The Problem with Current Automation Methods
Despite the advancements in automation, over 80% of business processes across industries are unstructured, dynamic, and beyond the reach of traditional RPA. These include processes that involve decision-making, complex workflows, and frequent changes, making them unsuitable for rigid rule-based automation. Examples include customer service interactions, HR enquiries, procurement negotiations, and supply chain adjustments, where human intervention is still required to handle variability and exceptions.
What Is Agentic AI?
Imagine a digital workforce that doesn’t just follow scripts but thinks, adapts, and takes initiative. That’s Agentic AI. Instead of simply automating repetitive tasks like RPA, an Agentic AI agent acts as a dynamic decision-maker that understands, plans, and executes actions based on real-time business needs.
As an Agentic AI agent, I don’t just process a workflow—I analyse it, anticipate changes, and optimize it. I can:
- Adapt dynamically to evolving business processes without requiring constant reprogramming. Example: A company’s customer support team shifts its policies frequently. Instead of manually updating a traditional chatbot workflows, an Agentic AI agent adapts in real time, learning from customer interactions and policy updates to provide accurate responses.
- Understand unstructured data from conversations, emails, and documents to drive intelligent actions. Example: An AI agent extracts key details from thousands of supplier contracts, identifying compliance risks and flagging necessary updates without human review.
- Make decisions autonomously, collaborating with humans and other AI agents in real time. Example: A logistics Agentic AI agent detects a warehouse delay and autonomously reroutes shipments while coordinating with procurement systems to restock inventory.
- Execute entire workflows, not just individual tasks, significantly reducing manual effort and human oversight. Example: In HR, an Agentic AI agent manages end-to-end onboarding by collecting documents, verifying credentials, scheduling training, and answering employee questions dynamically.
Unlike RPA, which struggles with unpredictable and non-standardized tasks, Agentic AI seamlessly fills the automation gap, bringing intelligence to processes that were previously out of reach.
Use Cases of Agentic AI in Business
Agentic AI has the potential to revolutionize areas that rely on human-driven interactions and decision-making. Here are some key areas where it can have the biggest impact:
1. Contact Centres: Autonomous Customer Interaction and Issue Resolution
- Agentic AI agents can handle complex customer queries, moving beyond scripted responses to interpret intent, troubleshoot problems, and resolve issues without escalating to human agents.
- Instead of static chatbot workflows, AI-driven contact centres can personalize responses, adapt in real-time, and anticipate customer needs by analysing historical interactions.
- AI agents can automate customer interaction with systems for information retrieval, data entry and follow-ups, ensuring that issues are resolved end-to-end and providing seamless omnichannel support across chat, email, and voice.
2. IT Service Management: Intelligent Incident Resolution and System Monitoring
- Agentic AI agents can monitor IT systems, detect anomalies, and proactively resolve issues before they escalate, reducing downtime.
- Instead of relying on human intervention for troubleshooting, AI-driven IT support can autonomously diagnose and resolve recurring technical issues, improving system uptime.
- Agentic AI agents can automate ticket triaging and escalation, intelligently reading and understanding ticket content and categorizing incidents and routing them to the right teams for faster resolution.
3. Financial Services: AI-Driven Fraud Escalation and False Positive Resolution
- Agentic AI agents can read and analyse fraud and anti-money laundering (AML) escalations, intelligently identifying, classifying and closing false positives to reduce unnecessary human workload.
- Instead of manual investigations, Agentic AI automation can prioritize high-risk alerts, ensuring that only the most critical cases require human intervention.
The Future is Autonomous Agents, Not Just Apps
The shift from static automation to autonomous agents is inevitable. Instead of building isolated apps or deploying more RPA bots, businesses should be investing in Agentic AI that can work dynamically across functions and interact intelligently with enterprise systems.
Taking the Next Step
To prepare for this shift, businesses should:
- Assess Current Automation Gaps: Identify processes that require constant human intervention or frequent maintenance.
- Adopt AI-Driven Automation Strategies: Move beyond rule-based RPA to AI-driven automation that enables agents to adapt and learn.
- Invest in Agentic AI Platforms: Look for solutions that provide real-time learning, decision-making, and execution capabilities.
- Redefine Workforce Collaboration: Train employees to work alongside AI agents, allowing them to focus on strategic tasks while automation handles repetitive work.
The future of automation is not about more scripts, more dashboards, or more fragmented AI models—it’s about intelligent agents that drive meaningful business impact. Now is the time to take the next step towards Agentic AI and unlock a new era of business automation.
At BioQuest Advisory, we work with clients across industries to adopt the latest in automation, speak to us at [email protected]
