In pharmaceutics, bringing a single drug to market takes an average of 10-15 years and costs over $2 billion.

Most of that time isn’t spent in a lab. The companies wait to process trial data, select sites, optimise manufacturing, back and forth with regulators, etc.

Operations, not scientific experiments, consume lots of time.

In today’s AI at the Top, we will study Pfizer to learn how in industries like pharmaceutical biotechnology, enterprises use AI to solve for speed and efficiency.

Pfizer is one of the world’s largest pharmaceutical companies, with roughly 75,000 employees, operating across 150+ countries, and a product pipeline spanning oncology, immunology, cardiology, and rare diseases.

Today, we will explore how Pfizer deploys AI across drug discovery, clinical trials, manufacturing, and internal operations.

Traditional drug discovery, from identifying a promising target to proving it works in a living system, takes 2-4 years.

To reduce the drug discovery time, Pfizer found world-class AI models already in the market and partnered with them instead of building in-house.

These are specialised AI firms, each with deep proprietary data in a specific domain.

Think of it as a hub-and-spoke model.

Plus the AI initiatives don’t work in isolation. They learn from each other, making the entire pipeline stronger.

The milestones from this approach have come in 40% faster than what Pfizer has anticipated.

In one documented case, a compound went from a proof of concept to the first real-world evidence in under eight months (from the typical timeline of 2-4 years).

Before a drug can be approved, it has to be tested on humans (clinical trials). These trials involve thousands of patients, hundreds of monitoring sites across multiple countries, and enormous volumes of data flowing in simultaneously.

One of the biggest bottlenecks in this process is data management.

Every time a side effect, a lab result, or a missed dose happens with a patient, it gets recorded. Someone then has to review that data, flag inconsistencies, and raise a formal query to investigate.

At the scale of a major trial, this is done by teams of analysts working through records manually. It is slow by design because the stakes are high.

The problem with slow data processing is every extra week in the queue is a week patients aren’t accessing the drug.

To automate this process intelligently, Pfizer built the Smart Data Query (SDQ) tool with Saama Technologies. Instead of analysts reviewing records line by line, SDQ uses AI to scan the data, detect inconsistencies, and generate queries automatically.

During Pfizer’s vaccine trial during COVID-19 with 44,000 patients, the SDQ tool reduced the data capture-to-query generation time from 30+ days to 22 hours.

In the next agentic layer, the company’s focus is on a workflow that automates feasibility and site selection before every trial. This is weeks of analyst work.

YOU DECIDE

A single drug can involve hundreds of precise steps, each with tight tolerances for temperature, pressure, timing, and ingredient ratios. Miss one parameter, and an entire batch gets scrapped. At Pfizer’s volume, even a small inefficiency compounds into significant waste.

Historically, manufacturers solved this by studying their best-ever production run and trying to replicate it manually. But there are hundreds of variables in play simultaneously, and humans can only track so many at once.

But tables have turned with AI.

The models analyse every variable across thousands of production runs, identify which combination of conditions produces the best outcome, and flag deviations from that pattern in real time. All this before a batch is ruined, not after.

The results on actual production lines:

Most enterprises default to a single AI platform company-wide. It makes sense for simpler procurement and easier IT governance.

But a scientist searching through years of research documents and a marketer getting copy approved through legal have nothing in common workflow-wise. One-size-fits-all tools optimise for neither.

Pfizer runs three platforms, each built for a specific role:

For a pharmaceutical company, R&D is the business.

Every drug Pfizer sells today was a research bet made many years ago.

The pipeline being built now determines what the company looks like a decade from now, including which diseases it can treat, which markets it competes in, etc.

The CEO is aware and mentioned in the February 2026 earnings call (Q4 2025) that Pfizer is expanding to 1,200+ GPUs over the next two years, and the expansion is driven almost entirely by R&D.

This will help the company accelerate its ongoing flagship programs.

YOUR TURN