I certainly wasn’t an AI expert, but I was still tasked with AI deployment at a government agency. AI usage should have been ubiquitous: folks are smart and capable and engaged; it would have saved them so much time. And yet uptake remained low.
This is part admonishment of how we think about AI adoption and part guide to selling AI better.
AI adoption isn’t technical, it’s human
The prevailing sentiment in Tech is that adoption is a technical problem: map the process, redesign it, automate it, deploy. Maybe that works at the bleeding edge. At most companies, that leads to a perfect solution with no uptake.
Why?
If you’re deploying AI, you’re inevitably going to run into mission-critical people who simply do not use, or even resist, AI. Until you win them over, your project is dead.
Tech focuses on rigorous discovery, thorough testing, and complex training. But non-users aren’t convinced by process. Or it indexes towards a “sink-or-swim” model. But that’s not great for morale anywhere, and I’m not convinced it actually leads to meaningful productivity gains - certainly not in government.
What does work? Getting non-users to “aha” moments that demystify technology. But until people trust a tool, they won’t use it. Technical capability is irrelevant.
If you’re selling AI, it’s your responsibility to get people over that hump; to teach them to trust not just your tool, but AI in general. Trust is AI’s currency. It doesn’t sell without it. You have a fiduciary duty to build trust.
Trust is all about communication
Whether CxO or an analyst, building trust in AI requires answering four questions.
How does AI work?
Is it good?
Should I be worried?
What do I do?
AI users and non-users don’t speak the same language, and answers are fraught with mistakes. So, I’ve demystified the conversation for you.
Mistake #1: You answer the question directly instead of understanding what folks are really asking.
Q1: How does AI work?
Insight: Folks are not asking about AI’s fundamental building blocks, they want to know if they can trust it. Frame AI in a situation that is compelling and relatable.
My answer: Think about AI as if it were a new employee just out of college. They’re probably pretty smart. They have baseline capabilities but lack expertise. You ask them to build a model. How do they do that? You give them access to Excel. Maybe you share model templates and firm best practices. You give them detailed instructions covering exactly what you need. And you let them iterate and learn, getting better over time.
AI is the same. Your AI model has (1) some amount of reasoning and language processing capabilities, which we call intelligence. And (2) it can access tools and follow instructions, which we call context. It can even iterate and learn. The more you learn to use the tool - better understanding of underlying capabilities, more specific phrasing, wider context offered - the better the output.
Mistake #2: You use technical jargon when simplicity is more effective.
Q2: Is it good?
Insight: They’re pushing for a line in the sand to define AI capabilities in the context of their work. You must balance theory with pragmatism and craft an answer that feels real.
My answer: AI has a bunch of basic capabilities that might apply to any process. It might be good at research, or information synthesis, or data analysis, or coding. How “good” it is depends on how well those capabilities map to the problem you’re solving. Now if you want to define whether AI intelligence is closer to analyst work or VP work, you’re going to have to play with those capabilities yourself. But what I think is [insert whatever you think - after all, you do have to answer their question directly!].
Mistake #3: You lack empathy.
Q3: Should I be worried?
Insight: This is a very human moment; a mixture of fear of the unknown and fear of falling behind. Approach with empathy, not theory.
My answer: I don’t know. Realistically, in terms of intelligence, benchmarks show that LLMs are already pretty darn good. Then it’s about context. People are building massive libraries of prompts to describe work, scripts to give AI standardized tools, and even armies of pre-built AI tools (you might have heard them called agents) that can complete large tasks. Over time, chances are we’ll have smart enough models, enough prewritten context, and enough prebuilt agents to complete a lot of work as we know it today. But, does that mean humans will be replaced? Maybe? We still need people reviewing AI output. And just because work is more efficient doesn’t mean there’s less of it - in fact, historically it has meant the opposite. I guess we’ll see.
Mistake #4: You misread the moment and sell too soon.
Q4: What do I do?
Insight: Note that the question is “I”, not we. AI continues to feel deeply personal even in a professional context. This is an opportunity to pitch, but not a detailed deployment roadmap. It is just conversation 1.
My answer: I feel like the key is just getting familiar with AI. I know you’re quite busy, but could we find some time to sit and play with a few tools? Are there use cases you think are really painful and repetitive and AI might be valuable? Or maybe we can work with folks who are excited about AI and problem solve with them?
The Final Word
The cultural zeitgeist is AI replacement. My practical experience is that humans make AI effective. If you want to sell AI, start by building trust with humans.
