Artificial Intelligence in Software Engineering: Practical Applications

‍Artificial Intelligence is no longer a futuristic concept; it's a practical tool transforming how we design, build, and maintain technology solutions. In software engineering, its impact is undeniable, but so are its limitations. In this article, we'll share with you how we're using AI in our projects, the challenges we face, and why, despite its power, not everything should (or can) be solved with AI.

Why AI Matters in Engineering (and Why Now)

For years, software development focused on automating processes and improving experiences through explicit logic. Today, AI allows us to go a step further: instead of programming every rule, we can train models that learn from data and improve over time.

This opens the door to smarter, more adaptive, and more efficient solutions. However, it also requires us to rethink our project approach—from problem definition to validating results. The availability of large volumes of data and advancements in computing power have accelerated this adoption.

Where We Use AI Today: Real and Practical Use Cases

In our day-to-day work as an engineering team, Artificial Intelligence has become an ally in several areas. Here are some of the most impactful use cases:

1. Smart Document Processing: We use AI models to extract data from invoices, contracts, SWIFT transfers, and other complex documents. This helps automate what used to take hours of manual review. In a recent project, for example, we combined a language model (LLM) with business logic running on a .NET API to create a service that understands and structures unstandardized document data.
2. Ticket Classification and Prioritization: With models trained on historical data, we can automatically classify tickets by type, urgency, or complexity, improving response times and team workload distribution.
3. Predictive Analysis for Performance and Maintenance: In industrial or mission-critical systems, we apply AI algorithms to predict failures or performance drops, enabling proactive action.
4. AI Assistants for Development We leverage tools like GitHub Copilot and other AI-based coding assistants to speed up repetitive tasks and gain technical insights during development. “Developers using AI pair-programming tools see up to 55 % faster code completion.” — GitHub & Microsoft Research, 2023 Developer Productivity Report

What We're Not Doing (Yet) — And Why

While we're excited about the potential of AI, we're also clear-eyed about its current limitations. Here are some areas where we've consciously decided not to apply AI (at least for now):

1. Automating Critical Decisions Without Human Oversight We never delegate critical decisions entirely to a model. AI can inform decisions, but it doesn't replace technical judgment and experience.
2. Blind Use of Generative AI: “Generative AI investment is fueling a 42 % surge in data‑center infrastructure in 2025—AI is no longer experimental; it's foundational software.” — Gartner, July 15, 2025. Generative tools can be powerful, but also risky. They can produce insecure, low-quality, or simply unnecessary code. We only use them when they add value, not just because they exist. 
3. AI Without Quality Data We avoid implementing AI if there's no solid data foundation or if the problem can be solved more effectively with simpler, rule-based logic.

How We Decide to Apply Artificial Intelligence in a Project

The decision to use Artificial Intelligence starts early, during the discovery phase. We ask ourselves:

• Does the problem require learning from data, or can it be solved with deterministic logic?
• Do we have enough high-quality data?
• Will AI significantly improve outcomes or efficiency?
• Is the client ready to maintain this type of solution long term?

In some cases, we opt for hybrid solutions, combining deterministic logic for what's predictable with AI for ambiguity or variability.

“Leading enterprise adopters are combining data and AI governance in 2025, shifting from ad-hoc experimentation to structured, scalable deployment.” — Forrester Predictions 2025

Our Key Learnings So Far

• There's no magic: AI solutions take time to test, train, refine, and integrate properly.
• Data is everything: A model is only as good as the data it's based on.
• Integration matters: Having a working model isn't enough; it must be embedded within the product's architecture and exposed through robust APIs.

What's Next for Artificial Intelligence in Engineering?

Artificial Intelligence isn't a destination; it's a journey. As an engineering team, we're preparing for what's next: adopting agent-based systems, using frameworks like PromptFlow to orchestrate prompt pipelines, integrating services like Amazon Bedrock, and exploring vector search with Pinecone or OpenSearch.

We're also working on better governance, observability, and monitoring for AI-based systems—key topics for clients who are scaling their use of these technologies.

According to McKinsey, governance and observability become foundational as organizations scale AI — McKinsey Global Institute, The State of AI in 2023.

How We Do It at Switch

At Switch, we combine strong engineering capabilities with deep applied AI knowledge to solve real business problems. Our focus is always on practical applications: we use AI when it adds measurable value, supported by scalable architecture and long-term thinking. 

Final Thoughts

Artificial Intelligence is a powerful tool, but it doesn't replace critical thinking, engineering experience, or deep problem understanding.

In our view, the real value lies in how we apply AI: with intention, clarity, and a genuine focus on solving the right problems.

Curious how we turn AI into real-world results? Explore our Engineering Studio to see how we design, build, and scale intelligent solutions that actually work.