What does Prima Partners do?

Prima Partners is the advisory practice of Prasanna Walimbe. It operates in three modes: AI & IT strategy advisory for CXOs, corporate AI enablement and delivery, and angel investment plus mentoring for early-stage founders in fintech, supply chain and applied AI.

Who is Prasanna Walimbe?

Prasanna Walimbe is a deep-tech operator with 20+ years of experience across AI evaluation and safety, AI/ML architecture, and financial research. He is an alumnus of IIT Kharagpur and IIM Calcutta and a Certified Independent Director advising boards on cross-border AI investments, operations and compliance.

Which sectors does Prima Partners focus on?

Fintech and BFSI, supply chain, manufacturing, and applied AI across enterprise use cases. Assurance work is mapped to the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework.

How do I engage Prima Partners?

Email pw@prima-partners.com to start a conversation. Engagements run globally on a remote-first basis from Dubai.

What is AI governance consulting and what frameworks do you use?

AI governance consulting maps an organisation's AI systems, decisions and controls to a recognised risk framework — typically the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework — and closes the gaps. Prima Partners runs checklist-based evaluations, model-risk reviews and board-level assurance for financial services, supply chain and applied-AI use cases.

Do you serve boards as an Independent Director?

Yes. Prasanna Walimbe is a Certified Independent Director and advises boards on AI strategy, cross-border investment and operations, and compliance obligations across jurisdictions — with a specialisation in fintech, supply chain and applied AI.

Do you offer corporate AI training and enablement?

Yes. Corporate AI enablement includes executive workshops, leadership AI literacy, hands-on programmes for delivery teams, and practical playbooks — AI realism vs hype, ML deployment, GTM for AI products, and the founder's mindset inside corporates.

Do you mentor or invest in startups?

Prima Partners writes angel cheques and provides hands-on mentoring to pre-seed and seed founders in fintech, supply chain and applied AI. Support covers technical architecture, GTM, commercial modelling and board readiness.

Which AI model should an enterprise standardise on?

The Stanford 2026 AI Index shows the performance gap between top models has collapsed, so standardising is now a switching-cost and security decision rather than a capability one. Reframe long-running evaluations around integration risk, data-egress controls and exit paths instead of another round of benchmarks.

Should companies slow junior hiring because of AI?

The Index's data on entry-level task exposure argues for redesigning the junior role, not deleting it. Cutting the pipeline solves an immediate headcount problem but removes the bench that becomes your senior engineers and managers by 2028.

Where do AI agents actually belong on the enterprise roadmap?

The Index's agent-benchmark results locate a narrow band of workflows where agentic systems clear the reliability bar today — mostly bounded, tool-using tasks with human review. Broader deployments across open-ended enterprise workflows are a 2027 conversation and should be scoped as such.

Can enterprises trust reported AI productivity gains?

The Index places self-reported productivity uplift directly beside realised-output figures and the two diverge sharply. Boards should measure cycle time, defect rate and unit economics on specific workflows rather than treat satisfaction survey scores as evidence of P&L impact.

Who should sign off on enterprise AI governance?

The Index's transparency scores show foundation-model vendors are disclosing less each year, so control cannot be inherited from the vendor stack. Governance sign-off belongs with an internal owner running a checklist mapped to the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework.

The Verification Premium

2026-07-18 · Financial Inclusion · 12 min read

The poor don't pay more for money. They pay more for being verified.

The analysis

Financial exclusion is usually described as a distribution problem: not enough branches, not enough agents, not enough products designed for low balances. That framing has driven two decades of intervention and has produced real gains, but it misses where the cost actually sits.

The binding constraint is verification. Before a lender can price risk it must establish who you are, what you earn, and whether you have honoured obligations before. Where that evidence is thin, the institution does not simply charge a little more — it either declines, demands collateral, or prices in an uncertainty premium that has nothing to do with the borrower's actual behaviour. The premium is paid for the absence of a record, not for being a worse risk. That is why the same person can be uncreditworthy at a bank and perfectly reliable in an informal lending circle where verification is social and effectively free.

This is the point where AI changes something structural rather than cosmetic. Cash-flow inference from transaction data, document understanding for informal income evidence, and voice or vernacular interfaces all reduce the marginal cost of establishing a fact about a person. Lower verification cost expands the population that can be priced accurately at all.

The caution is equally structural. A verification system that is cheap but opaque reproduces exclusion faster and at greater scale, with less recourse. Responsible design here means explainability at the decision level, contestability for the applicant, and monitoring for proxy discrimination — treated as product requirements, not compliance afterthoughts. That is the argument I take to the AINext stage in Dubai.

Full essay on Substack: The Verification Premium.

Discuss this with Prasanna: pw@prima-partners.com · book a 30-minute call.