What African Healthcare Data Can Teach Project Managers About Delivering AI at Scale

Published On:

February 19, 2026

What African Healthcare Data Can Teach Project Managers About Delivering AI at Scale

By Brighton Mukundwi and Delvin Vengesai

Artificial intelligence is increasingly positioned as a game-changer for healthcare, promising faster diagnoses, better planning, and more efficient service delivery. Across Africa, governments, donors, and private organisations are investing heavily in digital health and AI-driven solutions. Yet many of these initiatives struggle to move beyond pilot projects or fail to deliver sustained impact.

Our recent research explored why this happens and what can be done differently. While our work focuses on healthcare systems in Africa, the lessons extend far beyond health. At its core, this is a project management story about complexity, fragmentation, governance, and scale.

The hidden problem behind failed AI projects

A common assumption in AI projects is that once the technology is available, value will follow. In practice, this rarely holds. Across African health systems, we found that the primary barrier to AI adoption is not algorithms or computing power, but fragmented, poor-quality data. Health data is often scattered across paper files, standalone digital systems, mobile apps, laboratory databases, and insurance platforms. These systems are rarely designed to talk to one another. As a result, project teams face duplicate records, missing information, inconsistent formats, and unclear data ownership.

From a project management perspective, this creates a classic failure pattern. Teams attempt to deliver advanced capabilities atop weak foundations. Timelines slip, costs rise, stakeholder confidence erodes, and pilots never scale.

Reframing AI as a data activation project

Our research proposes a different framing. Instead of treating AI as a standalone innovation project, organisations should first treat it as a data activation programme. These shifts focus from tools to foundations. We reviewed health information systems across multiple African countries and identified recurring design and delivery challenges. These included limited scalability, lack of standards, weak governance, and resistance to centralised systems. In response, we developed a reference architecture called the African Data Activation System for Health, or AfriDASH.

While AfriDASH is a healthcare platform, its principles are highly relevant to project managers in any sector that deals with complex data environments.

Key lessons for project managers

1) Solve organisational problems before technical ones

Many technically sound systems fail because organisational realities are ignored. In healthcare, institutions are often reluctant to share data due to legal, political, or trust concerns. Similar dynamics exist in large enterprises, public sector programmes, and multi-partner initiatives. AfriDASH addresses this by allowing both centralised and federated deployments. Data can be analysed collectively without forcing all stakeholders to surrender control. For project managers, this reinforces an important lesson: architecture choices must align with stakeholder incentives and governance realities, not just technical efficiency.

2) Design for phased delivery, not big-bang transformation

Large digital transformations frequently fail when they attempt to deliver everything at once. Our findings show that platforms designed with modular, phased implementation are far more likely to succeed. AfriDASH is built so organisations can start small, for example by improving reporting or data quality, and later expand into advanced analytics and AI. This mirrors good programme management practice: demonstrate early value, build trust, and scale capabilities incrementally.

3) Data quality is a continuous process, not a one-time task

A recurring theme in our review was the belief that data quality can be “fixed” during system rollout. In reality, quality degrades unless actively managed. Successful platforms embed validation, feedback loops, and accountability into everyday workflows. For project managers, this means treating data quality as an operational process with owners, metrics, and incentives, rather than a technical checklist item.

4) Governance is a delivery enabler, not bureaucracy

Governance is often perceived as slowing projects down. Our research shows the opposite. Platforms that fail to define data access, consent, accountability, and decision rights early often stall later due to disputes and loss of trust. AfriDASH embeds governance directly into the system through role-based access, audit trails, and consent management. For project leaders, this highlights that clear governance structures reduce risk, accelerate decision-making, and support long-term sustainability.

5) Build capacity, not dependency

Another reason projects fail to scale is over-reliance on external consultants or vendors. When they leave, systems stagnate. We emphasise capacity building as part of the platform itself, including training, documentation, and community engagement. For project managers, this reinforces a critical success factor: sustainable delivery requires investing in people and processes, not just technology.

Why this matters beyond healthcare

Although our research focuses on African health systems, the patterns we observed are familiar across industries. Fragmented data, siloed teams, unclear ownership, and misaligned incentives undermine digital initiatives everywhere. Project managers working on enterprise data platforms, smart cities, fintech systems, or public sector reforms face similar challenges. The lesson is clear. Advanced technologies only deliver value when foundational project disciplines are applied rigorously: stakeholder alignment, phased delivery, governance, quality management, and capacity development.

From research to practice

AfriDASH is not presented as a finished product, but as an actionable blueprint informed by real-world constraints. Its success, like any complex programme, depends on leadership, coordination, and sustained investment. For project managers, the takeaway is not to replicate a healthcare system, but to internalise the underlying principles. Treat data as a strategic asset. Design for scale from the start. Align technology with organisational reality. And remember that transformation is as much about people and governance as it is about platforms and tools.

As AI continues to shape how projects are conceived and delivered, these lessons will become increasingly important. The difference between stalled pilots and scalable impact often lies not in innovation itself, but in how well the project foundations are laid.

PML would like to extend a huge thank you to both Brighton and Delvin for sharing their knowledge and wisdom with the PML community! Learn more about them below and reach out to connect!

About the Authors

Brighton Mukundwi is a Senior Data Consultant and Cloud & AI Innovator with a strong background in data analytics, machine learning, and cloud architecture. He is an AWS Certified Solutions Architect (Professional) and ML Engineer, a Certified Data Analyst (IABAC), and a hackathon winner. Brighton is passionate about turning data into impactful solutions and evangelizing the power of analytics and AI to solve real-world problems.

Connect directly with Brighton on LinkedIn at: https://www.linkedin.com/in/brighton-mukundwi-1609b0250/

Delvin Vengesai is a Zimbabwe-based AI Product Manager and SaaS UI/UX Designer with six years of experience in product design and AI product leadership. His work spans AI-native enterprise systems and user-centred SaaS platforms in Healthcare and Fintech, and research-informed digital innovation, with interests in data systems, governance, and building technology that scales in resource-constrained contexts.

Connect directly with Delvin on LinkedIn at: https://www.linkedin.com/in/delvinvengesai/

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