Wafaa Amal, a veteran in the payments and banking sectors globally, can see trends before many others can. As CEO of Prisme.ai, a sovereign agentic artificial intelligence (AI) platform, she puts forward two considerate beliefs in a conversation with HT, at the India AI Impact Summit 2026. First, that AI no longer needs to be proven, but industrialised. And secondly, she says, “India is a case study for a lot of countries who have the same means and yet they are a step behind, especially with the same level of constraints with regulation 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 as well. From my point of view, India is a case study that we can learn from,” says Amal, observing India’s AI journey. French AI company Prisme.ai works with a global customer base, with particular focus on sovereign agentic AI solutions for enterprises — this includes private cloud and reversibility, which Amal insists are non-negotiable.
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This inversion is telling, particularly when general AI discourse positions US and parts of Europe as laboratories of innovation, as both regions embark on capital investment intensive momentum towards model supremacy and artificial general intelligence (AGI). India, in contrast, often with public-private partnerships in play, has remained focused on AI for masses. Infrastructure at scale is something that’s been demonstrated successfully time and again, including a digital payments push over the past decade led by the unified payments interface, or UPI.
While Europe and US navigate AI regulation, data protection, and economic implications of heavy spending on AI infrastructure, India offers a different lens to agentic AI platforms such as Amal’s Prisme.ai. There’s a balance to be found, between sovereignty, local infrastructure ambitions, enterprise digitisation, while being cost-sensitive. Amal has no doubt India will repeat UPI’s success at scale, with AI too.
Commodities and regulation
In time, LLMs or large language models that underline everything AI, will become a commodity. “China released models that are fast, highly qualitative, less consuming and less expensive. One of the signals 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 illuminating Amal’s opinion emerge from AI companies 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 till date. Rivals Anthropic released 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 of progress does worry Amal, and she questions if we are doing enough to ensure humans remain in control of the technology in due course, and whether solutions being built will remain fully auditable at any time. Existing regulations, which define industries such as banking and financial services as well as telecommunications, give Amal reason for positivity.
“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.
HT asked Amal if methodology to measure and validate quality of AI agent outputs is keeping pace with evolution, and she believes a multi-step process to ensure verification is essential. Importantly, she says an agent must “respect all exit scenarios and comply with high quality outputs”. Prisme.ai’s EDA, or event driven architecture solution, means enterprises have complete visibility over their data and agent actions, with real-time detection of any dysfunction or hallucinations.
Amal hopes India persists in its approach with AI, agents and AI 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 big market over the next few years.
From her perspective, India’s AI journey therefore, for a large part, has already been industrialised.