Latin American industry accelerates AI adoption for decarbonisation and efficiency

Latin American industry is shifting from experimental trials to operationalised artificial intelligence, driven as much by survival in a carbon-constrained economy as by efficiency imperatives, according to a new report by Siemens and Latinometrics. The Industrial Perspectives findings argue that “Industrial AI acts as a stabilizer that allows the pace of change to accelerate without compromising operational and strategic control, enabling organizations to decarbonize processes without losing efficiency,” and report wide early returns: 65% of firms cite energy savings averaging 23% and 59% report CO2 reductions of roughly 24%.

According to the report by Siemens and Latinometrics, the movement beyond pilots is already measurable: Reuters Events and Siemens research shows 63% of organisations have progressed past the pilot phase into directed use, moderate adoption or widespread integration, with only 13% still in exploration. The study maps where AI delivers the most immediate operational value, quality control (74%), energy management (65%), generative design (63%), production optimisation (62%) and predictive maintenance (60%), and highlights energy‑intensive sectors such as cement and water management as clear short‑term winners, with documented energy reductions in cement of 5–10%.

Mexico’s picture is more mixed. Siemens cites a 2024 INEGI survey showing 18% of Mexican manufacturers reporting AI use, a level the report notes is slightly more than half the EU average for its 27 member states. That contrasts with other market studies: for example, industry reporting from AWS indicates broader corporate uptake, around 38% of Mexican companies using AI in 2025, and suggests measurable commercial upside, with 88% of those firms reporting productivity gains and at least a 16% lift in revenue or profits. These divergent figures underscore a fragmented national landscape in which large corporate adopters lead while small and medium‑sized enterprises lag.

The uneven diffusion is primarily a question of scale, talent and infrastructure. Siemens and Latinometrics reiterate OECD guidance that four enablers, connectivity, skills, financing and access to inputs such as data and models, must be strengthened to bring SMEs on board. PwC analysis of Mexican labour markets between 2021 and 2024 further confirms the skills mismatch: demand for AI roles has grown fastest in information and communications, while manufacturing and other sectors remain behind, pointing to an urgent need for targeted upskilling. Industry programmes and public–private initiatives are beginning to respond; the AI Cluster of Nuevo León, launched with regional government and multinational support, positions AI as “fundamental infrastructure” and focuses on connectivity and workforce development.

Financing and partnerships are equally critical. The Siemens report notes that 23% of executives cite low ROI visibility as a primary barrier and 40% worry about reliability for mission‑critical systems. SMEs face acute constraints in attracting specialised talent, industry figures suggest 58% lack internal capabilities and 92% view shortage of AI experts as a major obstacle, making ecosystem approaches, shared platforms and interoperable solutions vital. Siemens positions its Xcelerator platform and the Siemens Industrial Copilot, developed with Microsoft, as examples of tools intended to lower the technical threshold: the company claims these allow personnel to interact with equipment using natural language, shortening implementation timelines and reducing dependence on bespoke programming.

On the factory floor the benefits are already pragmatic and measurable for early adopters: computer vision and sensors improving defect detection in real time, AI‑driven energy management systems trimming consumption peaks, algorithms producing lighter, more material‑efficient product designs and predictive analytics preventing costly unplanned downtime. Industry observers argue that once the basic digital backbone and data governance are in place, the next phase will be simulation and virtualisation at scale. Siemens and others envisage a convergence of AI with digital twins to create a “software‑defined” manufacturing environment, a de facto industrial metaverse, where entire plants or gigafactories can be modelled and optimised before physical resources are consumed.

For industrial stakeholders focused on decarbonisation, the strategic implication is clear: sectors that embed AI end‑to‑end will widen their competitive advantage as emissions constraints tighten and buyers demand lower‑carbon supply chains. According to the Siemens analysis, the technology’s role is not merely incremental efficiency but systemic change, if policy makers, financiers and industry leaders can close the connectivity, skills and finance gaps and scale shared platforms that make advanced AI accessible to smaller firms. The trajectory suggests that decarbonisation and digitalisation are now mutually reinforcing objectives rather than competing priorities for Latin American manufacturing.