In recent years, artificial intelligence has become one of the most widely discussed technologies in the global economy. Companies are investing billions of dollars in automation, analytics systems, and digital platforms capable of making data-driven decisions. However, behind the bold claims of a technological revolution lies a less obvious reality: implementing a technology is far easier than turning it into a sustainable part of a business with a new development model.
According to a study by McKinsey & Company, about 88% of organizations already use artificial intelligence in at least one business function, from marketing and analytics to operational automation. At the same time, most companies remain at the stage of experiments or pilot projects, while true transformation of business processes occurs far less frequently.
The gap becomes even more visible when examining the economic results of implementation. While companies often report localized benefits such as cost reductions or improved efficiency in specific processes, only about 39% of organizations say AI has had a noticeable impact on company profit at the EBIT level.
In other words, technologies are being adopted quickly, but their impact on the business model remains limited.
Key Takeaways
- AI adoption is widespread, but many companies struggle to see significant profit impact due to limited implementation.
- Transforming AI from an experimental tool to a sustainable business model requires a new development model.
- Maksym Mokiichuk emphasizes organizing development systematically to help integrate AI into digital products effectively.
- The LegalTech sector illustrates how coordinated development can turn AI capabilities into stable, operational platforms.
- Successful AI implementation hinges on both algorithm quality and structured product development organization.
Table of contents
Why AI Is Difficult to Scale
As long as artificial intelligence is used as an experimental tool or a standalone function, implementation is relatively simple. However, when companies attempt to turn AI into part of a new development model, the challenge becomes significantly more complex.
Artificial intelligence must operate not in isolation but within a complex technological infrastructure. It interacts with data flows, user interfaces, corporate services, analytics systems, and automated business processes.
As AI solutions move from the experimental stage to industrial use, a new professional specialization is emerging in the industry. These are specialists working at the intersection of product management, systems architecture, and the development of complex digital platforms. Their task is not only to create individual machine-learning models but also to integrate these technologies into sustainable digital products.
One such specialist is Maksym Mokiichuk, a project manager at the IT company Techlexity, which develops AI-oriented SaaS platforms. His professional work focuses on the critical stage when experimental prototypes transition into full-scale system development of technological products. At this point, it becomes essential to build a clear development architecture, synchronize the work of multiple engineering teams, and ensure the platform evolves as a unified technological system.
At a time when a significant share of AI projects within companies remains at the pilot stage, specialists capable of turning AI developments into sustainable technological products are becoming key figures in engineering teams.
In recent years, Mokiichuk has participated in projects that combine several technological directions at once: server-side development, data processing, process automation, and the implementation of AI functionality. This experience allowed him to develop a practical approach to organizing the development of SaaS platforms in which artificial intelligence becomes part of a comprehensive digital infrastructure.
How AI Platform Development Is Structured
Drawing on practical experience, Mokiichuk views the challenge of scaling AI not as a theoretical discussion about the capabilities of models but as a task of systematically organizing development.
His approach is based on coordinating several technological directions simultaneously, including backend development, data processing, machine learning, integrations, and user interfaces.
In such projects, synchronization of teams and technological processes becomes particularly important. Even small changes in a machine-learning model can affect data processing, user scenarios, or integrations with other systems. For this reason, a key task is building a development structure in which new functions can be implemented without disrupting the architecture of the product.
“Discussions usually begin with the capabilities of the model, what it can analyze and which tasks it can automate,” Mokiichuk says.
“But in a real product, the main question is different: how this model will interact with data, interfaces, and other services of the platform. If this is not considered in advance, the technology begins to exist separately from the product.”
According to him, this is precisely the stage at which many AI projects encounter scalability problems. When a new development model is not organized systematically, the product gradually breaks into separate solutions that become difficult to maintain and develop.
AI Solutions for the Legal Industry
The practical application of this approach is particularly visible in projects related to the digital transformation of traditional industries. One such area is LegalTech, a rapidly growing segment of technologies for law firms.
Mokiichuk plays a leading role in the development of an AI-oriented SaaS platform for legal firms, where he is responsible for organizing and strategically coordinating the development process. Within the project, he manages collaboration among several technical areas, including backend development, frontend teams, artificial intelligence specialists, QA engineers, and product support teams.
The platform is being developed as a comprehensive digital system that uses artificial intelligence to automate client communications, manage legal data, and optimize the internal processes of law firms.
Mokiichuk’s role involves translating business tasks into structured technical requirements, synchronizing the work of engineering teams, and ensuring the consistent development of the platform as a unified technological product.
According to Mokiichuk, systematic organization of development is what allows such platforms to remain stable as their functionality expands. When a new function appears in the system, it must be integrated into the architecture of the product in a way that avoids technical debt and does not disrupt the work of other components.
From Experiments to Industrial Systems
As the artificial intelligence market continues to grow, more companies are beginning to understand that successful AI implementation requires not only technological innovation but also a new development model of organization.
Artificial intelligence is gradually becoming part of the infrastructure of digital products rather than simply a standalone feature.
As AI solutions become more complex, it is becoming increasingly clear that the key barrier to implementation lies not only in algorithms but also in the architecture of digital product development.
In this environment, specialists capable of combining technological innovation with development architecture and the management of complex technological projects are beginning to play an increasingly important role in the development of the industry.
The practice of developing AI-oriented SaaS systems shows that successful implementation of artificial intelligence depends not only on the quality of algorithms but also on how product development is organized.
The work of Maksym Mokiichuk demonstrates that systematic coordination of development makes it possible to transform AI technologies from experimental solutions into sustainable digital platforms. By leading the development of AI projects in the LegalTech sector, he organizes development processes and engineering team collaboration so that complex technological solutions can function as a unified system and be applied in the real practice of legal companies.
This approach is what allows artificial intelligence today to move from the stage of experimentation to becoming a full-scale industry tool and helps establish new standards for AI platform development.
