent to wait for the hype to settle into proven technology, we believe that delaying to adopt agentic AI is a strategic risk for companies. 

By starting to experiment with and prepare for Agentic AI now, companies can build an advantage over their competitors, boosting efficiency and innovation, and quickly outpace slower adopters. This is because the real competitive advantage won’t come from buying software; it will come from the hands-on experience of integrating agents into your specific business and being able to provide your employees with the right tools to build agentic AI solutions for their high-value use cases.

Outlook

Agentic AI offers transformative potential for enterprises by enabling autonomous, intelligent automation across diverse functions. While significant benefits are evident, successful implementation requires careful consideration of governance, risk management, and organisational change. Our next articles in this series will delve deeper into the challenges of implementing Agentic AI, strategies for effective governance and scaling as well as a technical deep dive. Lastly, we will look at practical steps you can take to get started. So stay tuned for our next episodes of this series.

Sources:

1. Artificial Intelligence News
2. Nasscom
3. McKinsey
4. Gartner 
5. McKinsey
6. McKinsey 
7. Gartner