Pressure to Performance

You Cannot Outperform a System Working Against You

Wed, 15 Apr 2026 12:00:15 GMT

High performers do not fail randomly. When they consistently hit a wall, it is not a capability problem. It is a system problem. And most organizations never make that distinction.

When High Performers Hit a Wall

They were exactly the kind of person organizations say they want.

They delivered under pressure. They navigated ambiguity without losing momentum. When priorities shifted, they adjusted without complaint and got back to work. Their track record was real, and they knew it. They were also exactly the kind of person organizations can afford to lose the least.

The priorities were clear at the start of the quarter. They built the plan around them and executed. Six weeks in, the priorities shifted. Not because the work was wrong, but because a decision made three levels up had invalidated the work without anyone saying so out loud. The expectations remained. The conditions that made them achievable did not. They adjusted and got back to work.

Then it happened again.

By the third time, they shifted from trying to solve the problem to just surviving. High performers do not do that. Or they did not used to. The work kept moving. The ground kept shifting. They were delivering, but nothing they delivered seemed to land anywhere stable. Hours of careful execution were quietly invalidated by decisions they had no visibility into and no ability to influence.

They were not failing. But they were starting to feel like they were. And they could not quite locate the moment when that feeling had started to feel normal. What they could not see from inside it was that the environment had failed them systematically.

What High Performance Requires

Sustained high performance depends on conditions beneath the surface: clarity, perceived control, cognitive bandwidth, trust, and recovery. When those conditions erode, performance follows, regardless of talent or effort. This article addresses what leaders can do to restore those conditions, but more importantly, what individuals need to understand and do when leaders will not.

The System Is Not Broken. It Is Working as Designed

Most dysfunctional environments were not designed to be dysfunctional.

They were built to solve a real problem at a specific moment. A structure that rewarded autonomy made sense when the organization needed to move fast and trust its people to own their domains. A culture that deferred to senior leadership made sense when decisive direction was what stabilized a company through uncertainty. Those systems worked. They got rewarded. They became the way things are done here.

What rarely gets examined is whether the problem those systems were built to solve still exists. Contexts change. Markets shift. Organizations grow into different challenges than the ones that shaped them. But systems that worked do not dismantle themselves, and the people who rose within them, who were rewarded by them, have little incentive to question them. The system persists not because anyone is defending dysfunction, but because the system continues to serve the people with the authority to change it.

Economists have a name for this dynamic: the principal-agent problem. When the people making decisions (principal) do not bear the consequences of those decisions, the decisions reliably optimize for the wrong things. It is not a character failure. It is a structural one, and it is predictable enough that this dynamic has been studied for decades.

For the person inside that environment (agent), this matters for one reason. The conditions working against their performance are not a reflection of their value, their capability, or their fit. They reflect a system optimizing for something other than their performance. That is a meaningful distinction, and it is where an honest assessment of the environment must begin.

Is Change Structural or Performative?

Leaders who want to change these environments have a specific kind of work ahead of them. Not culture initiatives. Not offsites or engagement surveys or renewed commitments to transparency. Those are not useless, but they do not address the structural problem. What restores performance conditions is deliberate and often uncomfortable: changing what gets rewarded, redistributing decision authority, and absorbing the political cost of removing protections from people whose behavior is degrading the system.

That is harder than it sounds in most organizations. It requires a leader who can see the gap between what the incentive structure is rewarding and what the organization says it values. It requires willingness to name that gap publicly and act on it even when doing so creates friction with peers or threatens relationships that have been useful. And it requires patience for change that does not produce visible results quickly, which runs against almost every pressure a senior leader operates under.

Some leaders are doing this work genuinely. The signs are specific. Decisions about structure and incentives change, not just language about culture. People who were previously protected from consequence start experiencing it. People who were previously absorbing costs without recognition start being seen. The changes are structural and uncomfortable, not smooth and well-received.

Others are managing the appearance of change. Listening sessions happen. Task forces are formed. The language shifts. The conditions do not.

The question worth asking honestly is which one you are watching.

What to Look For

Reading an environment honestly is harder than it sounds. Most people inside a dysfunctional system have spent considerable energy adapting to it, which means they have also spent energy explaining it to themselves in ways that make continued effort feel rational. That is not weakness. It is how capable people survive in conditions that are working against them.

But at some point, honest assessment becomes more important than continued adaptation. The indicators worth paying attention to are patterns, not events.

The same dysfunction recurs. It is not a bad quarter or a difficult transition. It is the third or fourth time the same dynamic has played out with the same structural causes and the same people protected from consequence.

The cognitive cost keeps rising. More attention is being consumed by navigating the environment than by doing the actual work. Clarity, perceived control, and cognitive bandwidth, the conditions that make execution possible, are not temporarily constrained. They are structurally unavailable.

Naming the problem produces a predictable response. When the dysfunction is raised with someone who has the authority to address it, the response is defensiveness, reframing, or performative concern followed by no action. The messenger becomes the problem. The pattern continues.

The workarounds have become the work. When the informal systems people have built to survive the dysfunction are more reliable than the formal ones, the organization has already told you something important about whether it intends to change.

If any of these patterns are present, the next question is not how to fix them. It is what to do with that recognition.

Before moving forward, sit with these:

How many of these patterns are you recognizing?
How long have they been present?
Have they gotten better, stayed the same, or gotten worse?

For many people, the honest answers to those questions arrive before they are ready to act on them.

What Staying Costs

The most serious cost is the one that takes longest to recognize. It is not always self-doubt. More often it is quieter and more corrosive. A capable person who can see the dysfunction clearly, who knows the system is the problem, who has not lost confidence in their ability, arrives at a different conclusion: I cannot do my job here.

That realization does not produce motivation to push harder. It produces disengagement, not from lack of caring but as a rational response to a system that has repeatedly demonstrated that effort does not connect to outcome. It produces cynicism, not pessimism, but the specific kind that develops when someone who once believed the work mattered stops believing the environment will let it matter. And over time it produces something more permanent: a settled sense that no matter what they do, the conditions will undermine it. The person stops trying to change the outcome and starts managing their exposure to the disappointment of it.

The person has not changed. The environment has closed off the conditions that made their performance possible. That distinction matters enormously, and it is one most organizations never make.

Which raises the question most organizations will not ask on your behalf: what do you do when that recognition arrives?

Deciding to Adapt or Exit

Before that question can be answered honestly, two things need to be named that make honest assessment harder than it should be.

The first is resilience. Organizations select for it, invoke it under pressure, and treat it as a performance measure. And it is a genuine skill, the ability to absorb difficulty and continue functioning. But resilience is not infinite, and it cannot operate indefinitely in conditions that are actively consuming it. When leaders point to resilience as the reason people should continue absorbing the cost of a system that will not change, they are not describing a performance standard. They are describing a way of avoiding one. The question is not how resilient you are. It is whether the environment is giving resilience anywhere to work.

The second is sunk cost. The more someone has invested in an environment, time, relationships, identity, career capital built inside that specific system, the harder it becomes to weigh the actual cost of staying against the cost of leaving. What has already been spent feels like a reason to continue. It is not. It is a reason the decision feels harder than it is. Recognizing that does not make the decision easier. It makes it clearer. And clarity is the only honest place a decision like this can start.

With those two things named, the question of which approach to take becomes less emotional and more practical. Adaptation makes sense when there is credible evidence that the conditions are improving, when genuine change is underway rather than being performative, and when the cost of staying has not yet started to outpace what the environment is giving back. Adaptation under those circumstances is a legitimate performance strategy, not a failure of resilience.

Exit becomes the rational choice when the conditions are structurally unavailable, and the pattern has repeated across enough moments where change was possible and did not happen. When the cost of staying keeps growing. When the environment has begun closing off not just current performance but who you are still capable of becoming.

Before deciding which approach is best, sit with these:

Has anything structurally changed across the last several quarters or key moments where change was possible, or has the pattern held?
What is adaptation currently costing you that you are not fully accounting for?
If the conditions do not change in the next six months, what will that cost you that you cannot get back?

Where to Start

If you lead one of these organizations, the most useful thing you can do after reading this is examine what your incentive structure is rewarding, and whether the people absorbing the cost of it are the ones you can least afford to lose.

If you are inside one, the most useful thing you can do is stop measuring the environment against what it promised and start measuring it against what it has consistently produced. The evidence is already telling you something. The question is whether you are willing to act on it before the cost becomes one you cannot recover from.

When More Control Makes Performance Worse

Mon, 16 Mar 2026 18:56:54 GMT

How the layers added to protect outcomes create rework, friction, and hidden execution drag.

The signals are easy to miss. Quarterly results are on track. Meetings run smoothly, and decisions appear resolved. But some decisions are revisited weeks later. Fewer hard questions are asked. Strong operators who once challenged assumptions stop doing so. Nothing looks broken. The numbers are acceptable. But sustaining them requires more effort.

This pattern shows up under accelerating change. Markets move faster than planning cycles. Competitive pressure compresses timelines. Investors expect results. Boards expect progress. Conditions are less predictable, yet accountability remains near-term and visible. Slowing down is rarely an option. So the system adapts.

What Accelerating Change Does to Performance Systems

Under accelerating change, organizations move to reduce unpredictability. When conditions are unstable but outcomes must remain predictable, layers of control accumulate. Additional metrics are introduced. Approval checkpoints are added. Status updates become more frequent. Initiatives stack on top of existing priorities.

None of this is irrational. It is a disciplined effort to maintain control over outcomes in unpredictable conditions. If performance cannot slip, more control feels responsible. The assumption is simple: tighter control produces better performance. Over time, attention shifts from improving performance to managing the appearance of control.

The Observable Consequences

When control layers accumulate, nothing fails immediately. Delivery can still look acceptable on paper. Deadlines are met. Dashboards remain green. The cost is not yet visible in the numbers.

As oversight increases, people stop saying what they really think. Hard questions are voiced less often. Assumptions receive less scrutiny. Discussion becomes shorter and more streamlined. The team appears aligned, but the hard conversations have moved elsewhere.

When dissent and friction are filtered out of the room, they do not disappear; they relocate. Decisions that felt resolved resurface when concerns are raised downstream. The system maintains pace, but teams keep reworking the same issues.

The change is also visible in strong operators. They continue to execute, but they stop stretching the work. They deliver what is expected but no longer extend beyond it. The cost is lost discretionary performance.

None of this presents as crisis. It can resemble efficiency. It can even feel like progress. The cost shows up in execution: more rework, later surprises, and more friction during delivery.

This is often the point at which leaders are told to slow down or simplify. When pace cannot slow, simplification becomes the obvious answer. The problem is that simplification requires subtraction.

Why Simplifying Is Hard

Simplifying requires removing something that is already visible: a metric, a meeting, a checkpoint, or an initiative that has already been committed to. In most executive contexts, visible activity signals control. Removing work carries risk. The short-term risk of subtraction is easier to see than the long-term cost of carrying too much.

As a result, systems tend to add oversight rather than remove work. Simplification is correct, but it is harder to execute than it sounds.

What leaders need is not a mandate to simplify, but a way to remove controls without creating unnecessary risk.

Controlled Subtraction

Controlled subtraction is the deliberate removal of a single control layer, under defined conditions, to test whether it is still producing the outcome it was intended to protect.

Instead of attempting to simplify the entire system, leaders remove a single control layer tied to a specific outcome. A reporting step, a checkpoint, a metric, or a meeting is paused for a defined period while the outcome it was intended to protect continues to be monitored.

The objective is information. The test determines whether the risk of removing a control is real or assumed. If performance deteriorates, the control can be restored. If performance holds, the organization has identified a control that is no longer necessary.

Over time, these small removals reveal which layers are protecting performance and which are simply maintaining the appearance of control.

Recalibration as a Leadership Discipline

Accelerating change will not slow down. Control layers will accumulate again. The discipline is knowing when to remove them.

Controls should not be assumed to be permanent. A control earns its place if the outcome it was designed to protect cannot be sustained without it. The method is straightforward: pause the control, monitor the outcome for four to six weeks, and let the data answer the question.

The natural cadence is the reporting cycle. Leaders are already asking whether the organization is performing. The additional question is whether the controls in place are still necessary to sustain that performance. For most organizations, that means at least quarterly, though the principle scales to whatever rhythm the organization already runs on.

In volatile environments, the real discipline is not adding more control. It is knowing what no longer deserves to stay.

The Capacity Tax of Problemless AI

Sun, 15 Feb 2026 22:24:05 GMT

Why AI Fails When Nothing Comes Off the Plate

A couple of weeks ago, I was asked in an interview how my work on performance under pressure relates to AI. My answer was simple: AI is often rolled out as a solution looking for a problem, and that is how it turns into a pressure amplifier, not a reliever. When nothing comes off the plate, AI becomes one more thing. And in overloaded systems, “one more thing” is usually what gets dropped. This is what I call Problemless AI.

Problemless AI is what happens when AI is implemented before an organization can clearly name the performance bottleneck it meant to relieve. In practice, this makes AI additive rather than enabling. It is introduced alongside existing workflows instead of replacing or simplifying them. Usage stays uneven, and leaders often interpret this as resistance, a performance problem.

But the problem is not motivation or willingness to learn. It is capacity. When workflows, expectations, and incentives remain unchanged, the system around the work never adjusts. AI does not fail because people struggle to learn it. It fails because the system around the work does not change.

When Strategy Isn’t Explicit, Pressure Rises

Many AI initiatives do have a strategy behind them. The problem is not the absence of strategy; it is that the strategy may not be made explicit at the level where the work happens. People are told that AI will improve productivity, accelerate impact, or prepare the organization for the future, but they are not shown what problem it is meant to solve in their role or what is expected to change because of it.

This matters because AI is not replacing a broken system in many organizations. It is being layered onto work that is already functioning, under pressure. Traditional change efforts often begin by naming a clear pain point and replacing an existing tool or process. The relief is visible. With AI, the benefit is often abstract and downstream, while the cost is immediate: learning time, experimentation, cognitive load, and risk.

When strategy remains unclear, individuals are left to interpret it on their own. They must decide how much effort to invest, where AI fits relative to existing priorities, and whether using it will be valued. That uncertainty creates pressure before any performance issue appears. Attention fragments. Engagement becomes cautious. Learning becomes compliance-oriented rather than generative. This is not a failure of mindset or openness to change. It is a predictable response to systems that introduce new demands without clearly redefining what changes in how the work is done.

This Is Not Unique to AI

If this pattern feels familiar, it should. Organizations have long relied on learning initiatives to signal progress when performance problems are difficult to diagnose or slow to resolve. Completion is easy to measure, so it becomes a proxy for progress. Leaders can report attendance, modules finished, and certifications earned.

When outcomes lag, organizations often respond by increasing activity rather than changing conditions. More training is added. Expectations are reinforced. New tools are layered onto existing workflows. The underlying structure of the work remains the same. This creates the appearance of action without changing the conditions that shape performance.

AI inherits this pattern and intensifies it. Because the technology is new and its potential is broad, organizations default to scale. Broad enablement replaces targeted intervention. Adoption metrics stand in for performance impact. At its core, the issue is this: the system is asking for change without changing itself.

What This Looks Like in Practice

In practice, this pattern emerges less as outright failure and more as growing inertia. A team is told to increase AI usage to 80 percent while still meeting the same delivery targets. Nothing is removed. Adoption is tracked weekly. Deadlines remain unchanged.

Over time, usage becomes uneven. A small group experiments actively, while most people use the tools sporadically or only when prompted. The technology exists, but it never fully integrates into the flow of work.

Leaders respond by reinforcing adoption. Participation targets are set. Expectations are reiterated in meetings and communications. Activity becomes another proxy for progress.

At the role level, the experience is different. Employees are still accountable for the same outcomes, under the same timelines, with the same evaluation criteria. AI is framed as optional but encouraged, useful but not essential. Over time, AI use becomes performative rather than transformative . Something to demonstrate awareness of, not something the work depends on.

From the outside, this can look like resistance or lack of follow-through. From inside the system, it feels like another initiative layered onto already constrained roles. Nothing is explicitly broken, but nothing is meaningfully easier either. Work continues, but it requires more effort than it should. Capacity is quietly eroding.

Why “Problemless AI” Creates a Capacity Tax

Capacity is not skill or motivation. It is the finite ability of a system to absorb demand and still execute reliably. It includes attention, cognitive bandwidth, coordination, decision energy, and the flexibility people have to adapt when conditions change. When capacity is intact, performance holds even under pressure. When it is eroded, execution becomes fragile long before anyone notices a clear failure.

Problemless AI quietly taxes capacity. Learning a new tool requires attention. Deciding when and how to use it adds cognitive load. Switching between old workflows and new possibilities increases coordination costs. Unclear expectations force people to monitor themselves, their peers, and their leaders for signals about what really matters. Taken together, these demands steadily reduce the system’s ability to perform. Because the work still gets done, capacity loss is often misattributed.

AI does not create this dynamic on its own. It exposes it. When new demands are added without subtracting existing ones, capacity is consumed rather than created. AI rarely enters a system at full capacity. More often, it is introduced in response to existing performance strain: missed deadlines, slower execution, rising complexity, or pressure to do more with less. In that context, AI becomes one more attempt to restore capacity without changing the conditions that depleted it in the first place. When added to an already constrained system, the tool compounds the problem it was meant to solve.

The Misdiagnosis Loop

When capacity erodes, the early signs are subtle. Decisions take longer. Execution becomes less consistent. Coordination costs rise. Because the work still gets done, these signals are easy to overlook. Leaders interpret them as a performance problem rather than structural strain.

When performance lags, leaders try to explain why. In most organizations, those explanations default to the most visible causes: skill gaps, inconsistent effort, or lack of accountability. AI adoption becomes part of that story. Uneven usage is interpreted as resistance.

The response is predictable. More training is added. Usage is tracked more closely. Expectations are clarified through targets and reporting. Together, these actions increase demand on a system already operating under reduced capacity.

Over time, the organization becomes trapped in a self-reinforcing cycle. Capacity problems are treated as motivation or capability problems. Each corrective action adds load without changing conditions. What looks like a failure to adopt AI is, in fact, a failure to recognize how performance degrades under cumulative demand.

Treat AI Like a Performance Intervention

The pattern persists because AI is rarely treated as an intervention in a performance system. It is more commonly treated as a capability to be learned, a tool to be adopted, or a technology to be scaled. Those approaches focus attention on exposure and usage rather than on what limits execution.

Performance interventions start from a different place. They identify a specific bottleneck in how work is done. They require something to change in the system, not just something new to be added to it. Treating AI as a performance intervention shifts the question from Are people using it? to What pressure is this meant to relieve, and how will we know if it does? AI does not automatically remove work. Leaders have to deliberately remove something from the system for capacity to be restored.

If AI is meant to reduce effort, something must come off the plate. If it is meant to improve speed or quality, workflows, expectations, or incentives must change. Without those adjustments, the tool may still be impressive, but it will continue to tax capacity rather than restore it.

A Simple Test

Before rolling out an AI initiative, ask three questions:

What specific performance bottleneck is this intended to relieve?
What demand, decision, or coordination cost will be reduced as a result?
What will stop being required once this tool is in place?

If those questions cannot be answered clearly, the initiative is unlikely to create capacity. Instead, it will almost certainly add pressure.

AI does not fail because people are resistant or incapable. It fails when it is asked to solve problems that have not been clearly defined in systems already operating at their limits. When aligned to a clearly defined performance constraint, AI can be a powerful performance lever. When introduced without that alignment, it becomes one more thing. In overloaded systems, adding one more thing is often the most expensive mistake a leader can make.

Stop Spending Money to Make Performance Worse

Wed, 07 Jan 2026 23:43:32 GMT

How Organizations Fund Their Own Performance Decline

Most organizations respond to performance declines with fixes that sound responsible: more metrics, tighter oversight, more training, stronger accountability. The intent is rational; however, the problem is that these moves often make performance worse.

Not because leaders are careless, and not because employees suddenly forgot how to do their jobs. The problem is misdiagnosis. Under sustained pressure, organizations often treat capacity failures as competence problems, then invest in interventions that add pressure and further reduce capacity. The result is a widening performance gap: profit, productivity, engagement, and retention that the organization has already paid for become harder to access.

A Familiar Pattern

In an organization I am familiar with, performance was strong for years. Then market conditions shifted. Pressure increased. Leaders tried to reassure employees that layoffs would not happen, but layoffs eventually occurred and trust eroded. At the moment when clarity and transparency were most needed, priorities competed, decisions stalled, and the strategy kept shifting.

People still wanted to do good work, but execution became less reliable anyway. Coordination weakened. Follow-through slipped. The organization worked harder and got less back.

This pattern is common: performance declines without a sudden drop in talent. The conditions change, pressure rises, and execution becomes harder to sustain.

The Performance Gap

The performance gap is the space between knowing what to do and being able to do it reliably when pressure increases.

In this context, pressure is not “stress” in the casual sense. It is higher stakes and tighter constraints on time, control, or information. In practice, this shows up as compressed timelines, shifting priorities, heightened visibility, and unclear decision rights. Under those conditions, capable teams can start to look inconsistent, not because competence disappears, but because access to competence becomes constrained.

Why Leaders Misread It

When performance erodes, leaders tend to default to familiar explanations:

People do not have the right skills.
People are not motivated.
People do not understand the strategy.
People are not trying hard enough.
People need more rigorous performance management.

These explanations feel intuitive because they focus on what is visible and measurable. They also align with how most organizations are designed to solve problems: training, incentives, oversight, and consequences. So, the response is predictable: add structure, add monitoring, add reporting, add performance management.

The trouble is that many struggling performers were high performers under more stable conditions. From the outside, it looks like a discipline problem. From the inside, it often feels like overload.

What Pressure Does to Execution

The mechanism is straight forward. As pressure increases, people pay more attention to managing the environment and themselves. Worry increases. Self-monitoring increases. Outcome fixation increases. Teams start tracking threats, anticipating consequences, and managing impressions. That costs bandwidth. Less bandwidth remains for judgment, planning, prioritization, and coordination.

Attention can also narrow. Under pressure, people focus on what is urgent, visible, and risky. That can help with simple tasks. However, it tends to hurt performance when work requires flexibility, cross-functional coordination, and good judgment.

When these effects persist, execution gets noisier: more rework, more missed handoffs, more delays, more conflict. People are still competent. The system is making competence harder to access consistently.

The Performance Iceberg

Performance problems show up first as visible symptoms: missed deadlines, weaker execution, conflict, disengagement, slow decisions, inconsistent follow-through. Those symptoms matter, but they are often downstream.

Think of performance as an iceberg. What leaders see above the waterline is shaped by conditions below it. Under sustained pressure, execution becomes increasingly dependent on conditions that are rarely measured directly, such as:

Clarity: priorities, roles, success criteria
Perceived control: decision rights, autonomy, ability to act without excessive friction
Trust: willingness to raise risks early and tell the truth fast
Cognitive bandwidth: attention available for judgment and planning
Emotional regulation: steadiness under scrutiny, uncertainty, and conflict
Recovery: fatigue, pace sustainability, time to reset

When these conditions erode, performance symptoms rise. If leaders intervene only at the symptom level, the underlying conditions remain unchanged.

How Organizations End Up Paying To Make It Worse

Here is the loop that makes this expensive.

Pressure increases (uncertainty, change, visibility, constraints).
Capacity declines (attention, coordination, decision quality).
Performance slips (execution inconsistency, rework, slower cycles).
Leaders add surface-level controls (new metrics, more reporting, additional training, tighter performance management).
Those controls consume bandwidth and increase scrutiny.
Capacity declines further and performance degrades again.

A concrete example: after a downturn, leadership introduces new metrics, adds training, tightens performance management, and increases status reviews to “drive accountability.” The intent is understandable. The effect is often predictable. Meetings multiply. Reporting expands. Decision latency increases. Evaluation pressure increases. Focus fragments. Execution declines further. Leaders interpret that decline as evidence that even more oversight is needed.

This is why misdiagnosis matters. It turns a solvable performance issue into a persistent business cost, and it creates the illusion that the organization is acting while performance erodes in slow motion.

The Diagnostic Shift, Before you Spend

You do not need a new initiative to start seeing this differently. You need a better diagnostic question:

Is this a competence problem, or a capacity problem created by pressure?

A few questions can help you locate the source before you invest in fixes:

What changed recently in clarity (priorities, roles, strategy coherence)?
Where has control decreased (decision rights, autonomy, dependency chains)?
What has increased evaluation pressure (visibility, scrutiny, consequences)?
How much time is being consumed by coordination overhead (status meetings, reporting, approvals, rework)?
What are the signals that recovery is failing (fatigue, irritability, rising cynicism, preventable errors)?

These questions do not solve performance problems by themselves. They prevent you from funding interventions that quietly intensify the conditions causing the problem.

Closing

Yes, the performance gap should be closed, just not in ways organizations traditionally try to close it.

If performance is slipping and the usual fixes are not working, consider the possibility that you are not dealing with a competence problem at all. You may be dealing with a capacity problem created by sustained pressure. In that case, adding pressure will not be the cure. The fastest way to lose money is to keep investing in fixes that widen the gap.

If this pattern is familiar, I am developing a diagnostic assessment to help leaders identify the pressure conditions driving the gap before investing in performance fixes.