E-Paper
India is a case study that we can learn from: Wafaa Amal
India is a case study for countries who have the same means and yet are a step behind, especially with the same level of regulatory constraints and sovereign solutions, she said.
Wafaa Amal, a veteran of global payments and banking, sees trends before many others. As CEO of Prisme.ai, a sovereign agentic artificial intelligence (AI) platform, she shared two key observations in a conversation with HT at the India AI Impact Summit 2026. First, that AI no longer needs to be proven, but industrialised. Second: “India is a case study for countries who have the same means and yet are a step behind, especially with the same level of regulatory constraints and sovereign solutions.”
“We can say we are behind in Europe, as are some other countries, because regulation is very hard. I know India has similar requirements. From my point of view, India is a case study that we can learn from,” says Amal.
French AI company Prisme.ai works with a global customer base, focusing on sovereign agentic AI solutions for enterprises — including private cloud and reversibility, which Amal insists are non-negotiable.
The inversion
This perspective is telling, particularly when the general discourse on AI positions the US and parts of Europe as laboratories of innovation, as both regions embark on capital-intensive momentum towards model supremacy and artificial general intelligence (AGI). India, in contrast — often through public-private partnerships — has remained focused on AI for the masses.
Infrastructure at scale is something that has been demonstrated successfully time and again, including the digital payments push over the past decade led by the unified payments interface (UPI).
While Europe and the US navigate AI regulation, data protection and the economic implications of heavy spending on AI infrastructure, India offers a different lens to agentic AI platforms such as Prisme.ai. There is a balance to be found between sovereignty, local infrastructure ambitions and enterprise digitisation, while being cost-sensitive. Amal has no doubt India will repeat UPI’s success at scale with AI.
Commodities and regulation
In time, large language models (LLMs) that underpin everything in AI will become a commodity. “China released models that are fast, highly qualitative, less consuming and less expensive. One signal is that LLM providers are shifting their strategy to solutions that help create agents, orchestrate agents and so on,” she points out.
Two recent illustrations emerge from OpenAI and Anthropic. This month, coincidentally on the same day, OpenAI released the GPT-5.3-Codex agentic coding model, calling it the most capable of its kind to date. Rival Anthropic released the Opus 4.6 model, claiming it “extends the frontier of expert-level reasoning”. When used within the Claude Code tool, it enables agent teams to work together on tasks.
This rapid pace worries Amal. She questions whether enough is being done to ensure humans remain in control of the technology, and whether solutions being built will remain fully auditable. Existing regulations that define industries such as banking, financial services and telecommunications give her reason for optimism.
“They have had a governance strategy for the last 10 or 15 years, have the digital infrastructure and well-governed data. That makes it easier for them today to have digital infrastructure,” she points out.
Measuring agent quality
Asked if the methodology to measure and validate the quality of AI agent outputs is keeping pace with evolution, Amal believes a multi-step verification process is essential. Importantly, she says an agent must “respect all exit scenarios and comply with high-quality outputs”.
Prisme.ai’s event-driven architecture (EDA) solution means enterprises have complete visibility over their data and agent actions, with real-time detection of dysfunction or hallucinations.
Amal hopes India persists in its approach with AI and agents at scale, which will bear fruit in due course. “India adopted, on day one, a mindset to go into an industrialised mode. We see pragmatic tools, and India didn’t run after being a large model or an LLM provider. Instead, focus has been on how to make sure this technology is being used in a way that is useful for the population,” she says, looking at India as a significant market over the next few years.
From her perspective, India’s AI journey, for the most part, has already been industrialised.
