Blog Post
The Hidden Constraint in Emerging Markets
For companies building AI products in emerging markets, progress is often evaluated through visible outputs such as features shipped, pilots launched, or systems deployed. Inside most organizations, however, the primary constraint on performance is not software availability. It is the structure of information itself and how it is produced, reconciled, interpreted, and acted upon.
Multiple global studies on enterprise analytics and decision-making show that between 30 to 50 percent of knowledge workers’ time is spent on data preparation, reconciliation, and reporting rather than analysis or execution. This pattern holds across industries and is particularly acute in operations-heavy environments. Data exists, but it is distributed across tools that were not designed to work together. By the time reports are complete, decisions are often delayed or revised.
In time-sensitive environments, intelligence that arrives late has limited value.
The Cost of Latency
Let's take the case of Nigeria, our flagship market this inefficiency is amplified by macroeconomic volatility. When currency movements and inflation shift by the hour, late intelligence becomes a financial liability.
A reconciliation completed three days late can materially damage pricing strategy, inventory management, and cash flow exposure. In high-stakes environments, delayed intelligence is zero-value intelligence.
Learning from Our Customers: Why We Observed First
In 2025, Wamiri made a deliberate choice to slow down product development and observe how work actually happens. We bypassed discovery workshops for hundreds of hours of direct observation across logistics, commodities, and financial operations.
We watched how a single operational question travels a weary path through emails, spreadsheets, and manual approvals before becoming actionable. Our findings were clear:
Operational research across supply chain, finance, and logistics shows that delays in information flow increase working capital requirements, reduce forecasting accuracy, and weaken risk response, often translating directly into lost revenue or higher costs.
When figures require repeated validation, trust erodes, decision-making slows, approvals become conservative, and organizations adapt to latency rather than removing it. This environment shapes how enterprise AI must function if it is to create durable value.
Routine Questions, Repeated Work
Midway through the year, we conducted and published a research case study examining language-driven technology adoption in Nigeria. The findings aligned with broader evidence that tools fail less often due to technical limitations and more often due to poor fit with existing workflows and realities.
In Nigeria, workload intensity and resource constraints amplify this pattern. Systems that require users to change how they work before delivering value are often abandoned. Systems that reduce effort immediately are more likely to persist.
The Genesis of ALK: Built for Constraint
These observations shaped the early development of ALK, our operational intelligence platform. We prioritized natural language interaction, compatibility with existing data formats, and a focus on reducing time-to-answer rather than expanding feature sets.
The objective was not to replace existing systems, but to reduce the effort required to move from question to answer.
Working primarily in Nigeria has shaped how we think about enterprise AI. High-constraint environments expose failure modes quickly. AI systems that rely on ideal conditions struggle in this environment. Systems that can operate under constraint are more likely to generalize elsewhere. From this perspective, building in the Global South is a discipline that forces clarity about what matters in operational intelligence.
Moving Toward Actionable Intelligence
Operational intelligence is primarily an organizational challenge. Latency, not access, is the core problem. Language is a critical interface layer, and systems should adapt to work patterns, rather than the reverse.
The work ahead focuses on building infrastructure that reduces preparation overhead, integrates into existing workflows, and supports timely decision-making in complex operational environments.
