Artificial intelligence has become the defining tech narrative of this decade. It promises to boost productivity, cut costs, and free teams from repetitive tasks. Yet, between promise and execution lies a gap. Many Latin American organizations have tested pilots, invested in licenses and consulting, and still fail to see sustained impact on their bottom line. The key question is not whether AI works, but under what conditions it adds value, and when it is just smoke wrapped in marketing.
What does “productivity with AI” really mean?
Productivity is not just about speed. It means achieving better outcomes with less friction, fewer errors, and more informed decisions. In a well-designed scenario, AI automates low-value tasks, improves the quality of deliverables, and lets teams focus on sales, innovation, and customer relationships. In a poorly designed one, AI adds complexity, drains resources, and multiplies integration, security, and maintenance costs. The difference depends on the initial diagnosis, the quality of data, and model lifecycle governance.
Use cases that work (and why)
Where there is enough data, repeatable processes, and clear metrics, AI tends to perform. Examples include customer service with conversational assistants integrated into knowledge bases, demand forecasting to reduce stockouts, risk management with anomaly detection, and personal productivity with copilots that speed up writing, analysis, and synthesis. Gains appear when the tool plugs into an existing workflow instead of forcing a total reinvention. Success also requires measurable objectives—response time, NPS, error rate, cost per ticket—reviewed regularly.
By contrast, projects fail when AI is “shoehorned in” without a business case, with dirty or scattered data, or without clear ownership of processes. They also collapse when organizations fail to invest in training and change management. Adoption curves are as important as the model itself.
Roadblocks: data, culture, and hidden costs
The first barrier is data: quality, labeling, accessibility, and permissions. The second is culture: if teams see AI as a threat, they will resist it; if they see it as a tool, they will embrace it. The third is cost of ownership: beyond licenses, integration, MLOps, observability, privacy, compliance, and retraining all add up. Organizations that overlook these factors often conclude “AI doesn’t deliver” when the real issue was underestimating the project.
The smart path begins with targeted pilots and defined KPIs. Pick a process with clear pain points, available data, and a committed process owner. Establish a baseline and quarterly goals. Put in place basic governance: access control, bias evaluation, decision traceability, and contingency plans. Embed AI into tools already used by teams to reduce friction. And always measure, without metrics, you only have anecdotes. At this point, a practical guide aimed at small businesses shows how to prioritize high-impact cases without overspending, aligning with the progressive approach we propose here.
What if productivity doesn’t show up?
Sometimes it doesn’t, at least not right away. Common reasons include misaligned expectations, such as promising full process replacement instead of augmentation; shallow integration, where the model outputs results but workflows, incentives, or training never change; and lack of orchestration, meaning AI creates value in silos but never reaches business decisions.
Fixing this requires process redesign, not just “putting the model in the cloud.” Continuous feedback, prompt tuning, retraining, and closing the loop between operations and learning usually make the difference.
A sober approach for ambitious times
AI is neither a magic wand nor a passing trend. It is a general-purpose technology that requires discipline. Companies that extract value define problems precisely, measure before and after, and professionalize model lifecycle management. Those stuck in “perpetual pilots” end up with pretty dashboards and zero EBITDA impact. The difference is less about the algorithm and more about execution. For many organizations in the region, 2025 should mark the shift from isolated tests to production systems that integrate data, people, and decisions.
Sources:
- McKinsey: The economic potential of generative AI: the next productivity frontier
- McKinsey: From promising to productive: real results from gen AI in services
- Harvard Business Review: AI Won’t Replace Humans — But Humans With AI Will Replace Humans Without AI
- The Economist: Welcome to the AI trough of disillusionment
- Financial Times: AI presents growing risk to financial markets, US regulator warns
