Conversations about artificial intelligence often feel rather abstract. It’s hard to picture how AI might be rolled out in your own business, or where it would actually add value. That’s partly because the systems most often discussed in the media are large, transformative, and forward-looking, the kind of initiatives reserved for companies with extremely large, experimental budgets and teams of data scientists.
But there is a new category of AI system where the ROI is crystal clear. Where you can get started on a small scale, quickly, and have a huge impact on the efficiency and effectiveness of your business.
This category is AI agents.
What are AI agents?
AI agents are software systems that can autonomously perform tasks traditionally done by humans, often better, faster, and more consistently.
Unlike general-purpose AI tools (like ChatGPT in a browser), agents are goal-oriented, task-specific, and often work in the background, using logic and continuously executing actions based on changing inputs, rules, or environments.
You can think of them as focused, digital employees.
10 examples of AI agents for construction firms
1. Project planning & management
Schedule impact assessor
Problem: Many planners manually adjust timelines when delays occur.
What the agent could do: This agent would ingest project schedules (e.g., Primavera, MS Project), identify when tasks are falling behind, and instantly show how the delay ripples through the critical path. It could recommend changes (e.g., resequencing tasks, reallocating crews) and simulate multiple recovery options.
2. Procurement & supply chain
Material reorder assistant
Problem: Procurement teams often check stock levels manually or wait for site teams to request reorders.
What the agent could do: This agent would monitor project progress and material consumption from site reports or sensor data. It could then auto-generate and propose purchase orders before stockouts occur, ensuring just-in-time delivery while reducing excess inventory.
In real-world scenarios, agents like this have helped construction firms reduce buffer stock inventory by up to 23%.
3. On-site operations
Site diary summariser
Problem: Supervisors typically write daily site diaries by hand or into spreadsheets.
What the agent could do: This agent would take voice memos, WhatsApp messages, or raw notes and use AI to generate a structured, timestamped site diary. It could automatically tag issues (e.g. weather, deliveries, incidents) and save 1 to 2 hours of admin per day.
4. Design & engineering
BIM clash resolver assistant
Problem: Engineers often manually sift through BIM clashes and coordinate fixes across disciplines.
What the agent could do: This agent would review clash reports (e.g. from Navisworks or Revit), categorise them by severity, and suggest resolution sequences. It could also draft coordination emails to the appropriate stakeholders for each issue.
5. Estimating & bidding
Drawing analyser for take-offs
Problem: Estimators manually go through drawings to measure quantities (walls, doors, M&E elements).
What the agent could do: This agent could use computer vision to read PDF or CAD drawings, extract quantities for predefined elements, and populate spreadsheets automatically, speeding up pre-construction take-offs by 80%+.
6. Communications & administration
Meeting note compiler & tracker
Problem: Project managers spend hours writing up meetings and chasing actions.
What the agent could do: This agent would listen in on project meetings (via Zoom, Teams, etc.), summarise the discussion into structured notes, assign action items to attendees, and track whether they’ve been completed, integrating with tools like Monday or Asana.
7. Finance & cash flow management
Invoice checker
Problem: Accounts payable teams often cross-check supplier invoices manually against delivery notes and purchase orders.
What the agent could do: This agent would auto-match incoming invoices with supporting documents, flag discrepancies (e.g. overcharges, missing deliveries), and route exceptions to the right person, cutting down processing time and errors.
8. HR & labour management
Timecard anomaly detector
Problem: Site attendance data is often reviewed weekly to find fraud or overreporting.
What the agent could do: This agent would review timecard logs, compare them against historical patterns, GPS data, and equipment usage, and flag suspicious entries (e.g. clocked-in but no associated activity). It could send alerts to HR or supervisors for review.
9. Business development & CRM
Tender opportunity screener
Problem: BD teams manually sift through portals and emails to find good-fit tenders.
What the agent could do: This agent would scan tender portals and inbound requests, analyse the scope and requirements, and score the opportunity based on fit with past projects, margins, and resource availability. High-fit tenders could be summarised and pushed to the BD lead.
10. Sustainability & compliance
Embodied carbon estimator
Problem: Sustainability teams often manually calculate embodied carbon from spreadsheets and EPD databases.
What the agent could do: This agent would read material schedules and BIM models, estimate embodied carbon for each item using up-to-date emissions data, and suggest greener alternatives, generating a compliant carbon report in minutes.
How to know if a task can be done by an AI agent
There are two key categories of task:
- Those that are currently being done by humans: For these tasks, if an existing process is repetitive, error prone and/or taking up human time, it’s probably worth considering an AI agent.
- Tasks that are not being done at the moment but that would add to your efficiency or effectiveness: A good mental model to use is to ask “if labour was free and I had unlimited team members, what tasks would I have my team doing?” The answers to this question might be great candidates for AI agents.
How to get started
It’s almost always the case that once you begin learning about the capabilities of AI agents, new ideas start to emerge. The challenge then becomes how to focus and prioritise which agents to build first.
Steer73 offers free AI Agent Discovery & Roadmap Creation Projects, and services like this are typically a great place to start.
During the project, an experienced team will run a workshop with you to identify where the best opportunities for AI agents exist in your business. These opportunities are then mapped against key factors such as business goals, ease of implementation, available technologies, impact, budget considerations, and more.
The outcome is a clear understanding of which agents to build, their relative priority, and what would be required to bring them to life. A process like this ensures you’re focused on building the right thing, making sound technology choices, and putting the right guardrails in place around security and cost management.
If you would like to explore taking advantage of this service with Steer73, simply send the words “AI agent” to hello@steer73.com.