Leveraging AI to Enhance Workforce Capability

Danger, Will Robinson, we are about to talk about AI.

But don't worry, I'll use (A)ctual (I)ntelligence to bring across some points.

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In construction, we’ve learned the hard way that skipping preconstruction always shows up later - missed scope, blown schedules, safety incidents, and costly rework. The same can be true when we rush into new tools without a proper plan - unfamiliarity, poor adoption, under utilization, costly unused tools. Right now, artificial intelligence is having its preconstruction moment in our industry, and Learning & Development is uniquely positioned within organizations to upskill an organization’s workforce - or be left reacting.

If you are not already regularly using or experimenting with AI you are behind

AI is not new. Not anymore. It’s no longer theoretical or reserved for tech companies like Meta or Amazon. If you are not regularly using or experimenting with AI you are behind...Big Time. It’s already embedded in tools many organizations are using today, though often without a clear strategy for how it impacts people, roles, and capability. The risk isn’t that AI will replace construction professionals (not today at least!). The real risk is that we fail to intentionally prepare our workforce to work with it.

Construction Is Moving Fast - Learning Often Isn’t

There is a paradox in this header. The construction industry is known to be one of the slowest adopters of technology when compared to other sectors like healthcare, manufacturing, or finance. Yet, construction projects continue to operate under intense pressures: tighter margins, aggressive schedules, workforce shortages, and increasing complexity across projects. At the same time, our learning models often remain static. Programs are built once, deployed broadly, and rarely updated at the pace the field demands (this is not for lack of trying!). L&D teams spend many months, quarters or years building out formal programs, but once launched the content may already be stale due to updated SOPs, more refined job aids, new tools, etc.

This creates a familiar gap I hear in L&D forums. We expect people to adapt faster than the systems designed to support them can be created. AI cannot solve this problem on its own but when applied thoughtfully, it can dramatically reduce the lag between what a role requires and how quickly someone becomes capable of performing it.

AI Is a Force Multiplier, Not a Replacement

There’s understandable anxiety around AI in the global marketplace. Conversations often jump straight to job displacement or automation. I frequently reference a quote from Professor Scott Galloway because I believe it’s true, “AI will not take your job, but someone who understands AI will.” Just because a new tool like Joist AI can generate proposals for you, or Ediphi spits out estimates faster than you can say 'preconstruction', does not mean that these tools will replace the individuals in these spaces. Because for now, construction remains a deeply human industry. Individuals who are leveraging these tools certainly have a leg up over those who are not!

Judgment, experience, leadership, and situational awareness can’t be automated today.

What AI can do is amplify expertise. It can take what you or your organization knows and kick it up to 11. AI can help capture institutional knowledge that currently lives in the heads of a few seasoned leaders, your best builders - this is a KM’s dream. It can support early-career professionals as they build confidence and competence - with the tool acting as coach and teacher. And it can extend the reach of L&D teams that are already stretched thin - think chatbots, job aid creators, or course generation tools.

In an industry where knowledge walks off the jobsite every night, that matters.

Where AI Fits in Construction Learning & Development

The most effective use of AI in L&D isn’t flashy, it’s quite practical. It supports specificity, speed, and scale without losing context. Here are four concrete examples:

Personalized Learning Paths

1. AI enables learning journeys that reflect role, experience level, and project type. This moves us closer to the role-specific onboarding. This drives development, faster time to productivity, and cultural assimilation, without creating unsustainable workloads for L&D teams. Your learning management system (LMS) should be offering this for your users.

Just-in-Time Learning

1. Instead of relying on static courses or searching through document libraries, AI can deliver relevant information at the moment of need - on the jobsite, in the trailer, or between meetings. Learning becomes embedded in the work, not separate from it. For this to work you need to rely heavily on how you tag your content - some LMS systems will do this for you on the backend.

2. Think about this: a project engineer needs to review an RFI response related to firestopping. Rather than searching through the spec book or trying to catch their PM to help guide them in the right direction, they prompt an AI assistant, "tell me what the spec for project X says about firestopping at rated wall penetrations." The AI would then pull the exact spec section and provide links and additional relevant detail. Faster decisions with fewer interruptions. AND learning happens in the context of the prompting and review.

Capturing and Scaling Tribal Knowledge

1. Experienced superintendents, project managers, and foremen hold decades of insight. While our best builders are on site every single day, much of what they know is exclusive to them - only shared when requested or observed. AI can help translate that knowledge & experience into repeatable learning assets, reducing dependency on a small number of key individuals and strengthening bench depth. Start by recording targeted conversations or bring these groups together through a community of practice.

2. Here's a concrete example: Keeping Lessons Learned Alive!

   At project closeout, teams typically submit lessons learned that no one revisits (to be frank, these are exceptional resources only when leveraged). AI can instead:

   1. Analyze past lessons learned

   2. Group them by scope, risk, or trade

   3. Surface them proactively on future projects

      You prompt AI asking for elements to watch out for in a project that is over $50M. It could then say, “On projects over $50M with complex MEP scope, coordination delays most often occur during ceiling rough-in.”

Institutional learning becomes predictive, not archival

Manager Enablement

1. AI can support managers with prompts, coaching guidance, and development check-ins - reinforcing their role as the most critical driver of employee engagement, growth, and retention. Your L&D team can design a custom GPT built around this model so that responses are more nuanced to your organization.

2. Here is an example: A mid-sized GC recognizes a consistent gap: project managers and superintendents are technically strong but uneven when it comes to coaching, feedback, and early-career development (sound familiar?). L&D doesn’t have the capacity to individually support every manager and expectations around “being a good people leader” are vague.

   Instead of rolling out another generic leadership course, the L&D team designs a custom Manager Coach GPT aligned to the organization’s culture, roles, and expectations.

Guardrails Matter More Than the Tool

Just like onboarding, AI adoption cannot be rushed. Without clear intent, governance, and ownership, AI introduces risk, especially in a safety-critical industry like ours. Inaccurate information, cultural misalignment, or over-automation can erode trust quickly.

AI needs to support decision-making, not replace it. Human oversight, clear standards, and alignment to company values are non-negotiable. Remember the "those that use AI" from above.

A Shift for L&D: From Content Creators to Capability Architects

AI creates an opportunity for L&D teams to step out of reactive mode. Less time spent manually creating and updating content means more time focused on designing experiences, aligning stakeholders, and measuring impact.

This is less about adopting a new tool and more about evolving how we think about learning - moving from content delivery to capability building.

Before deploying AI broadly, organizations should pause and ask:

• Where does AI actually add value today?

• Which roles or processes should we pilot first?

• Are managers prepared to support this shift?

• How will we measure success beyond completion rates?

Starting small, with intention, is far more effective than reacting at scale.

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AI Is a Tool, Culture Is the Differentiator

AI will amplify whatever already exists in an organization. Strong cultures, clear expectations, and intentional development strategies will benefit. Weak foundations will crack.

Construction has always been about planning, coordination, and execution. AI in L&D is no different. Treat it like preconstruction - get the initial work right and everything built from there performs better.

AI will not take your job, but someone who understands AI will.