AI Isn’t a Miracle, But It Will Change Construction
A few weeks ago, I threw a rock in the LinkedIn pond and watched the ripples. Saying what a lot of folks were thinking, it seemed to hit a nerve.
The fact is, it hit a nerve because it’s true. We’re throwing around the phrase “artificial intelligence” like it’s capable of doing magic in a house where the lights aren’t wired, the doors don’t hang plumb and the drawings contradict themselves.
So, let’s slow down, breathe and figure out what AI actually is, what flavors we can buy and how to choose wisely. At least, before we just throw a chat-bot at a laborer because it sounds smart.
Step One: What AI Is (and Isn’t)
If you’re anything like me, you’ve probably gotten tired of the amount of ways people claim to have AI-ified their product. It leaves one scratching their head to understand what AI is technically.
Modern AI is a stack of math and software that learns patterns from data and uses those patterns to predict the next best output. Large language models (LLMs) predict the next word given context. Computer vision models predict the most likely object in an image. Recommendation models predict the next best action. But none of these models “think.” They approximate (albeit at breathtaking speed and scale) based on the data and instructions we give them.
But there are a lot of misconceptions about what AI can do. You see, it’s…
NOT a silver bullet for bad processes or missing leadership.
NOT a single brain that “understands the project.”
NOT an automatic truth machine.
It is a set of models that need clean, connected, permissioned data to be useful. If your inputs are stale, siloed or inconsistent, a fancy model will produce confident nonsense (but with citations).
And then there’s AI versus actual intelligence (the human brain). Humans reason across messy context: scope, contracts, personalities, weather, union rules, “that one detail the architect always forgets” and the ten thousand soft signals on a jobsite. AI is really only brilliant at narrow pattern tasks, like summarizing, extracting, matching, forecasting when we bound the problem and feed it the right data.
The winning play is human judgment + machine patterning, not either/or.
Step Two: Choosing Your Flavor
Understanding what AI is and isn’t really is just step one. As we’ve already admitted, everyone and their cousin is rolling out a new AI system. So, what’s the difference between them all?
The easy one to understand is the third-party, consumer option. Think ChatGPT or Claude, the general assistants that can be “directed” at construction work. While good at drafting emails or summarizing meeting notes, they don’t tend to handle construction-specific tasks with great context. Although you can integrate them with PMIS data, if you dare.
The safer bet is the AI your PMIS vendor is announcing from their annual conference stage, theoretically built inside the system that already runs your projects. Things like AI assistants embedded in a construction platform that search drawings/specs, analyze risks or copilot RFIs and submittals citing the exact paragraph. These tend to have less power but more unified data, native permissions, fewer integrations and smoother UX.
And then there’s the start-ups, independent vendors all clamoring to connect to your PMIS, file stores and mailboxes with their construction-specific solutions. These “Chat with my project” tools ingest your drawings, specs and RFIs and answer field questions with citations or auto-review things like submittals. While their specialty features are strong with a cross-platform reach, they are another vendor with another data lake and ongoing integration maintenance.
But that’s just the beginning of the tradeoffs. In reality, with both the third-party consumer options and the start-ups, there are quite a few risks that folks don’t often consider.
Security & Privacy: Your project data leaves the primary system and lives somewhere else, often several places. More copies = more opportunities for attacks.
Integration Dependence & Reliability: If APIs change or rate limits spike, your integrations slow down or fail taking your AI down with them. And of course, it always seems to happen at 4:30 on a Friday. Add in constant sync failures and your data freshness is out the window.
Continuity & Vendor Viability: Startups get acquired, roadmaps pivot, access can be throttled. What’s your fallback if the tap shuts off mid-project?
User Experience & Workflow Impact: Extra logins, different UI, context-switching and permission mapping fatigue wreck adoption. If it doesn’t live where the work lives, it won’t stick.
The Questions You’re Not Asking (But Should)
Now that we’ve the baseline out of the way, it becomes much easier to have conversations about AI with your teams (and vendors). When you evaluate any possible AI option (third-party, platform or start-up), these are the questions you should be asking out loud. Bonus points if you can get them in writing:
Data scope: Exactly what data do you ingest? Where does it live at rest? For how long?
Model use of our data: Is any of our content used to train your models or third parties’ models? Can we opt out? Is this contractually enforced?
Permissions: How do you enforce our project permissions? Is access evaluated on every query?
Citations & traceability: Will every answer cite drawing/spec/RFI with links? What happens if the source changes?
Freshness & sync: How quickly do changes in the PMIS propagate to the AI index? What’s the SLA on sync failures?
Accuracy controls: How do you reduce hallucinations? Can we pin approved sources or disable others?
Write-back & audit trail: Can the AI write drafts into RFIs/submittals/logs? Are AI-assisted actions labeled and auditable?
Identity & SSO: Do you support SSO/SCIM? Are actions tied to the human who clicked “accept”?
Resilience: What’s the fallback if your service or an upstream model is down? Do we degrade gracefully to search?
Exit strategy: How do we revoke access, purge indices and export work products if we leave?
Cost predictability: How are we billed (seats, projects, tokens, outcomes)? What guardrails stop runaway usage costs?
Roadmap fit: What’s coming in 3–6–12 months? Where do you partner vs. build?
Taking a Pragmatic Approach
AI will be most effective when it’s simplifying tasks that are closest to the work. Built-in platform AI can search project docs with your existing permissions, draft RFIs and submittals with citations and summarize meetings into action items without extra integrations. Bolt-on AI tends to fit better with a specific, sharp pain point (like submittal compliance). And of course, consumer AI can help with proposals, safety memos and training content.
But for long-term, sustainable success you must fix the foundation. By starting with connected, governed data and coupling that with an AI as close as possible to the system of record you reduce data silos, integration headaches and permission bottlenecks. Use a deliberate, pragmatic approach with platform AI and always keep humans in the loop. Treat the AI like a junior engineer who drafts quickly but never signs alone.
And to protect your investment, choose partners who are transparent about data handling, interoperable by design and committed to respecting permissions natively. Favor tools that cite sources so every answer can be traced and keep your architecture simple: identity → permissions → data layer → AI services → workflow tools. If an AI product can’t snap into that chain cleanly, think twice.
Being disciplined with your approach is what ensures today’s wins become tomorrow’s advantage.
AI Will Change Construction, If We’re Ready
AI won’t save us from chaos. It amplifies whatever we give it. If we hand it fragmented systems, untrusted data and duct-taped processes it’ll multiply the mess.
But if we do the hard work first, connecting the data, reducing waste and enabling real collaboration, then AI becomes a force multiplier for the people who actually build the world. That’s the game.
AI is going to change how we coordinate, communicate and control risk. The companies that stop chasing shiny objects and start architecting (data first, people-centric, workflow-native) will see AI turn good systems into great ones. Everyone else will keep buying louder buzzwords.
As I’ve said before: AI isn’t a savior; it’s a spotlight. Point it at a disciplined system and it will shine. Point it at chaos and it will blind you. Let’s choose wisely and build better.
Construction is cool, tell your friends!