Most people agree that preconstruction needs to modernize, but what's less clear is how.

Right now, most of the industry (and a lot of capital) is focused on generative design, AI-powered takeoff, and faster ways to extract quantities from drawings.

To put this investment into perspective, Cemex Ventures' 2026 industry report found that AI-enabled platforms captured 77% of all construction tech funding in 2025 — up from 35% the year before.

It sounds like progress, but it’s my belief that it’s aimed at the wrong problems…

After spending the better part of two decades building the tools GCs use to price and win work, these are the three shifts that I see actually playing out.

Prediction 1: Pricing will move ahead of design

We’ve been stuck in one, big, unforgiving loop. We design buildings first, then ask what they cost, then scramble to reconcile it.

Over the next five years, the leading GCs will flip it.

Pricing won’t be downstream because it will be the mechanism that defines the project itself. We won't price designs. We'll design to price. 

And the GC who can do this becomes the first call an owner makes — not because they're the cheapest, but because they can price an idea before anyone else can.

Read more: Why the Future of Preconstruction Starts with Cost, Not Design

Prediction 2: GCs will build teams with pricing engineers

Once pricing moves upstream, it has to become faster, more consistent, and more defensible.

That requires something the industry barely has today: dedicated pricing systems managed by specific people.

I’ve thought a lot about this role of the “Pricing Engineer” and its inevitable value to any preconstruction system that puts cost at the center.

Working within software like Ediphi, pricing engineers will be responsible for:

• Building reusable pricing logic
• Structuring cost data across projects
• Connecting estimating to buyouts and actuals
• Then, turning those cost systems into something that compounds

Estimators produce numbers, but Pricing engineers will produce the systems that create them.

Prediction 3: AI will only be as accurate as the cost infrastructure beneath it

AI promises that it'll reason for you. But few people are asking the question: reason with what?

Most of our preconstruction "data" are numbers without a lot of context. In a poll during an Ediphi webinar, most attendees reported that their cost information is split across a handful of estimating tools and almost none said it lived in one structured system. 

Finding small ways to layer AI on top of all that — a dash of this feature here, and a dash of that one there — doesn’t contribute anything meaningful, least of all profitable, to our preconstruction workflows.

AI needs the numbers + the reasoning.

In this new, better future, we will point AI towards our structured cost databases, alongside the reasoning of what changed along the way and what actually happened once the project was built.

Cost infrastructures enable AI that can reason, not just assume.

Where Ediphi fits

Building a cost infrastructure is really about building the foundation for process.

• When your people and your data are in the cloud, projects are more transparent
• When your cost models are connected to your estimates, projects are more accurate
• And when that data compounds over time, projects get smarter

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This is the layer we've spent years building at Ediphi.

When structured cost data is tied to a project, a decision, an outcome, any estimator on any new project can look at what a similar decision cost last time. And it continues to compound.

The GCs who build this now won't be scrambling to catch up in five years. They'll already be somewhere else.