AI is already reshaping AEC, and firms need leaders who can guide adoption with intention, training, and trust.
The question is no longer whether artificial intelligence will affect your AEC firm. It already has. The more urgent question is whether your leadership team is actually prepared to guide your people through what comes next.
At a recent SMPS AEC AI Masterclass, we spent 90 minutes talking through the really big issues, the ones that go well beyond which chatbot to try next. What emerged from that conversation was both a reality check and a roadmap. Leaders who are waiting for AI to stabilize before acting are already behind. But the firms doing it well share something in common: they are moving forward with intention, not just momentum.
Here is what we believe every AEC leader needs to confront, control, and cultivate in the AI era.
Confront: The seismic shift is already here
This is not just a tool
Early in the AI conversation, leaders across the AEC industry made a consistent and costly assumption: AI was a tool for IT, or maybe for marketing. It would live in a department somewhere and not require their attention.
That assumption is wrong, and the cost of holding onto it is growing daily.
Think about how previous waves of technology affected the industry. CAD transformed drafting. BIM changed project delivery. Both were significant, but both were largely contained to specific roles and disciplines. AI is different. It is a universal catalyst that touches every service line, every market sector, and every role, from the project manager buried in timesheets to the principal preparing a keynote to the field engineer supervising a site. If you think it will not reach a given function in your firm, you are already behind on that function.
The gap between leaders and employees is real
Here is a disconnect we see consistently when working with firms across the industry. When you ask leadership what is holding the firm back from advancing with AI, the most common answer is that employees are not ready. When you ask employees the same question, they say leadership is holding them back and nobody is providing training.
Both cannot be entirely true. What is true is that your employees are likely further along than you think. According to a Gallup survey cited during our session, only 8% of American workers use AI tools daily, and that estimate includes tools as basic as Grammarly. The firms represented in our session were largely ahead of that curve, with nearly 60% actively experimenting with AI tools. But the people in those seats were raising their hands while their firms were not keeping pace.
As a leader, the most important thing you can do right now is be honest about whether you are the bottleneck.
Control: Data, risk, and ownership
Who owns what your firm puts into AI?
One of the most pressing and underexamined questions in the AEC industry right now is data ownership. When your team uploads a proposal, a project narrative, or client specifications into an AI tool, what happens to that information?
The short answer: it depends on the tool, the version, and whether anyone in your firm has gone into the settings to toggle off model training.
Free versions of major platforms, including ChatGPT, Claude, and Perplexity, are typically using your inputs to improve their models. That means proprietary firm data, client information, and project content can all be absorbed into a shared model. Paid versions of these platforms offer settings to disable this, but those toggles are not turned off by default. Your team has to take the additional step.
The leadership action here is clear: audit which AI tools your team is using, determine what version of each tool they are on, and establish a firm-wide policy for how proprietary information is handled before it is uploaded anywhere.
SOC 2 compliance is your new baseline
In the absence of comprehensive federal data privacy legislation in the United States, SOC 2 compliance has become the most meaningful benchmark for evaluating AI tools in a business context. SOC 2 certification requires a third-party audit of how a technology company collects, stores, and grants access to data. It is not a guarantee of perfect security, but it is a meaningful signal.
When vetting any AI tool for use in your firm, SOC 2 compliance should be a minimum requirement. This is especially true for tools where your team will be uploading qualifications, RFQ responses, contract documents, or anything that touches client data.
The investment that is not paying off yet
A widely cited study found that 95% of firms that invested in AI reported no measurable return on that investment. At first glance, that number is alarming. But the reason behind it is entirely predictable.
Most firms purchased a tool before they established a reason to use it. They invested in the shiny object without first asking why their people needed it, what problem it was solving, or how they would be trained to actually use it. When employees are afraid the tool will replace them, and when no training is provided, adoption fails and the ROI never materializes.
Start with the why. Before any AI spend, define the specific challenge your firm is trying to solve. Then invest in training your people to address it.
Cultivate: Culture, skills, and the human edge
The skills that matter most are not what you think
For years, the most valued skills in AEC hiring were technical: engineering, math, science, and systems-level thinking. AI is inverting that hierarchy.
According to the World Economic Forum, the skills commanding the highest premium in the AI era are adaptability, critical thinking, communication, creativity, active learning, and the ability to collaborate across disciplines. These are the capabilities AI cannot replicate, and they are the ones that will differentiate your firm in interviews, on projects, and in client relationships.
We have been calling these soft skills for decades. It is time to stop. They are crucial skills, and they need to be trained with the same rigor we have applied to technical certifications.
In practice, the most successful firms we have worked with share a common profile: they are curious, flexible, experimental, and adaptive. These are not personality traits. They are organizational habits that can be built intentionally.
Budget for training or lose the people who would have used it
Training is the hill worth dying on. It always has been, and every roundtable in this industry eventually reaches the same conclusion. Yet firms consistently underfund it. In the AI era, that gap is closing faster than anyone expected.
Your employees are clamoring to learn. They want AI skills not because it is trendy but because they can see, even in glimpses, how it can make their work better. If your firm is not providing that training, you will lose the people who are most motivated to use it. And you will struggle to recruit the next generation, which is already building its career assumptions around working in AI-forward environments.
On the training investment itself, good news: you may not need to increase your overall budget. Look at where you are spending today. There are very likely line items that are less urgent than AI literacy for your team. Reallocate before you add.
A practical training approach for firms looks like this:
- Identify your early adopters and invest there first. These are the people who will move fast, pilot ideas, generate ROI, and then teach others. You do not have to pull the laggards along; you just have to get enough momentum going that they follow on their own.
- Combine tool training with hands-on application. Classroom or virtual learning must connect directly to real workflows. The built environment is a visual, tactile industry. People need to see these tools working in context.
- Leverage your senior talent as knowledge holders. Before they retire, capture what they know. Not just project history, but process wisdom, client relationships, and institutional memory. There is an emerging role for this, sometimes called a data curator, that may be one of the most strategically important hires your firm makes in the next five years.
- Train for critical thinking, not just tool operation. AI output requires human judgment. People at all levels, across all generations, need to develop the habit of pausing before accepting what a model produces.
Protect the knowledge that walks out the door
Every AEC firm has a version of the same problem: a senior employee retires or moves on, and they take with them 20 years of project knowledge, client relationships, and institutional wisdom that was never formally captured.
AI changes your options here. You can now collect information from your people in almost any format – text, interviews, audio, video, written summaries – and build a “Story Bank,” a repository of firm knowledge that does not disappear when a person does. That knowledge can be used for training, for proposal development, for onboarding, and eventually as a firm-specific knowledge base that any AI system can reference.
The firms that start building this now will have a structural advantage over those that wait.
The leadership responsibility no one is talking about
Leadership in the AI era is not primarily about understanding the technology. It is about creating the conditions for your people to evolve alongside it.
That means making decisions about org structure before AI forces them on you. It means asking whether your current hierarchy is designed for the next five years or the last 30. It means exploring questions that may feel uncomfortable: What roles will look fundamentally different? Could new employees build an AI assistant tailored to their function during onboarding? What happens to your firm's competitive positioning when AI automates another layer of technical work?
It also means being clear-eyed about what humans bring that AI does not. Not as a defense of the status quo, but as a strategic asset to develop deliberately.
AI is remarkably capable at processing information, generating content, and identifying patterns. It is not capable of the human judgment that wins a project interview, the relationships that bring a client back, or the intuition built over a career of complex problem-solving. The firms that will lead in this industry are not the ones who replace human capability with AI but the ones who use AI to amplify what their people already do well.
One of our favorite framings: human-driven, AI-powered. That is the organizational model worth building toward.
Where to start
If this conversation has surfaced more questions than answers, that is exactly where you should be. The leaders who navigate AI well are not the ones who have all the answers. They are the ones asking better questions, moving forward in spite of uncertainty, and refusing to use their employees' readiness as an excuse for their own.
A few places to begin:
- Audit your firm's current AI tool usage. Know what your people are actually using, at what subscription level, and what data they are putting into those tools.
- Identify your early adopters and give them the space and support to experiment. Then create a structure for sharing what they learn.
- Put training in the budget before the next fiscal year. Not as a line item you will revisit, but as a commitment.
- Start capturing institutional knowledge now, before the next wave of retirements takes it with them.
- Ask the big questions with your leadership team. What does your org chart look like in 2030? What roles are you designing for a world that does not yet fully exist?
The language of AI is learnable. The mindset required to lead in this era is cultivatable. The firms that move with intention and invest in their people will not just survive this shift. They will define what leadership looks like on the other side of it.
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Kristin Kautz is co-founder of JAM Idea Agency and Evidence Based AI, and serves as a fractional Chief AI Officer for AEC firms nationwide. Contact her at klk@JAMideaagency.com. |
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Troy Parkinson is principal at SagePresence, a firm that helps AEC professionals communicate with clarity, confidence, and impact. Contact him at troy@sagepresence.com. |