Frustrated with miscommunication, costly delays, and endless bottlenecks on your projects? You’re not alone. Many agencies and consultants are still early in their digital transformation journey, and legacy processes continue to slow down collaboration and project delivery.
Organizations that embrace digital-first workflows have found ways to overcome these challenges. That’s why we brought together leaders from HDR and MJ Engineering for our latest webinar, What Digital-First Engineering Firms are Doing Differently.
Our panel explored the shift from paper-based processes to digital-first workflows and how that mindset creates a foundation for AI readiness. Panelists included:
- Mike Koterba, PLS, Chief Technology Officer, MJ Engineering
- Mike Pianka, GSP, Director of Software Development, MJ Engineering
- Drew Dragoo, PE, Construction Services Manager, HDR
Below, we’ve summarized key insights from their discussion, along with a practical Digital-First Playbook your organization can adapt today. These excerpts have been edited for clarity and length. The full discussion is available here.
Proving Value to Owners
How do you get ownership buy-in for embracing new, digital delivery solutions?
Koterba: What we’ll do is try to prove the value to our clients of working with these digital models. So, a lot of times, your traditional project deliverables will be those paper plans and profiles. Meanwhile, on the backend, we’ll start an existing survey by capturing rich 3D data, building an existing conditions model, and that project will be designed in 3D. With the right contractor, that same design model can be used to construct a project. So, just kind of proving out the workflow of the project and showing the benefits that come from that workflow, with fewer transcription errors and things like that.
Takeaway: Demonstrating real project efficiencies is the fastest way to earn buy-in. Start small, show measurable improvements, and scale from there.
Connecting Construction and Asset Management
How does capturing GIS data during construction support the asset management phase of the transportation asset lifecycle?
Pianka: After construction is completed, and the project moves from construction to asset maintenance, having that data in GIS is a great first start. But then, having it in something like a digital twin viewer, where that spatially located data is referenced on top of 360 camera photos of the site and 3D scans of the site, provides a good immersive platform for those asset owners going forward. The GIS data is at their fingertips, and it’s integrated – it’s not a flat base map, it’s the real 3D data in a contextual environment.
Takeaway: Integrating GIS and 3D data helps agencies manage assets more intelligently, with spatial context and visual references that make data usable long after construction ends.
Driving the Digital-First Evolution
Are local and stage agencies pushing for these types of tools, or are engineering firms leading the charge for a better deliverable?
Dragoo: It’s kind of both ways. The expectation to deliver and get reports out to clients in a more timely fashion is what’s driving digital-first for consultants. We all want to have that collaboration between design teams, construction teams, and maintenance teams, and have those files in a way that can be utilized. So you can kind of see it from both sides of the consultant pushing it and the owner pushing it. They want to integrate those tools so they can better manage the lifecycle and assets of their projects for the future. Having these tools gives us the capability to sort out information and view it in real-time as the data is inputted and collected.
Takeaway: Digital-first success relies on collaboration—consultants and owners moving in tandem toward shared, data-driven systems.
Preparing for AI-Enabled Workflows
How will AI help solve the disconnect between design, construction, and asset management to create digital continuity?
Pianka: With digital transformation, you’re not going to go from 0-60. A lot of agencies that are more progressive have a lot of digital records, but not necessarily organization and cohesion across projects. The move to GIS is where a lot of them are finding themselves now. Using GIS as a means to track their records and documents from work over time, in addition to actually maintaining infrastructure layers, underground utilities, etc. Those records can live and breathe as projects evolve and new data comes in.
Going beyond that, the next step is, how do we leverage AI to really sift through this information and help people with organizing it? That’s happening now, and a big part of that is offering good metadata on the GIS layers. That additional context will empower the AI to help you more. That’s a big thing going forward – not just having digital documents, but having metadata that the AI can read and understand beyond the document data itself – that’s going to help with organization.
Dragoo: Adding to that, I think the way we design our different form fields when we are collecting the data and how we set expectations for nomenclature on line items – how you define those terms is going to allow us to better predict the projects. How we set the project up allows for AI to be utilized in the future to help generate estimates for cost. I have this much square footage, this much quantity – what’s the average cost based on all our projects over the last four or five years?
Takeaway: The foundation for AI readiness starts today, with clean, consistent data and a thoughtful approach to metadata.
Safety and Efficiency in Action
Can you share an example of how a digital-first process supports safety and efficiency?
Pianka: This traffic sign inventory project was one of our earliest projects where we really injected AI into the workflow and went digital-first from the start. Before that, the way to tackle this project would be a lot of boots on the ground with GPS units. Being a roadway traffic sign inventory, there are scalability concerns and safety concerns.
On this project, we mobile-mapped all the streets of interest. From that mobile mapping data, we had people doing traffic sign inventory from a desk environment. They were starting from a shortlist of detected signs that came from an AI model that we ran on the mobile mapping imagery to have a first cut on the signage, so the task was mainly review and classification. Overall, it was a really good capstone project for us to put into practice those technologies and bring efficiencies in terms of time and cost to the project.
Takeaway: A digital-first process not only saves time and money—it can also reduce risk and improve safety in the field.
The Digital-First Playbook You Can Adapt
- Define the first use case. Avoid scope sprawl.
- Standardize your toolkit and workflows.
- Replace file handoffs with shared system access.
- Instrument the field with validation and location context.
- Establish your data exchange (APIs, formats, governance).
- Teach the org: reverse mentoring, QMS, retraining for turnover.
- Show value to owners: deliver tools they can actually use post-project.
- Plan for change management.
As our panelists made clear, going digital-first isn’t just about adopting new tools, it’s about rethinking how data flows across the entire project lifecycle. From survey to design, construction, and maintenance, digital continuity reduces errors, improves collaboration, and lays the groundwork for AI-driven insights.
If you want to hear our full conversation with HDR and MJ Engineering, it’s available here. To learn more about how Infotech works with engineering teams to provide tools that support accurate source data capture and GIS integrations, visit our solutions hub for engineering consultants.
Author
Nate Binder
Digital Marketing Manager
A proud graduate of Florida State University, Nate works with subject matter experts and sales professionals to produce targeted marketing collateral.