What AI Changes About the Cost of Custom Software

AI changes the economics of custom software by making more implementation work accessible and helping teams explore alternatives faster. A business owner should welcome that capability while examining what a quotation actually includes. The cost of producing working software is only part of the cost of establishing a product that can support customers, employees, and future change.

Separate production effort from the whole investment

A hypothetical distributor needs a portal through which customers request repeat orders. AI may help a developer produce screens, routine functions, and alternative workflows quickly. The business must still decide which customers can order which products, how prices are confirmed, and what happens when stock information is unavailable.

Those decisions affect the required product even if implementation becomes faster. The project also includes review, preparing records, connecting necessary services, introducing the workflow, and maintaining it afterward. Compare proposals against the same responsibilities before concluding that a lower initial figure represents a lower total commitment.

Avoid treating faster generation as a reason to add every imaginable feature. Each additional capability creates business decisions and operating obligations. A restrained first scope can convert implementation speed into earlier learning, while an expanding wish list can absorb the benefit before the business sees any improvement.

Read productivity evidence in context

A 2023 controlled study of GitHub Copilot found that developers using the tool completed a specified programming task faster than the comparison group. The study demonstrates a benefit in that setting. It does not establish an equivalent reduction in the price of a complete commercial project, which involves additional work and different tasks.

Evidence also depends on context and changes over time. METR's early-2025 experiment found slower completion among experienced developers working in familiar open-source projects. In a February 2026 update, METR said later measurements were affected by selection problems and were unreliable indicators of the current productivity effect. It considered improvement likely, while describing evidence for its size as weak.

The practical interpretation is that AI's commercial contribution should be measured in the actual delivery setting. These studies neither justify dismissing AI nor support a universal savings percentage. Ask what work the proposed approach accelerates, how results are reviewed, and what remains dependent on professional judgment.

Identify responsibilities that remain

Important business rules require clear ownership. In the distributor example, the owner should specify who may receive negotiated prices and who can approve an order outside normal terms. A developer should determine how those rules are applied consistently throughout the product.

Review and security work remain material. NIST's Secure Software Development Framework recommends incorporating secure development practices into the development process. For an owner, the implication is to ask about how important risks are addressed rather than assuming that rapid implementation settles them.

Support, recovery, permissions, and future changes also require agreed arrangements. AI can assist with these activities, but the business still needs someone accountable for technical decisions. An owner capable of creating an impressive early application should not have to take on every continuing engineering responsibility simply because software generation has become easier.

Assess value through completed business work

Ask a delivery partner to show how its method affects the proposed scope. Which capabilities can be reused? Which require investigation? What evidence will demonstrate that the order process behaves correctly? How will new information change the estimate or priorities?

Track results the business understands: a completed workflow, fewer repeated handoffs, or earlier validation of a customer assumption. Lines of generated code and the number of AI prompts are poor substitutes for these outcomes. A system that requires continuing manual reconciliation may deliver less value than a smaller product with dependable records.

Treat early iterations as opportunities to improve the investment decision. If a prototype reveals that customers need approval before ordering, revise the workflow before funding extensive automation. The value of AI-assisted speed may include avoiding expenditure on the wrong design, rather than simply doing the original plan more cheaply.

Combine acceleration with a reusable foundation

Holism's model joins AI with Software Product Line architecture and a human developer. The architecture provides reusable capabilities and consistent foundations, reducing the need to recreate common elements for each product. The developer directs AI, adapts those capabilities, and applies judgment to the business's specific requirements.

For a non-technical owner, this is a way to pursue customized software while remaining focused on customers and operations. It aims to combine implementation speed with maintainability, meaning the product can be understood and changed over time. It does not establish a guaranteed saving or remove the need for a clearly defined engagement.

AI can materially improve how software is produced, but the responsible cost comparison includes the complete business outcome and continuing obligations. Evaluate the benefit through demonstrated delivery, explicit responsibilities, and realistic scope rather than assuming that faster generation translates directly into a fixed discount.