AI in construction scheduling software uses an organization’s historical schedules to suggest activities, sequences, durations, and risks for a new plan, and the scheduler accepts or rejects each suggestion. It doesn’t replace the planner. It gives them a realistic starting point and flags what past projects say is likely to go wrong.
Why does AI belong in the construction scheduling process?
Artificial intelligence (AI) is becoming increasingly prevalent in the construction industry. Once a somewhat misunderstood concept, there’s now growing acceptance and appreciation for its capabilities. Rather than replacing human intelligence, AI is intended to work in tandem with it. This human intelligence — the expertise and experience of your project team members — is what feeds AI’s ability to learn.
In turn, AI helps those team members better understand current and past projects to enable better decisions, which leads to better project outcomes. Recognizing this, project owners and contractors alike have their sights set on working AI into their processes.
One construction area where AI is making a particularly powerful impact is in scheduling and planning. Augmenting human judgment to create more predictive and realistic project timelines makes it a natural partner for construction scheduling software.
How does AI use historical schedule data?
Think of all the rich data each capital project accumulates. Then multiply that by all the builds you’ve done. How do you mine that vast amount of information to make it work for you? If substantial capital builds make up all or the bulk of your portfolio, it might seem overwhelming to comprehend. But for AI, the more data it has to work with, the better it can inform creation of more accurate project plans.
So, let’s say you’re creating a plan for a type of structure you’ve built before. Rather than starting from scratch with essentially a blank screen, AI recognizes a similar project in your history and suggests the past real-life schedule as a starting point, saving you an immense amount of development time, effort and money (not to mention potential manual entry errors).
Drawing from a knowledge base of historical data and template critical path method (CPM) schedules, AI-enabled construction scheduling software makes the complex scheduling process much more efficient. It can suggest activities, task durations, sequencing, and risks as it detects plan components that resemble those of past projects, and it shows how strong each match is. As you (or the main project scheduler) accept or reject these suggestions, it further improves AI’s ability to learn, resulting in a more accurate, realistic schedule that is more likely to earn buy-in from the broader project team.
How does AI help identify and manage schedule risk?
Of course, no construction project is without risk. And your capital projects in particular — with their longer durations, higher complexity, often larger size and more resources — are at the mercy of the anticipated and the unexpected. What risks are you likely to encounter that you have to plan for? What would the possible impact be? And what can be done to mitigate them or avoid them altogether?
AI-powered construction scheduling software helps answer these questions, giving you enough predictability to allow you to risk-adjust plans confidently. Keep in mind AI isn’t a decision maker, telling you definitively how to mitigate each and every risk factor. Rather, it’s more of an advisor meant to inform your own decisions.
So based on prior data you’ve entered for similar projects or components, AI can give you a data-driven idea of what you might experience and suggest how to best address them. This allows you and your project team to collaboratively decide whether to decline or approve the suggestions (which further “trains” the AI from a risk perspective) as you develop a more finely tuned, risk-adjusted schedule that meets the evolving circumstances of your project. The point isn’t a more precise date. It’s a more realistic one: a risk-adjusted schedule that separates everyday uncertainty, like productivity swings, from discrete risk events, like a late delivery, and shows you the range of finish dates each one implies.
Of course, risk isn’t just about internal or external “events.” That AI functionality within construction scheduling software can just as easily spot unrealistic task durations, an overly ambitious timeline, overlapping schedules for the same resource, or out-of-sequence task dependencies. As AI learns to identify these scheduling hiccups, it’s better able to provide alternatives to correct the schedule so it’s more realistic.
How should AI capture your team’s planning and scheduling expertise?
As AI extracts and processes the project data to deliver insights, and initial schedule baseline and risk-adjusted timelines, and ongoing risk identification, the project team is freed up to do the things only humans can do with all that carefully mined information — analyze flagged risks, make improvements and adjustments to plans, and collaborate on critical decisions.
But there’s one aspect of human intelligence that typically isn’t captured. Think of the deep well of experience and expertise your team members bring to your projects — their lessons learned, observations, insights and experience-informed decisions. That’s just as vital to successful scheduling and planning as the wealth of project data you’ve accumulated.
How do you collect and apply that knowledge going forward, long after they’ve left the jobsite or retired? By capturing this broader human intelligence in AI-powered construction scheduling software like InEight Schedule, you keep their expertise and feedback throughout your project. Reviewers mark up the schedule, the planner adopts or declines each markup, and the result informs future project plans, rooting them in a foundation of realism that couldn’t be achieved otherwise.
Relying on the actionable data in its knowledge base, you can use AI as an interactive tool to capture and apply historical data and create risk-adjusted schedules throughout your build as data and conditions evolve. That is what the practical AI tools in InEight Schedule are built to do.
