ARTICLE | SEPTEMBER, 10
The 2x to 5x productivity secret.
Is AI the end of dev teams?
By Pedro Mones
I speak with CEOs and CTOs in various industries, including fintech, healthcare, and oil and gas, every single day. The same conversation keeps coming up: everyone knows AI is a game-changer. They see the headlines, hear the buzzwords, and feel the pressure to innovate. The big question is no longer “if” but “how fast” to implement it.
But let me be blunt: if you think AI means you can slash your dev teams and solve all your problems, you’re missing the bigger picture.
At Patagonian, we’ve recently conducted an internal survey with our engineering team—the very people on the front lines building custom software, managing AI transformations, and augmenting client teams. The results were insightful. The study, which focused on the impact of AI across 25 real projects, revealed that while AI has become an indispensable tool, it has not replaced the need for skilled developers. In fact, it has elevated their importance.
AI is a force multiplier
The consensus is clear: AI has a powerful and positive impact on daily workflow. The numbers don’t lie.
Most of our engineers reported an estimated speed increase of 2 to 5 times when using AI tools. It’s important to note that this is a broad estimate; the project’s context plays a big role. For a mature project with a five-year history, the acceleration might be closer to 1.5x to 2.5x. However, for a brand-new project, the speed boost could easily surpass 4x.
This boost in productivity translates into:
- Faster feature delivery
- Higher output.
— According to Eduardo Cuomo, Engineering Manager
Some team leaders even stated that productivity has increased by up to 3x, freeing up their team to focus on other important areas like maintainability, security, and scalability.
This kind of efficiency gain is exactly what you, as a business leader, want to see. Your team can solve more problems and build more innovative solutions in a shorter amount of time.
But here’s the critical part: this isn’t a silver bullet. The same report highlighted some crucial challenges.
The hidden costs of AI
Implementing AI without a strategy can create more problems than it solves. Our developers pointed out that AI models often “hallucinate,” generating code with errors or using non-existent functions. This requires more time for review and correction. The code produced by AI also frequently fails to adhere to a project’s specific coding standards, which can lead to an accumulation of technical debt over time. This challenge is reflected in our survey data, where 21.9% of respondents reported a minor negative impact on quality or an increase in technical debt, and 12.5% reported a significant negative impact.
Eduardo put it this way: “This isn’t just a technical problem; it’s a strategic one. Relying too heavily on AI can cause developers to ‘detach’ from the code. It makes it harder to remember the core logic of a component and to understand the ‘why’ behind an architectural decision. For junior developers especially, this dependency can hinder their ability to learn fundamental concepts, such as design patterns and system architecture.”
When to use AI and when to lean on human expertise?
This brings us to the core of the matter: successful AI implementation is about knowing when and how to use it. Our team’s insights provided a clear roadmap, built on the principle of keeping a “human in the loop.” This model is about a strategic partnership:
Use AI for:
- Automation of repetitive and tedious tasks: Generating boilerplate code, writing unit tests, or creating documentation. These are the jobs AI excels at, freeing up your team to focus on more complex, strategic work.
- Rapid prototyping: Need to build a proof-of-concept fast? AI can quickly generate a working skeleton for a new feature or application.
- Exploring new technologies: AI acts as an intelligent assistant, helping developers get a quick start with a new library or framework.
- Customer-facing solutions: Creating agents based on conversational design to talk to your customers, applying sentiment analysis to segment prospects, or implementing computer vision to analyze patterns.
- Turning raw data into actionable messages: It’s the perfect tool for tasks like generating clear system alerts, initial diagnostics or personalized recommendations.
Do NOT use AI for:
- Complex business logic: AI lacks the full context of your project’s interdependencies and can’t be trusted with mission-critical code without a human to validate and refine its output.
- Security and architecture decisions: These areas require deep human expertise and a full understanding of potential vulnerabilities. A human must be in the loop to ensure the integrity and safety of the system. Risk must be managed, for example, through guardrails or a human-in-the-loop, allowing humans to review automated decisions in exceptional cases to maintain control over complex systems.
- Generative AI is not the best option for data analysis. Decisions must be made by a human based on principles and ethics, experience and bias, logic and reasoning, skills and style. These tools are built to create, not to interpret complex datasets or understand nuanced user behavior. Machines can provide visualizations, exploration alerts, and other support for human decision-making.
What part of your business are you most excited to transform with AI, and what are the biggest challenges you foresee in doing so?
At Patagonian, we combine technical expertise with cutting-edge AI tools to deliver superior software solutions. If you’re looking to integrate AI into your projects, get in touch to explore how we can unlock its full potential together.
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