> For the complete documentation index, see [llms.txt](https://doc.duaer.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.duaer.com/guides/pharma-ai-research-pilot-use-case.md).

# Sun Pharma Glenmark AI Research 2027: Choosing a Pilot Use Case

Drawing on Duaer's documentation, this guide shows pharmaceutical companies how to move from AI research headlines to practical pilots. It covers common use cases ranked by value and difficulty, pitfalls to avoid, and the metrics, stop rules, and governance requirements to set up front.

## Where the Real Value of AI in Pharma Lies

When a pharmaceutical company announces that it is 'doing AI research,' many business leaders read a headline rather than a plan. The research team may already have promising models, yet commercial, quality, and regulatory affairs teams still cannot say which workflow deserves resources first. The result is a familiar one: a few lab demos, a long backlog of ideas, and pilots that stall as soon as they meet real data, validation requirements, and compliance review. The useful question is not whether the industry is doing AI research, but where a pharma organization should start applying it and how to tell whether the work is ready to scale.

AI work in pharma spans many stages, from early drug discovery through post-market operations. The use cases that come up again and again fall into a few broad groups: literature and patent mining to support target identification; prioritization of compound screening; site selection and patient recruitment in clinical trials; triage of adverse event reports in pharmacovigilance; drafting and checking regulatory documents; and root-cause analysis of manufacturing deviations.

These categories are more useful than any single company's research agenda. A discovery-stage model and a pharmacovigilance triage tool have very different data needs, validation burdens, and owners. Managing them as one 'AI project' is the first mistake many organizations make. The better approach is to break the portfolio into specific use cases, assign a business owner to each, and state which decision each one is meant to improve.

## Rank by Value and Difficulty, Not by Buzz

The most talked-about research directions, such as generative molecular design, are usually not the best first pilot for a mid-sized or established pharmaceutical company. They tend to combine high value with high difficulty: experimental cycles are long, labeled data is scarce, heavy expert judgment is required, and the path to something with regulatory weight is long.

A more reliable method is to score each candidate use case on two dimensions. On value, ask how much it could shorten cycle time, reduce the cost of each decision, or cut risk exposure, and how often that decision comes up. On difficulty, ask how easily the data can be obtained, how much validation the output requires under a regulated quality framework, how many systems must be integrated, and how quickly an error can be detected and reversed. For example, summarizing literature for an internal medical team is typically low difficulty with moderate value; adverse event triage with human review usually sits at moderate levels on both; generative drug discovery is high on both. Ranking this way makes the trade-offs visible. For a deeper method for this step, see our guide: [Enterprise AI Use Case Prioritization Guide](https://doc.duaer.com/guides/enterprise-ai-use-case-prioritization/).

## Three Misconceptions That Stall Pilots

The first misconception is treating a research announcement as operational capability. A company may be investing seriously in AI research while its own teams still lack the data pipelines, review roles, and audit trails needed to use the results. Before copying any external direction, check whether your organization has these foundations in place.

The second misconception is trusting benchmark performance. A model that performs well on a public dataset may behave very differently on your historical reports, specialized terminology, and document formats. Always test with samples drawn from your own records, and track where the model fails, not just how often it is right.

The third misconception is assuming that another company's research focus transfers directly to your portfolio. Your product mix, geographic footprint, regulatory exposure, and partner ecosystem may all differ. External research is a source of hypotheses. Those hypotheses still need to be tested against your own constraints.

## How to Choose the First Pilot

A good first pilot is narrow, measurable, and reversible. It should answer a clearly bounded question, rely on data the organization already holds, have a qualified human reviewer check every output, and agree on pass or fail criteria before work begins. At the start, it is best aimed at internal use, so that errors are caught by staff rather than passed on to patients, regulators, or customers.

For many pharmaceutical organizations, a pilot that drafts or summarizes internal documents under mandatory expert review fits these criteria better than a pilot that proposes new compounds. The point is not that any one category is always right. The point is that the pilot should be chosen against explicit criteria, not against the most impressive demo.

## Measure the Pilot Like a Regulated Team, and Put Governance and Data First

Establish a baseline before the pilot begins. Measure the current process's cycle time, the share of AI-generated outputs that reviewers edit, the number of errors that get past review, the time needed to reach a decision, and whether the audit trail is sufficient for internal and external review. Compare the pilot against this baseline, not against vendor claims or numbers from research papers.

Set stop rules in advance. For example, if reviewers must rewrite more than an agreed share of outputs, or if any error reaches a downstream stakeholder, the pilot should pause until the cause is identified. Pilots without stop rules tend to turn into open-ended experiments that no one can judge.

Pharma data often involves patient confidentiality, limits set by informed consent, and validation expectations that vary by region and function. Before any model touches real records, confirm who owns the data, what purposes it may be used for, where it may be processed, and who approves any changes to the workflow. The same discipline that applies to clinical AI applies here. Our overview of [Healthcare AI Use Case Value and Risk Ranking](https://doc.duaer.com/guides/healthcare-ai-use-case-value-risk-ranking/) explains how to rank value and risk together, and the approach broadly carries over to pharmaceutical operations.

Consider scaling only after the pilot has produced stable metrics over a meaningful period and the governance lead has reviewed the audit records. Expanding from one team to the whole organization is a new project, with new data sources and new users.

## What You Can Do This Week

List five candidate AI workflows across your pharmaceutical organization, covering at least two functions such as R&D, medical affairs, quality, or regulatory. Score each workflow from 1 to 5 for value and for difficulty. Pick the one with the highest value relative to its difficulty, then write a one-page pilot charter that names the business owner, the human reviewer, the baseline metrics, the target improvement, and the stop rules. Keep the scope to one workflow and one team.

If you want to make this selection in a structured way and keep the charter, scores, and results in one place, you can sign up for a trial at https://www.duaer.com and start with your first candidate list.

AI research headlines show where the industry's attention is going, but a credible pharma AI program is built through a narrow, measurable, governed pilot that is carried out step by step.

## Questions

### How should a pharmaceutical company choose its first AI pilot use case?

Start by listing cross-functional candidate workflows and scoring each one on value and difficulty. Favor use cases with high value relative to difficulty, a narrow scope, data already in hand, and qualified human review. These are usually a better starting point than high-difficulty areas such as generative drug discovery.

### What metrics and stop rules should an AI pilot in pharma have?

Set a baseline before the pilot starts. Common metrics include the current process's cycle time, the share of AI outputs reviewers edit, the number of errors that get past review, decision time, and the completeness of the audit trail. Agree on stop rules in advance. For example, pause the pilot and find the cause if reviewers rewrite more than the set share of outputs or if any error reaches a downstream stakeholder.

### What data and compliance preparation does a pharma company need before an AI pilot?

Confirm who owns the data, what purposes it may be used for, where it may be processed, and who approves changes to the workflow. Take patient confidentiality, informed consent, and regional validation requirements fully into account. Consider expanding the pilot only after the governance lead has reviewed the audit records and the pilot has produced stable results.