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 Price of Productivity: Why AI Success Demands a Structural Rework

2026-02-11 · Enterprise AI · 9 min read

The AI J-curve isn't a tech problem — it's an organisational-design problem.

The analysis

General-purpose technologies reliably produce a productivity J-curve: measured output dips before it rises, because the complementary investments that make the technology pay — new processes, new roles, new measurement — take years and are expensive while the old structure is still running.

Enterprise AI is following the same shape, and the dip is being misdiagnosed. When the numbers do not move, the usual response is to change the model, add tools or expand licences. That treats a structural problem as a procurement problem. The constraint is almost never capability at this point; it is that work is still organised around the assumption that generation is the expensive step.

The complementary investments that actually matter are unglamorous. Roles need redefining where the value has moved from producing to specifying, reviewing and verifying. Review capacity has to be expanded and treated as a first-class function rather than an overhead. Incentives need to reward outcomes rather than output volume, or the organisation will optimise for the thing AI made cheap. And measurement has to be rebuilt around throughput and quality on specific workflows so the curve is visible at all.

The uncomfortable implication for leadership is that the dip is not evidence the strategy is wrong, and neither is it evidence to wait it out. It is a signal that the organisational rework has not started. Companies that do that rework see the curve turn; companies that keep buying tools stay in the trough with a larger licence bill.

Full essay on Substack: The Price of Productivity: Why AI Success Demands a Structural Rework.

More on Enterprise AI

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