Vertical AI and data integration: how to generate real ROI with Artificial Intelligence 

The corporate race to adopt Artificial Intelligence (AI) has reached its peak. However, while the market is flooded with promises of magical transformations and generalist virtual assistants, directors, decision-makers, and technology managers are facing a reality check behind the scenes. The verdict is unanimous among experts: 11 out of 10 companies discuss AI, but few understand that the success of this technology depends directly on the maturity of the data infrastructure and the integration of systems.

To discuss how to transform the hype surrounding AI into tangible Return on Investment (ROI) and operational efficiency, the Builders brought together leading figures from the Brazilian tech sector: Sérgio Fontana, CEO and founder of SysMiddle (a platform specializing in the orchestration and integration of complex systems), and André Oliveira, Head of Data and AI at Skyone (a leader in unified cloud, data, and artificial intelligence solutions).

Below, we analyze the key insights from this dense discussion, mapping the strategic role of systems integration, the concept of Vertical AI , and the indispensable governance structures that separate failure from success in the data age.

1. Why AI “eats data for breakfast” (and integration serves the table)

There's an inconvenient truth in data engineering: artificial intelligence is, essentially, a mechanism for making optimized decisions based on historical and real-time data. Without a continuous, clean, and structured flow of information, any AI model fails.

Historically, ERP (Enterprise Resource Planning) and legacy desktop systems have acted as the gatekeepers of a company's transactional and parameterization data. The major corporate challenge lies in the complexity of accessing these sources. Many of these systems operate in hybrid or on-premise architectures , predate the internet, and lack documentation or structured APIs.

As Sergio pointed out, extracting data via relational queries is the simplest part. The real complexity lies in the reverse flow: inputting and updating information. Entering a sales order generated by an autonomous AI agent requires validating a massive chain of interdependent dependencies, including customer registration, pricing, inventory rules, and complex local tax matrices. Without an intelligent integration platform (iPaaS) to consolidate these business rules, automation breaks the integrity of the main database.

2. Horizontal AI vs. Vertical AI: The Search for the Expert

One of the biggest catalysts for frustration in companies is the attempt to solve niche pain points using Horizontal AIs (such as ChatGPT or Gemini in their pure versions). Because they are generic language models (Large Language Models — LLMs) trained with the open knowledge of the internet, they are excellent for universal tasks, but fail when trying to solve deep operational pain points of a company.

It is in this scenario that Vertical AI stands out . As André Oliveira explains:

While a generalist assistant performs basic administrative tasks, complex tasks, such as payroll calculation with legal implications or managing the supply chain of an industrial industry, demand a niche specialist. A vertically integrated AI natively understands the jargon, metrics (such as churn rates and pricing dynamics), and regulatory nuances of the market in which it operates. It doesn't just serve to answer questions statically; it's designed to interact with data ecosystems and execute autonomous end-to-end actions

3. The phenomenon of vibe coding and the risks of shadow IT

The ease with which modern generative tools allow the creation of code and automation has given rise to the phenomenon of Vibe Coding. Today, professionals from business or humanities fields can act as developers, instructing AI models to build entire applications using natural language (prompts).

While this democratizes innovation and accelerates local prototypes (such as customized telemetry panels), it also opens the door to Shadow IT (the unauthorized use of technologies within the organization).

• The tool salad: different sectors adopting isolated tools discovered on social networks.
• Data breach: employees exporting client spreadsheets and feeding them into public LLMs on the web.
• Financial inefficiency: poorly structured prompts leading to excessive token consumption , inflating operational costs without generating a proportional ROI.

For technology managers, the challenge lies in implementing a robust AI Governance. Governance should not act as a bureaucratic barrier, but rather as a framework that provides audited connections, 24/7 monitoring (via SOC/SIEM), and data isolation through private LLMs.

4. Success metrics: how to measure the real impact on the business

Investments in AI and systems integration need to translate into measurable performance indicators, whether quantitative (top-line or bottom-line) or qualitative. Experts have listed the main metrics that demonstrate the efficiency of these implementations:

Table: Success indicators in integration and AI projects

The strategic path to intelligent automation

Modernizing infrastructure doesn't mean rewriting legacy code from scratch, but rather creating a scalable foundation. Integrated platforms like Skyone Studio eliminate corporate silos by unifying iPaaS solutions, cloud data storage ( lakehouse ), and agentic workflow creation into a single interface. In this way, the human role shifts from manually executing repetitive tasks to strategically reviewing and curating automated processes.

Want to delve deeper into this insightful review?

This analysis was based on insights and practical experiences shared on the Builders podcast. To understand all the operational details, check out the personal productivity hacks shared by the guests, and listen to the full discussion, click here and listen to the complete episode on Spotify!

Written by Skyone