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A CFO opens an invoice from their software development agency. They find that every hour is accounted for and that each developer logged their time accurately. Nothing on the bill is technically wrong. Yet the invoice still represents a failure.
Not because anyone acted in bad faith, but because the pricing model was based on effort instead of outcomes. The project delivered what was requested, but the final cost exceeded initial expectations due to extended timelines and evolving priorities. The invoice was transparent, but the investment was never truly predictable.
AI Is Changing the Conversation
Those invoices are more prominent than you might think. Why does that matter? Because now, the conversation includes how artificial intelligence is shaping software development. Stack Overflow's 2025 Developer Survey found that among more than 49,000 developers across 177 countries, 84% reported that they use or plan to use AI tools in development, while 51% of professional developers already use them daily. AI has already become a standard practice in software development.
If AI is changing how software is built, should software builds still cost the same?
The Limits of Time & Material Pricing
For decades, the Time & Material (T&M) model has been the default approach to software development. Until recently, it made sense. Software projects are inherently uncertain; requirements evolve and product teams often discover what they need only after development begins. Paying for time gave both clients and developers the flexibility to adapt without constantly renegotiating contracts.
The model compensated for the engineering effort. More work generally meant more hours, and more hours translated into higher costs. Enter AI, which is disrupting that relationship.
Productivity Gains & Their Implications
Developers can use AI to generate code, automate testing, draft documentation, and accelerate repetitive tasks. GitHub's controlled experiment on Copilot found that developers completed a programming task 55.8% faster with AI assistance. Productivity gains that once took years of process improvement are now happening through everyday development tools.
Many business leaders overlook the fact that the greatest financial risk in software projects lies in the commercial model behind the invoice.
Transparency Versus Predictability
When organizations evaluate software vendors, they often compare hourly costs or team sizes. While those metrics matter, they can distract from the bigger question: How much financial uncertainty is built into the engagement?
A vendor can provide detailed timesheets and perfectly accurate invoices while the total investment remains unknown until the project ends. As priorities evolve and new features emerge, costs continue to expand alongside the scope.
From an accounting perspective, everything is visible. From a budgeting perspective, very little is controlled.
Planning Considerations for Technology Investments
Financial advisors encourage clients to establish investment objectives., define acceptable risk levels, and rebalance as circumstances change. Technology investments benefit from a similar approach.
Without clear financial boundaries, organizations often postpone difficult prioritization decisions because expanding the budget feels easier than narrowing the scope.
According to Google's 2024 DORA (DevOps Research and Assessment) Report, more than 75% of respondents now rely on AI for at least one daily professional responsibility, and more than one-third reported productivity gains. The research also found improvements in documentation quality, code quality, and code review speed with AI.
The DORA report also found that AI adoption was associated with modest declines in delivery throughput and delivery stability. That finding does not suggest AI is ineffective. Rather, it highlights that writing code faster doesn’t automatically make software projects easier to govern.
Why Incentives Matter
Commercial incentives still matter. Teams must still manage risk, make architectural decisions, prioritize competing business needs, and ensure that every new feature contributes to the organization's broader objectives.
If AI enables development teams to accomplish more in less time, who should benefit from those efficiency gains? Should faster delivery simply result in a smaller invoice, or should it create greater business value within the same investment?
Rethinking Pricing Models
Organizations are beginning to rethink how development projects are priced. One emerging approach is the use of a max-price software model. This establishes a predefined budget ceiling while allowing priorities to evolve throughout the project.
Rather than locking every feature into a fixed contract or leaving costs completely open-ended, this model encourages teams to continuously evaluate which work delivers the greatest business impact within an agreed financial boundary.
This way, when new priorities emerge, teams make trade-offs within the existing budget instead of automatically extending timelines and increasing costs.
This approach shifts away from measuring success by hours and instead toward measuring success by business outcomes. It preserves the flexibility agile development requires while introducing the financial discipline executives expect for major technology investments.
A New Standard for Software Investment
Artificial intelligence is changing how software is built. The commercial models that govern software delivery should evolve as well.
The invoice companies should question is not necessarily the most expensive one. It is the one that quietly rewards effort instead of results. As AI compresses development cycles and increases productivity, business leaders should look beyond hourly rates and ask a more important question: Does the pricing model encourage better outcomes, or simply more hours?
Sylwia Maslowska is the chief strategy officer at Polcode, where she leads company-wide growth initiatives and long-term strategic planning. With more than 15 years of experience across IT services and banking, she has held senior roles at Netguru, 10Clouds, and BNP Paribas, managing global teams and driving commercial strategy in markets across the U.S., Europe, and the Middle East. Her expertise spans go-to-market strategy, revenue growth, and organizational transformation, backed by a proven ability to build high-performing teams and scale operations through data-driven decision-making.
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Advisor Perspectives welcomes guest contributions. The views presented here do not necessarily represent those of Advisor Perspectives.