Cognizant Romania unveils 10 insights on how AI is transforming software industry

As artificial intelligence continues to reshape the digital landscape, Cognizant Romania, one of the largest IT companies in the country, releases 10 findings and ideas about how AI is transforming software development. Cognizant is at the forefront of driving AI innovation globally, with a strong focus on helping clients harness the power of artificial intelligence to transform their businesses. In Romania, Cognizant plays a strategic role in advancing AI capabilities through skilled talent base, deep industry expertise, and commitment to responsible AI development.

These ideas and findings were presented during Programmers’ Week 2025 – Cognizant’s flagship event in Romania, which happened September 15 – 19 – a celebration born from the company’s legacy of innovation and knowledge-sharing. This year, the event explored how AI is transforming software development across the stack – from backend agents and mobile apps to DevOps, QA and project governance.

1. Accelerating Mobile Development with AI: The Cursor Experience

Mobile app development is evolving rapidly. Teams are under pressure to deliver faster, maintain architectural integrity, and ensure high-quality user experiences. Traditional workflows often struggle to keep pace with these demands, especially when dealing with legacy codebases, complex architectures, and fragmented documentation.

To address these challenges, engineering teams are turning to AI-powered tools and one such tool is Cursor, an intelligent code editor designed to assist developers through natural language prompts. It supports everything from project scaffolding to code refactoring, documentation generation, and security auditing—making it a powerful ally in modern development workflows.

Adopting AI-assisted development brings clear advantages, including significant time savings across development stages, improved consistency with architectural standards, and enhanced onboarding through better knowledge sharing. Automated security insights and fixes further strengthen code quality. However, limitations remain: complex business logic and platform-specific tasks still require manual expertise, generated code must be thoroughly reviewed for accuracy and security, and integration with third-party SDKs often needs fine-tuning.

While not a replacement for human expertise, Cursor empowers teams to work faster, smarter, and with greater alignment. Its adoption can lead to more disciplined engineering workflows and faster delivery cycles—making it a valuable tool for modern software teams.

• AI Adoption in Project Management: Navigating the Shift with Confidence

As AI becomes a strategic imperative in software development, project managers must lead the way in integrating AI into team workflows, stakeholder processes, and delivery models. To effectively harness AI within teams, Cognizant experts recommend to start by gauging readiness through structured checklists that clarify current capabilities and inform enablement strategies. Set realistic expectations by estimating the effort and outcomes AI tools can deliver, avoiding overpromises. Skill development is key—map out essential competencies and foster a culture of continuous learning. Proactively manage risks using frameworks like the NIST AI RMF, ensuring potential AI-specific pitfalls are anticipated and addressed. Finally, measure productivity and impact through well-established KPIs, leveraging models such as DORA Metrics, the DX AI Framework, and LinearB ROI to keep progress visible and aligned.

• GPT-4.1 Assisted Medallion Pipeline Construction Guide for Data Engineers

As data platforms grow in complexity, engineering teams face increasing pressure to deliver robust, scalable pipelines quickly. The Medallion Architecture – organizing data into Bronze, Silver, and Gold layers – has become a standard for modern data engineering. However, implementing this architecture requires significant effort in coding, orchestration, and documentation.

Integrating GitHub Copilot into DevOps workflows accelerates development and prototyping, improves documentation through automated README files and comments, and fosters collaboration via AI-human iteration. Its scalability supports modular, containerized deployments, making it a strong fit for modern CI/CD pipelines. However, prompt clarity is critical for output quality, and human oversight remains essential to validate and test AI-generated code. Successful adoption also depends on access to advanced tooling like GPT-4.1 and supporting infrastructure, alongside best practices such as prompt engineering, modular design, and iterative documentation.

• Building Intelligent AI Agents in .NET

Developers are now orchestrating AI workflows, not just writing code. The rise of agentic systems—autonomous, goal-driven AI agents—offers new opportunities for scalable automation, intelligent decision-making, and cross-domain collaboration. .NET developers can build intelligent AI agents using frameworks like Semantic Kernel and AutoGen.NET, a proof of the evolution from single-agent solutions to multi-agent systems capable of reasoning, planning, and acting autonomously.

These recommendations can guide a successful agent implementation: start with simple agents and add complexity gradually. Provide clear goals and context to improve performance, keep humans in the loop for critical decisions, ensure safety with sandboxing and logging, and choose frameworks aligned with your project’s maturity and integration needs.

• Enhancing .NET Development with GitHub Copilot

Modern .NET backend development is increasingly demanding—requiring fast delivery, clean architecture, and high-quality code. Developers often spend significant time on boilerplate generation, debugging, and documentation. As engineering teams seek ways to optimize workflows, AI-powered tools like GitHub Copilot are emerging as strategic enablers.

While it automates routine tasks and enhances productivity, its full potential is unlocked when paired with thoughtful engineering practices and human oversight.

These recommendations help ensure effective use of Copilot in development workflows: break tasks into small, reviewable prompts for better control, use instructions files to ground suggestions in domain language, and always review and refactor generated code before merging. Automate documentation and testing early and maintain prompt playbooks for recurring workflows to drive consistency.

Teams adopting Copilot strategically can expect faster delivery, better maintainability, and improved developer experience.

• Generative AI Assistants helps developers catch up and keep up

Generative AI assistants like ChatGPT, Claude, and TabNine are increasingly used to streamline software development, especially when working with unfamiliar libraries or frameworks. They efficiently generate boilerplate and repetitive code. Compared to manual scripting or regex-based solutions in IDEs like IntelliJ or Visual Studio Code, assistants streamline the process with less effort. Developers also use them for brainstorming features, drafting requirements, and identifying edge cases. In professional settings, these tools help automate unit and integration tests, as well as mocking, improving testing coverage with minimal effort.

Their effectiveness often reflects the quality of the underlying technology. When assistants fail to produce working code, it may indicate poor documentation or inconsistent design in the library itself—highlighted by challenges with frameworks like Next.js. In contrast, technologies with strong community support and clear documentation, such as Java, React, Kotlin, and Docker, yield better results. These ecosystems provide frequent updates and accessible examples, allowing AI models to stay current and remain reliable partners in modern development workflows.

• From API to MCP: Is GenAI the New Control Plane for DevOps?

MCP, introduced by Anthropic in 2024, is an open protocol designed to standardize how AI systems interact with tools, APIs, and data sources. By wrapping existing APIs in a structured and AI-friendly layer, MCP enables LLMs, agents, and copilots to securely query and act on cloud services, infrastructure, and developer tools.

For some, it represents a new era where GenAI becomes the control plane of operations. For others, it raises concerns about security, governance, and vendor lock-in. Ultimately, MCP’s impact will depend on how openly it is adopted and how well the ecosystem balances innovation with safety. The big question is not just whether MCP will succeed, but how DevOps engineers will redefine their role in a world where AI can automate the automation itself.

• GitHub Copilot in Mobile Development: From Evaluation to Team-Wide Adoption

Mobile development teams face growing pressure to deliver high-quality apps faster, while navigating evolving frameworks and user expectations. AI-powered tools like GitHub Copilot promise to reduce repetitive work and accelerate coding—but their effectiveness depends heavily on how they’re used. Prompt engineering and context engineering can unlock Copilot’s full potential in mobile workflows.

To maximize Copilot’s effectiveness, start by creating task-specific instruction files for activities like testing or refactoring, ensuring clarity and repeatability. Develop code iteratively, refining prompts and context as requirements evolve, while keeping context concise and resetting it regularly to avoid overflow. Encourage Copilot to surface assumptions for greater transparency and use comments to capture design intent rather than simply describing the code.

By investing in shared knowledge and structured guidance, teams can transform Copilot from a reactive assistant into a proactive contributor—accelerating delivery and improving code quality across the board.

• AI-Powered Application Development: Vercel AI SDK and Model Context Protocol

The Vercel AI SDK offers the practical tools and optimizations needed for production deployment, while MCP establishes the architectural foundation for sustainable, multi-provider AI applications. This combination represents the current state-of-the-art in AI development tooling and standards, enabling developers to build sophisticated AI applications with unprecedented ease and flexibility. These technologies work together to solve real-world challenges in AI application development, from initial prototyping through enterprise-scale deployment, making them essential knowledge for any developer working in the AI space.

Both technologies enable enterprise-grade AI development by supporting robust error handling, flexible integration with deployment pipelines, and architectures designed for interoperability and scale. While monitoring and analytics are typically provided by external platforms or integrations, the Vercel AI SDK and MCP together establish the practical and architectural foundation for building scalable secure and production-ready AI applications.

1. Transforming QA Challenges into Opportunities with AI-Driven Testing

Software teams today are racing against the clock. Features are built, tested and shipped in days, sometimes hours. In this whirlwind of releases, ensuring quality isn’t just a checkbox, it’s a lifeline. With AI embedded in products, from chatbots to recommendation engines, QA is no longer just about verifying correctness. It’s about ensuring trust in systems that produce probabilistic, often unpredictable outputs.

Traditional QA methods fall short when testing AI components. AI-driven testing is not just a technological upgrade, it’s a paradigm shift that redefines the role of QA in modern software development. And far from replacing testers, AI empowers them. AI-driven testing leverages Large Language Models (LLMs) to generate test cases, create synthetic data, heal flaky tests and triage bugs, shifting QA from reactive to proactive.

AI-driven QA is redefining how testing is approached by combining advanced evaluation methods with automation. It begins with AI Testing, where large language models are assessed for fluency, coherence, and semantic understanding using human judgment, LLM-as-Judge techniques, and metric-based scoring. Building on this, Agentic Systems bring automation to complex domains like healthcare and finance through scenario-based data generation, validation agents, and conversational test case creation. Finally, Automated Bug Triage streamlines defect classification and prioritization, reducing manual bottlenecks and accelerating delivery. Together, these innovations turn traditional QA challenges into opportunities for smarter, scalable, and more reliable testing.

About Cognizant

Cognizant (Nasdaq: CTSH) designs modern businesses, helping clients modernize their technology, reimagine processes, and transform experiences so they can stay ahead of the curve in a rapidly changing world. See how on www.cognizant.com or @cognizant.

Cognizant Romania has been present in Romania since 2018, when Cognizant acquired Softvision, a company founded in 1998, in Cluj-Napoca. Currently, Cognizant is a top employer in Romania, with offices in Bucharest, Cluj-Napoca, Iasi, Timisoara and Baia Mare, with 2500 employees, being one of the largest Cognizant software development centers in Eastern Europe. “Randstad România Employer Brand Research 2024”, the world’s most comprehensive employer brand research. Looking back in time, Romania studio  has a history of implementing some of the major tech innovations. Almost 30 years ago, being one of the software engineering pioneers in Romania, the company was exposed to Silicon Valley challenges.