Urgency Is Not a Culture. It Is a Countdown.
Urgency can move organizations for a while. Eventually it becomes the operating model. That’s when trust starts to erode.
Reflections on leadership, organizational pressure, and what happens when urgency stops being a strategy and becomes culture.
Urgency can move organizations forward for a while. Eventually it becomes the operating model. That’s when trust starts to erode.
This reflection came out of a recent Build Without Chaos conversation with Danny Speros on leadership, hypergrowth, and organizational pressure.
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Urgency Is Not a Culture. It Is a Countdown.
I recently sat down with Danny Speros on Build Without Chaos to discuss hypergrowth, urgency culture, leadership under pressure, and what organizations normalize long after it stops working.
What stayed with me most was not the pace. It was the cost.
This reflection came out of that conversation.
Most organizations that break under pressure do not break all at once. There is a slow accumulation of small decisions, each one reasonable in isolation, that eventually adds up to something that cannot be sustained.
I have been inside enough of those environments to recognize the early signs. The all-hands gets longer and more carefully scripted. People stop asking questions in open forums. Managers start hedging. The language around strategy becomes more abstract.
What is actually happening is a gap opening between what the organization is saying about itself and what people are experiencing from where they sit. That gap is where trust erodes.
Danny Speros has operated close to that gap multiple times, at Zenefits through a business model pivot, a CEO change, and an acquisition by TriNet, and now at Automation Anywhere. What he describes is not a cautionary tale. It is a pattern that shows up inside almost every company that grows faster than its internal infrastructure can absorb.
The Projection Problem Is Not What You Think
Every growing company tells a forward-looking story. It has to. The issue is not ambition. The issue is when that story is not connected to anything traceable. When someone two or three levels down cannot draw a credible line between what leadership is saying and what they are actually being asked to do, the narrative stops functioning as motivation and starts functioning as noise.
Speros puts it precisely: there has to be a believable path. Not a perfect one. But one that holds up under scrutiny.
What I have seen, and what he describes, is that companies often build the narrative outward before they have built the connective tissue inward. The people closest to the actual work are usually the first to notice. They do not always say anything, but they notice.
What Urgency Actually Costs
There is a useful timeline embedded in what Speros observed at Zenefits that most operators know intuitively but rarely say plainly.
Three months of sustained high pressure: manageable. Six months to a year: people start to flatten. Two years or more: the ones with options leave. The ones without stay, but they are not the same people they were.
That second group is the quiet risk. Attrition numbers look fine. What is actually happening is that the people who remain are holding on out of caution, not commitment. They are not asking hard questions. They are not pushing back on bad decisions. They are not taking the kinds of risks that move a business forward.
Speros connects this to a shift he started seeing in early 2023, when Silicon Valley Bank collapsed and tech pivoted from growth to profitability. The leverage that employees felt in 2021 and 2022 began to evaporate. What replaced it was a low-grade anxiety that looks like stability but functions like stagnation. Organizations that run on innovation cannot afford that, even when the spreadsheet says everything is fine.
Narrowing Is an Act of Leadership
What actually helped at Zenefits, in the later years before the TriNet acquisition, was not better communication or a reorganization. It was a deliberate narrowing of focus.
Pick one or two company-wide problems that are genuinely broken. Name one forward-looking opportunity worth real energy. Create urgency that has a defined endpoint rather than urgency as a permanent weather condition.
When urgency is attached to something specific and bounded, people can sustain it. When it is the ambient operating mode with no visible resolution, it accumulates in ways that do not show up on any dashboard.
The operational concept Speros reaches for is opportunity cost. If you are doing ten things at full intensity, you are doing none of them as well as they require. The prioritization work is not a soft skill. It is a core function of leadership, and in high-growth environments it is consistently underdone because it requires saying no to things that are genuinely important.
The Management Problem No One Wants to Name
Management in tech is structurally undertrained and then placed inside environments that reward delivery above everything else.
The promotion logic makes sense on paper. Someone is excellent at their job, has decent relationships with peers, gets a team. From that point forward they are expected to both drive results and develop the people under them, often with minimal preparation inside a context that signals, clearly and repeatedly, that the first obligation is the number.
The two-hour conversation about where someone wants to be in three years gets deprioritized. Not because managers are indifferent to their people, but because the incentive systems do not reward it. They reward throughput.
Speros is thinking about whether agentic AI changes this equation, whether it can absorb enough administrative load that managers have actual capacity for the relational work that holds teams together. That is a real design challenge worth taking seriously.
What to Actually Do Inside the Chaos
The practical advice is not complicated, but executing it requires political will that most organizations underestimate.
Make the list of everything competing for attention. Stack rank it. Draw the line at what can actually be done well given available time, money, and people. Then protect a small amount of capacity for learning and development. Not as a perk. As a survival mechanism. Organizations that spend every available hour on the present become less capable of responding to the future, faster than most leaders expect.
If everything is urgent, you are not operating with priorities. You are operating with a list. And a list does not make decisions. It just grows.
Clarity does not emerge naturally in high-growth environments. It has to be built deliberately and defended when the volume gets loud. That is the actual work of leadership. Not the urgency. The resistance to it.
Related:
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Why Enterprise AI Activity Is Not the Same as Enterprise Value
Enterprises are launching hundreds of AI initiatives, yet very few reach production or deliver measurable value. New research highlights the widening gap between AI activity and enterprise outcomes.
Key Insight
Enterprise AI adoption is accelerating across organizations, but measurable business value is not keeping pace. As companies launch hundreds of AI initiatives, fragmented ownership and weak operating models make it difficult to convert experimentation into reliable enterprise systems.
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A new AI Governance Benchmark Report from ModelOp highlights a pattern that many enterprise leaders are quietly observing inside their own organizations.
AI activity is accelerating.
Enterprise value is not keeping pace.
The report found that 67 percent of enterprises now manage between 101 and 250 proposed AI use cases. Yet 94 percent report fewer than 25 in production.
At first glance, that may look like a normal innovation funnel. But the deeper issue is not the volume of experimentation. It is the widening gap between AI activity and measurable business value.
Many organizations are now running hundreds of AI initiatives across multiple teams, tools, and vendors. Development cycles are compressing. New use cases are being piloted in months rather than years. GenAI, agentic systems, and third party platforms are expanding what teams believe is possible.
But speed and scale alone do not create enterprise value.
What the data reveals is something more structural.
Activity is not the same as value
The ModelOp report describes what it calls an emerging “AI value illusion.”
Enterprises appear highly active. New pilots launch quickly. Teams report progress. Tools proliferate across business units.
Yet very few initiatives reach production and even fewer produce measurable impact.
Part of the problem is visibility. According to the report, more than two thirds of organizations still rely on manual or projected ROI tracking for AI systems that are already in production.
In other words, enterprises are deploying AI systems faster than they can measure their impact. Without clear performance measurement tied to business outcomes, AI programs remain activity driven rather than value driven. This is why Measured Acceleration has become a critical discipline in enterprise AI programs.
That is not a technology problem. It is an operating model problem. In enterprise environments, scaling AI requires more than experimentation. It requires governance structures that define ownership, accountability, and how AI connects to business outcomes. This is the foundation of Governance as a Growth Lever.
The fragmentation problem
Inside large organizations, AI rarely develops as a single coordinated program. It spreads across teams.
Product teams experiment with AI features.
Marketing teams test generative tools.
Operations groups explore automation.
Data science teams build new models.
Each effort may be valid on its own. But when dozens of teams move independently, organizations end up with fragmented portfolios of AI initiatives that are difficult to track, govern, or scale.
The ModelOp report also notes that many agentic AI systems now connect to six to twenty external tools and services. Each new connection expands operational complexity and third party risk.
At a certain scale, the challenge is no longer experimentation.
The challenge becomes coordination and accountability. Enterprises that successfully scale AI introduce clear operating structures that define how AI initiatives move from experimentation into production. This level of discipline is what Operating Rigor looks like in practice.
The shift from experimentation to enterprise delivery
For the past several years, the dominant question around AI has been speed.
How quickly can we experiment?
How quickly can we launch new use cases?
How quickly can we bring AI capabilities to market?
Those questions made sense during the early adoption phase.
But as AI portfolios expand, enterprise leadership is starting to ask a different set of questions.
Which AI investments are delivering measurable value?
Which systems should be scaled across the organization?
Who owns the AI portfolio and how is performance measured?
These are governance questions.
And governance is not about slowing innovation. It is about ensuring that innovation translates into enterprise outcomes.
The next phase of enterprise AI
Enterprise AI is entering a new phase.
The advantage will no longer belong to organizations that launch the most pilots. It will belong to those that can convert experimentation into reliable, operational systems.
That requires more than technical capability. AI initiatives must also align with how organizations grow, compete, and allocate resources. That alignment sits at the center of Enterprise Growth Alignment. It requires clear decision ownership, alignment between AI initiatives and business priorities, and disciplined measurement of outcomes.
In other words, enterprises must move from AI experimentation to AI operating discipline.
Many organizations are already discovering that the hardest part of scaling AI is not building the models.
It is building the structure around them.
And that structure is what ultimately determines whether AI activity produces real enterprise value.
About the Author
Colleen Goepfert is a revenue growth executive specializing in AI governance, enterprise operating models, and AI-driven growth strategy. She is the founder of Build Without Chaos, a platform exploring how organizations scale technology, revenue, and leadership systems without operational chaos.