Standing in the Rain: Why Organizations Must Stop Optimizing for Prediction

This brief series is not intended to present a finished framework. It captures an observation and a growing conviction that organizations and leadership may be entering a fundamentally different era. My aim is not to provide definitive answers, but to ask better questions.

Every meaningful shift in my thinking has started with a simple observation. This one happened while gardening.

You may have seen a recent LinkedIn post I wrote about gardening in the rain without being bothered. I've found that I haven't stopped thinking about it. Not because of the moment itself, but because of what it revealed to me about how we've designed organizations.

For decades, we've been rewarded for eliminating uncertainty. We plan. We forecast. We standardize. We optimize. When uncertainty appears, our instinct is to create another process, another control, or another layer of governance to remove it. That approach built extraordinary companies and I've spent much of my own career helping organizations become more efficient, scalable, and predictable. I'm proud of that work. But I've started to wonder whether we optimized organizations for a world that no longer exists.

Today, uncertainty is no longer an interruption to the operating environment. It is becoming the operating environment itself. That changes the design objective. The question is no longer: How do we avoid standing in the rain? It is becoming: How do we build organizations that can continue to perform while standing in it? Or perhaps more fundamentally: How do we create the conditions for people to operate confidently when certainty no longer exists?

I have come to believe we have been solving the wrong design problem.

For decades we optimized for prediction. The next generation of organizations will need to optimize for conditions. Conditions that enable people to think clearly, exercise judgment, act with agency, learn continuously, and execute strategy even when certainty is unavailable.

Better Technology Isn't Enough

So far, the conversation around AI has largely focused on technology.

•  How fast should we deploy it?

•  Which tools should we buy?

•  Which jobs will change?

•  Which skills should we build?

These are important questions but I don't believe they are the most important ones.

Earlier this year, BetterUp published “The Case for Conditions,” arguing that the same AI technology produces dramatically different outcomes depending on the conditions surrounding the people using it. Their research demonstrates that trust, leadership, psychological safety, coaching culture, and organizational norms influence whether AI creates value or simply accelerates poor work. I believe this is an important contribution.

Their work crystallized something I had been observing in practice for years. It also led me to a different question. If conditions matter this much, who is intentionally designing them?

The Next Frontier of Organizational Design

Throughout my career, organizations have largely been designed around a simple assumption: If we build the right structure, define the right processes, and reduce enough variability; performance will follow. Historically, that assumption served us remarkably well.

The challenge is that today's environment changes faster than organizations can optimize around it. Yet our instinct remains the same: more governance, more controls, more planning, more process.

Ironically, the very systems designed to reduce uncertainty may now be creating the rigidity that prevents organizations from responding effectively. The issue is no longer that organizations cannot predict every disruption. The issue is that they continue trying to.

Don't Optimize for Prediction. Design for Conditions.

This realization has fundamentally changed how I think about organization design. I no longer believe the objective is simply to build efficient organizations. I believe the objective is to intentionally create the conditions under which people can exercise good judgment, learn quickly, collaborate effectively, and operate confidently even when certainty no longer exists.

That is a different design problem.

One of the biggest shifts in my own thinking has been to recognize that conditions and culture are not the same thing. For years, I suspect I treated conditions as though they were simply elements of culture. Today, I think they come before culture. Conditions are the environment leaders intentionally create through enterprise design. Culture is what emerges as people collectively experience that environment over time.

A condition is specific enough to point to: how much time people are given to think before deciding, how safe it feels to be wrong, how clearly people understand what actually matters right now. It's not a value or an aspiration. It's a design choice with an observable effect on how people act.

A leader mandating AI training is optimizing for adoption. A leader who instead protects time for people to experiment — and makes it safe to fail while they do — is designing a condition. The first produces a completion rate. The second produces the environment where the training actually gets used.

Every enterprise design decision shapes the environment people experience. Leadership shapes it. Governance shapes it. Operating models, decision rights, technology, people systems, rewards, and information all shape it. Whether intentionally or not, together these design choices create the conditions under which people think, decide, collaborate, learn, and perform.

Technology Amplifies. Enterprise Design Determines What Gets Amplified.

This has become my working hypothesis. Organizations will not create competitive advantage through AI alone. They will create competitive advantage by intentionally designing for the conditions necessary to enable performance. Technology has become an amplifier. Enterprise design determines what it amplifies. That changes the questions leaders should ask.

Here's what that shift looks like in practice:

Instead of asking: “How quickly are people adopting AI?”

Perhaps we should ask: What conditions is our operating model creating?

Instead of asking: “How many employees completed AI training?”

Perhaps we should ask: What conditions allow curiosity to become experimentation?

Instead of asking: “How do we improve engagement?”

Perhaps we should ask: What conditions make good judgment more likely?

A Leadership Imperative

Leadership is no longer defined primarily by reducing uncertainty. It is defined by intentionally designing the conditions that enable people to think clearly, exercise judgment, and act with confidence despite uncertainty.

Conclusion

The future isn't just an HR operating model. I've come to believe it's an enterprise performance model — one that begins with purpose, aligns strategy and values, intentionally designs the enterprise, creates the conditions people experience every day, and ultimately shapes culture, capability, and performance.

I believe organization design should no longer be judged primarily by its efficiency. It should be judged by the quality of the conditions it creates. Because in the end, I believe organizations won't be defined by the technology they deploy — they'll be defined by the conditions they create.

AI didn't create the need for this shift. It simply made it impossible to ignore.

Maybe that's why I haven't stopped thinking about standing in the rain. The lesson was never that we should enjoy discomfort. It was that we should stop assuming we can design it away.

Perhaps the ultimate measure of good enterprise design is not how much uncertainty it removes, but how much agency it creates. In the end, the future belongs to the organizations that stop waiting for the storm to pass and instead learn how to perform in the rain.

Questions Worth Asking

If uncertainty is no longer the exception but the operating environment...

•  What conditions is your organization intentionally creating?

•  Which of those conditions are helping strategy become enterprise capability…and which are preventing it?

•  If AI amplifies what already exists, what exactly are you asking to amplify?

•  Are we still designing organizations for predictability, or for performance in uncertainty?

•  What would change if we judged our operating model not only by its efficiency, but by the quality of the conditions it creates?

These are the questions I'll continue exploring in the coming briefs — starting with where conditions actually come from, and a discipline I've come to call Conditions Architecture.

Acknowledgment

This Executive Perspective was inspired in part by BetterUp's The Case for Conditions, which helped crystallize an organizational design idea I had been experiencing in practice but had not yet fully articulated. Their research demonstrates that organizational conditions shape whether AI creates value. This brief explores the next question: If conditions matter this much, how should enterprise leaders intentionally design organizations to create them?

Reference: BetterUp. The Case for Conditions: Why AI Success Depends on the Environment You Create. 2026. Available at: https://www.betterup.com/the-case-for-conditions

Intellectual Property Notice

The concepts, terminology, frameworks, models, diagrams, and written content contained in this publication are the intellectual property of Shannon Thomas and Titanium Vectors unless otherwise noted. No portion of this publication may be reproduced, adapted, or incorporated into other works without prior written permission.