Mikhail Zborovskiy: Why Time to Market is the main indicator of AI effectiveness in development

Why Time to Market is a business indicator

Time to Market measures the path of a product from idea to launch, and for different industries this distance differs by orders of magnitude: software can enter the market in a few months, while in pharmaceuticals this cycle is stretched for years due to regulatory requirements. But in any industry, the logic is the same: the later a product reaches the user, the more market opportunities and potential profits the company loses.

In high-load products, this dependence becomes even more acute, since competitors and user expectations change faster than in niches with a slow update cycle. A delay here does not just postpone revenue, it risks completely losing the window in which the product would be in demand.

Key reasons why Time to Market directly determines financial results:

• Earlier appearance on the market gives the company the effect of the first mover, higher market share and brand recognition;
• Launch delay reduces not only revenue, but also the quality of feedback that the company has time to collect before competitors appear;
• A long development cycle increases the risk that the product will become obsolete before it enters the market;
• Shortening TTM allows you to calibrate the product faster to real, rather than hypothetical, user needs.

That is why Mikhail Zborovskiy advises to consider any implementation of artificial intelligence (AI) in development not as a technical improvement in itself, but through the prism of how much it shortens the path from idea to real user.

Where exactly AI speeds up the development cycle

Acceleration of Time to Market through AI occurs not at one point in the process, but at several: from automated testing, which detects errors at early stages, to analytics, which helps teams make decisions about priorities without lengthy approvals. The automation of routine development tasks deserves special attention, namely, it frees up engineers' time to work on complex, non-standard parts of the product.

No less important is the role of AI in the decision-making process itself, when data on user behavior and market trends processed by the model allow teams to adjust course faster than would happen with manual analysis of reports and meetings.

Key points in the development cycle where AI implementation provides measurable acceleration:

• Automated testing and early detection of errors before the release stage;
• AI analytics to prioritize features based on real user behavior;
• Automation of repetitive development tasks, which frees up the team’s time for complex solutions;
• Predictive models that help to more accurately plan resources and deadlines during the development stage.

The effect of these acceleration points is multiplied if the company applies them not in isolation, but as part of a single, pre-thought-out development process.

Why speed without balance creates new risks

The desire to reduce Time to Market at any cost creates its own risk. A product released too quickly and without proper quality control can undermine user trust faster than it will benefit from early market entry. That is why Mikhail Zborovskiy emphasizes that the task of AI is not just to speed up the process, but to do so without losing control over quality.

Here again, the dual role of technology is evident. On the one hand, automated testing and monitoring help to detect problems faster than humans, on the other hand, the decision whether a product is ready for release still requires expert judgment, which cannot be fully delegated to an algorithm.

Conditions under which AI acceleration does not turn into a risk to product quality:

• Automated testing covers critical use cases, not just formal metrics;
• Launch decisions are made based on data, but approved by a human responsible for quality;
• The Minimum Viable Product (MVP) is used to collect real feedback, not as an excuse for defects;
• Development speed is not achieved by reducing resources to fix found problems.

The balance between speed and quality, not speed itself, determines whether a reduction in Time to Market will lead to the long-term success of the product in the market.

The material is informative and analytical in nature, reflects the personal opinion and professional experience of the author and does not constitute technical, investment or legal advice. Before implementing AI solutions in a specific business, a separate assessment with specialists is recommended, taking into account the specifics of the product, industry and regulatory requirements.