The AI agent market hit $5.4 billion in 2024 and is projected to grow at 45.8% annually through 2030 (Grand View Research, 2025). These numbers paint a clear picture of a massive market that's growing rapidly. With demand for these types of solutions increasing, I'm thinking about which construction AI tools will genuinely transform how we work, and which ones will become another pile of expensive software that nobody uses.

My hope is to see more role-specific AI agents. Tools designed for specific jobs rather than generic "do everything" platforms. Software solutions have long promised to completely streamline our jobs from end-to-end. But in my experience, the common approach is to layer on feature after feature, only to leave the user feeling confused. In construction this might be especially relevant, given the lower average threshold for tech. A superintendent needs different AI support than an estimator, who needs different tools than a project manager. If we're going to avoid repeating mistakes of past construction software rollouts, we need people from the field helping design these systems.

What makes role-specific AI agents different?

Traditional software gives you features. AI agents give you capabilities that adapt to how you actually work. A project manager agent could monitor your schedule, flag conflicts before they cause delays, and draft RFIs based on project documentation. A superintendent agent could cross-reference daily reports against the schedule and spec sheets, surfacing potential issues before they become problems.

These tools can contribute meaningfully by either reducing workload or accomplishing deeper analysis that otherwise might never happen. Role-specific agents have potential to reduce risk, keep schedules on track, and maintain budgets.

Claude Skills: Bringing us closer to real agents

Anthropic recently launched Claude Skills, which lets you package instructions and workflows that Claude automatically loads when needed. Think of it as teaching Claude your specific business processes once, then having it consistently apply them. This is a similar concept to one I have shared previously about using the personal preferences in Settings to write long, technical prompts with protocols that can be triggered by certain phrases. Now this way of operating is available in a more structured way. There are helpful tutorials online for how to set up Skills. My recommendation would be to watch one of these and then upload some of the off-the-shelf Skills available in settings. Specifically the skill-builder skill which, as the name implies, can be used to help build other custom skills. Claude then identifies which skills are needed and coordinates their use. No more explaining your requirements in every conversation. Early adopters report 73% reductions in repetitive prompt time (Anthropic, 2025). Skills represent a significant step toward AI that genuinely handles complex tasks with minimal intervention.

According to recent McKinsey research, companies with $500M+ in revenue are more than twice as likely to have dedicated AI teams and clear implementation roadmaps compared to smaller firms (McKinsey, 2025). This difference feels like it's more about intentionality rather than capability.

I was listening to Paul Hedgepath from MJ Harris Construction Services, LLC on the Bricks & Bytes podcast recently, and he mentioned something that stuck with me. Innovation budgets aren't just for Fortune 500 general contractors. Even $50 for coffee and snacks to host a 30-minute break-time discussion about AI could surface hidden opportunities. What if your team spent one lunch talking about the most time-consuming parts of their week? You might discover that three people are all manually solving the same problem in different ways.

Small businesses are actually closing the AI adoption gap faster than people think. Recent SBA data shows small firms (under 250 employees) increased AI use from 6.3% to 8.8% in just six months, and they're only about a year behind large companies in adoption trajectories (U.S. SBA Office of Advocacy, September 2025). The difference is that small firms can move faster once they commit. You don't need a massive budget; you need focus and a willingness to experiment.

Start small. Pick one painful process. Test one tool. Learn. Then scale what works.

I built myself an AI assistant using Chipp that is trained on Simpson Strong-Tie products. Simple yet effective. Instead of scrolling through PDFs on my phone or hunting for installation specs, I just ask questions. "What size nails are required for LUS10Z hangers?" The assistant pulls from the actual technical documentation and gives me exactly what I need.

You might be thinking "can't ChatGPT just do that?" The difference is specific training. When you ask a generalist LLM like ChatGPT to retrieve technical specs from the internet, it's prone to errors, hallucinations, and outdated information. A product-expert assistant trained on verified documentation gives you accurate answers every time because it's working from a curated knowledge base, not the open web.

This is the kind of practical AI application that I really appreciate. Not flashy. Not revolutionary. Just solving a daily friction point.

The potential here extends way beyond hardware. Every product we use has documentation. Warranty information. Installation best practices. What if your team could have a conversation with any of these knowledge sources instead of digging through hundreds of pages?

These product-expert AI assistants are part of the broader agentic AI construction trend. They're not replacing expertise. They're making expertise more accessible, which positively impacts folks in the field. This is exactly the kind of practical AI that can raise the digital floor for our whole industry.

Residential estimating has always been tedious and error-prone. Assembli AI addresses this by using AI to scan plans and generate detailed estimates with material quantities and pricing (Assembli, 2025). Contractors can upload plans and get accurate takeoffs across all trades. Fewer change orders, better client trust, and significant time savings on every project.

Trunk Tools has developed an AI agent trained on SOPs and is deploying this agent with major clients like Gilbane and Suffolk. This is exactly the kind of use case that first got me excited about AI for construction. It is a simple idea to understand - train an AI on your company best practices and then new hires get better onboarding support, field crews can quickly reference company standards, and you eliminate repetitive conversations about "how do we handle X?" Use cases like an SOP agent are one of the most high-impact ways we could adopt AI in our businesses whether you’re a large enterprise or a small remodeling contractor. I love to think about the level of cohesion across teams that an agent like this could support.

About 60% of construction decisions happen over the phone, but those conversations often lead to miscommunication and rework that costs 5-10% of project revenue. Hardline automatically transcribes all jobsite calls and meetings, extracts action items and key decisions, and provides English-Spanish translation. The voice-first approach works impressively well and requires minimal interruption to existing ways of working. The platform is flexible in how it is used, with features like Site Mode that can be used to record walk-throughs or client meetings. The Assistant feature lets you search past conversations for critical information. Rather than spending evenings trying to remember what was discussed on three different calls, you have searchable records of every conversation.

The technology works and is improving all the time. Here's what I'm focusing on:

Start with problems, not technology. Identify your biggest time-waster first. Answering the same client questions? Tracking down specs? Reviewing submittals? Pick one pain point and find tools built for that specific problem.

Experimentation is key. There are lots of folks out there who lack confidence in AI. Maybe they tried ChatGPT a year ago and weren’t too impressed. Putting uncertainty to the side and experimenting allows people to identify strengths and weaknesses when it comes to this new technology.

Budget for learning, not just buying. Even $50 for a team lunch about AI opportunities could be transformational. Small businesses closing the AI adoption gap are doing so by committing resources, however modest, to experimentation.Creator prompt: “What can I teach in 5 screenshots that saves someone 5 hours?”