People often say Africa is 10 years behind India, and India is 10 years behind Europe, the UK and the US. But advanced technologies are changing this pattern. Instead of skipping steps entirely (like leapfrogging straight from no phones to smartphones), businesses are now adopting new tools more quickly once they arrive. This faster adoption is closing the gap between markets.
In my work at Beyond Capital Ventures, I have seen this dynamic firsthand. The next wave of tech-driven value creation is unfolding now across Africa and India, following the tailwinds of global growth. In these markets, companies are applying Silicon Valley-level capabilities in lower-cost operating environments to deliver enormous value.
However, it is important to discern fact from fiction when deciding whether to invest in an advanced technology. Few technologies illustrate that challenge more clearly than Artificial Intelligence (AI). AI is being rapidly adopted in African and Indian markets but not all implementations create lasting value. Understanding how startups are integrating AI and where that integration is translating into real, investable value is key.
When diving into emerging market AI, it is important to identify the different ways businesses are using AI — which we classify as Gloss, Embedded Engine, Native Experience, DNA, and Infrastructure — and how AI is being integrated into the operating model to increase defensibility. This can help identify what is truly adding value and will grow with the rapid advancement of AI and what may not stand the test of time.
It’s sometimes easier to understand through metaphor, so let’s think of AI as a car.
AI as a gloss
Think of AI as the car hood – the shiny gloss you notice first, but not what makes the car move.
While African and Indian businesses are rapidly adopting AI, many are doing so at only a superficial level. AI as a gloss needs to be carefully reviewed and often avoided in both markets, as it involves low integration and low defensibility. Businesses using AI as a Gloss bolt on AI tools to enhance surface-level experiences without changing the core engine. While these bolt-ons may improve user experience, they do not fundamentally change how a business operates or create deep value in its products or services. Instead, they provide a glossy sheen on an existing business model, improving appearance without altering its DNA. For these businesses, significant risks exist from competitors that use AI to transform their core product or service. A clear example of AI as a Gloss is businesses using ChatGPT APIs to handle customer prompts and responses or using AI to generate blog posts and other content.
AI as an embedded engine
Think of AI as the engine – hidden under the hood, quietly powering performance.
True value for customers begins when AI is used as an embedded engine. In this use case, businesses weave AI into their operating fabric, incorporating it into core systems to drive measurable efficiency, accuracy or cost savings. This use of AI fundamentally transforms how the business performs, enhancing the value delivered to customers. In both India and Africa, businesses embedding AI into their core systems are already creating investable opportunities by lowering the cost for customers and increasing the market size – by leveraging non-traditional data sets to overcome digital infrastructure gaps and by enhancing edge processing to overcome physical infrastructure gaps. Risks remain, but the value is tangible. A strong example of AI as an Embedded Engine is insurers embedding AI into underwriting to dynamically price risk or reduce fraud in claims.
One example of a company using AI as an embedded engine is Eden Care. A Rwanda-based “insurtech” company, it embeds AI in its core systems to facilitate customer underwriting and improve claims management. AI-powered predictive modeling identifies high-risk cases early, while automated claims adjudication reduces fraud and accelerates decisions. For employers and employees, the result is lower costs, faster service and healthier outcomes.
AI as the native experience
Think of AI as the driving experience – the feel of the wheel, the sound system, air conditioning and the ride you actually experience.
When companies begin to use AI as the native experience, AI becomes the front-end interface, creating customer stickiness and retention. In this use case, the AI itself is the customer-facing product. AI is not only improving the core of the business, but the customer is also getting accustomed to the interface and gaining familiarity with the brand. Many African and Indian businesses are building demographic-centric AI platforms that adapt to the diverse languages, cultures and contexts across their regions. These localized AIs are being built with deep cultural awareness, from dialect and tone to purchasing behavior and social norms. By doing so, they are designing for the population of the future, as Africans and Indians are projected to represent nearly 40% of the global population by 2030. Key examples of AI as a Native Experience include AI companions, AI writing assistants and AI-native search.
For example, Snapplify, a South African education business, developed Book Buddy, an AI tutor and teaching assistant with interactive games, puzzles, Q&A and dynamic teaching that adapts to each student.
AI as the DNA
Think of this as the car design – the blueprint that determines how the car is built, how it runs and even how it evolves over generations.
Businesses with AI as the DNA have AI as the core architecture of their product or service, rather than just an add-on. They build proprietary models or systems from scratch, often using unique or hard-to-replicate datasets, to solve problems in ways that competitors cannot easily imitate. Unlike surface-level or operational uses of AI, here the technology is the business engine. African and Indian businesses are both excelling in this segment, powered by the rise of demographic-specific data and rich, localized customer insights. In Africa, this segment led to one of the best exits – if not the best exit – in the past decade. Instadeep, which delivers AI-powered decision-making systems for enterprise customers, was bought by BioNTech for over $650 million.
Another example of a business with AI as the DNA is XENO, a Ugandan business that introduced AI robo-advisory to millions of first-time savers across East Africa. Its AI generates personalized portfolio recommendations that users can easily fund via smartphone. By combining AI personalization with ubiquitous mobile distribution, XENO helps customers build toward financial resilience.
AI as infrastructure
Think of everything you need to be able to drive a car – the roads, traffic lights, automobile factories, etc. that make driving possible at scale.
Businesses using AI as infrastructure provide the foundational layer upon which others build and deploy AI. They power the ecosystem by offering computing capacity, data infrastructure, cloud access, and developer tools that enable AI applications to scale reliably and securely. When executed well, these businesses achieve the highest levels of defensibility and integration. While there are not yet many examples of AI as infrastructure in Africa, Indian businesses are beginning to compete with global giants like Nvidia, Amazon Web Services and OpenAI. As Africa’s digital and data infrastructure matures, the next generation of breakout companies will be those that build and own the continent’s AI infrastructure.
For example, India-based Neysa provides an AI acceleration cloud system that allows other businesses to train, test, deploy, and monitor their AI models from a single dashboard, without the complexity of managing multiple tools.
In all of these stages, it is important to note that businesses can move up the curve and further incorporate AI into their operations, processes, and value proposition. For example, a company that has AI as an Embedded Engine can move towards AI as the DNA over time. However, it takes money, time and innovation to move up the curve, with the largest jump coming from AI as a Gloss to AI as an Embedded Engine.
Sifting through AI opportunities to find real, investable value
Investors considering opportunities in the space need to be clear on how potential portfolio companies are using AI – and whether it is actually creating real and defensible value. To differentiate Gloss from DNA, here are some key questions to consider:
Signals that AI may just be “gloss:”
- Substitution test: If you swapped out your AI with another off-the-shelf tool (e.g. ChatGPT API, Jasper), would the business still work the same way?
- Value creation: How much of the customer value comes directly from the AI versus from existing processes?
- Dependency: Are you renting the AI from others (APIs, SaaS tools) or do you own anything unique?
- Impact measurement: Is AI only changing surface metrics (like faster responses, prettier content) instead of core KPIs (unit economics, revenue, churn)?
- Team experience: Is the company hiring dedicated data scientists or AI developers?
Signals that AI may be “DNA:”
- Core test: If you removed the AI, would the entire business model collapse?
Proprietary edge: What proprietary data, models, or IP are you building that others cannot easily replicate? - Integration depth: Is the AI baked into the company’s architecture, workflows, and product strategy?
- Defensibility: How does the AI create a moat (unique datasets, feedback loops, sector-specific optimizations)?
- Customer stickiness: Is the AI itself the engine of customer trust, retention and adoption?
- Team experience: Is the company run by data scientists or AI developers?
Startups in Africa and India are using advanced tools to meet urgent local needs while creating durable financial and social value. For many businesses, AI serves as an invisible enabler—compressing innovation timelines, bridging infrastructure gaps and strengthening competitive moats. Proprietary datasets deepen defensibility; automation reduces costs; and personalized services expand market reach. Innovations in personalization and new models of service delivery provide critical goods and services where the demand is both urgent and growing.
For investors, the value is clear. Companies in emerging markets that leverage AI blend rapid growth with lower operating costs. This combination enables them to deliver Silicon Valley-grade solutions with a fraction of the required capital, translating into stronger returns and greater impact per invested dollar.
The real opportunity lies in discernment—backing the businesses where AI is not just a feature but a foundation. Those investors who can tell the difference will capture both the financial upside and the lasting impact of this new wave of innovation.
Christophe de Montille is a principal at Beyond Capital Ventures. (Disclosure: Beyond Capital Ventures is an investor in Eden Care and XENO.)
Guest posts on ImpactAlpha represent the opinions of their authors and do not necessarily reflect the views of ImpactAlpha.
