Intelligent Automation vs Agentic AI

The rapid evolution of technology continues to present organisations with new tools and systems designed to optimise operations and drive innovation. Intelligent Automation (IA) has paved the way, and now, Agentic AI has emerged as a powerful evolutionary leap.

Both offer revolutionary capabilities, and while they tackle different scales of problems, Agentic AI is best understood as building upon the foundational strengths of IA. For CIOs and CTOs, understanding the nuances between these two approaches is critical for making informed decisions that align with their organisation’s goals.

What is Intelligent Automation?

Intelligent Automation combines artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to create a robust, hybrid system. Unlike agentic AI, which emphasises autonomy, IA focuses on optimising repetitive and rule-based tasks while also tackling more complex processes that require cognitive abilities such as pattern recognition and decision-making.

Key characteristics:

• Integration of Technologies: Combines AI to interpret tasks, ML to improve performance over time, and RPA to execute repetitive actions with precision.
• Streamlined Workflows: Automates both mundane tasks (e.g., data entry) and nuanced processes (e.g., interpreting analytics for decision-making).
• Efficiency-Driven: Designed to reduce human workload, improve speed, and minimise errors through consistent execution.

Use case example:

Retail Operations: Intelligent Automation is widely used in retail. For example, inventory management can dynamically adjust stock levels based on real-time analytics, ensuring the right products are available without overstocking. Chatbots powered by automation systems handle customer inquiries with efficiency while allowing human agents to focus on complex queries.

Benefits of Intelligent Automation

• Enhanced Efficiency: Automates repetitive tasks, reducing errors and manual bottlenecks.
• Cost Savings: Lowers operational expenses by reallocating human resources to strategic activities.
• Improved Customer Experience: AI-supported chatbots and analytics allow businesses to deliver seamless, personalised interactions at scale.

What is agentic AI?

Agentic AI represents the next frontier in automation, embodying a higher degree of intelligence and autonomy that builds directly upon the principles of Intelligent Automation. It moves beyond predefined rules and even beyond optimising existing processes, enabling systems to make autonomous, context-driven decisions and adapt dynamically to new information and changing environments without continuous human intervention. Agentic AI’s hallmark is its capacity for independent action in complex, unpredictable scenarios.

Key characteristics:

• High Autonomy: Operates independently, analysing environments and acting based on its insights without continuous human oversight.
• Deep Context Awareness: Processes vast and diverse data streams to make decisions based on real-time context.
• True Adaptability: Learns and adjusts its behaviour over time, enhancing its ability to handle dynamic situations.

Use case example:

Insurance Claim Processing: Agentic AI has the power to autonomously complete almost the entire workflow of an insurance claim procedure – reviewing submission, validating a claim’s eligibility, proactively contacting the customer, tracking their responses and answering questions they have. Unexpected issues can be reviewed by a ‘human in the loop’, which helps the agent continuously learn how to process new claims and requests moving forward.

Benefits of agentic AI

• Improved Decision-Making: With its ability to process context-aware data, Agentic AI can make decisions that are more accurate and aligned with real-world conditions.
• Reduced Human Intervention: Frees up resources by minimising the need for manual oversight in highly dynamic environments.
• Scalability in Complex Scenarios: Thrives in industries requiring adaptable solutions, such as logistics or healthcare.

Comparing core differences

While both Agentic AI and IA share the goal of optimising operations, it’s more accurate to view Agentic AI as an evolutionary leap from IA, offering a higher degree of autonomy and adaptability. IA sets the critical stage by streamlining foundational processes and creating the clean, optimised data environment upon which Agentic AI can truly thrive.

Here’s how they compare in their progression:

Choosing the right solution for your organisation

For CIOs and CTOs, the question isn’t always ‘which one,’ but rather, ‘what blend or progression of these technologies best suits our evolving needs?’ Deciding on your automation roadmap requires a strategic evaluation of your organisation’s goals, operational complexities, and readiness for technological transformation.

Firstly, consider your core priorities. Do you first need to streamline foundational processes and achieve immediate efficiency gains (IA)? Or are you ready to build capabilities that allow your organisation to adapt dynamically and innovate autonomously (Agentic AI, often layered on top of robust IA)? Clearly defining your overarching goals establishes the foundation for aligning the right technology with your business strategy.

Next, reflect on the nature of the tasks and decisions your organisation seeks to optimise or automate. Are these tasks predominantly repetitive and rule-based, or do they require context awareness and the ability to learn from evolving situations? Evaluating the complexity of operations will provide clarity on whether a more process-focused or decision-driven approach is necessary.

Another key consideration is organisational readiness. What technological infrastructure do you currently have in place, and how compatible is it with advanced systems like AI or automation? Assess whether your existing data practices, IT frameworks, and internal processes can support sophisticated implementations. Technological incompatibility or poor data quality can stall deployments or lead to less-than-optimal results, making this an essential checkpoint for decision-makers.

Equally critical is the workforce perspective. Do you have the expertise in-house to manage the deployment and maintenance of these technologies? If not, are you prepared to invest in training or bring in external specialists who can bridge these skill gaps? Both IA and agentic AI rely heavily on cross-departmental collaboration and technical fluency, making talent alignment a vital aspect of preparation.

Finally, consider the long-term implications of your choice. Is your organisation looking for a solution that provides tangible, incremental benefits in the short term, or does your roadmap prioritise foundational technologies that unlock new strategic opportunities over time? Think about how adaptable and future-proof your chosen approach is in a world where markets, regulations, and customer expectations are constantly shifting.