We talked about how to evaluate suppliers based on their ability to demonstrate fast and sustained value, with a milestone-based plan, a flexible architecture for changing models, and a team that maintains what has been implemented.
We analyze how the cost of AI is controlled from the design stage by choosing between token-based or license-based consumption, setting limits per user, reusing repeated responses with caching, and validating that the business case closes with the maximum monthly expenditure.
We'll tell you why governing data in the AI era requires going beyond data quality. It involves governing the data itself, the model, biases and regulations, and the conversations and documents an agent produces. Why, if the model isn't governed end-to-end, it won't reach production.
To determine if an AI is still performing well, observability is essential: automated metrics for response quality, resource consumption, biases, and model availability in production. We'll explain how to monitor model degradation over time.
Many organizations invest in ambitious governance frameworks that end up being difficult to implement. We propose an approach that moves from concept to implementation.
We present a comprehensive look at the characteristics of a modern AI-powered pipeline, the capabilities it must possess, and the challenges companies face when implementing it. We also analyze how to achieve the right balance between automation and human oversight.
We explore what data maturity really means from a business perspective, how to identify an organization's current level of data maturity, and why advancing AI without a solid foundation can generate more risks than benefits.
We analyze why many organizations get stuck in the experimentation phase, what the most frequent roadblocks are when trying to scale, and what concrete steps allow AI to be transformed into a real business capability.
The challenge is no longer adopting AI, but scaling it. We analyze how data fabric enables the creation of real business capabilities and positions itself as a strategic enabler for CIOs, CDOs, and data architecture leaders in data-driven organizations.
