One of the most valuable aspects of interviewing AI leaders is uncovering the real-world strategies that actually work. When I sat down with Michael Domanic, Head of Generative AI Business Strategy at UserTesting, I knew we had something interesting: concrete insights from someone actively transforming a major organization.

UserTesting, one of the world's leading platforms for rapid customer feedback, has successfully embedded AI into their everyday workflows under Michael's leadership. His approach offers tangible strategies that SMB leaders can immediately apply to their own AI adoption challenges.

This four-part series unpacks the key insights from our conversation, designed to give you practical, actionable strategies you can implement right away:

• Part 1: Launching AI Transformation Effectively
  Discover the practical strategies and clear steps to begin your AI journey, including how to identify real problems worth solving and secure quick wins that prove immediate value.
• Part 2: Measuring Impact and Learning from Setbacks
  Explore the concrete, measurable outcomes from UserTesting's AI initiatives, plus the surprising lessons and limitations they discovered along the way.
• Part 3: Strategic Leadership for SMB Success
  Learn the specific implications for small and mid-sized business leaders, including actionable frameworks for balancing top-down vision with bottom-up innovation.
• Part 4: The Essential Traits of AI Transformation Leaders
  Understand the four critical characteristics that make someone successful at leading AI adoption—including why creativity might be more important than technical expertise.

Whether you're just starting to explore AI or looking to accelerate your current initiatives, Michael's experience provides a roadmap for transformation that's both ambitious and achievable.

This series is based on our complete interview with Michael Domanic. You can watch the full conversation and discover additional insights at: Top-Down, Bottom-Up: How UserTesting Drove AI Adoption

Part 1: Deep Dive into AI Transformation at UserTesting (Michael Domanic)

One of the most enjoyable aspects of interviewing hundreds of AI experts and practitioners for Just Curious is hearing about the specific problems they've tackled and their real-world case studies. It immediately sparks ideas for how I can apply these insights to my own business—and I'm sure it resonates with you, too.

Last week, I had a fantastic conversation with Michael Domanic, Head of Generative AI Business Strategy at UserTesting—one of the world’s leading platforms for rapid customer feedback on websites, apps, prototypes, and other digital or physical products. Interestingly, Michael is also a Wilderness First Responder and a licensed NY State Outdoor Guide, showcasing a unique blend of structured strategic thinking and adaptability in unpredictable environments.

This is the first post in a four-part series where I’ll unpack key insights from our discussion. Each post is designed to:

• Immediately reward you with practical, actionable insights.
• Pique your curiosity by highlighting unconventional perspectives and measurable results.
• Encourage you to return by promising deeper strategic insights and leadership lessons.

Here’s what you can specifically expect:

• Part 1: Practical strategies and clear steps to launch AI transformation effectively.
• Part 2: Concrete, measurable outcomes and surprising lessons from UserTesting’s AI journey.
• Part 3: Strategic implications specifically tailored for SMB leaders and actionable recommendations.
• Part 4: Insights into the four critical traits of a successful AI transformation leader, including the often-overlooked role of creativity.

Michael is spearheading a significant internal transformation at UserTesting, embedding generative AI into everyday workflows. His approach offers tangible, real-world strategies relevant for SMB leaders navigating AI adoption.

Key Takeaways for Immediate Action:

1. Clearly Identify a Real, Painful Problem
Michael began by pinpointing a substantial internal issue: fragmented organizational knowledge that wasted valuable employee time. By carefully quantifying this problem, he clearly demonstrated the hidden costs and urgency.

"We were just losing way too much time because people were hunting through different resources to find answers to simple questions."

2. Secure Quick Wins to Prove Immediate Value
Michael’s team built a custom GPT that quickly unified and surfaced critical organizational knowledge. Specifically, this GPT was trained on their existing documentation repositories—such as Confluence and Highspot—allowing employees to query one centralized AI-powered interface instead of manually searching multiple platforms. Although exact numeric outcomes will be detailed in part 2, early results clearly indicated significant time savings and vastly improved access to internal knowledge.

"It was really clear we were saving a ton of time and increasing access to knowledge. This experiment made it obvious what was possible."

3. Generate Natural Virality Through Strategic Early Adoption
Michael stressed intentionally creating excitement and curiosity by initially offering GPT access to enthusiastic early adopters. This strategic rollout fostered genuine interest and enthusiasm, organically driving adoption across the organization.

Michael noted specifically:

"We handpicked individuals we knew would experiment and talk about their experiences. That created internal virality and excitement around AI."

Why It Matters:

If you're skeptical about whether AI can genuinely add value to your business, Michael’s approach provides clear evidence that successful AI implementation starts small, remains focused on tangible, measurable improvements, and naturally gains momentum from internal enthusiasm. This practical strategy can help you confidently introduce AI without overwhelming your team, ultimately transforming initial skepticism into genuine excitement and real business impact.

Post 2: Impactful Results & Surprising Insights from UserTesting’s AI Journey

Let's now dive deeper into the impactful outcomes and unexpected lessons they've discovered.

Impactful Results:

• Significant Time Savings: Before implementing their custom GPT, UserTesting's employees frequently struggled with fragmented information across platforms like Confluence and Highspot. Michael emphasized:
  "We significantly reduced the time employees spent hunting through different resources for answers, addressing a major productivity bottleneck."
• Operational Efficiency Boost: Easier access to critical information notably improved productivity, allowing teams to shift focus from routine tasks to strategic, high-impact projects. As Michael pointed out:
  "It was really clear we were saving a ton of time and increasing access to knowledge. This experiment made it obvious what was possible."
• Enhanced Marketing Performance: GPT-powered marketing campaigns became quicker to produce and launch, significantly improving the quality and responsiveness of marketing initiatives. Michael described:
  "We created marketing campaigns with better returns and could execute more rapidly, clearly demonstrating the value AI brought to our teams."

Unexpected Insights:

• Custom GPT Limitations: Although GPTs delivered immediate benefits, UserTesting identified notable limitations, particularly regarding dynamic data management and complex API integrations. Michael highlighted:
  "Initially, we thought GPTs would integrate seamlessly with dynamic data sources. In reality, certain workflows required additional custom-built solutions."
• Persistent Cultural & Operational Challenges: Maintaining adoption momentum proved challenging, necessitating structured experimentation and consistent internal communication. Michael emphasized:
  "Teams naturally resist altering familiar workflows. Frequent communication about tangible benefits and success stories was essential to drive sustained adoption."
• Critical Importance of ROI Measurement: Michael strongly advises consistent ROI tracking, emphasizing its crucial role in guiding investments and prioritizing impactful initiatives:
  "Measuring ROI isn’t optional, it’s critical. It clarifies priorities and helps effectively communicate the business case to leadership."

Why This Matters for SMB Leaders:

These practical results and insights underscore critical lessons for SMBs initiating AI transformations:

• Clearly define and measure tangible outcomes quickly.
• Be ready to adapt and refine your approach based on real-time learnings.
• Prioritize consistent experimentation and transparent internal communication.

Part 3: Strategic Leadership Lessons from UserTesting's AI Success

The Leadership Reality Check

Michael Domanic's experience at UserTesting reveals a critical truth: AI transformation isn't just about technology—it's about leadership strategy.

Here's what separates successful AI adoption from failed experiments:

1. You Need Both Top-Down Vision AND Bottom-Up Energy

The Challenge: Most SMBs pick one approach—either mandate AI from the top or let teams experiment freely. Both fail alone.

Michael's Solution: "Neither top-down nor bottom-up alone is sufficient. Leadership must clearly communicate vision and expectations, while still encouraging employee-led innovation."

For SMB Leaders:

• Set clear AI objectives and communicate them regularly
• Create safe spaces for experimentation
• Celebrate and share internal wins publicly

2. Governance Isn't Optional (Even for Small Teams)

The Mistake: Thinking "we're too small to need AI policies."

The Reality: Clear boundaries prevent costly mistakes and build employee confidence.

Michael emphasized: "Establish clear boundaries around data use from the start. Consistent communication ensures trust and compliance."

Your Action Items:

• Define what data can/cannot be used in AI tools
• Create simple usage guidelines (not complex policies)
• Communicate these boundaries regularly

3. CEO Involvement Is Make-or-Break

Key Insight: The CEO's attitude toward AI directly predicts adoption success.

Michael noted: "The CEO sets the tone. Regular, clear communication from leadership significantly enhances organizational buy-in."

For SMB CEOs:

• Use AI yourself in strategic work (board prep, strategy sessions)
• Share your AI experiments with your team
• Address concerns directly and honestly

Your SMB Action Plan

Start Here: The 30-Day AI Pilot Framework

Week 1-2: Problem Selection

• Identify one repetitive task that wastes 2+ hours per week per employee
• Quantify the current cost (time × hourly rate × affected employees)

Week 3-4: Quick Implementation

• Deploy a custom GPT or AI tool for this specific problem
• Train 2-3 enthusiastic early adopters
• Measure time savings daily

Example from UserTesting: Their knowledge fragmentation problem was costing significant productivity. A custom GPT solution provided immediate, measurable relief.

The Power of Custom GPTs for SMBs

Michael's team found GPTs excel at:

• Internal knowledge queries (replacing endless document searches)
• Routine document generation (reports, emails, proposals)
• Marketing campaign creation (faster, more consistent output)

Michael's advice: "Custom GPTs work exceptionally well for routine, repetitive workflows. Start there, achieve quick wins, and build momentum."

Measuring Success (Don't Skip This)

Why It Matters: Michael stressed that ROI measurement "isn't optional—it's critical for prioritizing investments and communicating value to leadership."

Simple SMB Metrics:

• Time saved per employee per week
• Quality improvements in output
• Employee satisfaction with new workflows

The SMB Advantage

Unlike large enterprises, SMBs can:

• Implement changes quickly without bureaucracy
• Get direct CEO involvement from day one
• Pivot strategies based on immediate feedback
• Create company-wide impact with small wins

Post 4: The Four Critical Traits of Successful AI Transformation Leaders

Throughout this series, we've explored UserTesting's AI transformation journey—from Michael Domanic's initial strategy and quick wins to measurable business impacts and leadership lessons. But one critical question remains: What makes someone the right person to lead AI transformation?

This isn't a trivial question. As Michael emphasized in our conversation, "This is not the side gig of a half dozen people in your company. You want to appoint somebody that this is going to be their dedicated mission."

The Challenge: AI Leadership is Different

Unlike deploying traditional enterprise software like Salesforce—where you teach users to "push that button, pull that lever, get that output"—generative AI is fundamentally different. It's abstract, unpredictable, and requires creative problem-solving.

As Michael put it: "We've never deployed a technology like generative AI in our businesses before. It's a pretty abstract technology, and you need someone who can think creatively about how to translate that abstract layer into something that feels real and tangible."

The Four Essential Traits

Based on his experience leading UserTesting's transformation, Michael identified four critical characteristics:

1. AI Tool Fluency

"You want somebody that's going to understand what the capabilities are and make sure we understand how those capabilities connect to the business."

This means more than just knowing how to use ChatGPT. The leader needs to understand:

• What different AI tools can and cannot do
• How capabilities are evolving
• Which use cases deliver the most business value
• When custom solutions are needed vs. off-the-shelf tools

2. Cross-Functional Business Knowledge

"You're not an expert in HR, but you understand what HR does. You're not an expert in marketing, but you know what marketing does."

The AI transformation leader must be a business generalist who can:

• Identify AI opportunities across all departments
• Speak the language of different functions
• Understand how departments interconnect
• Translate AI capabilities into function-specific benefits

3. Proven Execution Ability

"They should be someone that knows how to get stuff done."

This seems obvious but is crucial because AI transformation involves:

• Managing ambiguity and changing technology
• Coordinating across multiple departments
• Overcoming resistance to change
• Delivering results while learning and adapting

4. Deep Creativity (The Most Important Trait)

"These are deeply creative individuals. The reason why that's important is because we've never deployed a technology like generative AI in our businesses before."

This is Michael's most surprising insight. AI transformation requires someone who can:

• Envision new ways of working
• Connect abstract AI capabilities to concrete business problems
• Inspire others to embrace change
• Think entrepreneurially about opportunities

Finding Your AI Leader: It's Not Who You Think

Michael's advice on identifying creative leaders might surprise you:

"I don't think the AI transformation lead comes from any one specific discipline. There are creative individuals across all different roles in a company. By the way, in business, we have a term for this creativity—we typically call them entrepreneurs."

Look for people who:

• Have entrepreneurial backgrounds or mindsets
• Pursue creative hobbies outside work
• Think differently about business problems
• Have a track record of innovation within your organization

The key insight: Technical background isn't required. Creative problem-solving ability is.

Why This Matters for SMB Leaders

SMBs actually have advantages in AI transformation leadership:

1. Direct CEO involvement: Unlike large enterprises, SMB CEOs can personally champion the transformation
2. Faster decision-making: Less bureaucracy means quicker pivots and implementations
3. Closer team relationships: Easier to identify creative, entrepreneurial employees
4. Immediate impact: Small teams mean individual contributions are more visible

Your Action Plan

1. Assess your current team: Who demonstrates creative problem-solving?
2. Look beyond job titles: The right person might be in sales, operations, or marketing
3. Consider entrepreneurial backgrounds: Past or current side projects are good indicators
4. Evaluate learning agility: AI tools evolve rapidly—choose someone who adapts quickly

The Bottom Line

AI transformation success isn't about having the most technical expertise or the biggest budget. It's about finding the right leader who can bridge the gap between AI's abstract possibilities and your business's concrete needs.

As Michael concluded: "You just have to be a little bit creative about who that creative person is."

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