AI-driven Development: Experiment and Lessons From NIX

Artificial intelligence has quickly shifted from an experimental tool into a proven accelerator of business value, especially in software engineering. The rise of artificial intelligence in software development is redefining how teams approach planning, coding, testing, and delivery. By embedding intelligence into each stage of the software development life cycle (SDLC), companies can shorten release cycles, reduce costs, and improve quality at scale. For organizations balancing competitive pressure with efficiency demands, AI solutions are no longer a “nice-to-have”—they’re becoming a vital part of sustainable growth.

To explore this transformation in depth, we spoke with Max Ushchenko, Head of AI and Data Practice at NIX. With years of hands-on experience leading enterprise-level AI adoption, Max has guided both clients and internal teams in turning AI from theory into practice. In this interview, he shares how NIX approached the integration of AI into the SDLC through a structured, experiment-driven strategy, the challenges encountered along the way, and the measurable business benefits that come with doing it right.

Interviewer: Could you start by telling us why NIX decided to run an AI experiment in the software development life cycle?

Max: For us at NIX, implementing AI in software development wasn’t about following hype. We saw growing interest from clients in practical AI solutions and wanted to provide answers backed by evidence. Our challenge was clear: clients expect faster delivery, lower costs, and uncompromised quality. To meet those demands, we needed to see whether AI in software development could deliver measurable efficiency. That’s why we treated the experiment as a serious investment—designed, monitored, and scaled like any of our enterprise projects.

Interviewer: What was the main point of the experiment?

Max: The experiment was about building a “help system” for our teams. For developers, that meant AI tools capable of generating code templates, analyzing problems, and automating repetitive tasks. For business analysts, it was about using AI to speed up the creation of user stories, acceptance criteria, and documentation. But the point wasn’t just to try tools—it was to measure efficiency gains in the software development life cycle and understand how AI could fit into real-world enterprise workflows. We wanted hard numbers on productivity, quality, and ROI to ensure that when we recommend AI for business, it directly benefits our clients.

Interviewer: What challenges did you face while running this experiment?

Max: One of the biggest challenges was filtering out noise. There’s no shortage of AI tools, but not all are mature, reliable, or secure enough for enterprise use. Another challenge was change management—AI doesn’t replace developers or analysts, but it changes how they work. Some team members needed time to adjust to new workflows. That’s why we closely monitored adoption rates, effort reduction, and real outcomes. We wanted to make sure efficiency wasn’t just theoretical but actually visible in day-to-day delivery.

Interviewer: When companies introduce AI in software development, why is change management such a critical factor?

Max: Because AI doesn’t just add another tool—it changes how teams work, think, and deliver. Developers may fear job displacement, distrust AI’s reliability, or simply prefer their usual workflows. That’s why we apply structured change management. For example, the ADKAR model focuses on individual change: building 1) Awareness of why AI in SDLC matters, creating 2) Desire to adopt it, giving Knowledge through training, developing 3) Ability with hands-on support, and reinforcing 4) Adoption with 5) Recognition. We also use Kotter’s Eight-step Model, which creates urgency, builds champions inside teams, and secures long-term cultural adoption. These frameworks ensure that the benefits of AI in software development—like productivity gains and reduced costs—aren’t lost to resistance or confusion.

Interviewer: And how do you make sure the AI tools themselves are useful and not just shiny tech?

Max: That’s where human-centered design (HCD) comes in. It ensures the tools we introduce are intuitive, useful, and aligned with developers’ real workflows. In practice, this means we empathize with teams by studying their challenges, define the exact pain points AI should address, ideate possible solutions, prototype early versions, and test them in real projects before scaling. This process allows us to refine AI tools—whether it’s generative AI in software development for code suggestions or automation for documentation—so they’re actually adopted and deliver long-term value. When AI tools feel natural to use, teams are more likely to integrate them into their daily routines, making the benefits of AI in the SDLC sustainable and measurable.

Interviewer: How did you structure the experiment to get those insights?

Max: We approached it like a product rollout. We started with a small group of 10 developers, then scaled to 42 developers and 8 business analysts across projects in Python, .NET, Java, TypeScript, PHP, and front-end technologies. Each participant tracked estimates versus actual time spent, reported mental effort, and logged where AI made the most difference. On the BA side, we applied AI to core deliverables—user stories, spikes, technical stories, and task descriptions. By collecting structured data over 4–6 months, we built a clear picture of where AI adds value, and where it doesn’t.

Interviewer: And why is this important for NIX clients?

Max: Our goal is to deliver maximum efficiency for our clients, and our internal AI experiment clearly showed measurable results:

• Faster development: Developers completed certain tasks up to 65% faster.
• Reduced documentation time: Business analysts cut documentation work by several days.
• Lower cognitive load: Around 70% of developers reported spending less mental effort on routine tasks.
• Less time searching for resources:54% of developers spent less time looking for external code examples or documentation.

For clients, this translates to:

• Faster project delivery with more predictable timelines.
• Reduced development costs and better resource utilization.
• Confidence in AI adoption: By testing AI internally first, NIX can integrate artificial intelligence software development practices into client projects safely and effectively, maximizing benefits while minimizing risks.

Interviewer: How does implementing AI in the SDLC impact costs?

Max: AI adoption has shown clear cost efficiency for both development and BA teams:

• Development Team:
  
  
  • Average project cost savings of around 10%, with more experienced and motivated teams seeing higher impact.
  • Projects with reliable data achieved a code-helper cost effectiveness (CHCE) score exceeding 1, confirming a positive effect on development costs.
    
    
• Business Analysis (BA) Team:
  
  
  • Task-specific cost savings ranged from 18% to 45%, depending on complexity and individual involvement.
  • Forecasted average savings around 45% for artifact creation tasks.
  • CHCE score exceeded 2, highlighting significant efficiency improvements in BA operations.

For clients, this means:

• Lower overall project costs without compromising quality.
• Optimized resource allocation, focusing human effort on high-value tasks.
• Predictable cost efficiency, allowing better planning and ROI on AI adoption in software development.
  
  

Interviewer: What are the first steps businesses should take when deciding to implement AI into their software development life cycle?

Max: To successfully integrate AI into your software development life cycle, it’s essential to follow clear, practical steps, which are the following:

1. Define a Clear Roadmap

Before adopting AI, businesses should create a detailed roadmap outlining goals, priorities, and expected outcomes. This includes identifying processes where AI can have the most immediate impact, such as code generation, testing, and business analysis tasks.
Tip: Start with a small pilot project to measure efficiency and ROI before scaling AI adoption across all teams.

2. Establish an Adoption Framework for Teams

Motivating and training developers, tech leads, and other stakeholders is critical. Provide workshops on generative AI, AI-assisted coding tools, and best practices. Ensure that teams understand AI use cases in software development and how these tools integrate into daily workflows.
Tip: Identify early advocates among developers who can mentor peers and promote adoption.

3. Appoint AI Advocates and Champions

Designate key team members as AI champions to encourage adoption and serve as a first point of contact for questions and issues. They can also monitor the effectiveness of AI tools and provide feedback for continuous improvement.
Tip: Select advocates from different teams, including developers, BA, and QA, to cover the full SDLC.

4. Align Sales, Marketing, and Value Proposition

For client-facing teams, clearly define how AI-enhanced development creates value—faster delivery, higher quality, and reduced costs. Ensure sales and marketing teams can communicate these benefits accurately.
Tip: Use real internal data from pilot projects to demonstrate tangible results.

5. Plan Budgeting and ROI Tracking

Allocate budgets for AI tools, training, and pilot projects. Track key metrics such as time saved, cost reductions, and improvements in code quality to measure ROI.
Tip: Set short-term and long-term KPIs to justify continued investment and fine-tune AI implementation.

6. Identify Key Entrance Points

Determine which roles and processes will initially interact with AI, such as developers, architects, PMs, and BAs. Prioritize areas with the highest potential for efficiency gains.
Tip: Focus on processes where AI in the software development life cycle can reduce repetitive tasks or improve decision making.

7. Implement Project-wise

Roll out AI tools in a project-based manner. Define the why, when, and how for each project and provide PMs and tech leads with a framework for monitoring adoption and performance.
Tip: Collect feedback continuously to improve integration and prevent disruption of ongoing workflows.

8. Address Legal and Compliance Considerations

Ensure AI tools comply with relevant legal, security, and data protection requirements. Understand licensing, intellectual property implications, and privacy regulations when using AI consulting or third-party generative AI tools.
Tip: Engage legal teams early to avoid compliance issues and ensure safe deployment of AI in software development.

Interviewer: Can you share where NIX currently uses AI in its workflows?

Max: At NIX, we’ve integrated AI in software development and AI-assisted software development across multiple stages of the software development life cycle (SDLC). This isn’t limited to just coding—we use AI for requirements analysis, automated testing, documentation, code review, and business analysis. The goal is to enhance efficiency, reduce repetitive work, and ensure a secure SDLC.

For developers, AI tools provide code suggestions, template generation, and technical debt analysis, while for business analysts, AI assists with user stories, acceptance criteria, and artifact creation, saving days of manual effort. Across the board, AI helps teams make faster decisions, reduce errors, and optimize workflows.

Through our AI consulting, we guide clients in applying these practices safely and effectively. Below, we provide a schematic overview showing how AI fits into different stages of our workflows, highlighting where it delivers the most tangible benefits for both technical and business teams.

Interviewer: How can AI be effectively used in better project management for software development teams?

Max: AI can significantly enhance project management across the software development process by supporting both planning and execution. With AI-driven tools and machine learning algorithms, teams can analyze historical data to forecast timelines, allocate resources efficiently, and optimize task prioritization. Software development AI can assist in writing code, code automation, and testing processes, reducing manual effort for complex tasks. Natural language processing enables better documentation and communication across teams. By combining AI systems with human developers, modern software development becomes more efficient, helping software development teams improve quality, minimize security vulnerabilities, and streamline software application delivery.

Interviewer: How does AI enhance QA workflows in software development, and what specific benefits does it bring?

Max: AI-enhanced QA workflows transform the development cycle by supporting software developers and project managers with AI-driven solutions that automate testing and code analysis. Tools leveraging large language models can generate code snippets or even entire functions from natural language descriptions, improving coding efficiency and reducing coding errors. AI-driven code generation and AI-driven testing tools help with bug detection, optimize problem-solving skills, and produce optimized code. By leveraging AI alongside a human programmer, teams can accelerate project planning, perform deeper data analysis, integrate into continuous integration, and ensure faster, higher-quality delivery while allowing users to interact safely with new tools.

Interviewer: Could you explain how NIX implements AI into workflows? Do you use the Model Context Protocol, and if so, how does it fit in?

Max: We focus on secure, practical, and efficient AI integration across development workflows, and the Model Context Protocol (MCP) is central to this approach. MCP is essentially a standard that allows AI models to connect safely to tools, APIs, and data sources, so AI agents can perform real actions with controlled, permission-based access.

• Secure connections: AI agents connect to tools via MCP adapters—like GitHub, Jira, and internal databases—while access is strictly limited to specific actions.
• Chained workflows: Multiple AI agents can be linked to perform multi-step workflows, from code generation to automated testing and reporting.
• Internal and external integrations: MCP allows AI to work with in-house analytics or external services like Expedia and Zapier, providing flexibility and secure automation.
• Controlled and transparent: This ensures AI can enhance productivity without compromising security or exposing sensitive data.

Using MCP, we can safely embed AI in software development processes, streamline operations, and provide AI-assisted development that is both scalable and fully aligned with client requirements.

Interviewer: What are NIX’s best practices for AI-enhanced development, and why are they important?

Max: That’s a great question. Implementing AI isn’t just about adding tools—it’s about doing it safely, efficiently, and in a way that actually benefits the business. At NIX, we follow several core best practices for AI in software development and AI-assisted development to ensure our clients get real, measurable value:

• Security-first integration approach: We introduce AI only after rigorous security reviews. This ensures that your product remains secure from day one and mitigates potential risks from the start.
• Data quality and governance control: AI is only as good as the data it works with. We ensure clean, compliant, and reliable data at every stage to maximize generative AI benefits.
• Custom AI workflow optimization per project: Every project has unique needs. We tailor AI workflows to optimize speed, accuracy, and ROI, ensuring AI assists development where it matters most.
• Strict compliance with IP and data protection policies: We follow global regulations and proven legal best practices to protect intellectual property and sensitive data.
• Cross-team AI enablement and training: Every role in the project—developers, analysts, and QA—receives guidance and training to use AI effectively and confidently.
• Transparent AI usage documentation and reporting: Clients get full visibility into how AI drives results, making decision making clearer and more informed.
• Continuous AI model evaluation and improvement: We monitor AI outputs and refine models continuously, ensuring optimal performance over time.
• Human-in-the-loop quality assurance: AI suggestions are always overseen by our experts, ensuring deliverables remain accurate, relevant, and aligned with business goals.

Together, these practices allow clients to reap the generative AI benefits while minimizing risks—helping teams work faster, reduce errors, and improve the overall quality of software delivery.

Interviewer: What practices does NIX follow to mitigate risks when implementing AI-enhanced development?

Max: At NIX, we take a structured, human-centered approach to minimize risks and ensure reliable outcomes when integrating AI into development:

• Security-first Approach: AI tools undergo rigorous security reviews before integration to safeguard client systems.
• Intellectual Property Protection: We strictly manage IP and sensitive code to prevent unauthorized access.
• No over-reliance on AI: Human oversight ensures AI suggestions complement, not replace, expert judgment.
• Quality Assurance With Human Oversight: Our experts validate AI outputs for accuracy and alignment with project goals.
• Strict Confidentiality Controls: Sensitive project data is protected with robust access management.
• Compliance With AI Regulations: We adhere to evolving AI laws and industry standards to maintain compliance.
• Transparent AI Usage and Accountability: Clients receive full visibility on how AI contributes to the project.
• Proactive Legal and Compliance Measures: We anticipate regulatory changes and adjust practices to mitigate risks.

Interviewer: What are NIX’s future plans regarding AI in the software development life cycle? How do you see AI evolving in your workflows?

Max: At NIX, we see AI as a practical partner for developers, not just a tool. Our goal is to expand AI-assisted software development across all stages of the SDLC, helping teams work smarter, deliver faster, and maintain higher quality—while keeping human oversight at the center.

• Autonomous Agents: We’re exploring AI agents that handle routine coding, testing, and deployment tasks, and RPA 2.0 for business processes. These agents free our teams to focus on creative and strategic work, improving both productivity and job satisfaction.
• Open-source Model Adoption: We plan to use small, specialized models fine-tuned for client-specific needs. This reduces reliance on large commercial AI models, giving us flexibility, better security, and more tailored outcomes.
• Continuous Human-AI Collaboration: AI will never replace human judgment. We combine AI with secure SDLC practices, cross-team enablement, and real-time monitoring to ensure that every recommendation is accurate, relevant, and aligned with client goals.

In short, our vision is a future where AI and human teams work together seamlessly—enhancing efficiency, reducing repetitive work, and helping clients achieve faster, predictable, and high-quality results, all while maintaining transparency and control.

Interviewer: After everything we’ve discussed, what’s your main takeaway from NIX’s journey with AI in software development?

Max: What inspires me most is how our experiment started as a small initiative and quickly grew into a company-wide transformation. We proved that AI powered tools and AI development alone are not enough—what really makes them effective is the combination of cutting-edge software development tools, machine learning models, and top engineering talent. At NIX, our people bring expertise in multiple programming languages, judgment, and creativity, while AI enhances their speed, precision, and code optimization. By leveraging AI-generated code alongside human insight, we accelerate development, improve software quality, and deliver smarter, faster, and more reliable solutions for our clients. That’s why NIX is more than just a software development company—we’re a trusted partner who knows how to turn AI in software development into real business value.

Afterword 

For those who’d like to dive even deeper, Max recently joined Walter Hildebrand, CTO of Zendesk Latam, on the FTL Tech Meetup podcast. Together, they explored how AI can be embedded into the software development life cycle—and beyond. In this episode, Max shares more about our internal AI experiment: how we identified the right entry points, managed challenges across different roles, and measured real business impact. Whether you’re just starting with AI or scaling it across teams, this conversation is full of field-tested strategies worth hearing.

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