Three signals from the market are forming one clear picture – along with a few conclusions we’ve drawn from our own projects.
AI is changing the way software houses price, sell, and deliver projects. Traditional billing models (Time & Material), approaches to estimation, and even the definition of a company’s value are all starting to break down. In this article, we’ll show what is changing and how we’ve responded to it at fireup.pro.
Why the time & material model is breaking down in the AI era
The Time & Material (T&M) model charges for hours worked. The faster a team operates, the less revenue it generates. AI accelerates development, which means T&M structurally rewards slower delivery and penalizes efficiency.
This tension is becoming increasingly visible in conversations with clients and partners. Companies that have adopted AI in their daily workflows are delivering more in less time and immediately running into a problem. The traditional billing model means that the benefits of increased efficiency go to the client, not the delivery team.
This is not an implementation issue. It is a design flaw in the T&M model.
Alternative approaches are becoming mainstream: capped-budget models with flexible scope, outcome-based pricing, and hybrid engagement structures. At fireup.pro, we’re seeing the same shift on the client side. More and more often, clients ask, „What exactly will we get for this budget?” instead of „How many hours will it take?”
This is not a coincidence. Hours and value are two different things.
How AI is changing software house valuations
Investors are becoming less interested in the number of developers a company employs. Instead, they increasingly ask: „What is your product?”
A growing number of market analyses point to a clear trend: companies with standardized delivery processes, proprietary IP, and predictable pipelines are receiving higher valuation multiples. Those relying primarily on staff augmentation and body leasing are seeing lower ones.
AI is flattening the competitive advantage of „having available developers.” With GitHub Copilot and similar tools, available developers are everywhere. The question is no longer „Do you have resources?” but rather „What exactly do you build, and for whom?”
At fireup.pro, our IP is our specialization in regulated industries: expertise in MDR, HIPAA, HL7 FHIR, and experience working in highly regulated environments that require certification and compliance. A generic AI-assisted development team cannot easily replace that.
But to sell this expertise — and for investors or clients to properly evaluate it — you first need to define and communicate it. Many companies across the CEE region already have the foundations of productized services. They simply don’t recognize them as such.
Estimating AI projects: the third variable missing from your spreadsheet
In traditional software development, you estimate time and functionality.
In AI projects, you estimate time, functionality, and model accuracy and that third variable is by far the hardest to predict.
Model accuracy doesn’t depend solely on your team. It depends heavily on the quality of the client’s data, which often remains unknown until the project is already underway.
Data audits are regularly the first factor to disrupt timelines and budgets. Clients who expect 100% accuracy need an honest conversation: no one can guarantee that outcome at the proposal stage.
Based on our experience, there is only one practical answer: an iterative approach.
PoC first, not as a formality, but as a genuine validation of both the technical and business hypothesis. Each phase reveals new information. A client expecting a fixed-price quote for a full AI implementation after a single meeting needs to understand that such a quote is not an estimate – it’s a forecast.
What to do about it: 3 changes worth making today
First: review your pricing model.
If you’re still selling hours exclusively, you have a structural problem because AI is making hours cheaper.
Second: define your product.
Most companies in the CEE region already have the foundations of productized services. They simply haven’t named them yet. Industry specialization, proprietary tools, repeatable delivery processes – that’s intellectual property, not just human resources.
Third: rethink how you estimate AI projects.
Stop estimating the entire project upfront. Start with a discovery phase and a PoC that uncover critical unknowns, then estimate the next stages based on real evidence.
If you’d like to discuss how this works in practice for healthcare or fintech projects, let’s talk.
