Across boardrooms and board packs, a new phrase is echoing louder each day: Agentic AI. But behind the fanfare, there’s a growing undercurrent of concern. Are we on the verge of an Agentic Winter where the expectations for AI agents far outpace enterprise readiness?
At first glance, the surge of investment—hundreds of billions into AI infrastructure, LLMs, and orchestration platforms—suggests unstoppable momentum. But look closer, and the cracks begin to show. We see a widening gap between technical potential and enterprise value realization.
Can we build production-ready systems that actually deliver on agentic promises in real business environments?
Are We Automating the Wrong Things?
The central issue isn’t whether AI agents are powerful—it’s whether they are being applied effectively. Companies are racing to deploy AI agents without establishing the foundational infrastructure needed for sustainable success.
Much of the current agentic automation landscape focuses on discrete tasks, showcasing demos where individual actions are automated in isolation. But enterprises don’t generate value in isolation. True ROI lies in automating processes, not tasks.
And this is where many vendors—and enterprises—fall short.
We’re seeing efforts to implement agentic solutions without a clear understanding of:
- The current state of operations governed by human agency
- The optimal intersections where agentic automation can create value
- The target state where humans and agents coexist with clarity and purpose
What’s missing is a structured blueprint to evolve from human-led operations to agentic operating models—with an eye on preserving, not erasing, human agency.
How Do You Define the Agentic Blueprint? Beyond Pilots and Platform Demos
Most enterprises are still experimenting without direction. Pilot purgatory has become the norm—interesting use cases, impressive demos, yet no enterprise-grade transformation. The foundations are missing:
- Work telemetry and contextual data to train and monitor intelligent agents
- Process-level orchestration that reflects how real work happens
- Human-in-the-loop design to ensure agents enhance, not replace, human judgment
This isn’t about a new type of bot. It’s about rethinking how goals, actions, decisions, and learning are distributed between humans and AI agents.
Who Owns Agentic AI?
Here lies another unresolved question: Who drives agentic transformation in the enterprise?
Is it:
- The CIO or CTO focused on enabling tech platforms?
- The Chief Transformation Officer chasing cost takeout and agility?
- The Operations Head focused on throughput and compliance?
- Or emerging AI leaders focused on experimentation?
In reality, agentic AI cannot live in silos. It demands a cross-functional coalition with shared accountability: aligning strategy, operations, data, decision-making, and technology under a common operating vision.
Avoiding the Winter: A Call to Action
To prevent an Agentic Winter, enterprises must move from hype to habit:
- Bridge the gap between observation and orchestration with telemetry-rich platforms
- Focus on process-level automation with Large Action Models (LAMs), not one-off task bots
- Prioritize human-centered AI design, preserving agency while expanding efficiency
- Build a target state blueprint where humans and AI agents collaborate at scale
The financial stakes extend far beyond individual company losses. We're potentially facing a market correction that could derail AI automation progress for a generation. Enterprises who realize their substantial AI investments aren't generating promised returns will see funding evaporate rapidly.
This is not just about automation. This is about reinventing human agency—augmenting how we work, learn, and make decisions in a digital-first world.
At Skan AI, we help businesses make smarter decisions. That also extends to agentic AI initiatives.
Let's talk so we can show you how we guide businesses to decisions that unlock real value.
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