Gartner’s studies indicate that by 2028, 33% of enterprise software applications would incorporate some form of agentic-AI interventions. Their report finds that Agentic-AI’s usage which was around 1% in 2024 would soar to 15% of an enterprise’s day to day operations by 2028.
There is a notable shift from data to decisions to dispositions in play already and an acceleration seems likely given how enterprises are moving from asking questions such as “What happened?” to “What do we do?”
Understanding the phenomenon of automation
Techniques such as computer vision, decades ago, enabled enterprises to automate. While it was a mechanical automation, such as if X happens then do Y, it enabled coding business process into precise functions through scripts, trigger points or complex workflows.
Mechanical automation worked like a charm for predictable tasks such as invoice routing or ticket assignment. But leadership or customer behavior itself has never been predictable. So, dynamic environments, where static rules decayed quickly, someone had to constantly rewrite the business logic. This invariably resulted in backlogs and delays, but Agentic-AI removes this brittleness.
What makes Agentic AI different now is its ability to deploy autonomy instead of automation. So, instead of hard-coded flows, enterprises can design goal-oriented agents. Say, find objectives such as reduce churn, improve collections, optimize inventory, reconcile payments, turn reviews, etc… These agents figure out the path dynamically.Such Agentic-AI analyzes context, pulls data from various systems, recommend actions, and execute those actions themselves. Some of our customers have stopped thinking in terms of “tasks to automate” and moved to “outcomes to delegate.” This mindset shift is already in action and the system doesn’t need to be “reprogrammed” every time the world shifts as it learns.
Adaptive workflows & business benefits
Over weeks and months, workflows become smarter without anyone redesigning them. This is where agentic AI stops being a tool and starts behaving like a strategic co-pilot.
This agentic AI not only observes business-minutiae such as customer behaviour, risk signals, or vendor-relationships, but actually intervenes to enable business cycle-times. It can recommend and autonomously follow on execution steps instead of just waiting for a manager to act upon the intelligence.
The other compelling argument about rising agentic-AI adoption has been from Deloitte, which predicted that by end of 2025, nearly 25% of companies that were already using Gen-AI would likely have AI pilots or PoCs (proof of concepts) for Agentic AI. This number was likely to grow to 50% by 2027.
If the forecasts are interesting, the actual business impact is equally surreal. Companies have been reporting higher returns on investments as well exceeding their business objectives. A Google Cloud study of 3,466 executives found 53% attributing a consistent revenue growth of 6-10% from generative AI alone.
However, there was a distinct group, 13% of early adopters of Agentic-AI who witnessed additional benefits. The Google study found that at such organizations where at least 50% of their future AI budget was spent on AI agents, the enterprises saw agents getting deeply embedded across operations. In 88% of such instances, organizations were seeing ROI from generative AI on at least one use-case – compared to a 74% average across all organizations.
Internally too, we see a pattern – decisions which once took weekly reviews are happening continuously. Teams are shrinking approval chains; cycle times are getting compressed; and trust with the systems itself is increasing as humans trust the guardrails being implemented.
Many of these interactions tell us that nobody begins with a moonshot – that is, they don’t hand over their strategy or core revenue decisions on day one; but start gradually with small, contained, and reversible processes. These could be collections prioritization, service recovery, fraud signals, back-office operations.
Inside boardrooms the discussion has shifted from “Can AI help us?” to “where else can we safely let it act?” So, leadership had realized that this isn’t an automation project anymore. It’s an operating model change.
Rewiring the C-suite mindset
The most profound shift isn’t technical — it’s psychological. When intelligence moves from dashboards to action, leadership behavior changes fundamentally. And with this we can boldly state that Agentic AI doesn’t replace executives, but augments them.
Instead of supervising every step, they define guardrails and let the system operate within them. Instead of asking for another report, they ask whether the system has already acted. The posture shifts from reactive to proactive — from reviewing yesterday to shaping the next hour. That’s not just efficiency. That’s a different way of leading.
Risk feels different when every decision is data-backed and reversible and when leaders stop managing processes and start managing intent.
The common discourse around us needs to be corrected – that Agentic AI is replacing the C-suite; it is not. Agentic AI is compressing the distance between strategy and execution.
For years, enterprises collected more and more intelligence but struggled to move faster. Now, intelligence can act on its own, and that changes everything. Organizations stop behaving like reporting machines and start behaving like living systems, sensing, deciding, and responding in real time. Leadership evolves with it — not just better informed, not just better advised, but actively augmented. From data to decisions to dispositions, that’s how the modern C-suite gets rewired.
The author is Akshat Saxena, Co-founder and CEO, Vibrium.AI.
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
