A design-build construction company moved from zero AI governance to deploying custom chatbots for complex bids. Here's exactly how they did it.
Like most companies in 2024, Primus Builders didn't need to decide whether to adopt AI. Their team was already using it.
The problem? Nobody knew who was using what, for what purpose, or whether they were exposing sensitive bid data to public AI tools.
The question wasn't "Should we use AI?" It was "How do we make sure we're using it safely and getting actual business value?"
Here's what actually happens when you implement AI in a mid-sized construction company. Not the theory. The reality.
Not just a rubber-stamp group. Real decision-makers from operations, IT, and project management.
Simple rules that people would actually follow. See Company-wide AI Policy for framework details.
Identified team members already using AI effectively. Trained them to onboard others. See AI Training Programs for implementation approach.
The best trainers weren't IT staff or consultants. They were project managers and estimators who'd already figured out practical AI applications for their daily work.
Construction companies process hundreds of invoices monthly. Manual entry was eating 15-20 hours per week.
RPA sounds magical until you realize you need to standardize invoice formats from 50+ subcontractors first. That took longer than building the automation.
Related capability: RPA Platform
Implemented Aha.io to collect, evaluate, and prioritize AI use cases from across the company.
Your team sees inefficiencies every day. Most companies lose those insights. Primus captures them, evaluates feasibility, and implements the winners.
Related capability: Ideas Portal
When you're bidding large multi-facility projects, you can't have estimators digging through 1,200 pages of specs manually.
Instead of reading 1,200 pages to find specific requirements, estimators asked questions and got instant answers with page references. Cut bid prep time significantly.
Related capability: Custom Chatbots
Daily manpower logs and delay reports in Procore needed weekly executive summaries. That was 2-3 hours of manual work every Friday.
Related capability: Document Intelligence
Construction's biggest nightmare: specifications say one thing, drawings show another. Finding those conflicts manually took days.
This isn't perfect. AI still flags false positives. But catching 80% of real conflicts before breaking ground saves orders of magnitude more than the time spent reviewing AI alerts.
Related capability: Access to Approved LLM (Copilot)
Legal reviews on construction contracts take weeks. Most changes are standard negotiation points. AI handles the routine stuff.
Related capability: Document Intelligence
The real measure of AI implementation isn't how many tools you deploy. It's whether your team uses them after the consultants leave.
Here's what Primus Builders achieved:
Six months after implementation, the team is still using these tools. That's rare. Most AI pilots get abandoned when the consultants leave. Primus succeeded because they built governance first, trained their people, and created tools that solve real problems.
Primus didn't deploy AI first and figure out security later. They built the steering committee and usage policy before anyone touched a custom chatbot.
The AI Champions weren't consultants or IT staff. They were project managers and estimators who'd figured out practical applications on their own.
See: AI Training Programs
Every automation Primus built solved a problem people complained about daily. They didn't automate because they could. They automated because manual processes were killing productivity.
See: Ideas Portal for capturing team insights
The RFP chatbot wasn't impressive because it used cutting-edge AI. It was impressive because it solved a real problem: estimators couldn't manually review 1,200-page bid documents fast enough.
See: Custom Chatbots
The specs vs. drawings comparison tool isn't perfect. It flags false positives. But catching 80% of real conflicts before construction beats catching 0% while waiting for a perfect solution.
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