David Turk is Vice President of Digital Strategy & Solutions at GEI, leading digital strategy and AI adoption across the AEC industry.
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Organizations are rolling out AI faster than ever. But many are discovering the same reality: AI does not automatically change how work gets done.
Too many firms still treat AI like a software rollout instead of what it really is: a redesign of how people work, collaborate and make daily decisions. So they launch pilots, experiment with new tools and layer AI onto fragmented workflows, then wonder why adoption stalls and the impact never fully shows up.
In many cases, organizations are using AI before fully understanding which workflows actually need to change in the first place.
I’m seeing this firsthand across the architecture, engineering and construction (AEC) industry, where both leadership and clients are already expecting AI efficiency gains before many firms have meaningfully integrated AI into their day-to-day operations. That pressure carries real weight in an industry responsible for shaping both the natural and built environment, where accuracy, accountability and trust matter.
Many organizations assumed that once AI arrived, transformation would naturally follow. It hasn’t.
Instead, many firms have spent more time selecting AI platforms than thinking about how this technology actually becomes embedded into daily workflows. Pilots continue to accelerate, yet many organizations are still struggling to turn experimentation into operational change.
I’ve seen organizations spend months planning AI rollout strategies while very little changes in how work is produced and delivered. Teams debate platforms, governance models and roadmaps before people even begin using the technology in their daily work.
Meanwhile, the organizations making real progress are often the ones that simply started. One workflow. One repetitive task. One practical use case that solved a real problem.
That momentum matters.
Many organizations underestimated what transformation actually requires. Deploying AI is the easy part. Reimagining what work should be automated—and how—is much harder, but far more valuable.
Many workflows across the AEC industry still rely on manual coordination, disconnected systems and constant handoffs between planning, design, engineering and construction teams, often across multiple contractors.
Today, tasks that once required hours of reviewing drawings, counting materials and cross-referencing plans can now be accelerated through AI-assisted workflows. Yet many organizations are still trying to layer AI onto the same delivery models instead of rethinking how work should move across the entire project lifecycle.
That is where the disconnect begins.
Determining which workflows should actually change cannot come from leadership alone. The people closest to the work often understand best where delays exist, where duplication happens and where AI can improve outcomes.
To realize the full value of AI, organizations must rethink the operating model itself—not just automate pieces of it.
AI does not fix broken workflows. It scales them.
The organizations making the most progress with AI are not treating employees like bystanders to transformation. They are involving the people closest to the work in redesigning workflows, improving decisions and identifying where AI can improve how work gets done.
I’m increasingly seeing domain experts and practitioners become active builders of AI-enabled solutions themselves. Not because they are software developers, but because they understand why certain problems continue to exist, where work slows down and how decisions get made.
Even OpenAI is now investing heavily in helping organizations integrate AI into daily operations and workflows, recognizing that access to AI models was never really the roadblock. The harder challenge is that most organizations are trying to fit AI into yesterday’s workflows instead of redesigning work for tomorrow.
That shift matters.
AI transformation cannot stay trapped inside innovation teams or leadership slide decks. It has to be shaped by the people actually doing the work every day.
People should not ask for permission to participate in that transformation. The invitation has already arrived.
As AI becomes more embedded into daily operations, leadership, trust and employee ownership become more important, not less. Reimagining work is ultimately a human process before it ever becomes a technical one.
The technology is no longer the limiting factor. The real question now is whether people, teams and leaders are ready to rethink how work actually gets done.
The organizations making the most progress are not waiting for perfect roadmaps or fully mature strategies before they begin. They are creating momentum by improving one workflow, solving one operational problem and building toward real results.
That matters because transformation rarely happens all at once. It grows gradually as people begin integrating AI into the habits, decisions and workflows that shape and improve their daily work.
The future of AI will not be defined only by the companies building the most advanced AI systems. It will be defined by the people willing to rethink and reshape the processes and behaviors around them.
Progress will come from participation, from experimentation and from ownership.
Some people will wait for the future of work to become clear. Others will build it.
That difference will define the next generation of leaders.
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