Agentic AI: Enterprises build a new nervous system for work

Enterprises are entering a phase of digital transformation where the role of artificial intelligence (AI) is no longer confined to generating insights or assisting with tasks. Instead, AI systems are beginning to act—executing decisions, coordinating workflows, and responding to events without waiting for human instruction. This shift marks a fundamental redesign of how enterprises operate.

The Tech Trends 2026 report by HCLSoftware captures this inflection clearly. According to its primary research covering more than 173 CXOs and senior technology leaders, 81% of enterprises are already running or piloting autonomous AI agents, signalling that agentic AI has moved beyond experimentation into operational reality.

According to Kalyan Kumar, Chief Product Officer at HCLSoftware, the numbers signal a decisive transition. “When more than four out of five enterprises are running live or pilot autonomy initiatives, the experimentation phase is ending,” Kumar said.

“AI stops being automation when it no longer waits to be triggered—and instead continuously senses, interprets and acts across the enterprise in closed loops.”

What is emerging, the report suggests, is not another automation layer but a connective intelligence fabric—one that increasingly resembles a nervous system for the enterprise.

From Assisted Intelligence to Delegated Decision-Making

For much of the last decade, enterprise AI focused on prediction and recommendation. Systems analysed data, flagged anomalies, and suggested next steps, while humans retained decision authority. That model is now breaking down.

The report identifies 2026 as a crossover year in which AI transitions from intelligence augmentation to intelligence delegation. Agentic AI systems are designed to reason, act and learn continuously, coordinating across ERP, CRM, IT service management and security platforms.

Survey data shows where this shift is taking hold. Enterprises cited operational efficiency (61%) and outcome-oriented automation (55%) as the primary drivers of agentic AI adoption. Use cases such as lead conversion acceleration (42%), workflow automation (26%), and proactive threat detection (19%) are already being executed autonomously in production environments.

Kumar stressed that the inflection point lies in orchestration rather than task acceleration. “The shift happens when agents move from discrete task accelerators to domain orchestration—where the same shared context informs customer experience, the data layer and operational execution, and outcomes flow back to update decisioning in real time,” he said.

He added that this is when the nervous system analogy becomes structural rather than metaphorical. “A nervous system emerges when intelligence is embedded into the enterprise core—across experience, data and operations—and governed by design, not bolted onto isolated workflows.”

Where Enterprises are Embedding AI Agents Today

The report’s adoption data shows that agentic AI is not confined to a single function. Nearly 38% of organisations report visible process transformation, while another 34% are running active pilots, indicating imminent scale.

Operational domains are leading adoption. AI agents are being deployed to monitor infrastructure, reroute supply chains, optimise customer engagement and remediate IT incidents before they escalate. These systems increasingly interact with each other, forming what the report describes as ecosystems of autonomous agents.

This interconnection elevates agentic AI from automation to architecture. Instead of optimising individual tasks, enterprises are using AI agents to orchestrate workflows end-to-end. Decision latency shrinks, responsiveness improves, and operational complexity is absorbed by software rather than people.

Kumar noted that scale without integration risks fragmentation. “If agents remain trapped in pilots, you do not get compounding returns—you get tool sprawl,” he said. “Value shifts when systems are integrated and operating models are redesigned around autonomy.”

However, this scale also exposes fragility. The more decisions AI systems make, the more critical it becomes to understand how and why they make them.

Governance is the Real Constraint on Autonomy

Despite rapid adoption, the report highlights a growing imbalance: one in four enterprises deploying AI agents lacks adequate governance frameworks, while 56% operate hybrid governance models. Many organisations are still defining how human oversight, accountability and escalation should function when AI systems take initiative.

The report points to decision rights, data fragmentation and explainability gaps as structural barriers to mature autonomy.

Kumar reinforced this finding, describing governance as the defining differentiator. “The biggest blind spot is not principles—it is decision rights,” he said. “When an agent decides and acts, who owns the outcome—and what is the escalation path when confidence drops or risk rises?”

He argued that governance must evolve from policy statements to operational design. “At scale, governance cannot be a static approval layer. It must function as infrastructure—policy-as-code, auditable traces, bounded autonomy and clear human–machine decision thresholds.”

Trust, the report suggests, is becoming the limiting factor. Enterprises may trust AI to optimise processes, but remain cautious about delegating authority without transparency and clearly defined accountability.

Looking ahead, Kumar said scale will favour architectural clarity. “The winners will not be the ones with better models,” he said. “They will be the ones that re-compose the enterprise around autonomous decisioning.”

As AI agents take on a more central role, enterprises are discovering that intelligence alone does not create advantage.

Like a nervous system, AI must be governed, coordinated and designed for scale. Those who embed autonomy into enterprise architecture—rather than layering it onto existing workflows—are likely to define the next phase of enterprise transformation.

The image accompanying this story was created using AI-based tools.