Technology for Impact

Purpose of the document

This document looks broadly at how information technology can enable social impact at scale. This could be a government, a non-profit, an emerging market-focused large private incumbent, or a startup.

Please note:

1. This is not meant to be a scholarly article, but my attempt to synthesize my experience in a structure, admittedly for myself, first. I’m sure more thorough & rigorously vetted frameworks are out there — reviewing/enriching this document with their review is yet to be done.
2. Much of the context is India & my experiences in it, which may also be representative of most emerging markets, differing though in the stage of technology for impact adoption.
3. Impact interventions are never about only technology. Technology is one of the legs or enablers — but intervention has to be more holistic, covering skilling, sensitization, policy, outreach, tie-ups & more. When designing or implementing any intervention
4. Many technologist-led interventions have failed due to inadequate holistic focus. And many interventions, technology or otherwise, look promising until they are evaluated or rolled out. We do not touch upon the rigorous evaluation or counter-design of any intervention or counter-design. Instead, we only look at broad-stroke ideas to enable thinking.

Categorizing impact interventions

Even though this document is about technology, it’s helpful to step back & look at some likely possible intervention types. The categorization below is not exhaustive but helps put technology in perspective. A typical intervention is a combination of multiple.

Below are some categories:

1. Core Sector Advances: Fundamental innovations in the sector that make it more accessible or better and thus more impactful. These could be newer forms or tools of pedagogy, diagnostic mechanisms & more.
2. Skilling: Identifying any skills gaps, ways of training, or new strata of the workforce that may help better a benefit reach more people — eg ASHA workers in healthcare.
3. Capital: Interventions that lead to more effective or impactful capital access for activities that drive impact, either directly or by setting up technology platforms/innovations/scores that other organizations use to do so. The capital access could be in credit, insurance, or any other instrument.
4. Information Technology: Technology is an enabler & gives options to leapfrog reach or resource/human/capital dependency. We’ll talk more about this in the following sections. While there’s a bit of information technology in almost every intervention, some are inherently purely enabled by technology. Some of these, such as a new AI model for weather forecasting for fishermen, require domain expertise as well — and thus are core sector advancements. In this document, we’ll focus primarily on the Information Technology interventions.
5. Linkages (Tie-ups & Reach): Even existing interventions often struggle to reach the targeted beneficiaries. Innovation or efforts that help expand the reach, either on the beneficiary or provider side, fall in this category.
6. Policy: Good policy puts guardrails to scale what’s proven, often by the government, or ensures what’s done right doesn’t break.

To illustrate these categories, below are some top-of-the-mind examples in a few sectors:

Here are the impact organizations I have been associated with using the above framework:

Impact Technology

Benefits

Technology can help individuals or impact organizations in some of the following ways:

1. Help organizations reduce costs/wastage using digital tools to manage processes. Could be:

• SaaS (open-source / paid) for manufacturing operations, etc
• ML models — eg for automating processes, forecasts
• Public datasets — to allow use in planning, eg weather forecasts, soil data

2. Revenue and expansion opportunities by enabling new market partnerships, eg marketplaces, platforms, credit, and protocol standards.

3. Remove access barriers to services and skills, eg platforms, and direct digitization of credit.

4. Reduce quality inequity by automation & AI — eg, use of AI for content creation, support for teachers, doctors, etc.

Classifying Technology

One could classify technologies as follows:

1. Tools: Easy to use or deploy standalone software/tools + technology partners to support to make an organization more efficient and accessible:

• Language or agent tools — added to the existing software, broadening the access in different languages & modalities, both input & output or given a chat / NLP interface.
• Software tools — for adding or automating some of the existing processes and enabling other benefits of digitization. These could be privately offered, closed-source, or open-source. Irrespective, they should be easy to use, self-deployable (eg Shopify), multi-lingual (easy to tie with a language stack. NLP enabled), and must handle India reality (eg fragmented handicraft manufacturing in India)

2. Standards: These are standard APIs, often REST, for the commonly expected interactions between independent software in a sector for them to transact. All compliant software can then transact with others, thus allowing for transactions between entities that could not earlier.

3. Platforms: Multiple entities register on a Platform, allowing themselves to be discovered. These platforms bring trust for otherwise independent entities to transact. The platforms themselves may create & expose a standard or implement an existing one. By aggregating demand & supply, these help scale smaller entities’ business at a large company efficiency.

4. Public digital assets — eg AI models, DataSets

• AI models — to be used in custom impact applications. These AI models could be domain or use-case-specific, solving problems of a context. Language models enable broader access to services & tools. With sufficiently generic language & generation AI models, the Tools can complemented with Generation AI models & interfaced with Langauge/NLP models as below:

• Datasets — for training, benchmarking, and building solutions. Creating new AI models for country-specific problems needs data. Democrating this process requires this data to be exposed for external consumption as datasets.

Startups pick some of these because of technology’s capacity to scale non-linearly & India’s rapidly increasing technology adoption.

Digital Public Infrastructure (DPI)

Why DPI?

Indian economy is highly fragmented. There are pockets of innovation that don’t reach their due scale. DPIs allow interaction between separate entities. This aggregation enables wider innovation reach, economies of scale, global market-linkage optimizations, and more efficiency-inducing learnings.

But why aren’t market forces enough? The Indian customer market can be split into three fuzzily different customer segments.

1. Higher-income: The problems & likely solutions for this segment are similar to those in the Western world, and they are commercially feasible to provide because of higher paying capacity. The private sector can thus address them.
2. Middle income: It is a large growing market, and while solution providers may not be currently viable, they shall be so at scale over time. Thus, venture capital funds these.
3. Lower income: This segment’s low-paying capacity means that public entities must create standards and possibly even platforms.

Kinds of DPIs

More DPI standards & platforms need to be developed. One approach is to identify possible such standards and platforms from the customer segment categorization — look at problems venture capital-backed startups are doing for the middle-income segment, and ask:

1. Can a standard allow individual companies to talk to each other — and thus get VC-backed company scale/efficiency?
2. Should the above be a standard or a live platform? ie, is there to be centrally kept for security or history/optimization (eg Aadhar records)?

Below are some ideas: