How Pharma’s AI Moment Is Finally Here

For years, artificial intelligence in pharma has been something of a slow burn—often discussed in the abstract, hyped in headlines, but with limited real-world impact. That is, until now. According to a new report from health-tech investment firm Define Ventures, AI has officially moved from a "what if" to a "must do" for most major pharmaceutical companies.

The numbers tell a compelling story. Over 70% of pharma executives now consider AI an immediate priority. Among the top 20 pharmaceutical companies, that number climbs even higher to 85%. And this isn’t just talk—more than 80% of companies have increased their AI budgets in the past year, with only a slim 15% holding budgets steady. It’s a clear signal that we’re witnessing a strategic shift across the board, and the timing is no coincidence.

What’s driving this sudden urgency? Much of it comes down to maturity. Pharma is no longer experimenting with isolated AI pilots or buzzword-ridden proof-of-concepts. Instead, the industry is gravitating toward practical, low-risk, high-impact use cases—like medical writing, internal workflow optimization, and administrative automation. These are areas where AI can relieve chronic bottlenecks and deliver value quickly. One hundred percent of surveyed leaders agreed that reducing administrative burden is one of the clearest markers of success.

It’s not just about efficiency anymore. Companies like Genentech have reframed their approach entirely—calling AI an “effectiveness amplifier,” a tool that can magnify human insight and execution, not just accelerate repetitive work. That mindset shift is essential. AI isn't just speeding things up; it’s making them better.

Alongside this evolution in mindset is a quiet but critical change in how pharma companies are choosing to build AI. Gone are the days when internal R&D teams were expected to develop complex AI models from scratch. Today, many firms are adopting a hybrid approach—balancing internal development with external partnerships. In fact, 40% of companies now pursue this middle path, while the remaining split evenly between internal-first and external-first strategies. Collaboration with AI startups and tech firms is growing rapidly, suggesting that pharma has realized the real value lies not in owning every line of code, but in accessing the best tools for the job, wherever they may come from.

But with great power comes real responsibility. As AI takes on a more central role in clinical workflows, marketing, safety, and decision-making, concerns around data ethics, bias, and governance can no longer be sidelined. Encouragingly, around 80% of pharma organizations now report having formal AI governance structures in place, with the remaining 20% actively building them. Ethics, safety, and long-term accountability are becoming foundational—not optional.

What makes this moment even more striking is how fast the sentiment has changed. Just last year, a vast majority of pharma leaders—over 80%—still described AI as “overrated.” Many were skeptical. Most had only a few limited use cases in production. Today, that hesitation has all but vanished.

Outside analysts predict AI-related pharma investment will grow sixfold in the next five years—from $4 billion in 2025 to $25 billion by 2030. By 2034, the market for AI in drug development and marketing is expected to surpass $16 billion globally. The implications are massive: faster trials, smarter targeting, reduced waste, and possibly even new therapeutic insights born from machine learning models that think differently than we do.

Still, it’s not the numbers alone that make this feel like a turning point. It’s the tone. There’s a seriousness in the way pharma is now approaching AI—no longer as a shiny object, but as an operational pillar. A tool to accelerate real-world impact, from lab bench to patient bedside.

The next 12 to 24 months will be critical. Define Ventures calls this period the inflection point—and it’s hard to disagree. What companies do now will set the trajectory for years to come. Those who act decisively, embed AI into their core strategies, and collaborate wisely may not just gain a competitive edge—they may redefine what success in pharma looks like.

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