Launching our Fractional AI Engineer Program

Today, we are excited to introduce our Fractional AI Engineer program. Over the years, we have built one of the strongest AI teams in the social sector, working with NGOs such as Educate Girls, Indus Action, and Reach Digital, and LMIC country governments to amplify their impact through technology and AI. Despite AI’s potential, very few NGOs are able to adopt and scale AI solutions. This led us to ask: Why? And how can we lower the barriers to tech adoption?

To address this challenge, we are launching our Fractional AI Engineer program, which provides engineers of different profiles––data engineers, data scientists, full-stack engineers–– to work as embedded team members within organizations. This program is designed to fill critical technical gaps and ensure that AI solutions move beyond proof-of-concepts to full-scale implementation. In this post, we explain how we arrived at this solution and why we believe it is what the sector needs.

Why are there so few scaled AI solutions in the social sector?

Over the past year, we have spoken with nearly a hundred social sector organizations interested in AI. The overwhelming majority believe AI has the potential to transform their operations, from how they work to the services they provide. However, in 2025, while AI is deeply integrated into the private sector, the social sector remains largely on the sidelines with very few scaled AI solutions.

From proof-of-concept to scale: Why many AI projects stall

One of the first challenges organizations face is identifying the right AI use case. This requires deep knowledge of the sector, the geographical context, and the organization itself, combined with a realistic understanding of what AI can achieve today and the engineering effort needed to scale a solution. 

Even when a strong use case is identified, organizations need AI engineers who can build production-grade solutions. Many organizations lack the in-house technical expertise to build and scale AI solutions. While it is easier than ever to put together a proof-of-concept using online tutorials, Copilot, and Cursor, building cost-effective, scalable, and maintainable AI systems requires specialized skills.

Hiring AI talent is another major hurdle. Many organizations, seeking to minimize financial risk, opt to hire junior engineers rather than investing in experienced technical leadership. However, without senior guidance, projects often fail to scale. A better approach is to start by bringing in a senior leader on a fractional basis to develop the strategy and build the team. Hiring well is difficult without existing in-house technical capacity, and an experienced AI leader can ensure that the right skill sets are brought in at the right time.

A hot market further complicates hiring. With the private sector offering high salaries, mission-driven organizations struggle to attract engineers willing to work at a fraction of what they could earn elsewhere. Retaining these engineers is even harder—after months of searching for a data scientist, an organization may find itself repeating the process less than a year later.

Early-stage organizations also face the challenge of needing a broad range of technical skills. They often require data engineers to set up their data systems, data scientists and machine learning engineers to build models, and full-stack and DevOps engineers to develop and deploy solutions. First, finding all of these capabilities in a single person is nearly impossible. Second, hiring a whole team is simply unaffordable. Our Fractional program can solve both challenges, where the fractional engineer’s profile is tailored to the organization’s evolving needs.

The engineer’s perspective: Why being the only engineer is hard

For engineers working in the social sector, being the only technical expert within an organization can be isolating. Without mentorship or peer support, engineers lack opportunities for co-learning, troubleshooting, and knowledge-sharing. Junior engineers, in particular, struggle to define best practices, establish workflows, and shape AI strategy in the absence of experienced colleagues.

Through our Fractional program, engineers embedded in organizations remain part of IDinsight’s AI community. This means access to mentorship, peer learning, and structured frameworks, including our Ways of Working and Roles & Responsibilities documents, which outline best practices at each level of technical maturity. Engineers are not just placed in organizations—they bring the strength of an entire technical team with them.

Why IDinsight?

Our engineers are embedded in the regions we serve, providing them with a deep understanding of local contexts and challenges. We have built AI solutions specifically for NGOs and, as an NGO ourselves, we intimately understand the constraints our partners face. Unlike traditional technology vendors, we bring not just technical expertise, but a commitment to impact-driven problem-solving.

What’s next?

As we launch this program, we are eager to hear from organizations about two key questions. First, at what price point would this program be viable for your organization? Second, beyond cost, what other constraints prevent you from bringing on a fractional engineer?

We welcome your advice, feedback, or interest in getting started. If you’d like to discuss how this program could support your organization, email me at sid.ravinutala@idinsight.org.

Let’s work together to bring AI solutions beyond the proof-of-concept stage and into real-world impact at scale.