AI is already part of the operations of 48% of organizations in Mexico, up from 38% a year earlier. However, 63% still use it mainly for basic applications and incremental improvements, revealing a gap between experimenting with the technology and incorporating it structurally into the business.
This gap is particularly relevant for the supply chain, where decisions about inventory, transportation and customer service have direct effects on costs, response times and operational continuity. The next step is not just about acquiring a tool, but about connecting artificial intelligence with data, processes and responsible parties capable of turning results into actions.
The problem: isolated pilots and fragmented data
A test can show that a model works in controlled conditions, but it does not guarantee value when integrated into daily flow. If information remains scattered across systems, spreadsheets, contact channels and commercial platforms, AI will have an incomplete view of the operation.
Data quality also conditions the results. Forecasts, recommendations or alerts lose usefulness when fed with outdated records, inventories that do not match or processes without clear monitoring rules.
Evidence points to deeper integration
The growth of enterprise adoption confirms that AI has moved from an experimental technology to a business capability. About 550,000 Mexican organizations started using it in the last 12 months, but use remains concentrated in limited-scope cases.
In logistics, the most promising applications are those that allow anticipating changes and coordinating responses: analyzing demand, adjusting inventories, optimizing routes, identifying transportation risks and keeping the customer informed. Utility increases when these functions share information and can trigger tasks within the operation, rather than being limited to generating isolated recommendations.
Therefore, scaling a project requires establishing a baseline and measuring concrete results. Relevant indicators include processing time, error reduction, inventory accuracy, service level, team adoption and financial impact.
How to advance in a controlled way
- Seleccionar un problema operativo específico, como consultas de disponibilidad, seguimiento de pedidos o clasificación de solicitudes.
- Centralizar la información necesaria para que los equipos y sistemas trabajen con datos consistentes.
- Definir responsables, permisos y criterios de supervisión antes de automatizar decisiones.
- Medir el desempeño del caso de uso y compararlo con la línea base inicial.
- Ampliar la implementación únicamente cuando existan resultados repetibles y capacidad operativa para sostenerlos.
At Onix Board we help connect automation, communication and analytics within a single operation. The platform integrates channels such as WhatsApp Business, Facebook, Instagram and Telegram into a unified inbox; it also enables creating automated flows, managing customer information, handling inventories and orders, and consulting dashboards to evaluate process performance.
This integration enables AI not only to answer questions but also to collect data, update contacts, transfer conversations, accompany commercial processes and contribute to a more complete view of the relationship between demand, operation and service.
The opportunity for Mexican companies lies in turning AI adoption into measurable operating capability. The decisive step will be to move beyond disconnected tools and build processes where data, people and automation work toward common objectives.