Artificial intelligence, or AI, has become one of the most prominent topics across various industries, and construction management is no exception. For many, the potential advantages gained through AI in construction scheduling are a source of hope, promising greater efficiency and lowering costs. For others, the technology can be daunting, introducing unknown risk and adding complexity to systems that work today. As a tool in its early stages, AI’s use cases are still being tested and refined. For construction management applications, particularly in scheduling, AI’s iterative modeling capabilities show significant promise but warrant careful application.
The Complexity Behind Construction Scheduling
Historically, the construction industry has been slow to adopt new technology. This reluctance is due in large part to the sheer complexity of projects—and the risk of introducing unproven systems. Construction scheduling requires a deep understanding of the project-specific details including execution strategy, resource availability, regulations, and stakeholders’ expectations.
If AI can deliver accurate projections and reliable results, its efficiencies in schedule optimization could transform the industry. Currently, it is not a replacement for humans, but it can be used strategically by an expert and with reasonable caution.
Many AI tools are already being used in the real world, with even more in development. In construction scheduling, where construction managers are under pressure to deliver accurate projections across multiple build scenarios, AI is helping to expedite the process. While traditional modeling tools require manual input across hundreds or thousands of activities, AI can model in seconds what could otherwise take days or weeks. However, it cannot replace experience.
Construction managers plan activities such as permitting, excavation, site work, MEP, and other tasks to move projects forward. Modeling multiple scenarios can inform the optimal sequence and schedule, anticipate potential issues, and provide insights and solutions to avoid costly delays. During the modeling process, a construction management professional uses data from past work and their real-world experience to adjust variables logically. AI only has access to the data it is given. While it can analyze that data and synthesize convincing conclusions, these won’t necessarily be grounded in practical realities or accurate assumptions.
AI and Human Experience
Construction schedulers carry years of experiential knowledge with them. They also have access to a wealth of historical information stored in a multitude of files and databases. Creating a schedule is an intricate exercise that can involve researching native scheduling files, PDFs, years of meeting minutes, daily reports, change orders, and much more. These records contain invaluable insights that managers interpret and extrapolate to a given project. The historical data may reference delays, unforeseen constraints, safety impacts, weather challenges, and the human factors that impact schedules. A scheduler uses this data, together with their own experience and awareness of project variables and practical realities, to make decisions. AI is limited to its own algorithmic assumptions and cannot access what humans intuitively know.
For a simple illustration, a human scheduler understands that adding more framers, excavation crews, or MEP installers to a project does not necessarily increase productivity at a linear rate. On-site personnel need adequate space to work. Some may need training. Inclement weather in a given climate region could cause delays. AI tools may not understand site constraints or environmental factors. If a project schedule is based on an AI tool’s assumption that doubling the personnel on a site will double the productivity, it is likely to set up unrealistic expectations and potentially introduce new problems.
Still, AI should not be disregarded. While AI has its limitations, it will only get better, and construction managers who use AI to augment certain aspects of their work are seeing benefits today.
AI as Apprentice
Although AI is not yet a perfect tool for certain nuanced tasks, its speed and ability to process and analyze complex data sets is unmatched by the human intellect. Its sheer processing power can offset the considerable time it takes to set up a model, for instance. Schedulers can use AI to rapidly iterate multiple early planning drafts and compare alternatives side by side.
Because it can model more scenarios more quickly than traditional methods, AI helps illustrate tradeoffs between schedules in terms of cost, resources, and time. AI can also take a “first pass” at developing activity lists, suggesting durations, identifying comparable past projects, and other tasks that allow experienced managers to focus on higher-value work. In these cases, AI can greatly enhance efficiency, but its outputs must be reviewed carefully.
Work with AI in a construction scheduling environment as if it were a developing team member. Outputs should be treated as preliminary models, and all schedules must be reviewed for accuracy before progressing. Like an apprentice, AI needs oversight to correct inaccuracies or misinterpretations.
Corrections take time, but because models learn from input, refining and training them becomes an investment in future efficiency. With consistent “teaching” and broader access to real-world construction data, an AI tool’s knowledge base will grow and its assumptions will become more sophisticated.
Teams can also input relevant historical data, reducing AI’s learning curve. Fully realizing its potential can take time and dedication, such as providing it with the datasets it needs to deliver more reliable outcomes. The vast number of data sources and formats to mine can pose a daunting challenge, but the labor that AI could save over time is enormous.
Internal AI Models as a Competitive Advantage
AI is already reshaping today’s workforce. While the complexities of construction may have slowed adoption compared to other industries, the growing availability of AI scheduling tools is evidence of the important role this technology will play in the future. Teams that invest today in internal, company-specific AI models built on proprietary historical data will likely have a competitive advantage tomorrow.
Teams can start by taking inventory of existing data and incrementally feeding it to their AI tools as time allows. Entering old data builds a foundation of knowledge that, if maintained, will continue to offer greater efficiency and reliability. Organizing and standardizing schedules and project documentation with tags and consistent naming conventions can help to facilitate this learning process. Centralizing storage locations can help, as well.
It’s also important to identify the pain points that teams need AI to address. Software can’t solve problems that are undefined. By thinking critically about where actual challenges or bottlenecks exist, teams can be more strategic in what documentation they provide.
Don’t Ignore AI in Construction Scheduling—Use it Carefully
Despite its risks, AI has already shown it can deliver measurable benefits to construction scheduling. Used properly, it offers unmatched speed in scenario modeling and gleaning insights from unstructured data. Without sufficient context or historical data, however, AI can produce deeply flawed schedules.
AI cannot replace the experiential knowledge of a seasoned construction management professional, who understands the nuances that make every project unique. What it can do is reduce time-consuming administrative tasks and process massive volumes of information at incredible speed.
Thinking of AI as an apprentice can dramatically streamline early-stage construction scheduling tasks. Teams will need to provide the AI tool with relevant data and review its output. Building processes for strategic long-term adoption and developing robust internal models based on proprietary historical data, can produce significant advantages as the technology and industry continue to evolve.
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