*By Rennan Sanchez
Artificial intelligence has gone from being a futuristic promise to becoming a general-purpose technology, transforming industrial sectors, value chains and business models with an impact comparable to previous revolutions such as electricity and the internet. Recent data shows that more than 601,000 companies already adopt some form of AI in their processes, with exponential growth expected as autonomous agent solutions take hold.
In the corporate world, companies seeking to capture the value of AI face a dual challenge: preparing themselves culturally and technically. Cultural change involves educating leaders about the potential of technology and creating a data-driven mindset. Technical preparation requires adopting AI solutions based on private corporate data, while respecting security, privacy, and compliance requirements.
The main barrier to the success of these initiatives lies in the quality and organization of corporate data. The metaphor that data is the new oil gains a practical extension: just as crude oil needs to be refined, corporate data needs to be processed and organized so that AI can extract relevant insights. Large-scale language models function as digital refineries, but without a structured database, even the most advanced algorithms become ineffective.
In practice, data is often scattered across ERPs, CRMs, spreadsheets and isolated databases, creating silos that need to be overcome. When this barrier is broken and agents gain access to organized data, the results are transformative. Use cases emerge that multiply productivity by up to 10 times, redefining how work is performed.
In finance, agents extract data from multiple financial sources to perform automatic reconciliations, forecast cash flows, and suggest tax optimizations. Activities that previously required weeks of manual work are completed in hours. In technical support, agents access internal knowledge bases and service histories to resolve more than 80% of queries without human intervention, freeing up teams to deal with complex cases.
In B2B sales, agents use CRM data and purchase history to suggest personalized approaches and prioritize opportunities with the highest likelihood of closing. In industries like pharmaceuticals and engineering, agents analyze millions of technical documents and patents to accelerate innovation hypotheses. In industrial operations, they integrate data from IoT sensors and maintenance history to predict failures and optimize production lines.
The choice of technology platform becomes critical to success. Solutions that natively integrate connections to diverse data sources, scalable storage, ETL/ELT pipelines, and AI development tools offer practical advantages: they simplify the architecture, reduce multiple integrations, provide agility in implementation, and ensure data governance.
We have seen organizations with these platforms accelerate their digital transformation, generating value from data more quickly and securely. Considering that projections indicate that AI will contribute up to US$15.7 trillion to the global economy by 2030, companies need to adopt the technology in a structured and results-oriented manner.
The practical journey involves adopting enterprise AI agents, building intelligent data warehouses, integrating ETL/ELT processes, and developing models based on private data. Each step represents a transformation in how organizations operate and compete.
The challenge is not whether AI will transform business, but whether your company is prepared for this transformation. The answer lies in the ability to transform data into actionable intelligence through well-implemented agents and appropriate technology platforms.
*By Rennan Sanchez, CTO at Skyone
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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