When AI Accelerates Software Development, Competitive Advantage Depends on Product Decisions

AI & Software Development

Generative AI has changed how software is built: mockups in minutes, working prototypes in hours, and code produced in much less time. AI lowers the threshold for building, exploring, and testing solutions that previously required more time, more skills, or more resources.

This transformation also changes the work of people who design and develop products. The number of alternatives increases and the range of opportunities expands. Teams can explore more ideas, develop more variants, and test market hypotheses more frequently.

More opportunities also mean more decisions: what to explore, what to build, what to test, and what to leave out of the product.

Users remain the people each decision is ultimately made for. Competitive advantage therefore shifts from the ability to produce software to the ability to choose what to experiment with, validate, and scale: what creates value for people and for the business.

AI Multiplies Opportunities and Increases the Number of Decisions

AI makes it possible to produce one solution, compare it with ten others, modify them, and generate more.

This makes it possible to explore options that previously required more time, resources, or skills.

The range of opportunities expands, but so does the need to filter what is actually worth turning into a product.

Every component involves a decision: how an interaction works, how information is presented, which behavior is encouraged, and which exception is handled.

The same applies at a broader level. Which problem to address. Which opportunity to pursue. Which technology to use. Which process to automate. Which feature to develop. Which idea to abandon.

A solution can be technically feasible and still have little value.

The question therefore becomes less “Can we do it?” and more “Is it worth doing?”

Why Producing Faster Can Accelerate Debt

AI accelerates what it finds. A clear process becomes faster. A confused process produces confusion faster.

With weak criteria, greater production capacity makes it possible to accumulate features, variants, exceptions, and code much more quickly.

Every choice that enters the product creates work to manage. A feature requires testing, maintenance, documentation, security, accessibility, and support. A new interaction pattern has to be understood by users. A new exception increases system complexity.

A feature can be simple to generate and difficult to understand or maintain. An automation can reduce work in one part of the system and increase the work required from the people who have to check its output.

Technical debt is the future cost created by technical choices that will require correction or maintenance. UX debt is the accumulation of problems in the user experience caused by postponed, inconsistent, or poorly validated decisions. With AI, both can accumulate faster.

AI also accelerates the effects of a poor direction. A solution generated in a few minutes can require months or years of maintenance. A wrong decision made early and replicated quickly can spread across many parts of the product before the problem becomes clear.

Competitive Advantage Shifts From Knowing How to Build to Knowing How to Decide

When development and design can more easily create solutions that previously required more time, skills, or resources, the ability to produce becomes less of a differentiator.

The difference lies in understanding which solution deserves to be built.

Which problem to address first. Which opportunity to pursue. Which technology to use. Which feature to develop. Which idea to discard. Knowing how to choose becomes part of competitive advantage.

The ease with which we can now build a solution makes it easier to explore, but also makes it more important to understand whether that choice should actually become part of the product.

An option that seems easy to add creates costs as soon as it enters the system: development, testing, documentation, release, maintenance, support, training, exception management, and rework.

A poor choice can have effects beyond the individual component and increase product complexity over time.

Competitive advantage therefore comes from the quality of the decisions an organization can make as the range of what it can build and explore increases.

How to Decide What Is Worth Building

Understanding what is worth building involves the whole team. It is a product decision and comes before the technology choice.

Business, product, and development teams contribute to defining the problem and sharing the criteria used to evaluate a solution. These definitions need to converge before a solution is turned into code.

Making these decisions requires understanding users. Who will use this feature? What are they trying to achieve? Where do they lose time? Where do they make mistakes? What problems do they encounter? Which outcome do we want to improve? What benefit should it create for the business?

User research allows us to answer these questions through evidence rather than assumptions. Interviews, observation, behavioral analysis, usability testing, and support data help identify which problems actually exist, how relevant they are, and who experiences them.

KPIs must also be defined to measure the success of the solution. Adoption, task success, time needed to complete a task, errors, conversion, retention, or support costs make it possible to establish from the beginning what result is expected and how it will be measured.

User research and KPIs therefore serve two different purposes: understanding which problem is worth addressing and establishing how to measure the result we want to achieve.

At that point, the role of technology can be evaluated.

Data on agentic AI shows why this distinction matters. According to Gartner, more than 40% of agentic AI projects will be cancelled by the end of 2027, mainly because of rising costs, unclear business value, and inadequate risk controls.

Capgemini reports that only 2% of organizations have deployed AI agents at scale.

Cases from products already on the market also show that a technically effective solution in one part of a system can create unexpected consequences elsewhere. Klarna invested heavily in AI automation for customer service, but later revised its model, keeping AI for simpler requests while strengthening human support where the quality of the interaction matters more. Air Canada was held responsible for incorrect information provided by its chatbot.

The point goes beyond individual cases: technology creates value when it addresses a real problem and improves something that matters to users and the organization.

AI Makes It Easier to Test Hypotheses

Identifying the problem is not enough. We also need to understand which solution works best.

Testing with prototypes makes it possible to explore alternatives before turning them into a product. It helps identify problems and compare different approaches.

The cycle becomes: hypothesis, prototype, test, evidence, adjustment.

AI makes this process less expensive. A prototype that previously required weeks can be built in hours. More alternatives can be explored and compared before deciding which one to develop.

This makes it possible to use greater production capacity to gather evidence before investing more.

Producing quickly is useful when it also allows teams to verify quickly. Building a solution quickly when it does not address a real problem, creates no value, or creates new problems simply means reaching the wrong solution sooner.

The earlier the verification happens, the lower the investment required to correct direction.

Measure the Impact on Users and Business

Verification continues after release through the KPIs defined at the beginning of the project.

The data collected makes it possible to understand whether the solution is producing the expected result and whether the observed effects match the defined objectives.

A feature that reduces errors can reduce support costs. A simpler process can increase conversion. Better understanding can reduce training time. A useful feature can increase adoption and retention.

Frequent corrections, manual checks, escalations to human operators, abandonment of a feature, or reduced use can also indicate that the expected result has not been achieved.

What matters is therefore the overall result for users and business, not only the performance of an individual feature.

Product analytics, session replay, support data, usability testing, and user feedback make it possible to monitor over time what works, what changes, and when a solution needs correction.

Continuous measurement therefore makes it possible to verify whether the initial hypotheses remain valid once the product is used in real conditions.

Five Criteria for Starting a Software Project With AI

1. Start with the problem, not the technology
Identify users, needs, and business objectives. Establish which result will demonstrate the value of the solution before deciding how to build it.

2. Define KPIs and success metrics before writing code
Agree on metrics and targets from the beginning. Adoption, task success, errors, time, conversion, retention, and support become shared references for guiding decisions and verifying the value produced.

3. Validate with real users from the early stages
Define a validation process using prototypes and real users. Frequent cycles make it possible to observe, learn, and correct direction while the cost of change is still low.

4. Define how decisions are made
Establish objectives, shared criteria, and responsibilities across business, product, design, and development. Also define how AI-generated outputs will be evaluated before they enter the product.

5. Maintain consistency over time
Every new feature affects code, interface, processes, support, and maintenance. Continuing to evaluate each choice against the defined objectives and criteria helps maintain a shared direction and limit complexity and debt.

Conclusion

AI expands what a team can build and explore with the resources available. This creates new opportunities, but also increases the number of decisions that need to be made.

The quality of these decisions determines what enters the product, what costs it creates, and what value it produces.

For this reason, business, product, design, and development need clear objectives, shared criteria, validation with real users, and metrics that make it possible to evaluate results.

Competitive advantage shifts from simply knowing how to build to knowing what is worth building.

Part of the opportunity created by AI lies here too: using greater capacity to build and explore to gather evidence earlier, reduce uncertainty, and make more informed decisions.

Main Author: Gabriella Moro, UX Manager @ Bitrock

Sources and Further Reading

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