How to Build Curiosity in a Profession Built on Precision

I was in a partner meeting last month when someone said something that stopped the entire conversation cold.

We were discussing AI adoption timelines, and a senior partner raised her hand: "I understand we need to experiment with these tools. But we're accountants. We don't experiment. We get it right the first time."

The room went silent because everyone knew she was both completely right and completely wrong.

She was right that accounting culture is built on precision, compliance, and risk avoidance. These are good things. These are necessary things. These are the cultural values that keep clients' financials accurate and keep firms out of liability nightmares.

She was also right that you can't just tell accountants to "be more comfortable with mistakes" and expect that to work.

But here's what she was wrong about: You cannot adopt AI successfully without curiosity, experimentation, and permission to fail. If your culture doesn't have those things, your AI initiative is doomed before you start.

Welcome to the Accounting Culture Dilemma.

The Paradox Nobody Talks About

Let's be explicit about the tension:

Accounting culture is built on:

• Precision (numbers must be exact)
• Compliance (rules must be followed)
• Risk avoidance (mistakes have consequences)
• Getting it right the first time
• Thoroughness over speed
• Proven methods over experimental ones

AI adoption requires:

• Experimentation (try different approaches)
• Iteration (first attempts will fail)
• "Good enough" outputs that you refine
• Learning through trial and error
• Speed over perfection in testing
• Trying unproven methods to discover what works

These aren't just different—they're opposing values.

And here's what makes it worse: both sets of values are correct for their respective contexts.

You absolutely should not experiment with how you file tax returns. You absolutely should get financial statements right the first time. Mistakes in client-facing work have real consequences—regulatory penalties, damaged reputations, lost trust, potential liability.

But you also cannot learn AI without making mistakes. Your first attempts at prompts will be terrible. AI outputs will need human refinement. You will need to try things that don't work to discover what does.

This is what I call the Two Truths That Seem to Contradict:

• Truth #1: Mistakes are unacceptable
• Truth #2: In AI adoption, mistakes are required

Most firms try to ignore this tension. They announce AI adoption while maintaining a culture that punishes any form of failure. Then they wonder why no one experiments, why adoption is superficial, why innovation never happens.

You can't bolt AI onto a culture that discourages experimentation. The culture will win every time.

What Kills AI Initiatives (It's Not Technology)

Let me tell you what actually kills AI initiatives in professional services firms.

It's not bad technology. It's not lack of budget. It's not even resistance to change.

It's cultures that punish curiosity, reward perfection, and conflate mistakes with incompetence.

I've watched it happen dozens of times. A firm invests in AI tools. Provides training. Announces the initiative. Creates expectations.

Then someone tries using AI for a client proposal. The output is mediocre. They spend time refining it, but it's still not quite right. They eventually do it the old way because that's safer.

A colleague notices and asks how the AI experiment went. The person's response? "Eh, it didn't really work." The subtext? "I wasted time on something that didn't deliver immediately, and I looked less productive than if I'd just done it the traditional way."

That story spreads. Not explicitly, but through absence. People notice who's using AI and who isn't. They notice who's getting recognized and promoted. They notice what gets celebrated and what gets quietly ignored.

And they make the entirely rational decision to stick with what works.

In a culture that rewards perfection, experimentation is career risk. In a culture that punishes visible failure, innovation dies in silence.

What Curiosity Actually Looks Like (And What It Doesn't)

Before we go further, let's be clear about what we're talking about. Building a curious culture doesn't mean:

• Chaos or "anything goes"
• Ignoring risk or compliance requirements
• Letting people experiment on client work without guardrails
• Valuing innovation over execution
• Accepting sloppy work or missed deadlines

Those are straw-man fears that prevent firms from making necessary changes.

Here's what curiosity actually looks like in a professional services context:

Asking "What if we tried this differently?" Not about client deliverables, but about internal processes. Not about whether to comply with regulations, but about how to make compliance more efficient.

Viewing AI outputs as drafts, not final products. Understanding that the first output is a starting point for refinement, not a replacement for professional judgment.

Learning from failed experiments without punishment. Creating space where "I tried this and it didn't work" is treated as valuable information, not evidence of poor judgment.

Partners modeling curiosity, not just demanding it. Leadership sharing their own experiments—including the ones that failed—and talking about what they learned.

Protected time and space for experimentation. Not expecting people to learn AI on top of existing client work, but building it into capacity planning.

Celebrating insights from failures. Recognizing people who discover what doesn't work just as much as people who discover what does.

This isn't about becoming a different kind of firm. It's about creating space for the kind of learning that AI adoption requires while maintaining the precision that client work demands.

The Mindset Shifts That Bridge the Gap

So how do you actually do this? How do you build curiosity in a culture designed around precision?

You make explicit mindset shifts that acknowledge the tension rather than ignoring it:

From: "Don't bring me problems, bring me solutions"
To: "Bring me interesting problems worth solving"

This shift says: we value people who identify challenges and think creatively about solutions, not just people who execute flawlessly.

From: "We've always done it this way"
To: "That way worked well—what else could we try?"

This shift says: tradition is valuable AND we're open to evolution. You don't have to reject the old to explore the new.

From: "I need the right answer"
To: "I need three possible answers to compare"

This shift says: there are multiple valid approaches, and part of expertise is evaluating options, not just identifying one path.

From: "Don't experiment on client work"
To: "How can we test safely before we deploy?"

This shift says: experimentation is valuable, and part of being professional is knowing how to experiment responsibly.

Notice what these shifts have in common? They don't reject the core values of accuracy, compliance, and professionalism. They expand the definition of what professional excellence looks like in an AI-enabled environment.

Creating the Container: Where Mistakes Are Safe

Here's the practical solution to the Two Truths paradox: You create different containers for different types of work.

Container 1: Client-Facing Work

In this container, the traditional rules apply. Precision matters. Compliance is non-negotiable. You get it right before it goes to the client. AI outputs are thoroughly reviewed and refined before anything client-facing happens.

Container 2: Learning Environment

In this container, different rules apply. Experimentation is expected. First attempts are supposed to be rough. Failure is information. AI outputs are drafts to learn from, not deliverables to judge.

The key is making the distinction explicit. When someone is in the learning environment, they know they're protected from the judgment that applies to client work. When they're doing client work, they know AI outputs must meet the same standards as any other output.

Most firms fail because they blur these containers. They expect people to learn on client work, which triggers all the risk-avoidance instincts that prevent experimentation. Or they create "innovation time" but continue to judge people by client billability, which sends the message that learning is optional.

You need both containers. And you need to be crystal clear about which one you're in at any given time.

The Cultural Foundation That Enables AI

Building curiosity isn't about changing everything about your culture. It's about adding one critical element that's probably missing: psychological safety for intelligent experimentation.

Psychological safety doesn't mean everyone feels comfortable all the time. It means people can take appropriate risks without fear of punishment. It means saying "I tried this and it didn't work" doesn't damage your reputation. It means asking questions isn't seen as evidence of incompetence.

Here's how you build it:

Leadership goes first. Partners and senior leaders share their own AI experiments, including failures. "I spent an hour trying to get AI to draft this client memo, and it was terrible. But I learned that it's great at summarizing data but bad at strategic recommendations."

Celebrate the learning, not just the outcome. Publicly recognize people who experiment thoughtfully, even when the experiment doesn't yield immediate results.

Separate learning metrics from performance metrics. Don't judge people's client performance based on their experimentation velocity. Judge their experimentation based on thoughtfulness and learning, not immediate ROI.

Make space for practice. Give people actual time—not theoretical time, but protected capacity—to experiment with AI outside of client deliverables.

The firms that succeed are the ones that acknowledge the tension, create appropriate containers, and build psychological safety for the learning that AI requires.

The ones that fail are the ones that announce AI adoption while maintaining a culture that makes experimentation feel dangerous.

Which one are you?

Is your firm's culture enabling or preventing AI adoption? Winsome's consulting practice helps professional services firms build curiosity without sacrificing precision. We'll show you how to create the psychological safety and structural containers that make AI transformation possible—without compromising the excellence your clients expect. Let's assess your cultural readiness for AI.