Just as in the late 1990s the internet redefined commerce, work, and education, in 2026 Mexico, like many countries in the region, is entering a new inflection point driven by the rapid evolution of an exponential technology as artificial intelligence stops being an assistant and becomes an executor.
This shift, which signals an agentic revolution, is what turns autonomous commerce into the factor capable of multiplying the country’s productivity. This will not happen by adding more technology, but by enabling systems that make decisions, remove friction, and coordinate operations across a digital ecosystem that is already mature. When AI begins to act, the impact stops being incremental and becomes structural.
Mexico reaches 2026 with a digital commerce ecosystem that is no longer in an adoption phase, but in a scaling phase. According to the “2025 Online Sales Study” by the Mexican Online Sales Association (AMVO), retail e-commerce reached MX$789.7 billion (US$46 billion) in 2024, with annual growth of 20% and more than 67.2 million active digital shoppers, representing 84% of the country’s internet users. With an operation of this magnitude, which already accounts for 15.8% of total retail sales, every logistical friction, every delayed pricing or inventory decision, and every manual process directly impacts margins and profitability.
It is at this point that autonomous commerce emerges as the nervous system that allows Mexico’s digital muscle to move with precision, speed, and coordination, multiplying productivity and financial results.
In practical terms, the productivity leap occurs when the “latency” between what the market signals and what operations execute is reduced. In Mexico, where competition coexists with cost volatility and supply availability, that latency is paid for in immobilized inventory, returns, and defensive discounting. A well-governed autonomous model turns signals (demand, traffic, stockouts, incidents) into coordinated micro-decisions that protect margins and elevate service levels.
Let us remember that in the late 1990s, the internet brought three paradigms: e-commerce, remote work, and online education. Today, we are facing another exponential wave. Artificial intelligence — already mature but only recently mass-adopted — once again is reshaping these three axes, but from a different perspective: autonomous commerce, autonomous work, and autonomous education, all built on decades of digitalization, mobile-first adoption, and the platform economy.
It is exponential technology raised to the Nth power, but with one key difference: this technology acts. In commerce, for example, while traditional e-commerce optimized catalogs, checkout, and last-mile delivery, autonomous commerce began orchestrating decisions in real time — agents that adjust prices, prioritize inventory, anticipate stockouts, resolve incidents, and coordinate campaigns based on demand, elasticity, and logistical constraints. And here, hard data matters because it translates directly to business outcomes: in pricing, for instance, revenue improvements of 2% to 5% and profit increases of 10% to 40% are reported when optimization is driven by AI, precisely because of the speed and precision of decisions that were previously manual.
A similar shift is happening in work. In Mexico, the transition from remote work to augmented work is already underway, where people rely on intelligent systems to gain focus and efficiency. According to a study by IBM Mexico, 58% of workers say artificial intelligence already helps them be more productive, and 65% report that it enhances their creativity, especially by reducing repetitive tasks and accelerating decision-making.
At the same time, the report shows that while teams advance in everyday AI usage, many organizations still fail to scale adoption due to a lack of processes, governance, and strategic clarity. It is in this gap that autonomous work emerges as a complement. When systems absorb operational friction and execute tasks under clear rules, people can focus on strategy, creativity, and relationships with customers and partners. “Doing more with less” stops being an aspirational slogan and becomes a feasible and measurable way of working.
Autonomous education, for its part, will become critical in Mexico in the face of a talent gap that already constrains productivity. Data from the Inter-American Development Bank shows that demand for digital skills is growing faster than the traditional education system’s ability to supply them, forcing millions of workers to retrain and update their skills throughout their working lives.
This structural pressure is compounded by what IBM observes in its study of the Mexican workforce: artificial intelligence is already positively impacting productivity and creativity, but companies face difficulties in training, retaining, and scaling talent with AI competencies, mainly due to the lack of continuous training models and clear adoption frameworks. In this context, learning becomes a permanent, personalized, and contextual process, supported by technology that adapts content to the real pace of work. “Learning to unlearn and relearn” is no longer aspirational, it is part of the job to be done to sustain employability, competitiveness, and growth in Mexico’s digital economy.
That said, the challenge of the agentic revolution goes beyond the technologies being deployed. It is more closely tied to the governance of decision-making: what is delegated, within what limits, how it is audited, and who is accountable when a system executes. This is where trust is built. Solving complex problems, removing friction, and generating impact remain the true north. Behind every screen and every algorithm, there are people: someone who demands and someone who supplies.
Mexico has a unique opportunity: market scale, demanding supply chains, and accelerated digital adoption. Considering that the agentic revolution today is not a promise but an ongoing race, what players in this digital ecosystem must do is understand and adapt organizational culture to operate with systems capable of executing decisions, coordinating processes, and learning in real time — because productivity will no longer grow incrementally, but exponentially, and the ability to execute in the most human and intelligent way possible will become the new oil.
