Change Management
How do you know who or what to trust?
Do you trust someone who has a great personality? I hear Ted Bundy was quite the charmer. Do you trust someone who seems to be part of your social group? There is a great Netflix documentary you should watch titled Inventing Anna.
As we’re growing up, our parents or caregivers are usually the first people we trust. As we go out into the world, they tell us who we can trust, such as teachers, friends, and other family members. Trust is borrowed. It is handed to us before we know how fragile it can be.
Then life happens.
People we trusted hurt us. People we counted on let us down. People we believed in become disappointments.
Not just people. The systems we counted on to be steady, the technology that was going to make life easy, and the rules that everyone was supposed to follow.
Over time, trust erodes in quiet, disorienting moments that make us question our own judgment.
This is something I was thinking a lot about as I was writing my book, Learn to Love the Roller Coaster. I realized that yes, I have trust issues, but so do almost all of us. And for good reason. I wanted to learn how to trust again, but not be naïve about it. And so, after a lot of research, and discussion, and thinking, I came up with an equation.
Trust = Alignment + Truth + Consistency
I didn’t develop this as a leadership framework or a business model. I developed it for myself, as a way to stay grounded while navigating change. But the more I’ve lived with it, the more I’ve seen how universally it applies to relationships, organizations, communities, and especially to moments when humans meet technology.
Why trust feels harder in the age of technology
There’s a specific flavor of fear that comes with tech change.
It’s not just “Will this tool work?” It’s:
- Will this tool replace me?
- Will it make me look stupid?
- Will it track me?
- Will it judge me?
- Will it decide something about me that I can’t appeal?
- Will leadership use it to squeeze more output with less support?
- If the tool is wrong… who pays the price?
That fear exists even when leaders have good intentions.
Because for employees and customers, “technology” doesn’t feel like a neutral upgrade. It often feels like a decision being made at speed—without enough explanation, without enough testing, and without enough input from the people who have to live with the consequences.
So when leaders say something like, “Don’t worry, it’s going to be fine,” it rarely lands the way they hope.
Trust has to be built, not declared.
That’s where the equation becomes useful.
Trust = Alignment + Truth + Consistency
1) Alignment: Values have to show up in the design
Alignment is when actions match values.
If a company says, “We’re people-first,” then the technology choices need to reflect that. Not just in the press release.
Alignment sounds like:
- “We’re adopting AI to remove busywork, not to quietly reduce headcount.”
- “We’re not using automation to avoid talking to customers.”
- “We’re keeping humans in the loop where the stakes are high.”
- “We’re not measuring productivity in a way that punishes good judgment.”
Misalignment is what employees sniff out immediately. It’s when the stated intention is uplifting, but the lived experience is dehumanizing.
Examples of misalignment that erode trust fast:
- Rolling out monitoring software while preaching “culture.”
- Replacing customer support with chatbots while promising a “premium experience.”
- Saying “innovation” but punishing people for learning in public.
- Talking about ethics but refusing to discuss where the data comes from.
When alignment wavers, clarity dies. People stop believing the story you’re telling, even if parts of it are true.
2) Truth: No distortion, no spin, no “magic AI” language
Truth is where most tech initiatives quietly fail.
Not because leaders lie intentionally, but because the pressure to sound confident is intense. And technology is complicated. And admitting uncertainty feels risky.
But people don’t need you to be omniscient. They need you to be honest.
Truth sounds like:
- “Here’s what this tool can do well, and here’s where it struggles.”
- “Here are the risks we’re watching.”
- “Here’s what we’ll do when it’s wrong.”
- “Here’s what we don’t know yet.”
- “Here’s how we’re measuring success beyond cost savings.”
Truth also includes clarity about data:
- What data is being used?
- Who has access to it?
- How long is it stored?
- Is it being used to evaluate employees?
- Are customers aware of what’s happening?
When truth falters, connection breaks. People feel manipulated or managed instead of included.
And in the AI era, this matters even more because AI can sound confident while being wrong. If your organization starts treating “the tool said so” as truth, people will either revolt or quietly disengage.
3) Consistency: The “how” matters as much as the “what.”
Consistency is what makes trust durable.
It’s the repeated experience of: “You show up the same way. You respond predictably. You don’t disappear when things get messy.”
And technology adoption gets messy. Always.
Models drift. Automations break. Edge cases explode. A workflow that looked elegant in a demo becomes chaos in production. Customers hit weird loops. Employees get blocked. Someone gets blamed.
Consistency is what determines what happens next.
Consistency sounds like:
- “We don’t punish people for reporting problems.”
- “We fix issues fast and communicate clearly.”
- “We follow the same standards across departments.”
- “We don’t move the goalposts after rollout.”
- “We make it safe to say: this isn’t working.”
When consistency breaks, confidence collapses. People can’t trust what they can’t predict.
Technology doesn’t just need to work. It needs to be trustworthy.
This is the part leaders often underestimate:
A tool can be functional and still be rejected because adoption isn’t only a technical decision. It’s a human decision.
Employees and customers are constantly asking:
- Is this respectful?
- Is this fair?
- Is this safe?
- Is this reliable?
- Is this reversible?
- Is this being used to help me… or manage me?
Trust is the filter people use to answer those questions.
And if trust is low, people will:
- avoid using the tool,
- work around it,
- undermine it with sarcasm,
- blame it for everything,
- or comply outwardly while disengaging inwardly.
That’s not an employee “attitude problem.” That’s a trust signal.
Using the trust equation during AI adoption
If you’re integrating AI or making a major change and want to protect your culture, trust needs to be assessed, not assumed.
I created a Trust Assessment to help leaders quickly evaluate where trust is strong and where it may already be eroding across alignment, truth, and consistency.
It is a practical way to spot friction early, before resistance, disengagement, or quiet workarounds take hold.
Because sustainable change starts with trust.
