Artificial intelligence in companies: avoiding common mistakes

Intelligence (AI) has gone from being a futuristic promise to becoming the "New Industrial Revolution." However, as the hype grows, so does the confusion about what this technology really represents and how it can, in fact, transform the bottom line of an organization's financial balance sheet.

Recently, following The Developers Conference (TDC) AI Summit in São Paulo, experts from Builders by Skyone discussed the main controversies and insights shaping the sector. The verdict is clear: it's not enough to have AI; you need strategy, robust infrastructure, and above all, a focus on business value.

In this article, we explore the technical and strategic nuances for implementing AI efficiently, avoiding common mistakes that drain investments without delivering returns.

The "backyard model" misconception: what is AI really?

One of the biggest controversies raised by Renata Klein, data scientist and architect at Skyone, is the confusion between consuming an API and creating a Learning Language Model (LLM).

Many professionals believe they are developing proprietary technologies "from scratch," when in reality they are simply plugging interfaces into existing models, such as OpenAI or Gemini. Creating a trained model requires:

• Deep neural networks: where artificial intelligence is truly deepened.
• Robust machines: high-level processing (GPUs) not found in home computers.
• Mathematical and statistical knowledge: complex calculations that go far beyond simple software integration.

For companies, understanding this distinction is vital for cost planning and for the security of intellectual property.

The difference between "having AI" and "generating value"

Beatriz Fujii, a data analyst at Skyone, points to a latent pain point in the market: many companies create AI agents simply to say they have the technology, without identifying what business pain they are addressing or what numbers they want to impact.

AI should be seen as a value mining. Beatriz uses a powerful analogy with the mining industry: while the focus may be on cobalt, AI can identify "precious stones and gold" (data and indicators) that would otherwise be wasted at the end of the production line.

The role of vibe code and human appreciation

Another point of debate was the concept of Vibe Code and AI-generated software. Although automation tools can reduce development time from months to hours, human oversight remains irreplaceable. The machine acts as a scaling tool, but the creative process, design, and final quality control depend on the qualified professional.

The AI ​​ecosystem: the technological "systems experts"

Just as the automotive industry has "system integrators" (companies that produce specific components such as dashboards or tires for automakers), AI has created a similar ecosystem.

Skyone positions itself as the central system that connects various suppliers and technologies to deliver a coherent end product to the customer. This ecosystem was visible at TDC, attracting everyone from the judiciary and public authorities to large banks and startups.

From data lake to agent: the practice with Skyone Studio

To materialize this evolution, Skyone demonstrated the potential of Skyone Studio, a platform that integrates data, has its own Data Lake, and allows for the accelerated creation of AI agents.

The key differences of the low-code approach:

1. Team unification: eliminates the need for segmented teams (one for integration, another for DBA, etc.), allowing work to flow end-to-end through a single interface.
2. Speed ​​of delivery: building complete agents, from integration to data output, in a matter of minutes.
3. Governance and control: centralized administration that ensures AI is not a "black box," but a manageable tool.

Skyone Studio supports various LLMs (such as GPT-4, Llama 3, and Gemini) and focuses on the application layer, where innovation truly happens by solving real-world, everyday problems.

Small hacks for big scale

To conclude the discussion, the experts shared practical tips on productivity and mindset:

• Openness to listening: Renata emphasizes that technical and personal success comes from being open to listening to all aspects (data and people), transforming feedback into real benefit.
• Questioning and daring: Beatriz encourages nonconformity. "Don't do things just because everyone else is doing them. Question yourself to do things in the best way and dare to step outside the box.

Conclusion: the next step in your AI journey

AI isn't just about technology; it's about reallocating talent to higher-value services and automating repetitive processes. To lead this digital transformation, your company needs a solid database and tools that simplify complexity.

Listen to the full podcast!

This article is based on an engaging and controversial conversation on our podcast. To catch every detail, tone of voice, and the complete tips from our experts, click the link below and listen now on Spotify:

👉 Link to the podcast on Spotify

Written by Skyone