By: Kevin Woeste, PS, PLS, LS
CEO
Kevin is a Professional Surveyor licensed in Ohio, Kentucky, and Indiana. He earned his degree in Finance from the University of Toledo, and MBA from the University of Dayton, and a degree in Surveying & Mapping from the University of Akron.
AI and automation can help businesses work faster and more efficiently. But when technology changes how work gets done, it can also change how people gain experience and develop professional judgment.
That raises an important question for any organization adopting new technology: What were people learning through the work that is now being automated, and how will they learn those same lessons going forward?
Technology has long been part of how we serve our clients at McSteen. It supports our processes, improves efficiency, and helps projects move forward, but it has never changed the need for accurate information, professional judgment, and responsive communication.
Land surveying has been shaped by technology for decades. GPS, robotic total stations, digital data collection, drones, and advanced software have changed what survey professionals can accomplish in the field and in the office. These advancements have made the work more efficient and expanded the amount of information we can collect and process. Yet our core responsibility remains – to provide reliable information people can use to make important property decisions.
As businesses across industries consider how AI may change their workflows, surveying offers a useful perspective. Technology can remove steps from a process, but it can also remove the learning that once happened as people completed those steps.
That does not mean organizations should hesitate to adopt technology. It means they should be thoughtful about what changes along with it.
What Can Surveying Teach Us About Automation?
Surveying once relied more heavily on multi-person field crews. As equipment became more capable, many assignments could be completed with fewer people in the field, which was an important step forward for efficiency and productivity.
Modern surveying technology allows professionals to collect and process information more efficiently while maintaining the precision the work demands. However, larger crews also created learning opportunities that were not always visible on an organizational chart or included in a formal training plan.
Less-experienced team members were not simply helping operate equipment. They were working alongside experienced survey professionals and seeing how the work came together in real time. They had opportunities to watch how someone approached a difficult deed description, compared recorded information with physical evidence in the field, or recognized when conflicting information required a closer look.
Much of that learning happened naturally through observation, repetition, and conversation with more experienced colleagues. As the workflow changed, those opportunities changed as well.
That is not an argument against modern equipment or more efficient processes. It is simply a reminder that work often serves two purposes: it produces an immediate result, and it helps people build the knowledge and judgment they will need for more complex work later.
Technology Does Not Eliminate the Need for Judgment
Technology has made surveying more productive, but it has not eliminated the need for trained professionals who understand how to evaluate the information technology provides.
Surveyors still need to reconcile discrepancies between deeds, plats, recorded documents, evidence found in the field, and current property conditions. A drone can capture images, software can organize data, and GPS can help establish precise positions, but those tools do not replace the professional judgment required when the information does not neatly agree.
The same principle applies to AI.
AI can support routine work such as drafting, research, analysis, organization, and communication. Used well, it can help people spend less time on repetitive tasks and more time on work that requires experience and decision-making.
But faster output does not automatically mean better output, particularly when the work involves incomplete information, exceptions, competing interpretations, or consequences for a client.
Someone still needs to understand the work well enough to recognize when an answer is incomplete, inaccurate, or missing an important detail.
What Happens to Entry-Level Learning When Work is Automated?
Many entry-level responsibilities are structured, repeatable, and relatively easy to review. Those same qualities can make them good candidates for automation, which is one reason AI is likely to affect early-career work across many industries.
The challenge is that these responsibilities can also be how someone begins learning a profession. Preparing a first draft, researching records, organizing information, or working through a routine issue may not be the final responsibility a person holds, but it can be part of how they gain the context needed to handle more complex work later.
If AI completes the first draft, conducts the initial research, or performs the routine analysis, organizations need to think about where employees will develop the experience needed to review that work well. It is difficult to evaluate an answer, whether it comes from a person or a technology tool, without understanding how the answer was reached and what questions should be asked along the way.
Surveying has already experienced a version of that shift as technology changed how field and office work is performed. AI is creating a similar conversation in many knowledge-based professions.
The Opportunity is Better Workflow Design
The answer is not to automate less. It is to adopt technology with a clear understanding of how it affects the full workflow, including quality control, training, and client service.
Organizations should begin by looking beyond the task itself. A process may involve several people for good reasons, even if only one person appears to be responsible for the final deliverable. A junior employee may catch an inconsistency; a manager may provide context, and an experienced professional may make the final judgment call. If technology changes one part of that process, the organization still needs to make sure the right review steps and safeguards remain in place.
Efficiency without appropriate quality controls simply allows an organization to reach the wrong answer faster.
Training also needs to become more intentional when fewer learning opportunities happen naturally through day-to-day production work. Organizations should identify what employees once learned by observing experienced professionals, handling routine assignments, and working through exceptions, then determine how those lessons can be taught in a technology-enabled workplace.
That may include mentoring, structured reviews, case studies, simulations, or more deliberate exposure to difficult and unusual projects. In some cases, technology may even improve the learning process by allowing newer employees to work through a wider range of scenarios with guidance from an experienced professional, rather than waiting years to encounter those situations naturally.
The goal is not to preserve every manual task simply because it has always been done that way. The goal is to preserve the learning and judgment that those tasks helped develop.
Where Expertise Still Matters
Every organization should be clear about where people add the most value, rather than thinking only about which tasks can be automated.
In surveying, technology can help collect information, organize records, manage projects, streamline workflows, and improve communication. However, professional expertise matters most when the information does not line up as expected, when a property issue may affect a transaction or project, or when a client needs to understand what a finding means for their next step.
Clients do not simply need an automated status update when an unexpected issue arises. They need clear, timely communication from someone who understands the work, the possible implications, and the path forward.
That is not a shortcoming of automation. It is a deliberate decision about where expertise, accountability, and service create the greatest value.
At McSteen, we have spent decades adapting to changes in surveying technology while staying focused on the fundamentals our clients rely on: accuracy, responsiveness, professional expertise, and a smooth experience from start to finish.
AI is another significant technological change, and businesses should consider how it can support their processes and people. But adopting the tool is only the beginning. The more important question is whether the organization has considered what the new workflow will require from its people, how they will develop the judgment to do their jobs well, and where that learning will happen.
The strongest approach is not choosing between technology and people. It is using technology to make work more efficient while continuing to develop the people whose expertise, judgment, and service will matter most when the work is not straightforward.