The future of enterprise automation: Synergizing agentic AI and intelligent automation

As enterprises deepen their automation strategies, a critical question arises: Will agentic AI replace intelligent process automation (IPA) and robotic process automation (RPA), or will these technologies converge to drive hyper automation?

A client recently approached us, buzzing with enthusiasm for agentic AI. “It’s the future!” they exclaimed. They’re right—agentic AI is transformative and adaptable. But before diving into building intelligent agents, we asked: “What problem are you trying to solve?”
There was a pause.

In the rush to adopt new technology, it’s easy to overlook the why. Agentic AI, GenAI, RPA, and intelligent automation dominate strategy discussions, but jumping into solutions without understanding the problem can lead to costly detours. Sometimes, a process tweak or better integration is enough.

Before choosing a tool, we need clarity on the business outcome, pain points, required intelligence, and integration needs. Understanding the automation landscape is essential for impactful decisions. Without it, you’re navigating blindly in a fast-moving world.

It started with bots—simple, rule-following workers that never slept. Then came intelligent automation, adding brainpower. Now, agentic AI steps in, not just to follow instructions, but to make decisions, learn, and collaborate. Agentic AI, powered by large language models (LLMs), generative AI, and large action models (LAMs), offers human-like decision-making, while RPA and IPA provide reliability for structured tasks.

Managing a multi-cloud security strategy comes with unique challenges:

• Reliability: RPA’s deterministic nature ensures consistent outcomes, making it indispensable in regulated industries.
• Cost efficiency: Organizations report up to 30% cost reductions in back-office operations.
• Market growth: The global RPA market is projected to grow from $28.31 billion in 2025 to $211.06 billion by 2034, at a 25.01% CAGR (Vantage Market Research, 2025).

• Adding intelligence: AI capabilities like ML, NLP, and OCR add a layer of intelligence to traditional automation.
  
  
• Market growth: The intelligent automation market is projected to reach $37.2 billion by 2031 (SkyQuest Technology, 2023).

• Reactive agents: Focused on task processing, e.g., summarization, sentiment analysis, response generation.
• Single to multi-agent systems: From single agents built on LLMs to multi-agent orchestration at an enterprise level, supported by frameworks like MCP and A2A.
• Market growth: The AI agent market is expected to reach $47.1 billion by 2030 (Meticulous Research, 2024).

Picture a high-performing café. RPA is the automated coffee machine—programmed to make consistent cappuccinos, lattes, or espressos with speed and precision. Intelligent automation is the operations manager, monitoring customer flow, adjusting staffing, and predicting pastry demand. Agentic AI is the master barista, remembering customer preferences, sensing moods, and crafting personalized drinks. Every role is essential.

Some fear agents will compete with intelligent automation, but their synergy is powerful:

• Cognitive and execution layers: Agentic AI provides reasoning and adaptability, while RPA ensures precision for structured tasks.
• Legacy system integration: RPA’s non-invasive UI automation enables agents to interact with outdated systems, critical for legacy applications lacking modern APIs.
• Dynamic adaptability: Autonomous agents handle unstructured data and dynamic interfaces, while IA ensures reliability for high-volume processes.

For example, a computer use agent (CUA) can launch apps, navigate websites, and complete tasks using natural language. RPA excels in deterministic tasks but lacks reasoning, making it complementary to CUAs. While CUAs rely on compute-intensive LLMs, introducing latency and potential inaccuracies, RPA delivers predefined tasks without deviation.

Seller support agents

A client’s older support platform struggled with dynamic e-commerce customer service needs, causing delays and limited visibility. We deployed query processing agents with a human-in-the-loop (HITL) architecture, handling routine queries autonomously and escalating complex ones.

• 25%+ reduction in turnaround time
• Enhanced seller experience
• Scalable support operations

Advisor productivity agents

Another client lacked real-time visibility into advisor performance. We introduced real-time productivity agents to monitor, analyze, and act on live data, transforming a reactive environment into a precision-driven ecosystem.

Impact:

• 8% uplift in operational performance
• Proactive performance management
• Actionable insights for frontline teams