How to combine data, processes, and governance to scale AI in businesses

With the rapid advancement of Generative Artificial Intelligence and the popularization of vibe coding, the corporate market is experiencing a paradoxical moment. On the one hand, it has never been so simple and agile to build functional prototypes or automations in a matter of minutes. On the other hand, the vast majority of enterprise AI projects continue to fail in the transition from the test environment to the production environment.

Why do companies find it so difficult to extract real and lasting value from Artificial Intelligence? What is the true role of data in this equation, and why does the human factor remain the most critical link?

In a rich and enlightening conversation on Builders , Skyone 's podcast , we welcomed Lucas Andrade , founder of Mia IA (a company specializing in strategic AI implementation). With experience at major corporations such as Azul Linhas Aéreas, JPMorgan, PicPay, Móbly, and Vivara, Lucas shared the vision of someone at the epicenter of digital transformation and revealed the methodological architecture necessary to transform AI into a profitable and secure business engine.

1. The “Biznerd” profile: the fundamental intersection for the digital age

Historically, there was a clear division within corporations: business teams mapped operational pain points, and the Information Technology (IT) team developed the solutions. However, this traditional workflow was often slow, hampered by lengthy sprints, and failed to translate business needs into technical specifications.

In the Builders episode, Robson Del Fiol defined the figure that fills this gap: the Biznerd.

Lucas Andrade, a Business Administration graduate with a strong track record in data and business intelligence, embodies this definition. During the episode, he highlighted how this multidisciplinary perspective is crucial in the current landscape:

2. The AI ​​implementation triad: data, processes, and people (DPP)

One of the most relevant points discussed in the podcast was the reason why so many corporate Artificial Intelligence fail. The answer, according to Lucas, lies not in the technology itself, but in the prior methodological infrastructure.

The challenge of structuring and the myths of the market

At Mia, approximately 60% to 70% of project time is dedicated to understanding human pain points, mapping processes, and organizing the database before writing a single line of code or training an agent.

• Disorganized data: attempting to implement LLMs (Large Language Models) on a polluted or unstructured data environment only results in faster hallucinations. AI requires efficient data governance, unification of private data via Lakehouse or Data Warehouse, and clean pipelines.
• Confusing processes: if an internal operational flow involves manual workarounds, communication noise, and a lack of standardization, automating it with AI will only generate large-scale automated confusion.
• People and change: without acculturation and focused training, teams tend to view automation with distrust or resistance, hindering the adoption of the system.

Read also: iPaaS, Data Lakehouse and AI Agents: how to eliminate data silos

3. Practical case: operational efficiency in the purchasing sector with AI Agents

To illustrate the real impact of combining processes and Artificial Intelligence, Lucas shared a practical case developed by Mia IA within the Skyone Accelerate, a strategic initiative aimed at accelerating the innovation and AI journey for Skyone clients.

The customer's scenario

At one client with a large purchasing team, it was found that approximately 60% of buyers' time was consumed by operational and bureaucratic tasks, rather than strategic negotiation. The process involved:

1. Receive weekly requests for the purchase of supplies.
2. Filter lists of suppliers and send emails requesting quotes.
3. Receiving proposals scattered across multiple formats (PDFs, Excel spreadsheets, WhatsApp messages, and phone calls).
4. Manually consolidate all this data into a comparative spreadsheet.

You may also be interested in: AI Governance: from ChatGPT to intelligent agent management

The Agent-Driven Solution (Agentic Workflows)

Leveraging the infrastructure of Skyone Studio (an integrated platform that brings together iPaaS, Lakehouse, and AI Agent orchestration), the team developed end-to-end automation:

1. Automatic triggering: AI accesses system demands and sends standardized requests to suppliers.
2. Multi-format ingestion: an intelligent agent reads, interprets, and extracts data from proposals submitted in PDF, email, or spreadsheet format.
3. Consolidation and ranking: the agent organizes quotes in a structured interface via Vibe Coding, automatically ranking the best offers based on cost, timeframe, and history.

Result: the buyer stops wasting days typing data and can now focus 100% of their time on strategic negotiation and the search for new high-value suppliers.

4. Vibe Coding and the technological solution: the role of Skyone Studio and Skyone Autosky

During the technical demonstration on the podcast, Robson and Lucas demonstrated the power of Vibe Coding. Using Skyone Studio Creator, they created a complete financial management application fully connected to a database and with a responsive interface in less than three minutes.

However, to support this speed of development without compromising the stability of companies, a robust ecosystem is essential.

The Skyone ecosystem as an enabler of AI

Skyone Studio (Integration, Data and Agents):

• Integrated iPaaS: enables connecting over 400 legacy systems (SAP, TOTVS, Salesforce, Zoho, etc.) without the need for custom APIs built from scratch.
• Lakehouse and Data Warehouse: unifies dispersed data sources, creating the trusted foundation that powers LLMs privately and securely.
• Multi-Agent Orchestration: enables the construction of agentic workflows that interpret complex tasks, make context-based decisions, and execute automated actions.

Skyone Autosky (Modernization and Infrastructure):

• Zero-Change Migration: allows you to move ERPs and monolithic legacy systems to the cloud without having to rewrite the original source code.
• AI-Ready Cloud: provides centralized and scalable databases, essential for powering machine learning and generative AI models with full enterprise security (ISO 27001, Zero Trust architecture and MFA).

5. Practical productivity tip: using your voice in LLMs

To conclude the conversation, Lucas shared a hack for professionals and managers to increase their day-to-day efficiency: using the voice interface of Learning Management Models (LLMs) to feed continuous context to the AI.

Build your Artificial Intelligence journey

Artificial Intelligence is no longer a promise for the future; it is an operational reality that is already redefining market competitiveness. However, technology alone is just the spark. The true strategic differentiator lies in preparing your data, structuring your processes, and empowering people to lead this transformation.

If your company wants to accelerate this journey, understand how to apply vibe coding with governance , or modernize your legacy systems for the AI ​​era, access the Builders by Skyone education platform .

🎙️ Listen to the full episode on Spotify!

Want to check out all the details of this incredible conversation, see the applications being built live, and learn more about the future of the tech market? Click here to listen to the full episode of Builders 31 with Lucas Andrade on Spotify!

Written by Skyone