Future Frontier

Joint opening for both halls, either projected or broadcasted simultaneously depending on the venue's final configuration. This block introduces the focus of the 2026 cycle: moving from general conversation about artificial intelligence (AI) to an applied implementation agenda in real companies. It will explain the division of the halls, the focus on manufacturing, maquiladora, logistics, and infrastructure, and the logic of a day designed to connect productivity, operations, data, automation, regional competitiveness, and decision-making.

Artificial intelligence (AI) does not enter a plant through a broad promise, but rather through specific problems: inspection, maintenance, quality, rework, downtime, safety, planning, or traceability. This workshop addresses how to identify the first use cases without disrupting operations or overloading internal teams. Criteria will be reviewed for prioritizing processes, recognizing available data, defining responsibilities, anticipating operational risks, and establishing measurable indicators from the start. The cultural dimension of adoption will also be considered: how to prepare teams, communicate the value of technology, and move forward without generating internal resistance.

Breakout space designed to allow real mobility between rooms, facilitate networking, and prevent subsequent sessions from starting with attendees still in transit.

Computer vision is one of the clearest entry points for applying artificial intelligence (AI) in manufacturing. This use case will address how to use images, cameras, visual data, and historical failure data to support quality control, defect detection, scrap reduction, product sorting, rework, and traceability. The focus will be on understanding what problem needs to be solved, what type of data is required, how a model is trained, how its accuracy is validated, and how it is integrated into the actual shop floor workflow. It will also explain why many companies should start logging data now, even if they are not yet ready to implement a complete solution.

A brief pause to close the previous segment, handle technical preparation, and transition smoothly into the next content.

Before scaling artificial intelligence (AI) in an industrial operation, companies need ground rules. This panel will address how to organize data, responsibilities, permissions, security, traceability, usage policies, and vendor evaluation criteria to prevent pilot projects from growing in a disorganized manner. Governance is not presented as bureaucracy, but rather as a condition for scaling with control: knowing which data is sensitive, who can use it, what information can leave the company, who validates the results, and how to prevent each department from implementing isolated solutions without a common architecture.

Intermediate space for networking, connection between companies, conversations with speakers, and potential demonstrations or partner activations. It must serve as a continuation of the program, not just as a logistical break.

Many companies have already tested artificial intelligence (AI), automation, or analytics tools, but the difficult leap occurs when a pilot must connect with real operations. This in-depth case study addresses what differentiates an isolated trial from a scalable implementation. It will review how to connect AI with existing manufacturing, planning, maintenance, sensor, quality history, and operational dashboard systems. The goal is to show how to move from an interesting demonstration to an installed, stable, measurable, and replicable capability within the plant.

Short break for room preparation, mobility, and program continuity.

Robotics applied to manufacturing should no longer be understood only as heavy automation or large rigid lines. This workshop addresses flexible and modular solutions: mobile robots, collaborative cells, internal material transport, picking support, coordination between people and machines, ergonomics, safety, and operational continuity. The discussion will focus on where it makes sense to incorporate robotics and where it does not, how to evaluate return on investment, what data physical automation generates, and how that data can feed future decisions in artificial intelligence (AI), computer vision, sensors, and management systems.

A break designed to allow mobility between rooms before the last sectorial block and to encourage conversations between attendees, speakers, and technical teams.

For artificial intelligence (AI) to advance within a company, it is not enough for it to be technically attractive. It must make financial sense. This executive clinic will address how to build a business case for AI projects in manufacturing and maquila operations, translating productivity, scrap reduction, decreased rework, time savings, equipment availability, response speed, and error reduction into understandable metrics for general management, finance, and operations. The closing proposes a 90-day roadmap: what to measure, what internal capabilities to develop, what suppliers to evaluate, and how to prevent AI from remaining a mere experiment without a return on investment (ROI).

A space to transition or reconnect both audiences toward the common closing session, ensuring continuity between sector-specific learnings and the final executive summary.

Common closing for both rooms, projected or broadcast simultaneously depending on the venue's final configuration. This block brings together insights on manufacturing, maquiladoras, logistics, and infrastructure into a regional perspective. The final conversation must address what capabilities companies need to build over the next 12 months to move from isolated pilots toward a productive, measurable, and scalable adoption of artificial intelligence (AI). It also serves as a bridge to the upcoming sector-specific cycles of Frontera Futura.