Why AI matters at this scale

Brandsafway is a leading provider of scaffolding, shoring, and industrial access solutions, serving complex construction, maintenance, and turnaround projects across North America. As a large-scale enterprise with over 10,000 employees, the company manages a vast, geographically dispersed fleet of equipment and a massive workforce executing safety-critical tasks. In the traditionally low-margin construction services sector, operational efficiency, asset utilization, and safety performance are the primary levers for profitability and competitive advantage.

At Brandsafway's size, the sheer volume of data generated from equipment telematics, project schedules, safety inspections, and logistics creates a significant AI opportunity. Manual processes and intuition-driven decisions cannot optimize a network of this complexity. AI provides the analytical horsepower to identify patterns, predict outcomes, and automate decisions, turning data into a strategic asset. For a company of this scale, even a single-percentage-point improvement in fleet utilization or reduction in safety incidents can translate to tens of millions of dollars in annual impact, funding further innovation and solidifying market leadership.

Concrete AI Opportunities and ROI

1. Predictive Logistics for Fleet Optimization: By applying machine learning to historical project data, real-time GPS locations, and maintenance records, Brandsafway can build a digital twin of its asset network. This system would predict demand for specific equipment types at various job sites weeks in advance, optimizing transportation routes and reducing idle time. The ROI is direct: lower capital expenditure (needing fewer assets overall), reduced fuel and transportation costs, and higher revenue per asset through improved utilization rates.

2. Proactive Safety with Computer Vision: Deploying AI-powered cameras on sites and drones for inspections can automatically detect safety protocol violations—such as missing guardrails or improper personal protective equipment (PPE)—in real time. This moves safety management from periodic human checks to continuous, objective monitoring. The financial return comes from drastically reducing costly incidents, lowering insurance premiums, minimizing project delays, and protecting the company's reputation, which is paramount for securing large contracts.

3. Intelligent Project Planning and Scheduling: Machine learning algorithms can analyze thousands of completed projects to identify the key factors that cause delays or cost overruns. For new projects, AI can then generate optimized work schedules that account for crew skills, equipment availability, weather, and subcontractor dependencies. This leads to more accurate bidding, improved on-time performance, and higher client satisfaction, directly impacting win rates and profitability.

Deployment Risks for a Large Enterprise

For a company in the 10,001+ employee size band, the primary risks are not technological but organizational. Successful deployment requires seamless integration of new AI tools with entrenched legacy systems like ERP and field management software, which can be costly and slow. Change management is also a massive hurdle; convincing thousands of field supervisors and crews to trust and adopt AI-driven recommendations requires extensive training and a clear demonstration of value. Furthermore, data quality and standardization across dozens of branches and business units must be addressed before models can be trained effectively. Finally, given the safety-critical nature of the work, any AI system must be exceptionally reliable and explainable, requiring robust testing and validation protocols before full-scale rollout.