Drugmakers, CROs and life-science services · examples checked October 2026
Top 10 AI Use Cases in Pharma with Indian Examples (2026-27)
The shortlist
Ten AI use cases in pharma, at a glance
Each use case is backed by something a drugmaker, a CRO or a technology partner has published. Most examples come from Indian companies; where we could not verify an Indian deployment by a drugmaker, we say so and use a named vendor or global example. All sources were opened in October 2026.
- AI-assisted drug discovery: molecules designed and screened faster
- Clinical-trial patient identification: eligible patients found in records
- Pharmacovigilance case processing: adverse-event reports sorted and checked
- Regulatory and medical writing: first drafts of study reports and protocols
- Yield optimisation and predictive maintenance: better batches, fewer breakdowns
- Smart-factory analytics: plant-wide data for cost, quality and energy
- Quality documentation and investigations: routine QA checks automated
- GPTs for sales and field teams: sharper messaging, measurable field performance
- Patient adherence chatbots: reminders and correct-use tips
- Supply and inventory prediction: shortages spotted before they happen
This list is published on Skillopedia’s own site. Skillopedia is a training firm; we sell no pharma software and no company named here has paid to appear. Nothing here is medical, legal or regulatory advice.
Comparison table
The ten pharma use cases compared
| Use case | Example | Main team | Data it needs | Human check |
|---|---|---|---|---|
| Drug discovery | Aurigene (Dr. Reddy’s) | Discovery chemistry | Compound and assay data | Medicinal chemists |
| Trial patient identification | Veeda Lifesciences | Clinical operations | Patient records, protocols | Investigators |
| Pharmacovigilance | Tech Mahindra (vendor) | Drug safety | Adverse-event reports, emails | Safety physicians |
| Regulatory writing | Novo Nordisk; Indegene | Regulatory, medical writing | Study data, templates | Writers and reviewers |
| Yield and maintenance | Lupin | Manufacturing, engineering | Process and sensor data | Production, QA |
| Smart-factory analytics | Cipla, Dr. Reddy’s | Plant leadership | Plant-wide operational data | Plant heads |
| Quality documentation | Lupin | Quality assurance | Batch and test records | QA officers |
| Field-force GPTs | Mankind Pharma | Sales, marketing | Product and field data | Managers, medical review |
| Patient adherence chatbot | Cipla (Nepal) | Patient programmes | Opt-in patient contacts | Programme team |
| Supply prediction | Sanofi (global) | Supply chain | Inventory and demand data | Planners |
The ten use cases
How each pharma use case works, with a published example
For each: what it is, how it works, a public example, the risks and the people who need new skills. In a GxP environment, AI drafts and flags; qualified people decide.
1. AI-assisted drug discovery
What it is
Generative and predictive models that propose new molecules, predict how they will behave and help chemists decide which ones to make and test.
How it works
Models trained on large libraries of compounds and assay results suggest structures likely to hit a target and score them for properties such as potency and safety. Physics-based simulation narrows the list further. Chemists choose which candidates to synthesise, and lab results feed back into the models.
Real example
Aurigene, a Dr. Reddy’s company. Medical Dialogues reported in April 2024 that Aurigene launched Aurigene.AI, combining physics-based simulation, generative and predictive AI and computer-aided drug design. Its database holds 180 million compounds and 1.6 million validated bioassay data points, and the platform runs on Google Cloud. The company said an internal case study showed a 35% cut in cycle time from chemical design to synthesis and testing.
Risks to manage
Promising predictions that fail in the lab; models trained on data that does not match the target; and intellectual-property questions about data and outputs.
Skills the team needs
Medicinal and computational chemists who frame problems for models; data scientists who curate assay data; and project leaders who decide when a prediction is good enough to test.
- Who runs it
- Drugmakers, CRDMOs
- Team
- Discovery chemistry, computational science
- Data
- Compound libraries, assay results
- Typical tools
- Generative chemistry, simulation
Sources: Medical Dialogues (5 Apr 2024), checked October 2026.
2. Clinical-trial patient identification
What it is
Language models that read patient records and match them against a trial’s inclusion and exclusion criteria, so sites find eligible patients faster.
How it works
The model reads unstructured notes, reports and test results, compares them with the protocol and produces a ranked list of possible candidates with reasons. Investigators review each one and approach patients through the normal consent process.
Real example
Veeda Lifesciences. BioXconomy reported in July 2025 that the Indian CRO invested in Mango Sciences and would use Mango’s large language model, Querent, to automate patient identification, with a focus on oncology. CEO Mahesh Bhalgat said the partnership would make Veeda an AI-driven oncology drug development organisation. Deal terms were not disclosed.
Risks to manage
Bias if the records under-represent some groups; patient data used without the right consent; and over-trusting a match without checking the full history.
Skills the team needs
Clinical operations and site teams who verify matches; data-protection officers who approve data use; and medical monitors who check that selection stays fair.
- Who runs it
- CROs, sponsors, hospitals
- Team
- Clinical operations
- Data
- Patient records, trial protocols
- Typical tools
- LLM-based record screening
Sources: BioXconomy (15 Jul 2025), checked October 2026.
3. Pharmacovigilance case processing
What it is
AI that reads incoming adverse-event reports and safety emails, pulls out the key facts, sorts cases by seriousness and prepares them for a safety reviewer.
How it works
Agents classify each report, extract patient, drug and event details, check for duplicates and prioritise serious cases. Safety associates and physicians review and complete the case before it is reported to regulators.
Real example
Tech Mahindra with NVIDIA. In a March 2025 release, Tech Mahindra announced an agentic AI pharmacovigilance solution that triages and prioritises cases and checks pharmacovigilance emails. It says the solution reduces turnaround times by up to 40%, improves data accuracy by 30% and cuts operational costs by 25%. These are the vendor’s own figures with no client named.
We could not verify a published deployment at an Indian drugmaker, so we use this vendor example.
Risks to manage
Missed or misclassified serious events; reporting deadlines breached if automation fails; and validation gaps in a regulated process.
Skills the team needs
Drug-safety associates who review AI-prepared cases; safety physicians who make medical judgements; and validation and quality teams who qualify the system.
- Who runs it
- Drugmakers, PV service providers
- Team
- Drug safety, pharmacovigilance
- Data
- Adverse-event reports, emails, literature
- Typical tools
- Agentic case intake, NLP
Sources: Tech Mahindra release (19 Mar 2025), checked October 2026.
4. Regulatory and medical writing
What it is
Generative AI that drafts clinical study reports, protocols and other submission documents from templates and source data, for writers to check and finish.
How it works
Writers set up templates and point the model at approved data tables and earlier documents. The model drafts sections, keeps terminology consistent and flags gaps. Medical writers and regulatory reviewers verify every statement before approval.
Real example
Novo Nordisk (global). Anthropic’s customer story says Novo Nordisk built NovoScribe with Claude and cut the time to produce clinical study documentation from 10+ weeks to 10 minutes, with a 95% reduction in resources for device verification protocols. This is a vendor case study.
Indegene (India). BioSpectrum India reported in July 2025 that the Bengaluru-based company launched NEXT Medical Writing Automation, built on its Cortex GenAI engine, which drafts regulatory documents inside Microsoft Word. No figures were given.
Risks to manage
Errors or invented statements in regulated documents; confidential data sent to unapproved tools; and reviewers who skim because the draft reads well.
Skills the team needs
Medical and regulatory writers who become editors and verifiers; regulatory affairs teams who set review rules; and IT teams who keep data inside approved systems.
- Who runs it
- Drugmakers, writing service firms
- Team
- Regulatory affairs, medical writing
- Data
- Study data, templates, past submissions
- Typical tools
- LLM drafting inside document tools
Sources: Claude customer story: Novo Nordisk; BioSpectrum India (14 Jul 2025), checked October 2026.
5. Yield optimisation and predictive maintenance
What it is
Analytics and machine-learning models that find the process settings that give better yields, and sensor-based models that warn before equipment fails.
How it works
Historical batch data is analysed to link parameters such as compression settings with yield and quality. Sensor feeds from machines show early signs of wear. Engineers test the recommended changes under change control and schedule maintenance before a breakdown.
Real example
Lupin. In an article by Lupin’s CIO published on lupin.com in November 2025, the company says advanced analytics on compression parameters for five key products at its Nagpur Unit-1 delivered significant yield improvements, and predictive dashboards at Tarapur are improving uptime. Lupin says its digital and AI work delivered ₹40 crore in cost savings and reclaimed 12,000 man-days in FY25.
Risks to manage
Process changes made without proper change control; models that drift when raw materials change; and savings claimed without a clean baseline.
Skills the team needs
Process and production engineers who test recommendations; maintenance teams who act on alerts; and QA teams who manage change control.
- Who runs it
- Drug manufacturers
- Team
- Manufacturing, engineering, QA
- Data
- Batch records, process and sensor data
- Typical tools
- Process analytics, IIoT dashboards
Sources: Lupin, CIO perspective (18 Nov 2025), checked October 2026.
6. Smart-factory analytics across a plant
What it is
Plant-wide programmes that connect machines and systems and apply advanced analytics to many problems at once: cost, quality, energy and emissions.
How it works
A site builds a common data platform, connects equipment through industrial IoT and runs dozens of analytics and automation use cases. Results are tracked at plant level. Strictly, this is advanced analytics and Industry 4.0 rather than generative AI.
Real example
Cipla and Dr. Reddy’s. Medical Dialogues reported in October 2022 that both companies’ plants joined the World Economic Forum’s Global Lighthouse Network. Cipla’s Indore site implemented 30 Fourth Industrial Revolution use cases, improving total cost by 26 percent and quality by 300 percent and reducing greenhouse gas emissions by 28 percent. Dr. Reddy’s Hyderabad site, using IIoT and advanced analytics, improved manufacturing cost by 43 percent and reduced energy by 41 percent.
Risks to manage
Programmes that stall after the flagship site; data platforms that are expensive to maintain; and shop-floor teams left out of the design.
Skills the team needs
Plant leaders who choose and fund use cases; digital and OT engineers; and shift supervisors who need data skills to act on dashboards.
- Who runs it
- Drug manufacturers
- Team
- Plant leadership, digital, OT
- Data
- Machine, quality and energy data
- Typical tools
- IIoT platforms, advanced analytics
Sources: Medical Dialogues (12 Oct 2022), checked October 2026.
7. Quality documentation and investigations
What it is
Digital and AI tools that check batch and test records, help write quality documents and guide investigations into out-of-specification results.
How it works
Routine checks on records run automatically and flag gaps. Assistants suggest likely causes and the next investigation steps for an out-of-specification result, drawing on past cases. QA officers decide, sign and own every record.
Real example
Lupin. The same Lupin CIO article says tools such as In-Process Quality Assurance, Quality Co-Author and OOS Navigator are reducing manual errors and documentation lapses by automating routine checks, and that its digital in-process QA platform has been rolled out across four sites.
Sun Pharma. BioSpectrum India reported in April 2026 that Sun Pharma’s Dilip Shanghvi has proposed using AI for root-cause analysis in CAPA. That is a stated plan, not a deployment.
Risks to manage
Data-integrity failures if AI edits records; investigations closed too quickly on a suggested cause; and tools used before they are validated.
Skills the team needs
QA and QC officers who review AI suggestions; investigation leads who keep root-cause discipline; and computer-system validation specialists.
- Who runs it
- Drug manufacturers
- Team
- Quality assurance, quality control
- Data
- Batch records, test results, past investigations
- Typical tools
- Record checks, investigation assistants
Sources: Lupin, CIO perspective (18 Nov 2025); BioSpectrum India (1 Apr 2026), checked October 2026.
8. GPTs for sales and field teams
What it is
Custom AI assistants that help medical representatives and marketing teams prepare messages, work in several languages and track field activity.
How it works
Teams use approved custom GPTs to prepare call notes, adapt approved messages for different doctors and languages and summarise field reports. Medical, legal and regulatory review still approves promotional content before use.
Real example
Mankind Pharma. In a September 2025 release on its work with OpenAI, Mankind said it had deployed a wide range of customised GPTs across its workforce since July 2025, notably for sales and marketing teams, sharpening messaging, enabling multilingual communication and supporting demand planning and procurement. It said field force performance is now measurable in real time.
Risks to manage
Off-label or unapproved claims in AI-drafted messages; doctor data handled carelessly; and field teams judged by numbers they do not understand.
Skills the team needs
Medical representatives who use assistants within approved content; managers who coach with the data; and medical, legal and regulatory reviewers who set guardrails.
- Who runs it
- Drugmakers
- Team
- Sales, marketing, commercial excellence
- Data
- Approved content, field activity
- Typical tools
- ChatGPT Enterprise custom GPTs
Sources: Mankind Pharma release (17 Sep 2025), checked October 2026.
9. Patient adherence chatbots
What it is
Messaging assistants that remind patients to take their medicine, show how to use devices correctly and share disease-management tips, with consent.
How it works
Patients join a programme, often by scanning a code and agreeing to the terms. The chatbot sends reminders and answers routine questions on a messaging app. Anything clinical goes to a doctor or pharmacist.
Real example
Cipla (Nepal). The Kathmandu Post reported in May 2025 that Cipla launched Breathefree 90, a WhatsApp chatbot programme that sends inhaler reminders, correct-usage tips and disease management advice to asthma and COPD patients for three months, with patients joining by QR code and giving consent. The example is from Nepal, not India.
Risks to manage
Health advice that drifts beyond approved content; sensitive health data on messaging platforms; and patients who stop reading reminders.
Skills the team needs
Patient-programme managers who design journeys; medical affairs teams who approve every message; and privacy teams who check consent and data handling.
- Who runs it
- Drugmakers, patient-support partners
- Team
- Patient programmes, medical affairs
- Data
- Opt-in contact details, programme data
- Typical tools
- WhatsApp chatbots
Sources: The Kathmandu Post (7 May 2025), checked October 2026.
10. Supply and inventory prediction
What it is
Models that predict low inventory positions and supply risks early, so planners can act before a shortage reaches pharmacies or hospitals.
How it works
The model watches stock, demand, production plans and lead times and flags items likely to fall below safe levels. Planners decide whether to expedite, reallocate or adjust production.
Real example
Sanofi (global). Pharma Manufacturing reported in June 2023 that Sanofi’s internal AI app, plai, would enable accurate prediction of 80% of low inventory positions in its biopharma supply chain. This is Sanofi’s own claim.
We could not verify a comparable published example from an Indian drugmaker, so we use this global one. Mankind’s release above also mentions AI in demand planning.
Risks to manage
Alerts that planners ignore because of false alarms; shortages caused by suppliers outside the model’s view; and over-stocking to be safe.
Skills the team needs
Supply planners who act on alerts; procurement teams who work with suppliers on risk; and commercial teams who share demand changes early.
- Who runs it
- Drugmakers, distributors
- Team
- Supply chain, planning
- Data
- Inventory, demand, production plans
- Typical tools
- Predictive planning apps
Sources: Pharma Manufacturing (16 Jun 2023), checked October 2026.
Also worth knowing: ACG’s capsule plant at Pithampur joined the WEF Global Lighthouse Network after an assessment of 25+ use cases (PR Newswire, December 2023). MediaNama reported in August 2026 that CDSCO has not adopted AI for approvals and is finalising guidance for AI-enabled medical device software. Sources checked October 2026.
Choosing where to begin
Which pharma use case should come first?
Ask two questions. Is the work GxP-relevant? And how much expert time does it consume today?
Non-GxP, high-volume work first. Internal emails, meeting notes, training material, market research summaries and first drafts of non-regulated content give quick wins with low risk.
GxP work needs validation. Anything that touches batch records, safety cases or submissions needs a validated system, clear human review and an audit trail, so plan longer pilots.
Use our guide on how to identify AI use cases to score ideas. For a facilitated session with R&D, quality, manufacturing and commercial heads, talk to us.
Quick wins
- Literature and market research summaries
- Training and SOP explainers for new joiners
- Meeting minutes and action logs
- First drafts of internal reports
Larger programmes
- AI in discovery chemistry
- Pharmacovigilance case intake
- Regulatory writing automation
- Plant-wide analytics
Questions to answer early
- Is the process GxP-relevant?
- How will the tool be validated?
- Who signs off each output?
An adoption path
From pilot to validated practice in a pharma company
Pharma adds one step most industries skip: validation. Build it into the plan from the start.
Which pharma teams need which AI skills
| Team | What to learn | Where to start |
|---|---|---|
| Leadership | Portfolio of use cases, risk appetite, partners | AI for CXOs and business leaders |
| Manufacturing and quality | Shift reports, deviation write-ups, SOP assistants | AI training for manufacturing teams |
| Supply chain | Demand signals, supplier follow-ups, shortage alerts | AI training for supply chain teams |
| Sales and field force | Call preparation, approved messaging, reporting | AI training for sales teams |
| Marketing and medical affairs | Research summaries, content drafts for review | AI training for marketing teams |
| IT, data and R&D informatics | Data access, agents, validation support | AI training for IT and engineering teams |
| HR and L&D | Mapping AI skills across plants, labs and field | AI skills gap analysis |
Where Skillopedia fits
Models propose; qualified people still sign
Skillopedia builds no discovery, safety or manufacturing systems. We are a corporate AI training firm, Skillopedia LLP, founded in Mumbai in 2017, with programmes delivered for 335+ organisations across 13 sectors. In live, in-company sessions, pharma teams use the AI tools your company has approved on non-regulated tasks from their own work: research summaries, training material, meeting notes, internal reports and draft emails, with your review rules in every exercise.
Engagements follow our FLUENT method. Surveyed participants (300+) rate our programmes 4.8/5 on average. For pharma clusters in Gujarat, see AI training for manufacturing in Ahmedabad and Vadodara; for Hyderabad, see corporate AI training in Hyderabad.
Governance
CDSCO, GMP and data protection in plain terms
CDSCO and clinical trials. The Central Drugs Standard Control Organisation regulates trials under the New Drugs and Clinical Trials Rules, 2019. AI used in trials does not change sponsors’ and investigators’ responsibilities.
Revised Schedule M (GMP). Notified in December 2023, with a conditional extension to 31 December 2025 for smaller manufacturers. Any AI that touches GMP records or decisions needs validation and data integrity controls.
Pharmacovigilance. Marketing authorisation holders report adverse drug reactions under the Pharmacovigilance Programme of India. Automation in case processing must not delay reporting.
Health data is personal data. The DPDP Act, 2023 and its Rules notified in November 2025 apply to patient and doctor data. In general terms: consent or another lawful basis, purpose limits, security and breach notification.
Proving value
How to measure pharma AI without overclaiming
Company figures above are their own. Measure your baseline first, and in GxP areas count quality outcomes alongside speed.
| Use case | What to track | How |
|---|---|---|
| Discovery and writing | Cycle time, review rounds, rework | Project and document logs |
| Pharmacovigilance | Case turnaround, on-time reporting, error rate | Safety database |
| Manufacturing and quality | Yield, deviations, right-first-time batches | MES, QMS and batch data |
| Field force | Call quality, content compliance, active use | CRM and review logs |
| Any use case | Active users after 90 days, hours saved | Usage logs and surveys |
For pharma leaders, HR and L&D
Ready to build AI skills across your pharma teams?
Tell us which functions, roughly how many people and the AI tools you have approved. We reply within one working day and then send a written proposal that includes:
- sessions by function, from leadership to plant and field teams
- labs on your own non-regulated reports, notes and drafts
- a day-30 check on the workflows teams adopted
Ask for a pharma training proposal
We reply within one working day. No obligation.
FAQ
AI use cases in pharma: questions people ask
What are the top AI use cases in pharma?
Drug discovery, clinical-trial patient identification, pharmacovigilance case processing, regulatory and medical writing, yield and predictive maintenance, smart-factory analytics, quality documentation, field-force assistants, patient adherence chatbots and supply prediction.
How is AI used in the pharmaceutical industry?
AI helps design molecules, find trial patients, process safety reports, draft regulatory documents, improve batch yields, check quality records, support sales teams and predict shortages, with qualified people reviewing the outputs.
What is artificial intelligence in pharmaceutical industry?
It is the use of machine learning, analytics and generative AI across R&D, clinical, manufacturing, quality, safety and commercial work to speed up tasks and improve decisions, inside regulated processes.
How are Indian pharma companies using AI?
Published examples include Aurigene.AI at Dr. Reddy’s for drug discovery, Lupin’s yield, maintenance and quality tools, Cipla and Dr. Reddy’s smart-factory programmes, Mankind Pharma’s custom GPTs and Veeda’s AI patient identification.
How is AI used in operations in a pharma industry?
In plants, AI and analytics optimise process settings, predict equipment failures, check batch records and support investigations. Lupin reports ₹40 crore in savings from its digital and AI work in FY25.
How is AI changing the pharmaceutical industry?
It is shortening early research and documentation work and making plants more data-driven. Regulated decisions still rest with qualified people, and regulators are writing guidance as use grows.
Will AI replace pharmacovigilance jobs?
AI takes over intake, sorting and data extraction, but safety physicians and associates still assess cases and own reporting. Roles shift towards review, quality checks and signal analysis.
Will AI replace pharmaceutical sales reps?
Unlikely in the near term. AI helps representatives prepare and report, as Mankind Pharma describes, while relationships with doctors and compliant conversations remain human work.
Can AI write regulatory documents for drug approval?
AI can draft sections from approved data, as Novo Nordisk and Indegene describe, but medical writers and regulatory teams must verify every statement before submission.
Does CDSCO use AI for drug approvals?
According to an August 2026 reply in Parliament reported by MediaNama, CDSCO has not adopted AI for regulatory approvals and relies on scientific and technical review.
What AI skills do pharma employees need?
Everyone needs AI literacy and your company’s rules, including what is GxP. Scientists need to judge model outputs; QA needs validation awareness; commercial teams need compliant content skills; leaders need governance.
Does Skillopedia train pharma teams?
Yes. We run live, in-company AI training for pharma business and technical teams on approved tools, focused on non-regulated work. We do not validate systems or give regulatory advice.
Where does Skillopedia deliver pharma training?
On-site in Indian cities including Ahmedabad, Vadodara, Hyderabad and Mumbai, and live online. See corporate AI training in Mumbai or contact us.
Next step
Help your pharma teams use AI safely and well
Share your teams, the AI tools you allow and the work you want to improve. We reply within one working day with a few questions, then a written proposal at a fixed fee.
- Sessions for R&D, quality, plant and commercial roles
- Practice on your own non-regulated work
- Your AI, GxP and data rules in every exercise
Ask for a pharma training proposal
Reply within one working day.