A Fresh Perspective on AI Adoption in Assessment Offices with Trinity Taylor

About this Episode

Trinity Taylor is not an assessor. She's not an appraiser. She's a recent University of Arizona graduate with a psychology degree and a double minor in business and PR who landed at ValueBase doing outreach to assessment offices across the country. And that's precisely what makes this conversation worth paying attention to.

Trinity has talked to more assessors than most people in this industry ever will. In doing so, she's developed an outsider's pattern recognition about how offices respond to change — specifically AI adoption — and where the real friction points live. Her observations are unvarnished, free of industry jargon, and surprisingly incisive.

The Urban-Rural Divide Is Really a Mindset Divide

One of Trinity's most interesting observations is that receptivity to AI doesn't break down neatly by state. It breaks down by context. More urbanized offices with younger staff tend to be open to hearing about new tools. They know what AI is. They're not threatened by it. Offices in more suburban or rural areas — particularly those led by assessors who've held their seats for 25 years — are more hesitant.

But Trinity is quick to push back on the idea that age alone is the determining factor. She compares it to her own father, in his early 50s, who had never heard of ChatGPT until she introduced him to it. Once someone walks you through it, the fear dissipates. The issue isn't capability or willingness — it's exposure. Many assessors simply haven't had someone sit down and show them what these tools actually do in a property tax context.

This should be a wake-up call for the industry. If adoption is gated by explanation rather than resistance, then we have an education problem, not a technology problem.

The Terminator Problem

Trinity names something that anyone doing AI outreach in government has encountered: when assessors hear "AI," many of them picture a robot. The Terminator. Complete replacement. Jobs disappearing.

The reality, as Trinity describes it from her own experience using AI daily, is far more mundane and far more useful. She frames AI as a "helping hand" — something that handles the tedious, time-consuming research so you can focus on the work that actually requires human judgment. The example from a meeting she attended is telling: a staff member remarked that a particular task "takes forever" but acknowledged that an AI tool could consolidate the work into one place, eliminating hours of manual research.

This framing matters enormously. The assessment profession is already stretched thin. Offices are understaffed. The median age is north of 55. Telling these professionals that AI is coming for their jobs is not only inaccurate — it's counterproductive. The accurate message is that AI might get you home by five instead of eight.

You Have to Train It Before It Trains You

Perhaps Trinity's most sophisticated insight — impressive for someone who admits she wasn't using AI two years ago — is about the relationship between user investment and tool quality. She notes that the more you interact with an AI system, the more you tell it about your specific context, the better it performs.

This is the part most offices skip. They try a tool once, get a generic response, and write it off. But AI in assessment isn't a magic button. It's more like onboarding a very smart intern. You have to teach it what you care about, what's worked before, what hasn't. Trinity describes plugging in performance metrics and outreach data to help AI refine her own processes over time. The same principle applies to assessment work — ratio studies, sales validation, comparable selection. The tool improves as it learns your jurisdiction's specific patterns and priorities.

The offices that will benefit most from AI aren't the ones with the biggest budgets. They're the ones willing to invest the time upfront to make the tool genuinely useful.

The Hidden Complexity Behind Every Property Tax Bill

Trinity's candid admission about her own ignorance before joining ValueBase is telling. She lived in a house her whole life and never once thought about the machinery behind her property tax bill. When the team walked her through an example of poor vertical equity — assessments that are regressive, that burden lower-value properties disproportionately — she was genuinely shocked.

This reaction mirrors how most of the public relates to assessment. The work is invisible until something goes wrong. And that invisibility cuts both ways: it means assessors rarely get credit for doing their jobs well, and it means the consequences of doing them poorly go unnoticed until they compound into real economic harm.

If a 22-year-old with a psychology degree can grasp vertical equity and its implications after a single meeting, the industry can do a better job communicating these concepts to the public. The complexity is real, but it's not impenetrable.

Key Takeaway

The biggest barrier to AI adoption in assessment offices isn't technological sophistication or budget — it's the absence of a trusted guide who can demystify the tools and connect them to daily workflow. Trinity's experience suggests that once someone takes the time to explain what AI actually does in context, fear turns to curiosity and curiosity turns to adoption. The industry doesn't need to wait for a generational shift. It needs better translators.