Why “AI Maturity” Is the Wrong Goal for Construction Teams

Chasing “AI maturity” is the wrong goal for construction teams. I discuss the practical way to choose construction AI tools that get the job done without restructuring your team’s workflow.

The goal was never “AI maturity.” The goal is, plainly, to get the job done. Good tools meet you where the work already is. You don’t have to rebuild your company around them.

I was reading Guido Maciocci’s article this month, “The Point-Solution Trap: You Can’t Buy Your Way to AI Maturity.” It got me wondering: If you are not a tech company, how do you choose which direction to go with AI? Between point solutions, all-in-one solutions, intelligence layers to wrap around your firm, and DIY, the options are endless. The pressure to choose is especially high for those who work in construction. You have a physical job to do, you’re trying to get it done, but now, there are a hundred AI vendors knocking on your door.

Full disclosure: I build one of those point solutions. Discount me accordingly.

Here’s how I’d think about it.

AI is a means to an end. A very powerful one that promises to improve every workflow, whether it needs improving or not.

And it is perfectly fine to run several tools that each get the job done, instead of one tool that does many things mediocrely. Every company on earth uses different tools for different needs: Slack for internal comms, Salesforce for CRM, Zoom for calls. What they have in common is that each one is excellent in its own lane.

Our lane is submittal review. On a 155-unit build, we recently caught a duct vent sized wrong before anyone poured concrete. That one error would have otherwise cost the contractor about $600,000.

None of that was easy. Reading a submittal accurately, understanding what the spec actually requires, and knowing which of a thousand discrepancies is the one that matters is hard, and it took a long time to get right. But that difficulty is ours. The contractor did not build a data lake, define an ontology, or run a transformation program. They kept doing their job while we ran quietly in the background.

Now that we’re done stating the obvious, let’s get to the interesting part.

Timeframe matters

For full “AI Maturity,” you’d want a flawless data lake (which are rarer than Bigfoot sightings), as well as repeatable and immutable workflows all feeding your AI(s) so you could get compounding benefits at each interaction.

Having all your tools and processes learn from each other would indeed be phenomenal, but in the short term it is extremely hard. While being the same company, Salesforce and Slack’s integration into one another is neither sleek nor particularly useful. It is also not necessary. It would be magical if your Zoom call automatically updated your Salesforce Opportunity, or if your CRM fed your AI agents everything they needed to prep for the next call, but the reality is that plenty of very successful businesses run just fine without perfectly compounding across platforms.

In the long run, expect the best companies to stop requiring customers to show up with a pristine data lake and APIs and MCP connections ready to go. AI-native companies will ingest messy, poorly structured data, deliver value anyway, and invest in connecting to other tools to compound that value.

The trap of the all-in-one solution

After the craze of AI wrappers came the AI one-stop shop, which promises to do everything under the sun, as long as you bend to its precise data requirements and change how your team works.

The problems stack up. Doing many things without fully solving any of them is pointless, and it makes teams resent the product, because every failure turns into rework. Integrations are painful and costly when attempted all at once. And then you are trapped. Anyone who has lived through an ERP or CRM migration knows the shock is so brutal that companies will accept years of a subpar system rather than go through it again.

Solve the problem, ignore the noise

AI is overwhelming. Endless tools, endless models, endless takes: Watch out for garbage in, garbage out! Fable is a national security risk! Build a data lake! Do it yourself! Buy the thing that does everything! Cursor is the greatest tool ever built! Cursor will be killed by Claude Code!

Unless you work at an AI lab, report on the industry, or have unlimited time to keep up with the daily news cycle, let all of it fade into the background. You do not have to adapt your company to AI. AI will adapt to you.

Instead of executing a massive data overhaul or trying to find a solution that tries to solve everything all at once. Keep it simple. What are the biggest problems your company is facing? What is costing you the most time and the most money, and what is stopping you from growing? Is there an AI company solving exactly that? Yes? Great. Is it easy to integrate? Yes? Great. Does it deliver far more than it costs? Yes? Great.

Let’s simply get shit done.

Olivier Grinda

Entrepreneur, Tennis player, Gamer Know-it-all with a good heart.

I have launched and scaled 4 companies, raised over $60M in capital and had 2 successful exits in the last 15y. As COO of Clearco, I led a full operational pivot, deploying over $500M in capital, rebuilding product, risk, and data systems, and stabilizing the business. I am also an active angel investor in more than 70 companies.