Work in international development touches some of the most pressing issues today from security and healthcare to trade, infrastructure and economic development. These are not abstract problems; they directly affect individuals,families and entire communities.
At the same time, these challenges are becoming harder to address. With foreign aid budgets under increasing scrutiny and global pressures intensifying, there is growing demand for solutions that are not only effective, but scalable, sustainable and measurable.
Within my day-to-day work, I focus on helping how technology has the potential to transform some of their most complex organisational challenges. Increasingly, it’s clear that that same principle applied in AI and automation can, and should, extend into international development contexts.
AI has potential - but only when applied correctly
AI and automation already demonstrate value in structured business environments: improving supply chains, optimising finance operations and identifying operational inefficiencies.
In a development context, these same capabilities can be transformative in different ways. For example:
· Identifying early indicators of drought or food insecurity
· Improving the allocation of humanitarian aid during crises
· Supporting predictive healthcare interventions in vulnerable regions
However, the existence of technology alone does not create impact. The value comes from how it is applied, and whether it reflects the reality of the environments it is designed to serve.
Why context matters more than capability
A recurring misconception is that strong technology automatically leads to strong outcomes. In practice, this is rarely true.
As complexity increases, so does the importance ofunderstanding human, social and infrastructural context. At a conference I attended recently, an example was shared where AI is being used to improve maternal and child health outcomes in sub-Saharan Africa.
The concept was strong, but the real challenge wasn’t the model - it was the environment it needed to operate in:
· What if users don’t have access to smartphones?
· What if connectivity is inconsistent or non-existent?
· How do cultural and economic differences affect the relevance of health advice?
Without addressing these questions, even the most advanced system risks becoming unusable or irrelevant. This is where many well-intentioned initiatives fail. Not because the technology is insufficient, but because the context is not fully understood.
From implementation to co-creation
This highlights a wider shift that is needed in how AI solutions are designed and delivered: moving away from “implementation” as a one-way process, and towards genuine co-creation.
Co-creation means building with users, not for them. It involves:
· Engaging communities, not just stakeholders
· Designing around real-world constraints, not theoretical use cases
· Iterating based on lived experience and feedback
The idea that “if you build it, they will come” simply doesn’t hold in these environments. Solutions only succeed when they are embedded in the systems, behaviours and constraints of the people they are designed to support. Without this, even the most advanced AI becomes a technical exercise rather than a meaningful intervention.
The role of embedded delivery models
There has been increasing discussion in the technology and consulting space around “forward deployed engineers” - teams embedded directly with clients and users to build solutions in real-time.
While the terminology of forward deployed engineers may be new, the underlying principle is not. What it reinforces is something we already see in successful programmes: the closer you are to the problem, the better the outcome.
The forward deployed engineers model enables:
· Faster iteration based on real feedback
· Stronger alignment between solution and need
· Shared ownership of outcomes between providers and users
It represents a shift away from traditional “discover then build” delivery models towards continuous collaboration and adaptation.
Technology works best when it is shared
Across both private and public sector work, the most effective outcomes come from partnerships where technology, context and people are equally valued.
AI is a powerful enabler, but it is not a standalone solution. Its impact depends on how well it is integrated into the realities of the environments it serves. The challenge - and opportunity - is to move beyond implementation, and towards true co-creation.
For clients, partners and stakeholders, the message is simple: the best results come when we work together, stay close to the problem and build with the end user at the centre of every decision.
If you’d like to learn more about how Robiquity can help your organisation, get in touch with us today for an initial consultation.
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