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Hello, fellow humans! This one is especially for the managers out there trying to deliver AI solutions that their executive leadership is demanding. It’s important to understand what AI can and cannot do, but also how to push back against unrealistic pressure and deliver an AI solution that creates real value for you and your team.

Your CEO just came back from a conference. They've seen the demos. The keynotes were inspiring. The future is here. Now you have 90 days to "integrate AI across the organization."

You know exactly what happens next.

Your teams will race to build something—anything—that looks like AI innovation. Demo-worthy features will take priority over boring process fixes. Six months from now, you'll have impressive presentations and minimal customer impact. And somehow, everyone will claim success while your actual problems remain unsolved.

This isn't a story about AI failing. It's a story about what happens when AI adoption pressure meets organizational cultures built on managing upward instead of solving problems. And if you're a mid-tier leader—anywhere from manager to director—you're about to get squeezed from both sides.

In organizations with strong managing-up cultures, AI adoption mandates can create predictable pathways to failure. Teams optimize for the appearance of innovation rather than solving real problems with appropriate tools. This can cost wasted AI investments, or worse, erode the organizational trust and problem-solving capabilities you'll need for any transformation, AI or otherwise.

Let’s look at how this plays out, why it's especially dangerous with AI, and what signals to look for to tell you it's already happening in your organization.

Here's a scenario playing out right now in organizations everywhere:

A customer service team has a real problem—30% of tickets are routing errors. Customers get bounced between departments, wait times increase, and satisfaction scores drop. Clearly, there is a broken routing logic that's probably been accumulating exceptions and workarounds for three years.

But leadership wants to see "AI customer service." So the team builds an AI chatbot. It's impressive in demos. The presentation goes well. Leadership is pleased.

Of course, the routing errors continue. Now customers get the wrong department faster, with an AI chatbot layered on top of a broken process that likely has an organizational root cause unrelated to technology.

This is the demo trap, and it's the first sign that managing-up culture is distorting AI adoption. Teams prioritize implementations that are visible and impressive over those that are valuable. The chatbot gets built because it's easy to demonstrate to leadership. Fixing routing logic—boring, technical, invisible—doesn't make it onto the roadmap.

The pattern repeats across organizations. Innovation becomes performance. Teams optimize for what leadership will celebrate, not what customers need. And because managing-up cultures reward fast execution that looks responsive, you get speed over substance.

AI adoption requires slow, careful problem discovery. Leadership wants to announce AI initiatives. These two things are fundamentally incompatible, but only one side of that equation has career consequences for mid-tier leaders.

First throughline: Managing-up cultures default to performing AI adoption rather than taking on the complex problem-solving work needed to make AI deliver process improvements.

You might be thinking: "We've survived other technology adoption waves. Why is AI different?"

That’s a fair question; it’s important to understand that AI is probabilistic, not deterministic. It requires extensive evaluation and iteration. And it fails in ways that aren't immediately obvious—which means you can ship something broken and not realize it for months.

This creates a dangerous mismatch with managing-up dynamics. You can't build robust AI evaluation systems while optimizing for leadership presentations. You can't iterate honestly when iteration looks like lack of progress. And you definitely can't surface AI failure modes when your culture demands positive narratives upward.

Recent research from Product Talk analyzing nine real AI product teams found something crucial: successful teams evolved from simple spreadsheet evaluations to sophisticated assessment systems through iterative learning. But here's the problem—that evolution requires admitting what's not working, adjusting approach, and moving slowly enough to learn. In managing-up cultures, that looks like failure.

The technical debt compounds faster too. Bad AI implementations create worse technical debt than traditional software because the failure modes are harder to detect and more expensive to fix. Rushed AI implementations deployed without adequate evaluation create systems with unknown failure modes that compound maintenance burden and slow future innovation.

But the real multiplier is trust erosion. With traditional software, bad features annoy users. With AI, bad AI erodes trust in ways that are harder to recover from. Users stop trusting the system entirely. Teams stop trusting their own judgment. And leadership stops trusting the organization's technical capability.

Imagine: your sales team builds AI lead scoring to impress leadership. Pressure to ship means inadequate validation. Six months later, sales reps have discovered the system misses good leads and they've stopped using it entirely. Zero adoption. Damaged credibility. Wasted investment. And leadership's conclusion? "Our team can't execute on AI."

Second throughline: AI's complexity and trust requirements make managing-up dynamics exponentially more damaging than with traditional initiatives.

The good news: misalignment shows up early, long before projects fail. The bad news: most organizations ignore the signals because they look like progress.

Listen to how your teams talk about AI work. Are they describing customer problems first and AI as a potential solution second? Or does the "AI strategy" exist before anyone has clearly defined what problem you're solving?

Red flags show up in presentations that emphasize capability ("we're using GPT-4!") over outcomes. In project names that include "AI" but can't articulate success metrics. In roadmaps that look like technology evaluation rather than problem-solving sequences.

The healthy pattern sounds different. Teams that have alignment describe customer jobs-to-be-done before they discuss solutions. Their success metrics tie to customer outcomes, not AI utilization. And critically, they're willing to say "we tried AI here and simpler solutions worked better."

That last one is the real test. In managing-up cultures, admitting you chose not to use AI feels like failure. In problem-solving cultures, it's evidence of good judgment.

Follow the money and time. Where are your teams spending resources?

Warning signs: more budget for AI tools than for customer research. Teams spending over 50% of their time on internal stakeholder management. Heavy investment in demos and proof-of-concepts, but underinvestment in evaluation infrastructure. Hiring AI specialists without defining what problems they'll solve.

Compare that to what Product Talk's research found works: small teams of 2-3 people with deep domain expertise leading AI initiatives. Starting narrow with specific use cases. Building evaluation capabilities iteratively. Budget allocated for learning and experimentation, not just deployment.

The pattern is clear: domain expertise drives better AI decisions than AI technical knowledge alone. But managing-up cultures hire for credentials that impress leadership rather than domain knowledge that solves problems.

This one should make you uncomfortable: how is AI success being measured in your organization right now?

If you're counting "number of AI pilots launched," you're measuring activity, not impact. If your metrics focus on features shipped rather than customer outcomes improved, you're optimizing for the wrong thing. If there's positive internal sentiment despite lack of customer adoption, you have a managing-up culture that's decoupled from reality.

The alternative requires more discipline: clear customer outcome metrics defined before AI development starts. Regular measurement of both AI performance and customer satisfaction. Willingness to kill initiatives that don't show customer impact. Learning velocity metrics that track how fast you're improving AI performance based on real feedback.

Here's the most revealing question: who in your organization can say "AI isn't the right solution here"?

If that decision can only be made by leadership based on executive excitement, you're in managing-up mode. If teams implementing AI haven't first deeply understood the current process, you're in managing-up mode. If there are no clear decision rights about when AI is or isn't appropriate, you're in managing-up mode.

The healthy pattern pushes decision rights down to teams with domain expertise. Leadership sets constraints and desired outcomes; teams choose approaches. There's explicit permission to choose non-AI solutions if they're more effective. And there are regular reviews asking "why AI?" not just "how's AI going?"

Third throughline: Misalignment shows up in language (solution-first), resources (demo-heavy), metrics (activity-focused), and decisions (top-down) long before it shows up in results.

If you're trying to assess whether your organization is heading toward AI theater rather than AI value:

This brings us to you—the product manager, senior manager, or director reading this. You're in a unique and uncomfortable position.

You face pressure from above to show AI progress and responsibility below to deliver real value. You're close enough to problems to know what's real, but senior enough to influence the approach. And you face career consequences for appearing resistant to leadership's AI vision.

There's a trust equation at play here, and it creates real tension. Short-term trust comes from showing responsiveness to leadership's AI agenda—quick pilots, impressive demos, maintained upward perception. Long-term trust comes from delivering actual results that solve customer problems—slow discovery, maybe no AI, uncertain short-term optics.

The career risk asymmetry is real: short-term consequences of disappointing leadership feel more immediate than long-term consequences of failed initiatives. That's why most mid-tier leaders default to managing up. It's rational self-preservation.

But here's what makes your position powerful: you're uniquely positioned to translate rather than just transmit. Your job isn't to implement leadership's solution—it's to solve the problem leadership is trying to solve.

When your CEO comes back from that conference excited about AI, they're not really saying "go implement AI." They're saying "I'm worried about competitive pressure" or "I see efficiency opportunities" or "the board is asking what we're doing about AI."

Your job is to understand that actual concern and address it effectively. Sometimes that involves AI. Often it doesn't. Always it requires courage to reframe the conversation from "here's our AI plan" to "here's how we'll solve the problems you're concerned about, including when AI is and isn't appropriate."

Fourth throughline: Mid-tier leaders face unique career risks in managing-up cultures, making it harder to resist AI-for-AI's-sake pressure—but they're also uniquely positioned to create alignment.

We've established the problem: managing-up cultures create predictable AI adoption failures. We've identified the signals: language, resources, metrics, and decision-making all reveal misalignment early. And we've clarified your position: squeezed between pressure and responsibility, but uniquely able to create change.

The question now is: what do you actually do about it?

That requires practical strategies for reframing conversations upward, creating accountability infrastructure sideways and downward, and modeling the behavior you want to see. It requires understanding how to measure long-term trust building rather than short-term wins. And it requires accepting that culture change is measured in years, not quarters.

But before any of that: you have to decide whether you're willing to play a different game than everyone around you. Because creating alignment in a managing-up culture means building trust through problem-solving even when it's slower, messier, and less immediately impressive than AI theater.

The AI adoption pressure in your organization isn't the problem. It's the catalyst. The question is whether you'll use it to reinforce managing-up culture or to build the problem-solving culture you'll need for whatever transformation comes next.

If you're a manager to director level leader navigating AI adoption pressure:

The managing-up trap is seductive because it works—until it doesn't. AI adoption pressure is revealing which leaders are willing to build trust through honest problem-solving versus those optimizing for perception. Six months from now, the difference will be obvious. Three years from now, it will be career-defining.

Choose accordingly.