Mike Winn is the CEO and co-founder of DroneDeploy, a robotics and AI platform used on over 3 million sites worldwide.gettyConstruction's biggest overruns aren’t engineering failures; they’re documentation failures. A 2025 McKinsey review (registration required) of more than 300 billion-dollar-plus megaprojects found average cost overruns of 80% and schedule delays of 50%. On a $1 billion data center, the difference between catching an install error during foundation, steelwork or framing and discovering it after the walls are closed can easily run seven figures. Whether anyone catches it comes down to whether a current picture was taken and analyzed before the next trade started work. What exists instead is often a memory of a site walk from last week, photos on someone’s phone and a disagreement about what was where.From Walking The Site To Getting StreetviewA decade ago, documenting a jobsite involved walking every floor with a clipboard and a camera. Drones started the change. From 2016, project teams were flying weekly missions and creating a Google Earth-consistent aerial record of earthworks at centimeter resolution and accuracy. From 2017, consumer 360-degree cameras began to be used to capture interiors and create a timestamped, location-based walkthrough of every floor. Together, these tools created what the industry now calls “reality capture,” a navigable record of a project as it actually exists rather than as the schedule says it should.It was a step forward, but every part of the workflow still required someone to schedule the flight, walk the floor with a 360-camera and upload it before anyone could reference the results. Documentation improved, but the manual dependency persisted.What Changes When The Robots Do The WalkingOver the last few years, two things happened in parallel that fundamentally changed this. Docked drones started flying, repeating missions on their own, producing a fresh 3D model, ready for the crew stand-up each morning. Ground robots have begun navigating active interiors without a handler, capturing 360-degree images. Running daily, they produce a complete visual record of a site, saving the field team thousands of steps.The second shift was what became possible with AI. The same capabilities rewriting software engineering, medicine and your high schooler’s essay are now trained on construction data. No superintendent has the hours to review a full site capture every morning, so AI does the interpretation, comparing today's site against the plan, flagging what changed overnight and surfacing what needs attention before the morning stand-up. Instead of spending the first hour reconstructing what happened, the superintendent starts each day with an AI-generated brief and walks on site, knowing where to focus. ​What Separates The Deployments That StickEven after teams pick the right platform, implementations can still fail. The biggest blind spot is treating reality capture as a tool purchase rather than a behavior change. People are slower to change than technology. The hardware and software can be ready on day one, but getting a superintendent to open an AI brief before the morning huddle is a different project entirely. It requires deliberate training, repeated reinforcement and someone with enough authority to make the new routine stick. Most teams underinvest in that work because it's less visible than the procurement decision. The teams that get full value from reality capture treat human change as the real implementation. They build the new workflow before deploying the tool; identify who needs to change which habit; and run change management in parallel with the rollout rather than hoping adoption happens on its own.​That said, the technology still has to earn trust. Accurate visual documentation is the foundation on which everything else depends. If the data doesn’t align with design files across repeated captures, field teams stop trusting it almost immediately. One superintendent who sees misaligned data will close the app and not come back. Many vendors have built capable software on an insufficient positioning foundation, and it shows the first time someone attempts a real building information modeling (BIM) comparison.​Full-site autonomy matters as much as documentation does, because aerial-only platforms miss the interior, and any platform that still requires a dedicated operator creates the same friction as the manual process it was supposed to replace. I’ve also seen AI tools that can identify elements in imagery but have no awareness of the project schedule or design intent. A vision model that flags a floor slab without knowing it was due two weeks ago hasn’t told anyone anything useful.Better Decisions, EarlierWhat constrains superintendents is how much time every day gets consumed by documentation and piecing together what happened, rather than applying that judgment to what comes next. Teams working from a continuously updated, AI-interpreted view of their site start each day from a fundamentally different position. Over hundreds of daily decisions across multiyear projects, this quickly compounds into real money and real time back.Reality capture entered construction as a way to prove what was there after the fact. Autonomous robotics and AI are turning it into a live operating picture of a physical asset under construction. For the people who build and own these assets, and for the insurers and investors behind them, that changes how they evaluate risk on every active project in their portfolio.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?