01-32_GENERALS_SUMMER25_PT - Flipbook - Page 23
INNOVATION
BETTER DECISIONS AND BETTER
MARGIN WITH AI AGENTS
BY DAVID BOWCOTT, Executive Vice President, PLATFORM
OR OVER 20 YEARS, the construction industry has been seriously experimenting with various technologies, equipping
jobsites with a digital web of solutions that help reduce
risk and improve productivity. These technologies,
ranging from the simplest, like email, to more complex,
multi-technology integrated project management platforms, generate massive amounts of data. When aggregated, this
data represents a tremendous opportunity for every contractor.
What has happened over the past two years to make this aggregated data such a massive opportunity? We now have access to AI
models that are approaching artificial general intelligence status,
allowing them to analyze contractor data in ways that help leadership make better decisions and significantly improve margins
using agentic AI.
Before we define agentic AI (or AI agents), let’s examine the
various data sources generated by contractors and other stakeholders on every project, the digitally captured data that forms the
component parts of the digital twin. These data sources include:
BIM and Specification Technologies
Scheduling Technologies
Supply Chain Technologies
Project Management Technologies
Reality Capture Technologies
Internet of Things (IoT) Backbones
External Event Monitoring Technologies
Risk Management Information System (RMIS) Technologies
Digital Documents (not stored in project management technology)
Emails and Text Messages
When aggregated into a central location (i.e., a data lake) and
properly structured with construction-centric ontologies, this data
becomes high-octane fuel for AI models. Once connected to this
data, AI models unlock better decisions and improved margins.
The following diagram illustrates how data is aggregated and analyzed to create powerful dashboards and AI agents that complement your workforce:
As noted above, the two primary outputs from a well-organized
data lake are dashboards and AI agents. Before advancements in
AI, dashboards were the primary reason companies created data
lakes—de-siloing data proved valuable for generating insights.
However, the more powerful and transformative output today is
the AI agent (or Agentic AI).
An AI agent is a system that acts autonomously toward achieving specific goals by perceiving, reasoning, and taking actions.
These agents interact with tools, environments, and other agents
and can be applied in several ways:
REPETITIVE AND PROCESS-DRIVEN TASKS – Tasks with clear steps, rules, and
standard formats.
KNOWLEDGE-INTENSIVE AND DOCUMENT-HEAVY TASKS – Work that involves
understanding, summarizing, and identifying key components in
large amounts of text, such as contract reviews.
TOOL-USE TASKS – AI agents interact with external sources to take action,
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such as confirming deliveries through project management software.
RESEARCH & ANALYSIS – AI can analyze internal and external data sources to provide research reports, market analysis, due diligence, and
competitor benchmarking.
ORCHESTRATION AND COORDINATION – Managing and monitoring workflows
across systems or teams.
These are the best current use cases for AI agents, but as models
evolve, new applications will emerge. Many of these tasks take up
significant amounts of employee time. Studies from McKinsey,
IDC, and Coveo estimate that employees spend between 1.8 to 4.2
hours per day searching for and gathering information, 20% to
50% of working hours. This is a substantial cost. Imagine how
much margin could be recovered if AI agents handled these tasks
instead.
Now that we’ve outlined the steps and benefits of organizing
your data for AI, it’s time to consider which construction tasks
could benefit most from AI agents. Here are some potential AI
agent use cases that could drive better decisions and reduce costs,
thereby improving margins:
RFI ASSISTANT – Reads RFIs and routes them to the appropriate party.
CHANGE ORDER TRACKER – Reads change orders and updates schedules
accordingly.
PREQUAL AGENT – Reviews subcontractor qualifications for specific
projects based on contract terms and prequalification data.
SUBCONTRACTOR SCOPE AGENT – Ensures subcontractor bids align with the
required scope of work.
EQUIPMENT MAINTENANCE AGENT – Manages equipment logs and predicts
maintenance needs.
SAFETY AGENT – Reviews daily logs, safety checklists, and reality capture data to identify safety risks early.
PROGRESS MONITORING AGENT – Uses reality capture data and project
management insights to track project progress by division.
This is just a selection of existing and potential AI agents that
can improve decision-making and margins in construction. AI
is transforming industries, and construction is no exception. At
a minimum, organizations must start considering how AI will
impact them, both positively and negatively. Ignoring this rapidly
evolving technology is simply not an option.
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