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.

From Waterfall to Agile to AI-Native: Same Goal, Very Different Journeys

2026-04-07 · Enterprise AI · 8 min read

Same goal. Very different journeys — and the need for control has never been greater.

The analysis

Each delivery methodology is a response to where uncertainty sat at the time. Waterfall assumed requirements were knowable and put the control points at specification and acceptance. Agile assumed requirements were discovered through use and moved control into short iterations with working software as the checkpoint.

AI-native delivery shifts the uncertainty again. The requirement may be clear and the code may be correct, while the system's behaviour still varies with the model, the data, the prompt and the version. Determinism — the property both earlier methodologies quietly relied on — is no longer free. A build that passed yesterday can behave differently today with no change in the repository.

That does not argue for less governance; it argues for governance in different places. The control points that matter now are evaluation harnesses run continuously rather than at release, versioning of prompts and models as first-class artefacts, human review positioned where consequences are irreversible, and monitoring of output quality in production rather than only system health. Sprint ceremonies and acceptance criteria do not detect behavioural drift, which is why teams that ported Agile unchanged onto AI systems are the ones getting surprised.

The practical migration is to keep the iteration cadence, replace 'tests pass' with 'evaluation suite holds within tolerance' as the definition of done, and give someone explicit ownership of behaviour over time rather than at the point of merge.

Full essay on Substack: From Waterfall to Agile to AI-Native: Same Goal, Very Different Journeys.

More on Enterprise AI

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