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 info@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 and pre-Series A cheques and provides hands-on mentoring. Current positions span defence technology, artisan-led premium design and manufacturing, AI-powered franchisee onboarding for D2C brands, and memory-module hardware and software, alongside 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 an AI Decision Is Collapsing. The Cost of a Wrong One Isn't.

2026-09-29 · Enterprise AI · 5 min read

The price of an AI decision is collapsing. The cost of a wrong one isn't.

The analysis

Two September signals look contradictory. Mastercard's white paper on the new economics of AI argues that variable inference cost makes most agentic initiatives unprofitable by design. In the same weeks, TypeSafe's Jev — a decision-only model — made a machine decision roughly a hundred times cheaper than a typical LLM call. Both are right, and together they say something neither says alone.

The essay's frame is Jevons' paradox: more efficient steam engines raised coal consumption, because cheap energy found new uses. Cheap decisions will do the same. When routing a ticket or flagging an invoice costs a fraction of a cent, the freed budget is absorbed by more automation — Mastercard, citing Gartner, expects total inference spend to rise even as unit costs fall. Unit-cost savings are therefore a temporary advantage everyone gets. What does not fall is the cost of a wrong payment approval, a misrouted complaint or a missed fraud — and at scale, rare errors stop being rare.

Running decisions on probabilities is now technically trivial; confidence scores come out of the box. The gaps are organisational. Scores must be calibrated on your own data, since a 90% confidence is only useful if it is right 90% of the time. Ground truth is usually missing because human decisions were never recorded with their outcomes. And nobody owns the cut-off — a risk-appetite call that falls between IT, the business and risk. Banks, card issuers and insurers have treated thresholds as policy for decades; most of the enterprise has not.

The practical sequence is three experiments: build labelled ground truth for three to five high-volume decisions; run models in shadow and set thresholds by the cost of being wrong; and log input, score, action and outcome for audit and recalibration. Beyond that sits the real question as agents begin acting for each other across company boundaries: who granted the authority, within what limits, and who answers when it is wrong.

Full essay on Substack: The Price of an AI Decision Is Collapsing. The Cost of a Wrong One Isn't..

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