Grupo Intelsis took part in SAP House Chile 2025, a gathering of leaders, specialists and companies focused on the trends shaping digital transformation. Process integration, operational efficiency and artificial intelligence led the conversations, with technology framed as a way to move the business forward rather than an end in itself.
For our team, the event delivered two clear wins. The first was time with clients and partners, which strengthens relationships and opens new opportunities to collaborate. The second came from one of the talks, which tackled a question many companies put off: what data will AI actually run on?
A space to listen to clients and partners
The close, dynamic format of SAP House Chile encouraged networking and active listening. Alongside the talks, the event worked as a forum to share ideas, experiences and the real challenges organizations face.
Conversations like these tend to surface what never shows up in a proposal: the integration nobody documented, the KPI each department calculates its own way, the AI pilot that never left the drawing board. Understanding those needs up close is what makes it possible to design solutions that create real value. That is why showing up at events like this is part of Grupo Intelsis' commitment to innovation and to staying close to clients.
SAP BTP as the data foundation for AI
In his talk, Matías Müller explained how SAP BTP enables data management and what that means for artificial intelligence projects. His core point was that the platform integrates, governs and transforms data from many sources into a single, reliable layer, so AI models work with information that is high quality, current and grounded in business context.
In practice, that means pulling data out of the silos where it usually sits: the ERP, satellite systems, spreadsheets and partner applications. SAP BTP services such as SAP Integration Suite, which connects SAP and non-SAP systems, and SAP Datasphere, which organizes data and makes it available, help build that layer without customizing the ERP core, keeping it clean. For a business-level overview of the platform, read what SAP BTP is.
Why data quality sets the limits of AI
Müller also stressed that data quality directly determines three attributes of any AI model: accuracy, reliability and scalability. In day-to-day business terms, the effect looks like this:
- Accuracy: a model fed with duplicate master records or incomplete history gets things wrong, and does so with apparent confidence.
- Reliability: if sales, finance and operations don't recognize the source numbers, the recommendation gets ignored, no matter how good the algorithm is.
- Scalability: a pilot that depends on manually cleaned spreadsheets works once, but it can't be replicated in another business unit or country.
According to Müller, by reducing silos, improving traceability and enabling advanced analytics, SAP BTP supports smarter, data-driven decisions. He also made the case that investing in sound data management is a competitive advantage, not just a technology choice.
Checklist: is your data ready for AI?
To turn that message into action, we put together five checks to assess whether your company's data can support an artificial intelligence use case. Run them before you choose a tool or a model.
- Start with the use case. Pick one concrete decision, such as forecasting demand for a product line or prioritizing maintenance on critical assets, and map only the data it needs. Trying to organize all of your company's data first tends to stall the project.
- Give every critical data domain an owner. Customer, material, supplier and pricing data need a business owner who defines what each field means and approves changes. Without owners, quality becomes IT's problem alone and nobody fixes the source.
- Write quality rules and measure them. Define what a valid record looks like in terms of completeness, uniqueness, format and freshness, track those metrics and fix issues in the source system, not in the report.
- Document lineage. Know which system each data point comes from, what transformations it goes through and when it was last updated. Without lineage, nobody can explain or audit an AI recommendation.
- Govern access. Set role-based profiles, protect personal and sensitive data and log who accesses what. An AI model should never become a shortcut to information people aren't supposed to see.
Let's talk about your data
If your company is evaluating AI use cases, or already has a pilot that hasn't scaled, the Grupo Intelsis team can help you find out whether your current data can support that ambition. Our data and analytics and artificial intelligence practices cover everything from assessing sources and governance to architecture on SAP BTP, always anchored in a concrete use case. Tell us about your scenario and we'll work out the best starting point together.