Why the firms seeing results from AI are investing in people, training, and culture before they invest in more tools.
Most small firm architects are feeling the pressure. Clients expect faster responses. Projects are becoming more complex. Competition continues to increase. At the same time, we’re being told that artificial intelligence is about to transform everything. New tools appear every week, each promising to save time, improve productivity, and give firms a competitive advantage. For owners already balancing project deadlines, staffing challenges, business development, and leadership responsibilities, it can feel like one more thing demanding attention.
So we do what architects have always done when faced with a challenge. We start looking for solutions. We sign up for ChatGPT. We experiment with new platforms. We watch webinars and listen to podcasts. Before long, we have a growing collection of AI tools and subscriptions, yet very little has actually changed inside the firm.
That observation struck me while speaking with Michelle Hamilton on the EntreArchitect Podcast. Michelle has spent decades working in the architecture, engineering, and construction industry before transitioning into AI adoption and change management. Early in our conversation, she shared a simple insight that completely reframed the discussion for me. The firms struggling with AI are not usually struggling because they chose the wrong technology. They’re struggling because they approached AI as a software purchase instead of an organizational change initiative.
That distinction matters.
Many firm owners are searching for the right tool when they should be asking a different question entirely: How do we prepare our people to succeed with this technology?
As our conversation unfolded, it became clear that AI adoption for architecture firms has very little to do with technology and almost everything to do with leadership.
The Mistake Most Firms Make
As architects, we are trained to solve problems. When we identify an inefficiency, our instinct is to find a better system, a better process, or a better tool. That mindset has served us well throughout our careers, but AI requires a different approach.
Michelle shared that one of the first questions she asks organizations is surprisingly simple: What are you trying to improve?
Many firms struggle to answer that question with clarity. They know they want to save time. They know they want to become more efficient. They know they don’t want to fall behind. But they haven’t identified exactly where the problem exists or how success will be measured.
Before introducing AI into any workflow, Michelle recommends identifying repetitive activities that consume significant time. Tasks that occur several times each week and require fifteen minutes or more are often ideal candidates for improvement. Once those activities have been identified, firms should document the current process and establish a baseline.
The reason is simple. If you don’t know how long something takes today, you won’t know whether AI improved it tomorrow.
This is the same discipline we apply to project management, financial performance, and business development. We track utilization, profitability, and project schedules because measurement drives improvement. AI adoption deserves the same level of intentionality. The firms seeing meaningful results are not chasing every new tool that enters the market. They are identifying specific business challenges and using AI strategically to solve them.
Michelle recommends approaching AI implementation the same way we approach any important business initiative. Establish a baseline, run a focused pilot program, measure the results, and make informed decisions based on evidence rather than assumptions. That process may not be as exciting as experimenting with the latest AI platform, but it is far more likely to generate meaningful results.
Leadership Readiness Comes Before AI Readiness
One of the most important parts of my conversation with Michelle centered on a topic many leaders avoid discussing: fear.
According to Michelle, employees frequently ask whether AI is being introduced to replace them. Sometimes the question is asked openly during training sessions. More often, it surfaces later in private conversations. That concern is understandable. Employees hear the same headlines we hear. They see the same predictions about automation and workforce disruption. When leadership begins talking about AI, many naturally wonder what it means for their future.
What I appreciated about Michelle’s perspective was her honesty. She doesn’t dismiss those concerns or pretend they don’t exist. Instead, she focuses on helping employees understand that learning to work effectively with AI can increase their value rather than diminish it.
I think many small firm owners underestimate how much uncertainty exists inside their studios right now. When leaders don’t address difficult questions directly, people fill the silence with their own assumptions. Those assumptions are often driven by fear rather than facts. Trust begins to erode, resistance increases, and adoption slows.
Successful AI adoption requires leaders to communicate clearly about why AI is being introduced, how it will be used, and what role employees will play in the process. These conversations have very little to do with technology and everything to do with leadership.
Before a firm becomes AI-ready, it must first become leadership-ready.
The Hidden Risk of Shadow AI
Another topic Michelle introduced was something many small firm owners have never considered: shadow AI.
This occurs when employees use AI tools that have not been approved by the firm. In many cases, leaders believe limiting access reduces risk. The reality is often the opposite. When employees are curious about AI but don’t have approved tools available, they frequently seek alternatives on their own.
The issue is not experimentation. Experimentation is often how innovation occurs. The issue is data.
Employees may upload project information, client communications, specifications, contracts, or internal documents into tools that have never been reviewed by the firm. Often, they do so without fully understanding the implications. This creates risk not because employees are acting irresponsibly, but because they are acting without guidance.
Michelle emphasized that firms should focus on creating safe pathways for adoption rather than attempting to prevent adoption altogether. Providing approved tools, establishing expectations, and educating employees on best practices creates an environment where innovation can happen responsibly.
People generally want to do the right thing. They simply need leadership to show them what that looks like.
Governance Doesn’t Need to Be Complicated
The word governance can sound intimidating, especially for small firm owners who already wear too many hats. Fortunately, governance does not need to be complicated.
Michelle recommends starting with a simple governance statement. Most firms already have employee handbooks that address workplace conduct, technology use, confidentiality, and professional expectations. AI can be incorporated into those existing policies without creating unnecessary bureaucracy.
A basic governance policy might address which AI tools are approved, what information may be shared, what information should never be uploaded, and what level of human review is required before work is delivered to clients.
The objective is not control. The objective is clarity.
When employees understand the boundaries, they gain confidence. They know what is expected and can focus on learning how to use the technology effectively. Clear expectations eliminate uncertainty and create consistency across the organization.
For many firms, that clarity will provide more value than the technology itself.
Training Is the Real Investment
One observation Michelle shared stayed with me long after our conversation ended.
Most firms are willing to pay for software. Far fewer are willing to pay for training.
Yet training is where the value is created.
Think about how we approach software like Revit. We don’t purchase a license and expect someone to become proficient overnight. We invest in onboarding, mentoring, education, and ongoing support because we understand that software alone does not create expertise.
AI deserves the same level of commitment.
Too often, firms purchase subscriptions and expect employees to figure everything out on their own. The result is inconsistent adoption, uneven results, and widespread frustration.
The organizations seeing the greatest return from AI are investing in learning. They encourage experimentation. They share discoveries. They create opportunities for employees to develop new skills together. Most importantly, they recognize that AI literacy is becoming an essential professional competency.
Just as architects learned CAD, BIM, and cloud-based collaboration, we will also learn how to work effectively with AI. The firms that embrace that reality today will have a significant advantage tomorrow.
The Future Is Bigger Than Productivity
Most conversations about AI focus on efficiency, and those benefits are certainly valuable. Reducing administrative work, improving documentation, and accelerating routine tasks can create measurable gains for any firm. But Michelle encouraged us to think beyond productivity and consider something much more interesting.
She described architects using AI as a thinking partner.
During our discussion, we explored how architects are beginning to use AI to evaluate drawings, identify inconsistencies, test assumptions, and think through design challenges. Rather than simply generating content or automating repetitive tasks, AI can help architects explore ideas, identify blind spots, and evaluate possibilities that might otherwise go unnoticed.
That distinction is important because architecture has never been about producing drawings alone. It is about judgment. It is about solving problems, balancing competing priorities, and helping clients make better decisions. AI does not replace those responsibilities.
What it can do is help us explore more options, ask better questions, and process information more efficiently. The architect remains at the center of the process. The expertise remains essential. The judgment remains irreplaceable. AI simply becomes another tool that helps us apply those skills more effectively.
Building a Culture of Learning
One of my favorite moments from our conversation involved the relationship between generations inside architecture firms.
Experienced architects bring decades of professional judgment, technical expertise, client management skills, and real-world experience. Younger professionals often bring greater comfort with emerging technologies and a willingness to experiment with new workflows.
Both groups have something valuable to contribute.
The firms that thrive over the next decade will create environments where that exchange happens naturally. Senior architects can mentor younger team members in the art of professional practice. Younger architects can help experienced leaders understand new technologies and opportunities. Together, they become stronger than either group could be independently.
That kind of collaborative learning culture will become increasingly important as AI continues to evolve. The firms that learn together will adapt faster than the firms that leave learning to individual initiative.
Leadership Before Technology
As I reflect on my conversation with Michelle Hamilton, I keep coming back to a simple conclusion. The future of AI adoption for architecture firms will not be determined by technology alone. It will be determined by leadership.
The firms that thrive over the next decade will be led by owners who invest in training, communicate openly, create cultures of learning, and help their teams navigate change with confidence. They will understand that technology adoption is fundamentally a human process.
AI has the potential to improve efficiency, strengthen decision-making, and expand what small firms can accomplish. But those benefits will not come from software licenses alone. They will come from leaders who invest in their people and create an environment where learning and innovation can thrive.
If you focus on training before tools, communication before implementation, and leadership before technology, you’ll be far more likely to realize the promise of AI while strengthening your firm in the process.
To hear my full conversation with Michelle Hamilton, including our discussion on AI governance, shadow AI, training, and the future of architectural practice, visit https://entrearchitect.com/665 and listen to Episode 665 of the EntreArchitect Podcast.
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