The words both rooms use.

A large neural network trained on broad data that can be adapted to many specific tasks. GPT-class language models, Claude, Gemini, and Llama are foundation models; so are the multimodal and protein-folding variants.

In IndiaIndian pharma commercial and data teams overwhelmingly evaluate hosted foundation models from Anthropic, OpenAI, and Google before considering open-weights deployment, primarily because of data-residency and procurement comfort with named US vendors.

A pattern where a language model answers a question by first retrieving relevant documents from a private corpus and then composing the answer from them, rather than relying only on what was in its training data.

In IndiaA natural fit for the commercial function: RAG over the company's own sales reports, brand plans, IQVIA and SMSRC extracts, and territory masters, behind the firewall, queryable in plain language by a Brand Manager or sales-excellence lead.

Continuing the training of a foundation model on a smaller, task-specific dataset so it learns the patterns of that domain. Distinct from prompting (which uses the model as-is) and from RAG (which adds documents at query time).

In IndiaLess common in Indian pharma than RAG today: the data governance work needed to assemble a fine-tuning set responsibly often exceeds the value of fine-tuning over a strong base model with retrieval, and most teams reach the latter first.

The engineering discipline around productionising machine-learning systems: data pipelines, model versioning, evaluation, deployment, monitoring, and rollback. The pharma equivalent of GxP discipline applied to ML systems.

In IndiaOften the gating constraint on Indian pharma AI projects moving from proof-of-concept to production: a working model demonstrated on a laptop is two thirds of the way through the engineering work, not nine tenths.

Language-model systems that take actions in a software environment over multiple steps: reading a document, calling a tool, writing to a database, asking the user a follow-up. Distinct from a single-prompt chatbot in that they have a loop, tools, and state.

In IndiaThe category of system currently being piloted for commercial work at several Indian pharma companies in 2025 and 2026: re-ranking a Territory Manager's call plan each cycle, drafting Brand Manager review decks from IQVIA and SMSRC, and bringing primary, secondary, and Rx data onto one shared territory map.

The systematic measurement of an AI system's performance against a defined set of tasks and acceptance criteria, run repeatedly as the system changes. For language-model systems, evals are typically a mix of automated grading and human review.

In IndiaThe most under-invested layer of Indian pharma AI deployments today: a model that is good enough to demo but does not have eval discipline behind it cannot defensibly be put in front of a regulator or a patient-facing workflow.

The face-to-face promotional conversation a Medical Representative (MR) has with a prescriber, presenting a brand's clinical message and reminders. The core unit of pharma field promotion, logged as a call.

In IndiaDetailing quality is almost entirely unmeasured in Indian pharma today: the call report captures that a visit happened, not what was said or whether it moved a prescription. Closing that measurement gap is one of the most-discussed commercial AI opportunities.

The schedule that decides which doctors a Medical Representative visits, in what order, and how often per cycle. Built by the Territory Manager from the doctor list, brand priorities, and last cycle's coverage.

In IndiaThe call plan, not the MR's tablet, is where the leverage sits. Most MR-productivity AI pitched in India targets the MR's daily flow and misses the call plan one layer up, where the decisions are actually made.

Ranking prescribers by their prescription potential and assigning call frequency accordingly. A doctor's A/B/C grade drives how often an MR is expected to visit and how much promotional spend they attract.

In IndiaIndian pharma segmentation is still largely static and manually graded. Re-grading doctors dynamically from recent Rx trend and switchability is a concrete, high-value AI use case that Brand Managers ask for by name.

A field-intelligence method where the MR checks a chemist's records to see which brands in a therapy area are actually selling, used to validate what a prescriber claims and to spot competitor movement on the ground.

In IndiaRCPA is the MR's ground-truth check against what a doctor says they prescribe. Structured RCPA capture is patchy, and making it reliable is a prerequisite for any trustworthy territory-attribution model.

The share of prescriptions in a therapy area written for a given brand. The commercial outcome metric that sits closest to the prescriber, distinct from market share measured by value or by units moved through chemists.

In IndiaRx share, read from SMSRC and chemist audits, is the number a commercial AI workload ultimately has to move. Internal activity metrics that do not translate into Rx share are the most common way a pilot looks successful and changes nothing.

The trade channel between the manufacturer and the chemist: the manufacturer ships to stockists (primary sales), stockists supply retail chemists (secondary sales). The structure that the primary-versus-secondary gap measures.

In IndiaThe stockist layer is where primary and secondary diverge. A forecasting or attribution model that does not account for stockist-level inventory will misread a quarter where the company shipped to stockists but the product never reached patients.

The standard way Indian pharma companies organise what they sell. A therapy area (or 'cluster') contains several divisions; each division sells a portfolio of brands; each brand ships as one or more stock-keeping units (SKUs) by pack size, strength, or formulation.

In IndiaMost AI roadmaps inside Indian pharma confuse the layers: a model that predicts at the SKU level is useless to a Brand Manager planning quarterly campaigns, and a model that aggregates to division-level is useless to a Territory Manager building next cycle's call plan for a specific brand. Naming the layer is the first conversation.

The reporting pyramid of the in-person sales organisation. National Sales Manager at the top; under them, Regional Sales Managers (RSM) covering large multi-state regions; under each RSM, Area Sales Managers (ASM); under each ASM, Territory Managers (TM); under each TM, the Medical Representatives (MR) who actually visit doctors and chemists.

In IndiaThe MR is the only person in the field who actually meets the prescriber. Any AI workload aimed at commercial productivity that does not have a clear answer to 'how does this change what the MR does on Monday morning' is targeting the wrong layer of the pyramid.

An HQ-side marketing role that owns one or more brands across the country: positioning, campaign strategy, sales targets, KOL engagement, promotional materials, launch plans. The marketing counterpart to the field-force pyramid.

In IndiaBrand Managers are the most frequent operational consumers of AI for Indian pharma commercial functions. The questions they ask of AI are concrete (which territories are under-indexing on Brand X relative to IQVIA market share, which MRs are over-detailing low-Rx product, which doctors are switchable) and they hold the budget that turns AI pilots into production deployments.

How Indian pharma slices the country for sales planning, target-setting, and attribution. A territory (often called an HQ, short for headquarter) is the unit a single MR covers; territories aggregate up through region/state, zone, and ultimately all-India.

In IndiaAlmost every interesting commercial AI question in Indian pharma is a territory-level attribution problem in disguise: did secondary-sales movement in HQ X come from the MR call plan, from a Brand Manager promotion, from a chemist incentive, or from a market shift the IQVIA layer would have detected?

Primary sales are what the manufacturer ships to stockists; this is what the company books as revenue. Secondary sales are what stockists ship to retailers (chemists); this is what is actually moving into the market and, eventually, to patients. The two numbers can diverge significantly in any given month.

In IndiaThe gap between primary and secondary is one of the most-misunderstood concepts by AI vendors selling into Indian pharma. A demand-forecasting model trained on primary-sales data will mis-predict shelf availability; a sales-attribution model that ignores secondary will reward MRs for moving stock into a stockist rather than moving product to patients.

The dominant third-party market-intelligence dataset Indian pharma companies subscribe to. Reports the brand's market share (MS), its rank within its therapeutic class, and the absolute market value of that class, sliced by geography and by SKU. Published monthly with a lag.

In IndiaAlmost every Brand Manager review in Indian pharma opens with the latest IQVIA numbers, and almost every AI use case that touches the commercial function eventually has to line up against them. A workload that improves an internal metric while moving IQVIA in the wrong direction is dead on arrival, regardless of how technically interesting it is.

The standard Indian prescription-audit dataset. Audits a panel of doctors to estimate, for each brand, how many prescriptions are being written in each therapy area and geography. Distinct from IQVIA market-share data, which tracks what moved through chemists, not what was prescribed.

In IndiaSMSRC Rx and IQVIA secondary are the two halves of the commercial truth in Indian pharma: one says what doctors prescribed, the other says what patients (or chemists) actually moved. The interesting AI work begins where the two disagree, and the team that can explain the disagreement usefully is the team worth listening to.