Insights from Lokesh Venkatappa, Sr. Vice President – Solution Consulting at EvoluteIQ
A couple of decades ago, Radio-Frequency Identification (RFID) was hailed as the breakthrough that would revolutionize supply chains. When Walmart mandated RFID tagging for its top 100 suppliers, many predicted the humble barcode’s demise. The potential was immense: every product tagged, tracked seamlessly from farm to checkout, with details like country of origin instantly available.
Reality proved different. Instead of tagging every can of beans, RFID found its niche in tagging pallets and high-value goods, where the benefits justified the cost and complexity. It became useful, but far from ubiquitous.
Today, Agentic AI, capable of perceiving, reasoning, planning, and acting autonomously, or seamlessly engaging humans when needed, is having a similar moment in the spotlight. For Chief Digital Officers and transformation leaders, the promise of orchestrating white-collar equivalents of human–machine collaboration, once confined to shop floors, is alluring.
The mission: deliver a level of collaboration through AI Agents that was once unimaginable, streamlining operations, bridging gaps between ERP systems and real-world processes, integrating countless SaaS tools, and taming legacy platforms. In short, to build a bespoke digital ecosystem at a speed and scale that seemed impossible even five years ago.
From Hype to Hard Work
To unlock the potential of Agentic AI, businesses must move beyond hype. They need roadmaps that acknowledge context of their business, talent transformation needs, clarity on desired outcomes and existing technology footprint.
Five Keys to Making Agentic AI Deliver
1. Clarity on what Agentic AI is not
It is not a chatbot, nor just a technology wrapper around existing RPA bots (what Gartner calls “Agentwashing”). In early deployments, the focus should be on processes where autonomy is backed by strong controls, audit trails, and human-in-the-loop execution. Pick clear, high-value problems worth solving, not vague experiments or endless pilots.
2. Accuracy vs. Confidence
Most vendors emphasize accuracy and speed. Few highlight confidence, the system’s ability to measure and communicate certainty about its actions. Without confidence benchmarks and governance frameworks, companies risk diverting resources to manually verify automated decisions, draining value. Industry bodies and integrators must step up to define these standards.
3. Match capabilities to the right jobs
Agentic AI brings “human-like” capabilities… perception, reasoning, planning, learning, and adaptation. It should be applied to problems that benefit from them. For instance, EvoluteIQ has enabled:
- Denials prediction and appeals management in healthcare
- Complex invoice processing in logistics
- Fault prediction and remediation in telecom networks
4. Own human–agent orchestration
The orchestration layer, where data, processes, and people converge, will likely become the digital “fingerprint” of every business. While the core AI may come from vendors, the integration layer should be built internally to preserve flexibility and competitive advantage.
5. Measure, learn, and refine
Deploy in phases. Track meaningful metrics. Adjust course as results evolve and as autonomous behaviors reveal new opportunities.
Learning from RFID: The Road Ahead
If history is a guide, Agentic AI will follow the classic hype–stabilization curve: initial enthusiasm, tempered expectations, and eventually, focused adoption. RFID never replaced every barcode, but it became vital for asset tracking, inventory management, and theft prevention in select industries.
Agentic AI may follow the same path, transformational in certain areas, incremental in others. RFID’s story proves that even without fulfilling every lofty prediction, technology can deliver lasting competitive advantage when applied wisely.
For Agentic AI, the winners will be those who choose their battles carefully, build for adaptability, and remain relentlessly focused on outcomes.
