The AI Hype Cycle Avalanche: Agentic AI and Governance Leadership in 2026

In our last article, we argued that AI is neither revolutionary nor magical. For most enterprises, AI has been part of the operating environment for years, even if it has recently acquired new labels and ways of interacting with it. The pace may feel faster, and the language more dramatic, but for enterprise analytics leaders the underlying challenge remains unchanged: how to introduce advanced capabilities into complex organizations without losing clarity, control, or value.

What has changed is the growing gap between the number of decisions leaders are being asked to make and the clarity available to support them, driven by overlapping hype cycles and rising expectations.

The rapid rise of “agentic AI” brings the challenge of this gap into sharper focus.

In the span of a single year, enterprise conversations have moved from generative AI to agentic AI, accompanied by new expectations and urgency. Business stakeholders hear a new term and assume a new category, even when the technical foundations remain closely related to automation, orchestration, and long-standing analytics patterns. When those distinctions fade away, AI governance leaders inherit a far more difficult problem than tool evaluation. They are asked to govern an expanding set of behaviors without a shared mental model of what those behaviors represent.

What makes this moment difficult is not the technology itself. It’s the work of interpretation happening around it. Sensemaking, in this context, is the discipline of helping the enterprise correctly understand what it is being asked to adopt, what category a capability belongs to, and what changes as it moves from idea to operation. When new labels arrive faster than shared understanding, governance leaders are left managing expectations instead of outcomes. Agentic AI simply makes that gap more visible—and it does so by compressing yet another wave of hype into an already crowded AI narrative.

A familiar cycle, accelerating again

Agentic AI fits neatly into a pattern we’ve seen many times before. A capability emerges, vendors frame it as transformational, and organizations rush to adopt it before fully understanding how it changes their operating assumptions. The narrative shift from GenAI to agentic AI happened unusually fast (as most enterprises are just beginning to wrap their arms around enterprise GenAI capabilities), which makes it feel like an unforeseen avalanche as opposed to ski-cutting to stabilize the slopes. In reality, it represents a micro-cycle of hype embedded within a much longer AI adoption curve.

That compression matters because it leaves little time for enterprises to recalibrate how they think about use cases, ownership, or scale. Instead, teams default to treating agentic AI as the next logical step for any workflow that feels complex or high value. The result is a growing portfolio of initiatives that sound innovative but vary widely in purpose, structure, and risk profile.

For AI governance leaders, the speed of this transition creates a constant tension. The business wants to move fast and stay ahead, and you’re the one responsible for making sure that speed doesn’t come at the expense of things holding up once they’re in the wild.

Where AI governance starts to strain

Most large enterprises already have governance structures for AI in place. They review proposed use cases, assess risk, involve legal and privacy partners, and monitor what makes it into production. Those practices still matter, but agentic AI introduces operating conditions they were not designed to handle, particularly when agents are embedded inside platforms that are themselves still figuring out how agentic behavior should work at scale.

One of the defining characteristics of agents is that they tend to spread. An agent built for a narrow purpose quickly attracts interest from adjacent teams. Variants emerge. Integrations expand. Before long, something that began with clear intent becomes part of a loosely connected ecosystem that spans platforms, functions, and business units. At that point, governance is no longer about approving a discrete capability. It becomes about understanding how behavior propagates across the enterprise and where responsibility sits.

What makes this harder is that many of the major platforms enterprises rely on are still early in their own agentic journeys. Agentic capabilities are new, unevenly implemented, and often optimized for activity within a single environment rather than visibility across environments. Evaluating how an agent behaves inside one platform is difficult enough. Understanding how agents interact across multiple platforms, each with different levels of transparency and control, is even harder. Governance leaders are often asked to oversee behavior they cannot fully observe, measure, or compare.

This creates a growing gap between accountability and visibility. Leaders remain responsible for risk, compliance, and operational stability, yet lack consistent insight into where agents are being reused, how they are being adapted, or how usage is evolving over time. Confidence erodes quietly in these conditions because governance is being asked to operate without a clear line of sight into an increasingly dynamic system.

Analytics techniques classification matters more than novelty

One of the most consequential governance conversations unfolding right now centers on classification. Enterprises are struggling to distinguish between automation, agentic behavior, and generative interfaces in ways that meaningfully inform decision-making. When everything is labeled AI, evaluation becomes superficial and governance turns reactive.

The distinction is practical (see eBook Bridging Traditional Analytics with AI below). Some processes benefit from deterministic logic and well-defined flows. Others require flexibility, interpretation, and probabilistic reasoning. Treating these categories as interchangeable leads organizations to apply the most expensive and complex solution to problems that do not require it, often under the banner of innovation.

Governance leaders play a critical role here by forcing clarity. Asking whether agentic behavior is truly necessary reframes the discussion around purpose rather than trend alignment. That reframing often reveals simpler, more resilient paths to the same business outcome.

GenAI and agentic cost is part of the operating model

Another pressure point that agentic AI brings into focus is cost. For years, governance discussions have centered on risk, compliance, and ethical considerations. Economic behavior has received far less structured attention, even as AI systems have become more consumption-based and variable in their cost profiles.

Agentic systems amplify that variability. Open-ended interactions and probabilistic decision paths introduce cost dynamics that are harder to predict and harder to explain to business stakeholders. Without explicit governance checkpoints that consider cost alongside value and risk, enterprises accumulate exposure that does not show up until scale is reached.

Cost discipline sits squarely in the operating model, and governance leaders are often the ones best positioned to bring it into focus. When it’s treated as an afterthought, organizations mistake activity for progress and struggle to reconcile AI ambition with financial reality.

Analytics capability maturity before tooling debates

Agentic AI has also reignited familiar debates around building versus buying. Those discussions tend to focus on platforms, vendors, and feature comparisons, while overlooking a more fundamental consideration: the enterprise’s ability to operate what it adopts.

Organizations with deep engineering capacity and strong governance muscle can support more bespoke solutions. Others benefit from external capabilities that reduce internal complexity. What separates these paths is operational readiness and the ability to sustain what gets built. Tools do not compensate for unclear ownership, fragmented standards, or immature governance practices. In fact, agentic AI tends to magnify those gaps.

Framing decisions around capability maturity rather than product selection leads to outcomes that hold up over time. It also shifts the conversation away from speed for its own sake and toward sustainability.

A moment for AI governance leadership

Agentic AI has arrived at a moment when many enterprises are still digesting generative AI. That overlap creates confusion, but it also creates opportunity. Governance leaders can use this moment to recalibrate how the organization thinks about AI altogether.

The most effective leaders we work with are grounding conversations in practical questions that reconnect technology choices to business intent. They ask what problem is being solved, how solutions will evolve over time, who owns ongoing oversight, and how success will be measured beyond initial deployment. These questions cut through hype without slowing progress, and they give executives a clearer view of tradeoffs that are otherwise obscured.

Agentic AI does not alter the fundamentals of enterprise analytics and AI. It exposes them. Organizations that respond with clarity, discipline, and intent will move forward with confidence. Those that chase labels will continue to feel like they are racing to keep up, even as complexity accumulates beneath the surface.

For AI governance leaders, that distinction defines the work ahead.