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Today's Agenda
Hello, fellow humans! Today, we’ll look at how to build effective relationships of trust as we navigate the AI space together. While this is specifically written about managing-up organization cultures, a lot of this can be generalized to any large organization that is exploring how to use AI tools.
Measuring Trust and Building Culture in the Age of AI
This is the final piece in our series on AI adoption and managing-up cultures. In part one, we identified the problem—how managing-up dynamics distort AI adoption into performance theater. In part two, we covered three practical strategies: reframing conversations upward, building accountability infrastructure, and modeling transparent problem-solving.
Now we need to talk about the hardest part: playing the long game when everyone around you is optimizing for quarterly wins.
The difficult part is that a trust-building approach to AI adoption shows results more slowly than managing-up. Your peers will ship impressive demos while you're still talking to customers. They'll present polished roadmaps while you're sharing messy learning. For six months, maybe longer, it will look like you're behind.
But here's what I've learned watching leaders navigate this: the ones who build trust through honest problem-solving compound that trust over years, while those who manage up see their credibility evaporate the moment their initiatives fail to deliver.
Let me show you how to measure what actually matters, understand the timeline you're operating on, and recognize what success looks like when you're building culture instead of just shipping features.
Measuring Trust: The Indicators That Actually Predict Success
You can't manage what you don't measure, and most organizations measure the wrong things when it comes to AI adoption. They track pilots launched, budgets allocated, and executive satisfaction scores. Those are activity metrics dressed up as outcomes.
Trust is different. It's harder to quantify, but the signals are clearer if you know where to look.
Upward Trust Indicators (From Leadership)
These tell you whether leadership is actually trusting your judgment or just tolerating your presence:
Do they ask you for advice on approach, not just status updates? If conversations are always "how's it going," you're reporting. If they're "what do you think we should do about X," you're trusted.
Do they accept your recommendation NOT to pursue AI in certain areas? This is the real test. In managing-up cultures, saying "we shouldn't use AI here" feels dangerous. When leadership accepts that recommendation, you've built credibility.
Do they reference your team's work as examples for others? Not just praising results, but holding up your approach as the model. That's influence.
Do they give you more autonomy over time? Trust shows up as decreased oversight, not increased supervision.
Track these qualitatively. Keep a simple log. When did leadership last ask for your strategic input versus status? When did they last approve a non-AI solution you proposed? You're looking for trend lines, not snapshots.
Peer Trust Indicators (Sideways Influence)
Your peers watch what you do more than they listen to what you say:
Do other mid-tier leaders seek your input on their AI initiatives? If they're coming to you for advice, you're building reputation.
Do they copy your evaluation frameworks or customer discovery practices? Imitation is validation. If your infrastructure is being adopted by peers, you're creating cultural shift.
Do they share their failures with you? This is huge. In managing-up cultures, people hide problems. When peers are willing to share what's not working with you, they trust you won't use it against them.
Downward Trust Indicators (From Your Teams)
This is where you see whether your modeling is actually working:
Do team members surface problems honestly rather than hiding failures? If your reports bring you bad news proactively, you've created psychological safety.
Do they propose non-AI solutions confidently? If team members feel safe saying "we should solve this without AI," you've successfully transmitted that problem-solving beats tool adoption.
Do they say "I don't know" instead of projecting false confidence? Managing-up cultures punish uncertainty. If your team admits knowledge gaps, they trust you to handle that honestly.
Do they stay in customer conversations rather than rushing to implementation? Discovery takes time. If your team prioritizes learning over shipping, they've internalized the right values.
These indicators matter more than any quarterly metric because they predict long-term capability. Teams that surface problems, propose appropriate solutions, and admit uncertainty will solve harder problems over time. Teams that perform confidence will hit walls.
The Timeline: Why Culture Change Is Measured in Years
Here's the uncomfortable truth: if you start implementing these strategies today, you won't see full results for 12-24 months. Maybe longer.
That timeline makes most people abandon the approach. The managing-up path shows results in weeks—positive executive feedback, visible progress, maintained perception. The trust-building path requires enduring a period where you look slower, messier, and less impressive than your peers.
Let me break down what the journey actually looks like:
The Short-Term View (3-6 Months)
This is when managing-up approaches look most attractive. Your peers are racking up wins:
Number of AI pilots launched: High
Executive satisfaction with AI progress: Positive
Budget allocated to AI initiatives: Growing
Visibility in leadership meetings: Maximum
Meanwhile, you're doing customer discovery, building evaluation infrastructure, and admitting what you don't know yet. Your metrics look different:
Customer problems deeply understood: Growing
Evaluation frameworks in place: Developing
Team clarity on when AI is appropriate: Improving
Honest learning shared: Consistent
The gap feels painful. Your career instincts scream "move faster, look better, ship something impressive." This is where most people break and revert to managing up.
But here's what's actually happening beneath the surface: Your peers are building technical debt, eroding trust with end users, and setting up for failure that just hasn't manifested yet. You're building capability that will compound.
The Medium-Term View (12-18 Months)
This is when the divergence becomes visible. The AI initiatives launched six months ago are starting to show their true colors:
Your peers' implementations:
Adoption rates are lower than projected
Users are finding workarounds to avoid AI features
Technical debt is creating maintenance burden
Leadership is asking harder questions about ROI
Your implementations:
Customer outcomes are measurably improved
AI systems are still in use (because they solve real problems)
Team learning velocity is accelerating
Clear evidence of when AI works versus when it doesn't
The trust you built starts paying off. Leadership begins asking you how you're making decisions. Peers start copying your frameworks. Your team's problem-solving capability is noticeably stronger.
The Long-Term View (24+ Months)
This is where compound trust creates real differentiation. The managing-up approach hits a wall—repeated initiative failures erode credibility, leadership trust drops, and career capital evaporates.
The problem-solving approach creates momentum—demonstrated results attract resources, teams want to work with you, leadership gives you harder problems to solve because they trust your judgment.
Here's what that looks like concretely:
Performance capital (what managing up built):
Visible to current leadership only
Vulnerable to leadership changes
Based on perception, not results
Measured in projects shipped, not outcomes delivered
Impact capital (what problem-solving built):
Portable across organizations
Durable across leadership transitions
Based on demonstrated results
Measured in customer outcomes and team capability
When you interview for your next role, do you want to explain impressive demos that didn't deliver, or customer problems you actually solved? The answer determines which timeline you optimize for.
The 90-Day Roadmap: Getting Started When You're Starting Now
Knowing the long-term timeline is one thing. Starting the journey is another. Let me give you a practical roadmap if you're beginning this week.
Weeks 1-2: Assess and Document
Your mission: Understand current state with brutal honesty.
Actions:
Audit your calendar: Calculate what percentage of your time goes to upward communication versus customer discovery versus team problem-solving. Be honest about the numbers.
Map decision rights: Who currently decides if/when AI is used? Write it down. Share with your team and leadership. Ask: "Is this actually how we operate?"
Identify pressure points: Where is leadership pushing hardest for AI adoption? What's driving that pressure?
Interview your team: Do they feel permission to say "AI isn't right here"? What stops them from honest problem-solving?
Deliverable: One-page assessment that documents the managing-up versus problem-solving balance. This is your baseline.
Weeks 3-4: Reframe One Initiative
Your mission: Pick one AI mandate and translate it into a problem-solving approach.
Actions:
Pick strategically: Choose something leadership cares about but that's early enough to redirect
Deep-dive the real problem: Talk to customers. Understand current state. Map the actual workflow.
Create problem statement: Define the customer job-to-be-done, not the AI solution
Define success criteria: What customer outcomes actually matter?
Present the reframe: "Here's the problem we're solving, here's how we'll know if we succeed, here's why we might or might not need AI"
Deliverable: Problem-first proposal that shows responsiveness to leadership while maintaining solution flexibility.
Weeks 5-8: Build Evaluation Infrastructure
Your mission: Create systems that make customer evidence required, not optional.
Actions:
Start simple: Evaluation spreadsheet tracking customer metrics, AI performance, and learning
Install customer loop: Weekly customer conversations become mandatory for AI initiatives
Create learning log: Share what you're discovering, including what's not working
Define decision criteria: When will we deploy? When will we stop? When will we pivot?
Deliverable: Evaluation framework in active use, shared transparently with leadership and peers.
Weeks 9-12: Model and Share
Your mission: Make your learning visible and give others permission to be honest.
Actions:
Share first results: Include what didn't work and what you learned from it
Document decision: Why you did or didn't use AI for specific problems
Hold retrospective: What's working in your approach? What needs adjustment?
Teach one peer: Share your evaluation framework. Offer to help them implement it.
Deliverable: First transparent learning report shared broadly. You're establishing a pattern of honesty over polish.
Beyond 90 Days: Sustain and Scale
This is where the long game begins. You need quarterly and annual practices that keep you focused on trust-building when pressure pushes you toward managing up.
Quarterly practices:
Review trust indicators across all three dimensions (upward, sideways, downward)
Assess: Which AI initiatives from three months ago are still delivering value?
Share: What have we learned about when AI works versus when it doesn't?
Adjust: What needs to change in our approach based on evidence?
Annual practices:
Measure long-term outcomes: How have customer metrics actually moved?
Evaluate culture shift: Are others adopting problem-solving approaches?
Career reflection: Am I building performance capital or impact capital?
Strategic reset: What does the next year of trust-building look like?
What Success Actually Looks Like: The Integrated Organization
After 18-24 months of this work, what have you actually built? It's not just better AI implementations—it's organizational capability that will matter for whatever comes after AI.
The culture you've created:
Teams have clarity on customer problems worth solving
Decision rights are clear about who decides when AI is appropriate
Evaluation infrastructure exists before deployment, not after
Learning is visible and actively informs strategy
Leadership trusts teams to choose appropriate approaches
Customer voice is structurally louder than internal politics
The capabilities you've developed:
Mid-tier leaders willing to risk short-term perception for long-term trust
Systems that make customer feedback impossible to ignore
Incentives aligned with outcomes rather than activity
Transparency about what's working and what isn't
Problem-solving capability that transfers to any transformation
This is bigger than AI. You're teaching your organization how to handle change by putting customer problems ahead of internal performance. Today it's AI adoption. Tomorrow it's whatever comes next. The capability to clearly define problems before selecting solutions is the most valuable thing you can build.
Final Takeaways: The Trust Arbitrage
Let's bring it all together with the core insight: most people over-index on managing up, creating opportunity for those who focus on impact.
Managing-up cultures are optimizing for short-term perception at the expense of long-term capability. With AI specifically, this is more dangerous because AI complexity hides failures longer but makes recovery harder. AI requires evaluation infrastructure that managing-up cultures systematically underinvest in. And AI affects customer trust in ways traditional software doesn't.
You, as a mid-tier leader, are uniquely positioned to create alignment where others can't:
You can translate leadership pressure into customer-focused problem-solving
You can build evaluation infrastructure that makes results impossible to ignore
You can model transparency that gives others permission to be honest
You can measure and build the trust that compounds over years
The career calculation is clear: managing up gives you quick perception wins that are fragile when initiatives fail. Problem-solving gives you slow trust building that's durable across leadership changes.
The arbitrage exists because most people can't wait 12 months for results. They need to show progress this quarter. They optimize for short-term perception because that's what feels safe.
But six months from now, when the impressive AI demos haven't delivered customer value, who will leadership turn to? The person who built trust through honest problem-solving or the person who managed perception?
Three years from now, when you're interviewing for your next role, what story will you tell? The impressive initiatives that looked good in presentations, or the customer problems you actually solved?
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 build the problem-solving culture you'll need for whatever transformation comes next.
Your Move
Here's what I want you to do this week:
Pick one AI initiative to reframe: Use the problem discovery conversation framework. Understand what leadership actually needs, not what they specified.
Install one customer feedback loop: Make it mandatory, visible, and tied to go/no-go decisions. No customer evidence, no deployment.
Share one learning transparently: Find something that didn't work. Share why. Test whether your culture can absorb honesty.
Find one peer to collaborate with: Share your evaluation framework. Offer to help them implement customer discovery practices. Culture changes peer-to-peer.
Set your 12-month calendar invite: Schedule a retrospective for one year from now. Ask: Which AI initiatives from today are still delivering value? What did customer metrics actually do? What did we learn about when AI works?
The managing-up trap is seductive because it works until it doesn't. AI adoption pressure is revealing which leaders build trust through honest problem-solving versus those who optimize for perception.
The difference is obvious in six months. Career-defining in three years.
Choose accordingly.
This concludes our series on AI adoption and managing-up cultures. The core insight: organizational alignment, not technical capability, determines AI success. And mid-tier leaders who build trust through transparent problem-solving create compound advantage in a world where most people optimize for quarterly perception.
The long game wins. Play it.
Radical Candor
Without trust, we don't truly collaborate; we merely coordinate or, at best, cooperate. It is trust that transforms a group of people into a team.