The Rise of Agentic AI: Why Today’s Digital Co-Workers Are Rewriting the Rules of Business

The co-located AI & Big Data Expo and Intelligent Automation Conference kicked off with a clear signal: artificial intelligence is no longer just a tool—it’s evolving into a digital co-worker that thinks, plans, and executes. While headlines have focused on AI’s potential to augment human labor, the technical sessions revealed a deeper truth: the real revolution lies beneath the surface, in the infrastructure and governance frameworks that make agentic AI possible.

From Passive Automation to “Agentic” Intelligence

The exhibition floor buzzed with talk of a seismic shift—from rigid, rule-based automation to dynamic, reasoning systems. Amal Makwana from Citi explained how these new “agentic” systems operate across enterprise workflows, making decisions and adapting in real-time. This marks a stark departure from traditional robotic process automation (RPA), which follows predetermined scripts.

Scott Ivell and Ire Adewolu of DeepL framed this evolution as closing the “automation gap.” They argue that agentic AI doesn’t just assist—it collaborates, functioning as a true digital co-worker. Brian Halpin from SS&C Blue Prism added a crucial caveat: organizations must first master standard automation before they can successfully deploy these advanced systems.

The Governance Challenge: Controlling the Unpredictable

With autonomy comes complexity. Steve Holyer of Informatica, joined by voices from MuleSoft and Salesforce, stressed that architecting agentic systems requires robust governance. Unlike deterministic software, these AI agents produce non-deterministic outcomes—meaning their decisions can’t always be predicted. A governance layer is essential to control how agents access and use data, preventing operational failures and ensuring accountability.

Data Quality: The Foundation of Trust

The output of any autonomous system is only as good as its input. Andreas Krause from SAP was blunt: AI fails without trusted, connected enterprise data. For generative AI (GenAI) to deliver value in a corporate setting, it must access data that is not only accurate but also contextually relevant.

Meni Meller of Gigaspaces tackled the notorious “hallucination” problem in large language models (LLMs). His solution? eRAG (retrieval-augmented generation) combined with semantic layers. This approach allows models to retrieve factual, real-time enterprise data, dramatically reducing errors and increasing reliability.

Real-Time Analytics: The Competitive Edge

A panel featuring experts from Equifax, British Gas, and Centrica highlighted the necessity of cloud-native, real-time analytics. For these organizations, the ability to execute scalable, immediate analytics strategies is becoming a key differentiator in a data-driven market.

Physical Safety and Observability: AI in the Real World

AI’s integration into physical environments introduces new safety challenges. A panel with Edith-Clare Hall from ARIA and Matthew Howard from IEEE RAS explored how embodied AI is being deployed in factories, offices, and public spaces. The consensus: safety protocols must be established before robots interact with humans.

Perla Maiolino from the Oxford Robotics Institute offered a technical deep dive, discussing her research into Time-of-Flight (ToF) sensors and electronic skin. These innovations aim to give robots both self-awareness and environmental awareness, crucial for preventing accidents in manufacturing and logistics.

In the software realm, observability remains paramount. Yulia Samoylova from Datadog explained how AI is changing the way teams build and troubleshoot software. As systems become more autonomous, the ability to observe their internal state and reasoning processes is essential for reliability.

Infrastructure and Adoption Barriers: The Human Factor

Implementation demands more than just technology—it requires a receptive culture. Julian Skeels from Expereo argued that networks must be purpose-built for AI workloads, featuring sovereign, secure, and “always-on” fabrics capable of handling high throughput.

Yet, the human element remains unpredictable. Paul Fermor from IBM Automation warned against the “illusion of AI readiness,” noting that traditional automation thinking often underestimates the complexity of AI adoption. Jena Miller reinforced this, emphasizing that strategies must be human-centered to ensure adoption. If the workforce doesn’t trust the tools, the technology yields no return.

Ravi Jay from Sanofi suggested that leaders must ask both operational and ethical questions early in the process. Success hinges on deciding where to build proprietary solutions versus where to buy established platforms.

The Bottom Line: Building the Foundation for Agentic AI

The sessions from day one made one thing clear: while technology is advancing toward autonomous agents, deployment requires a solid data foundation, robust governance, and a culture ready for change. CIOs should focus on establishing data governance frameworks that support retrieval-augmented generation, evaluating network infrastructure to ensure it meets the latency requirements of agentic workloads, and running cultural adoption strategies in parallel with technical implementation.

Tags & Viral Sentences:

• Agentic AI is the future of work
• Digital co-workers are here—are you ready?
• The automation gap is closing fast
• Data quality is the new oil for AI
• Hallucinations in AI? Not anymore with eRAG
• Real-time analytics = competitive advantage
• Robots in the workplace—safety first!
• Observability is the new observability
• AI readiness: more than just tech
• Human-centered AI adoption is non-negotiable
• Build vs. buy: the AI strategy dilemma
• Agentic AI needs agentic governance
• The network is the new battleground for AI
• Trust the tool, or the tool won’t deliver
• AI is evolving—are your systems ready?

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