Drugmakers are using AI to cut months out of clinical trials and regulatory filings, and it is happening fast, and without waiting for miracle drugs.

This week at the JP Morgan Healthcare Conference, executives made it clear that AI is no longer a science project.

Right now, bringing a drug to market still takes about 10 years and costs roughly $2 billion.

That number has barely moved in decades. AI is attacking the slowest, dumbest parts of the process.

Site selection. Patient recruitment. Regulatory paperwork. The stuff that burns time, cash, and morale.

Companies are using AI to draft regulator-ready submissions that can run thousands of pages and must line up perfectly across countries.

They are using it to scan safety data, cross-check manufacturing records, and prep filings that once required armies of consultants.

They are also using AI to find patients faster and keep them enrolled, which has always been a weak point in trials.

Novartis said AI cut site selection for a late-stage trial of its cholesterol drug Leqvio from several weeks to a single two-hour meeting. GSK said digital and AI tools reduced manual data work and lowered costs in late-stage trials for its asthma drug Exdensur. Teva Pharmaceutical Industries said AI is already freeing staff from routine work so they can push more programs forward.

The disruption behind the news: Timelines are the real bottleneck in pharma, AI helps reduce them.

Every month shaved off development is worth tens of millions in earlier revenue, longer patent-protected sales, and lower burn.

If AI cuts even 5% off a 10-year timeline, that is six months. On a drug with $1 billion in annual sales, that is roughly $500 million in additional lifetime revenue.

The bigger disruption is structural. Administrative work has been one of the last protected labor pools in pharma.

Regulatory writers, data managers, trial ops teams, and outsourced document shops all exist because the process is slow and brittle.

AI is already good enough to replace a meaningful chunk of that work. Not someday. Now.

This also changes who can compete. Smaller biotechs have always been crushed by paperwork and trial logistics. If AI turns regulatory prep and enrollment into software problems instead of headcount problems, the costs reduce hard.

The market implication is simple. Productivity gains arrive before scientific breakthroughs. Stocks will reprice on margins and speed, not on AI-discovered wonder drugs.

What to watch next

First, watch trial cycle times.

If average Phase 2 and Phase 3 timelines start compressing by even 10 to 15 percent over the next 24 months, this becomes unavoidable.

Second, watch spending on external contractors for regulatory and data work. That line item should fall.

Third, watch how many parallel trials companies run. Faster ops means more shots on goal with the same budget.

The first true AI-discovered drug can wait. The real disruption is already here. And it is about making drugs faster and cheaper.