AI-assisted SDLC is becoming increasingly popular, both within our company and across the industry. It is no longer an experimental concept — it is already an integral part of how many organisations approach software development. There is a growing expectation that traditional SDLC will evolve into AI-driven processes, and many companies are actively exploring frameworks and methodologies to support this shift.
We sat down with Dmytro Plotnikov to discuss a growing misconception: that AI can replace not only developers, but also the underlying technological foundation of software development.
Can AI replace the technology stack that software is built on?
AI can accelerate development. It can generate code, assist with tasks, and boost productivity. But it does not replace the systems we build on — the technology stack, frameworks, architectural decisions, and component systems remain essential. AI operates on top of this system but does not redefine it.
In practical terms, this means that technologies such as JavaScript frameworks, component libraries, database solutions, and cloud infrastructure do not disappear. Instead, AI is trained and guided to work with them more efficiently. This is why developers can now handle more tasks — effectively supported by AI agents that function as a micro-team, similar to junior engineers assisting a senior developer.
What made you decide to build your own internal JS framework?
One of the key challenges in development is the lack of standardisation. Different teams often start projects using different frameworks and libraries — whether it’s Tailwind, Material UI, Carbon Design, Ant Design, Kendo, or others. These decisions may seem minor at the beginning, but they have long-term consequences.
In many cases, there are no predefined technical standards at the start of a project. When teams independently choose different tools, it leads to fragmentation, inconsistency, and long-term inefficiencies. Over time, as teams scale and projects evolve, these early decisions become costly to maintain or change.
To address this, we focused on standardisation. We analysed existing frameworks, defined evaluation criteria, and selected the most suitable option based on our project needs — particularly enterprise-level, data-heavy desktop applications. In our case, this led to choosing Material UI as a base.
Based on this, we developed ELEKS UI — a structured internal system tailored to our delivery needs: extended components, internal libraries, and documentation tailored specifically to our use cases. This resulted in a comprehensive ecosystem — not just a UI framework, but a standardised foundation for development.
How does a structured foundation improve AI-assisted development workflows?
When integrating AI into development workflows, this foundation becomes even more critical.
AI should be treated as a stakeholder in the development process. Like any team member, it needs a clear structure, context, and well-defined building blocks. Without this, its effectiveness is significantly reduced.
That is why we focused on building a structured system that combines:
- A standardised component model
- Integrated documentation
- Predefined patterns for implementation
- Supporting tools for seamless integration
Together, these elements form a consistent environment where both developers and AI tools can operate predictably.
To make this system usable in practice — especially in AI-assisted workflows — we introduced additional tooling, including CLI tools and MCP integrations, that allow teams to set up a full development environment in just a few steps. This includes access to codebase structure, component libraries, and contextual documentation — effectively replacing a large portion of manual onboarding.
Additionally, we provide predefined instructions and context for AI agents, allowing them to understand available components, follow established patterns, and generate more accurate and relevant outputs. This means that developers no longer need to build everything from scratch. Instead, they can start working within an already structured system where AI understands how to operate effectively.
What is the strategic value of an internal unified framework like ELEKS UI?
There is a tendency to think of AI-assisted SDLC as a replacement for traditional frameworks. But this is a misunderstanding of what AI actually does.
AI is not a framework. It is not the foundation of development. It is a tool that operates within a system.
A useful analogy is a car: AI may improve how efficiently the car operates, but it does not replace the engine, the wheels, or the underlying mechanical system. Without those, the car cannot function. Similarly, without a structured technological foundation — frameworks, components, architecture — AI has nothing to work with. Without a reusable system, AI tends to generate new solutions rather than building on existing ones, which leads to inefficiency and fragmentation.
In reality, development is already moving toward standardised, unified solutions. The question is not whether this will happen, but how quickly and effectively it can be implemented.
Adopting a unified internal framework — such as ELEKS UI in our case — is a logical step. It provides consistency across projects, faster onboarding, better integration with AI tools, and in-house expertise and support. Unlike external frameworks, internal solutions come with dedicated teams that understand the company's context and can continuously improve the system.
What does the future of AI-driven development look like?
Artificial intelligence can significantly accelerate development — in some cases by up to 85%, particularly when combined with strong domain expertise. However, this efficiency gain depends on having a solid foundation in place. AI does not eliminate the need for systems — it amplifies their strengths and exposes their weaknesses.
The future of development is not AI replacing the stack — it is AI amplifying a well-structured, standardised system.
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