“Team dynamics are really important to us,” says Jamie Miller, Director of Engineering Development at Sellen Construction. “We don’t just throw people on a project without thinking about how they’ll work with each other. We look for strengths that complement each other.”
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Miller is describing what good team building looks like. The problem is that doing it well, considering experience, relationships, collaboration history, availability, and fit for every staffing decision, takes more time than most contractors have. So they check availability, apply judgment on a few key factors, and move on. The decisions are reasonable. But relevant information goes unconsidered because there’s no time to dig for it.
Build-type experience is the factor 59% of construction leaders weight most heavily when assembling teams. Industry experience follows at 53%, and market-sector experience at 50%. But how often does experience actually get factored into routine staffing decisions versus the high-stakes ones? For most contractors, thorough evaluation happens only when the project is big enough to justify the research time.
AI changes the math by making thorough evaluation fast.
AI increases capability without shrinking teams
The goal of AI in construction workforce planning isn’t to reduce headcount. It’s to increase capability. Think about the go-to person in your organization for institutional workforce knowledge. They’ve likely been with your company for more than a decade, and possess an uncanny ability to recall past projects and experience for countless team members.
AI can’t replace that person. What AI can do is help others in your company achieve a similar degree of efficiency and workforce data access, without having to spend a decade to acquire it. AI sharpens teams by allowing them to analyze and optimize what was never doable manually.
Evaluating everything at once
You need someone for the Morrison healthcare project. Manually, you’d start with who’s available, then check who has healthcare experience, then see who’s worked with this client, then verify certifications, then consider commute distances. Each check takes time. Most get skipped.
AI evaluates your entire workforce against all criteria simultaneously. Who has healthcare experience? Who among those is available when you need them? Who has worked with this client? Who has the required certifications? Who lives within reasonable commute distance? Who has worked successfully with team members already assigned?
“When you’re placing a person, you’re not just placing a robot,” says Matthew Walsh, Senior Operations Technology Manager at Power Construction. “You’re placing a human. And understanding their relationships can be a big part of a project team.”
AI returns ranked candidates in seconds. You make the decision, but you make it with full information instead of whatever you had time to check.
Surfacing crucial data points that are often hidden
A contractor in Texas now prioritizes commute time above all other factors when staffing projects. This didn’t used to be the case. But after repeatedly assigning key team members to projects that were too far from their homes, only to have them resign a few months later due to the long commute, they discovered that this missing data point was critical to their success.
Two people you’re considering for the same team worked together on three projects over the past four years. That history might mean established communication patterns and mutual trust. Or it might mean unresolved tension. Either way, it’s relevant.
You probably didn’t know to check. AI surfaces collaboration history automatically because it can scan records you don’t have time to review. The same applies to other hidden information: certifications expiring mid-project, someone coming off a difficult assignment who might benefit from a different pace, experience with a specific architect the client works with.
“I think we’ve been building better teams from the project’s inception,” says Miller at Sellen. “We have the RFP in hand and are all on the same platform. We collaborate on the team we assemble immediately, which can help us win a project.”
Explaining why each person is recommended
Good AI shows its reasoning. “Sarah is recommended because she has healthcare experience on three projects totaling $120M, has worked with the proposed superintendent on two previous jobs, and has lower current utilization than alternatives with similar experience.”
AI that provides scannable summaries helps you know when to trust the recommendation and when your knowledge of factors AI cannot see should override it. If AI recommends someone and you know from recent conversations that they’re planning to leave, you can factor that in. If AI recommends someone you’d dismissed and you see the reasoning is solid, maybe you reconsider.
Where AI helps most with team building
Complex staffing with multiple constraints
When you need someone with specific experience AND specific certifications AND availability in a narrow window AND reasonable commute, the constraints compound. Checking each constraint manually multiplies the research time. AI handles compound constraints well because it evaluates all dimensions simultaneously.
“Bridgit has become a critical tool for our workforce planning,” says Keyan Zandy, CEO of Skiles Group. “It ensures our data is accurate and up-to-date, leading to better decision-making and efficient resource allocation.”
Pursuit teams before you bid
Before you bid, you need to propose a team. The team you propose affects whether you win. AI can identify who gives you the strongest positioning: relevant experience with this project type, relationships with this client or architect, past wins on similar work.
“Running scenarios where we can run specific projects like we’ve got 25 active projects and ten pursuits, 3 of which are 95%, so I can factor them into my forecasting,” says Shawn Gallant, COO at Columbia Construction. “That’s priceless to be able to do that.”
This is often more valuable than optimizing staffing after you’ve won. Getting the pursuit team right affects whether you win at all.
Finding capacity you didn’t know you had
You need healthcare experience for an upcoming project. Your gut says you don’t have anyone available. AI might surface someone whose current project is winding down sooner than you realized, or someone with healthcare experience you forgot about because they’ve been on industrial projects for two years.
“The power behind the tool allows us to look across our business to see if there are resources available in other parts of the country that we can bring in to help a different group that might be short on resources,” says Andy Sparapani, Project Technology Leader at The Boldt Company.
The person you need might exist in your organization. You just don’t know about them because they’re in a different office or working on a different type of project.
Factoring in development opportunities
You have a junior PM ready for more responsibility. Which projects would stretch them while providing support from experienced team members? This isn’t a question you’d typically have time to research, but AI can factor development goals into recommendations when you configure it to do so.
“Having Bridgit has helped us develop a much clearer picture of our staffing needs,” says Lisa Villasmil, VP of People & Culture at Cauldwell Wingate. “This lets us see gaps and give people within the company opportunities to fill them, first and foremost.”
Development-informed staffing builds your talent pipeline. Without it, the same experienced people get the complex assignments while others never get the exposure they need to grow.
What AI cannot see
AI works with data in your system. Information that exists only in your head or in hallway conversations is invisible to it.
Whether a collaboration actually went well: AI knows two people worked together. It doesn’t know if they clashed or clicked, whether there’s unresolved tension, or whether they’d actively request to work together again.
Personal circumstances: Someone going through a difficult time at home. A recent conflict with a client that wasn’t documented. An informal conversation about career direction that hasn’t been formalized.
Political dynamics: Which team combinations leadership favors. Relationships between offices that affect how people are shared. History that doesn’t show up in project records.
“Bridgit has shifted us from siloed decision-making to a more inclusive, team-based approach,” says Shelby McEntire, Director of Human Resources at Skiles Group. “Focusing on taking the best possible care of our people matters to us.”
You know these things from conversations, observations, and institutional knowledge that never gets documented. Your judgment incorporates them. AI suggestions should be filtered through what you know.
Your data has to be there first
“Trusting your data is foundational to achieving value with AI,” says Lauren Lake, co-founder at Bridgit. “If your team doesn’t trust the accuracy of the data, your AI strategy will ultimately fail.”
AI cannot recommend based on healthcare experience if healthcare experience isn’t tracked in your system. It cannot surface collaboration history if past team assignments were never recorded. It cannot consider certifications if certifications aren’t maintained.
The capabilities that matter most require:
- People records with project history, not just current assignments
- Experience tracking across build types, market sectors, and client relationships
- Assignment records showing who worked on what projects, in what roles, for how long
- Certifications with expiration dates that someone keeps current
If this data doesn’t exist, building it is the first step. AI features deliver value after the foundation is in place. The 100-day guide to building an AI workforce planning strategy walks through establishing your single source of truth and preparing your data for AI.
Companies that effectively use AI will beat companies that don’t
“Within the next two years, AI-powered workforce planning will be table stakes,” says Lake. “Contractors who can prove that they deploy the right teams, not just available teams, will win out.”
Owners are already asking about tech maturity in bids. A robust AI strategy underpinning how you bid projects and build project teams is a powerful signal that you’re leading the way.
The contractors getting ahead now are the ones building clean data and developing the habit of using AI for staffing decisions. The AI gets better over time as it learns your patterns. Starting late means catching up to competitors whose systems have had more time to learn.
You don’t need to trust AI completely. You need to use it as a tool that expands what you can practically consider. When it surfaces a candidate you would have missed, that’s value. When it confirms the person you were already considering is the right choice, that’s confidence. Both help you build better teams.
