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.

Nobody Called It a Model

2026-08-03 · Enterprise AI · 11 min read

The models that hurt you are, almost by definition, the ones your inventory does not count.

The analysis

Ask a chief risk officer how many models run inside the institution and the number will be confident, documented and wrong — because it counts the models the organisation knows about. Read the RBI's draft model-risk guidance, or the UAE and Qatar equivalents, as rulebooks for machine learning and you aim at the wrong decade. The question underneath them is far more embarrassing: do you know these systems exist at all?

The failure pattern is consistent across four decades of examples. JPMorgan's London Whale Value-at-Risk figure ran through manually copy-pasted spreadsheets containing a formula that divided by a sum instead of an average, halving apparent risk — a critical model that no validation process had seen, because on paper it was a workbook. Knight Capital's fatal deployment reactivated dormant code retired in 2003 and never deleted, a system the firm had stopped counting a decade earlier. The Dutch childcare-benefits algorithm was governed as an efficiency tool while wrongly accusing roughly 26,000 families and ultimately bringing down a government. And today's version is unmanaged: employees pasting source code and internal material into consumer AI tools, with the majority of AI users at work bringing their own.

None of those systems failed because the modelling was unsophisticated. They failed because nobody classified them as models, so no inventory, ownership, validation or decommissioning discipline attached to them. That is the practical remit of the new guidance, and it is why compliance projects that start with the LLM roadmap start in the wrong place.

The work that actually reduces risk is inventory-first: define a model by the consequence of its decisions rather than the technology behind it, sweep the spreadsheets, rules engines, dormant code paths and shadow AI tools into that definition, assign a named owner and a review cadence to each, and set an explicit retirement process so nothing sits dormant waiting to be reactivated. It is unglamorous work, and it is the part regulators will ask about first.

Full essay on Substack: Nobody Called It a Model.

More on Enterprise AI

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