How Contractors Can Adopt AI Agents Without Chaos
Construction finance is in the middle of the fastest tech shift the industry has seen. AI agents that read invoices, call superintendents for project updates, and chase change orders are no longer hypothetical — they’re being deployed in real contractor finance departments today.
But adoption is messy. Most contractors are running a stack of legacy systems, fragmented data, and teams that didn’t sign up to become AI operators. The ones rolling out agents successfully aren’t the ones with the biggest budgets. They’re the ones who got the foundation right before turning the agents loose.
In a recent episode of Builders, Budgets and Beers, Reece sat down with Ryan Rademann, who leads construction technology consulting at Wipfli, to talk through what actually works. Ryan has spent his entire career implementing systems for construction companies, watching the industry move from on-premise ERPs to cloud-based platforms to the current race toward agentic AI. The conversation is a practical playbook for any contractor who wants to adopt AI without breaking what’s already working.
Why construction is poised to leap ahead
For decades, the consensus was that construction lagged behind every other industry on technology adoption. Ryan pushed back on that framing — and made a case for why it might end up being an advantage. There’s a chance, he argued, that it actually becomes a benefit that construction companies didn’t go all in on systems pre-AI. The industry gets to skip right over the old school systems that every other industry fought through and now has to figure out how to uncroft in terms of tech debt. Construction gets to move right to intelligent systems.
Construction wasn’t able to adopt earlier waves of enterprise software cleanly because the jobsite was the last place to get connectivity, mobile devices, and usable interfaces. Now that those constraints are solved, contractors aren’t carrying the tech debt that other industries are spending years untangling. The opportunity is to skip directly to AI-native systems rather than layering automation on top of broken processes.
But skipping ahead requires getting three things in place first.
The three foundations contractors need before deploying agents
Ryan was direct about what he tells clients who want to start deploying AI agents tomorrow: slow down, then go fast.
1. Get your systems out of on-premise environments
The first prerequisite is straightforward: AI agents need clean, accessible API endpoints to do their work. On-premise systems make that nearly impossible. As Ryan put it, if you’re on a non-cloud ERP, you can’t keep kicking that priority down the path anymore. He sees a lot of contractors who want to spend all of their time on agentic systems and agentic engineering, but they end up pushing a rope uphill if they’re fighting with a gateway to an on-prem ERP’s back end.
The takeaway: if your ERP is on-premise, migrate to cloud before investing in agents. The migration itself can feel painful, but it’s the unlock for everything that comes next.
2. Map your processes — but map the right version
Process mapping is the second foundation. Ryan was specific about how to do it well. He’s careful, he said, about too much as-is process mapping, because if you map what you’re doing right this second, a lot of what you’re doing is in a goofy way because of a goofy system limitation. The better approach is to focus on what it would look like if the inefficiencies were managed out of the system.
Mapping the current state too literally bakes in workarounds and limitations that exist only because of broken software. The version worth mapping is the one that describes how the work should flow if the inefficiencies were removed — because that’s the version the agents will be running.
3. Build the data foundation
The third foundation is data infrastructure. Ryan talked about two parallel investments contractors are making: building a centralized data warehouse so that agents have one place to query, and investing in true integration platforms like Boomi, MuleSoft, or Workato rather than brittle point-to-point connectors.
He supports the basic Procore to Sage Intacct connector approach because it’s simple and easy. But one reason organizations are investing in a more bespoke middleware strategy on a true iPaaS platform is that the way you make systems talk today is different from how it used to be. It used to just be “I need to make the table in system A map left to right to system B.” Now, that’s where the intelligence actually needs to live. The integration layer is where AI now lives. Brittle connectors that worked fine for static data syncing aren’t enough when the data flowing between systems needs to be reasoned over by agents.
Where AI delivers the most value in construction finance
The biggest opportunity Ryan sees isn’t embedding AI into any single system — it’s the handoffs between systems and departments. The pain point, he explained, exists in the handoffs between departments. It’s the stuff nobody thinks they own. They kind of think it’s the other guy or gal’s problem, so there’s a lot of hot potato going on. Data moving between systems is where AI has the opportunity to make a huge difference in starting to act.
That’s exactly the gap Adaptive’s Project Accounting Agents are built to close. Agents that read invoices and code them to the right job, call the field for missing information, draft change orders, and track lien waivers all work on the seams between systems — the places where construction finance has traditionally lost the most time and money.
Ryan described his first experience with one of Adaptive’s voice agents. He was on the phone with Matt Calvano when the agent called him and asked for approval on an AP element. He said he hadn’t seen anything like that before. The shift he described matters: instead of finance teams chasing field workers and approvers for information, the agent calls them directly with a specific question. The friction inverts.
Structured vs. unstructured data — the new conversation in the C-suite
One of the more interesting trends Ryan flagged is how the conversation with CEOs has shifted. A few years ago, he said, a C-suite executive couldn’t be bothered to understand the basics of their relational database or their ERP. Today, those same executives are willing to explore concepts that are very intangible — concepts that may or may not ever apply to their specific architecture — because they want so badly to get ahead.
Specifically, executives are now thinking about the difference between structured data (the rows and columns in an ERP) and unstructured data (the conversations, photos, emails, and PDFs that contain critical context about a project). Agents can extract value from unstructured data in ways that traditional systems couldn’t, which changes how contractors should think about what data is worth capturing — and where to store it.
For construction finance teams, the practical implication is significant. A superintendent’s verbal update about a delayed electrical sub, captured by an AI agent during a routine call, can now feed change order drafts, update payment timing in AP, and inform future bidding decisions — without anyone manually entering anything.
How AI changes scaling for mid-market contractors
Ryan works primarily with contractors between $50 million and $1 billion in annual revenue. For that segment, growth is the most common cause of failure. He’s heard from impressive leaders that the main thing that can kill a construction company is growth. Right now he’s seeing a lot of contractors who’ve grown 100% in the last couple of years, and he flagged two things they need to be concerned about: whether they’re resilient to a potential retraction, and how the culture is going to handle a drawdown if they’ve doubled overhead to keep up with doubled top-line revenue.
AI agents change the math. Instead of doubling overhead to scale, contractors can scale operational capacity without proportionally scaling headcount. Ryan made the ROI case simply. Take a contractor with five or ten people on the accounting function. Naturally, one of those people leaves every year, with a multi-six-figure cost to replace them. What if that investment went into something arguably more reliable than adding another person?
For private equity–backed contractors running roll-up strategies, the impact is even sharper. Ryan described seeing acquired companies, who used to resist having their systems replaced, now actively pitching the technology platform as a reason to be acquired. He’s overheard management teams of acquired firms saying they should be selling the technology platform itself. It’s a big departure from the kicking-and-screaming dynamic that used to define those acquisitions. Now the platform is the reason firms are rolling up — because it’s what supports the scale.
The last-mile problem isn’t the technology
Ryan’s biggest warning was about the part of AI adoption that doesn’t show up in the demo: the people. He can’t promise contractors can totally upgrade and uplevel everybody in the organization, he said, but they can control who gets promoted and who gets hired in.
Every accounting and finance department has a spectrum. Some people are leading the way, already running their own AI experiments. Others are morally opposed to the technology and aren’t going to come around. Most are in the middle — neither for nor against, just waiting to see what’s expected of them.
The leverage point, in Ryan’s experience, is hiring and promotion. Before approving a new hire, run the job through a filter: can the money go into an agent that creates the capacity instead? If the answer is no and a body is genuinely needed, write the job rec to require two specific things — data literacy and AI curiosity.
That’s the last-mile problem in AI, Ryan said. More than the technology deployment itself, it’s getting folks rallying around their own retraining and their own refocus on what they add value to in the process of getting something built efficiently.
What contractors should do this quarter
The conversation surfaced a practical sequence for contractors who want to adopt AI agents without creating chaos:
- Audit your systems. If any core system (ERP, project management, accounting) is on-premise, the migration to cloud should be the first investment. Everything else is gated by this.
- Map your processes — the future state, not the as-is. Identify the workflows where handoffs between systems and departments create the most friction. Those are the highest-leverage places to deploy agents.
- Get your integration layer right. Either invest in a true integration platform, or work with a vendor whose agents can sit across multiple systems without requiring you to rebuild your stack.
- Set hiring and promotion criteria. Make data literacy and AI curiosity explicit requirements for finance and accounting roles. Promote the people already running their own AI experiments.
- Start with the highest-friction handoff. Most contractors find that invoice processing and field-to-office communication are the two workflows where agents deliver the fastest return.
The contractors who win the next five years won’t be the ones who deployed AI first. They’ll be the ones who got the foundation right — clean cloud systems, mapped processes, integrated data, and a workforce that’s curious about what the technology can do.
To hear the full conversation between Reece and Ryan, listen to the latest episode of Builders, Budgets and Beers.