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Thank you, Mexico City!
We recently wrapped up On The Dot, bringing together financial services and healthcare leaders for an evening of sharp insights, practical strategies, and engaging conversations on AI, emerging technologies, and what’s next for Latin America.
The sessions looked beyond the excitement around AI to examine what enterprises need in order to turn pilots into business impact: stronger data foundations, clearer strategy, better governance, and higher AI literacy across leadership teams.
Sriram Rajagopalan
Associate Director
LatentView Analytics
Welcome Note
Sriram Rajagopalan opened the event with a brief introduction to LatentView’s two-decade journey in data analytics and consulting. He highlighted our experience with Fortune 500 organizations across sectors, including technology, financial services, CPG, retail, healthcare, and industrials, and outlined our capabilities across data engineering, analytics, visualization, and predictive intelligence.
Parijat Banerjee
Chief Business Officer
KEYNOTE ADDRESS
Era of Decision Intelligence
Parijat Banerjee’s keynote centered on the shift from AI as a technology tool to AI as an enabler of better business decisions.
He began by tracing how major technologies have reached Latin America over time. ATMs, credit cards, online banking, and mobile banking all saw a lag between global adoption and regional adoption. With AI, he noted, that gap has nearly disappeared. Adoption is now happening almost simultaneously across markets, creating a significant opportunity for Latin America.
Parijat framed this moment as both promising and challenging. He pointed to the trillion-dollar potential of AI in the region and used life sciences as an example of how AI could compress long, expensive processes such as molecule development. But he also noted that many companies are investing in AI without seeing measurable returns.
He illustrated the framework through examples from healthcare, fintech, and payments. In healthcare, LatentView helped a large provider move from more than 1,100 dashboards to 81 CXO-level dashboards by creating a unified member DNA structure. That foundation enabled business questions that once took 10 to 14 days to answer to be resolved in around 45 seconds. He also discussed ongoing work in dynamic decision boarding for fintech and Mira, a multi-agent AI solution for a UK-based payments company.
Across these examples, Parijat distinguished between secondary AI, which improves productivity and accuracy within existing workflows, and primary AI, which changes the operating model itself. His larger point was that AI delivers value when it is tied to growth, business outcomes, and better decisions.
Eduardo Cerda Leal
Digital Transformation Strategy Advisor
TECH TALK
AI with a Compass: Turning AI into Business Value in Regulated Organizations
Eduardo Cerda Leal’s session brought a candid and practical lens to AI adoption, opening with a question he said too many companies skip: What problem are we trying to solve?
He described a familiar scenario: a CEO returns from an AI event and asks the IT team for an AI project, without defining what it should do, why it is needed, or whether AI is even the right solution. For Eduardo, that lack of clarity is one of the first reasons AI initiatives struggle to deliver value.
He pointed to leadership literacy as a major gap. Before executives can ask for AI, they need to understand what the technology can realistically do and whether the organization is prepared to receive it. That idea sits at the center of his book, AI with a Compass.
Eduardo then moved to strategy, using Seneca’s idea, “There is no favorable wind for a ship without a port”, to stress the importance of direction. AI projects, he argued, need to be tied to a clear business solution. Without that, companies risk using AI simply because leadership asked for it, making the project more expensive and complex than it needs to be.
Data readiness was the next major point. Eduardo said he has rarely seen a project where the data was ready at the start. In many organizations, teams may spend months aligning definitions and cleaning up inconsistencies before AI work can begin. He used the example of retail, where operations, sales, and finance may each define “yesterday’s sales” differently because they rely on different KPIs.
He also compared AI transformation with ERP transformation, arguing that AI is more complex because it has to learn processes, operate within governance structures, and produce outputs that may evolve over time. This makes explainability, oversight, and auditability essential.
Eduardo called for organizations to establish an AI Council early, but cautioned that the council and senior leadership need to be trained first. Governance, he noted, cannot work if the people responsible for it do not understand the technology they are guiding.
He closed by distinguishing between training and adoption. Sending teams to a course does not mean AI has been adopted. Adoption requires usability, trust, governance, and eventually an AI culture that helps people understand when and how to use the technology.
The takeaway was clear: AI needs a compass. Strategy defines the port, data provides the fuel, and governance creates the trust and explainability needed to turn AI into business value.
