We are engineers. That means we understand the use of a good tool. To us, AI isn’t a novelty or a shortcut, it’s simply another tool in the arsenal, and one that we think has its value when supported by a solid team of knowledgeable people. As the tale goes: a client once complained about the bill sent by an engineer.
The engineer simply shrugged and sent over a revised invoice. The new, itemized bill reads; Hammer: $5. Knowing where to hit the machine with hammer: $4995.
Leading BFW/Marcum’s AI effort is Jimmy Riemer, the firm’s AI & Business Innovation Manager. His goal is simple: find where AI can make life easier for engineers and prove its value through real work.
“Education and training are an important part of any new technology rollout, especially with emerging technologies like AI where the pace of change has been hard to keep up with,” Riemer said. His approach is direct. “I provide our employees with detailed demonstrations of how to use AI in their daily workflows and empower them to experiment themselves. By keeping open lines of communication and encouraging experimentation, employees can begin to lead training sessions themselves. This secures buy-in at the ground level and ensures individual wins and progress can be shared.”
That kind of peer-to-peer learning helps make AI adoption feel less like a mandate and more like collaboration. Many companies struggle with this balance. Studies have shown that most organizations fail to move from small AI experiments to large-scale, profitable deployment because they focus on the technology before they focus on the people using it (source).
Starting With the Work, Not the Tools
Riemer approaches every project from the workflow backward. “More AI tools and platforms are being created every day—it can be overwhelming to cut through the noise to find value,” he said. “The important thing to keep in mind when evaluating external tools or designing in-house solutions is your work process.”
Instead of shopping for technology first, he and his team start by identifying real pain points. Once they know what’s slowing down a process, they look for ways to fix it. It could be either with an existing tool or something custom. “Before looking at solutions, we define specific challenges and then design or buy,” Riemer said. “By using this bottom-up approach we know what we’re looking for. We explore what’s already been created and what features are easily replicated in-house. Once that’s clear, we can see the gaps and make informed decisions.”
That mindset keeps the firm from chasing every new trend. It also ensures that when AI shows up in a workflow, it serves a clear purpose and has a way to be measured.
Measuring What Matters
Riemer admits that proving the return on the investment is one of the hardest parts of adopting AI. “ROI can be a difficult subject when it comes to AI deployments,” he said. “If an employee uses Copilot to assist in drafting a document, that may cut their time from eight hours to thirty minutes. That’s a material return in operational efficiency but if the efficiency gain isn’t tracked, how can it be documented?”
He’s right. The returns on AI often start small and invisible. They show up in minutes saved, fewer re-dos, and better focus. “The key lies in open, frequent communication with end users,” Riemer said. “We capture ROI in the form of time saved in addition to dollars on the balance sheet.”
This approach mirrors broader findings in the industry. GitHub’s study of its Copilot users showed that developers completed coding tasks about 55% faster on average (source). While the savings didn’t immediately show up as higher revenue, the difference in productivity and job satisfaction was substantial.
Where AI Fits in Engineering Workflows
Inside an engineering firm, AI is beginning to prove its worth in predictable places. It helps draft technical memos, summarize meeting notes, and prepare first-pass reports that human engineers can review. It can search old project archives to find similar past work and help project managers prepare clear client updates from raw data. It can also clean up and organize spreadsheets before a licensed engineer steps in to interpret the numbers.
Each of these tasks saves time. More importantly, they free people to focus on analysis, design, and decision-making. These are the non-negotiable parts of engineering that require human judgment. Research continues to show that when AI supports the early, repetitive stages of a process, teams move faster and make fewer mistakes (source).
Turning Experiments into Everyday Practice
Riemer’s method for introducing AI is steady and transparent. He begins by working closely with small groups who are open to trying new ideas. Together they track how long tasks take before and after a new tool is introduced. When something saves time without sacrificing quality, it becomes a repeatable process that others can adopt. When it doesn’t, it’s revised or dropped.
That cycle creates a sense of ownership among employees. They aren’t being told to use AI but instead helping to inform how it’s used. Over time, that builds confidence and trust. It also gives leadership real data about what’s working.
What This Means for Clients
For clients, the benefits are subtle but real. Reports come faster and with fewer clerical errors. Meeting summaries are clearer and timelier. Schedules tighten not because people are rushing, but because tools are handling the repetitive work that used to slow them down. The result is smoother communication and a more predictable project experience.
Looking Ahead
Riemer sees the next few years as a period of quiet integration. “I think we’ll see AI embedded in more of our daily tools,” he said. “It’ll handle the setup work—organizing, formatting, summarizing—so engineers can focus on design decisions and problem-solving.”
That’s not a dramatic prediction. It’s a practical one. BFW/Marcum isn’t trying to automate engineering judgment. It’s trying to give engineers back their time. If a tool can turn eight hours of routine writing into thirty minutes of editing, that’s a win worth repeating. And if that gain is measured, shared, and taught, the impact multiplies across the company.
Riemer’s approach captures the spirit of the firm itself: curious, careful, and grounded in real results. In an industry full of hype about what AI might do someday, BFW/Marcum is quietly proving what it can do today.
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