If your company is experimenting with AI, here’s a statistic worth pausing on. Recent MIT research says “95% of AI initiatives fail to show real business value”. Well, that’s not a rounding error, that’s almost all of them.
In our recent webinar, leaders didn’t try to gloss over that statistic. They confronted it head-on, then broke down what the remaining 5% are doing differently with Agentic AI to actually see impact.
Missed the live session? Watch the full webinar on demand to see real demos and hear from Saama’s clinical AI experts about what’s actually working in production today.
The Real Problem with AI Implementation
AI experimentation is everywhere. About 80% of companies have tested tools like ChatGPT for internal tasks (Microsoft). It sure feels like progress, until the momentum stalls.
Only about one in five of those experiments evolves into an actual pilot. And from that small pool, just 5% make it to production. The rest? Stuck in endless “proof of concept” loops, burning through time, resources, and finances.
The issue isn’t enthusiasm. It’s execution and figuring out how to go beyond a good demo to something that actually sticks (and scales).
The Shift to Agentic AI
Most AI systems are reactive; if you ask a question, it spits out an answer. Agentic AI goes further. It can plan, reason, and execute multi-step tasks that traditionally needed human oversight.
Saama’s platform processes a 200+ page protocol and automatically extracts over 360 structured data points: study design, inclusion/exclusion criteria, visit schedules, risk indicators, and more.
But it doesn’t stop there.
- The eligibility criteria agent converts those textual requirements into executable logic, builds database queries, and generates patient funnels using real-world data.
- The site selection agent runs thousands of combinations using genetic algorithms, balancing speed, cost, and diversity; even running Monte Carlo simulations for risk modeling.
- And the protocol insights agent compares your study design against similar trials, estimates patient burden and operational complexity, and quantifies the impact of any design change on cost and experience.
That’s not keyword matching. That’s reasoning.
Making AI Work for You, Not the Other Way Around
If your AI strategy still revolves around pilots and proofs of concept, it’s time to change course.
The leaders in this space have already moved from experiments to production-ready AI ecosystems that deliver massive efficiency gains across critical workflows.
And here’s the thing, AI isn’t something that might help pharma “someday.” It’s already happening. The difference is whether you’re using something purpose-built for life sciences or just hoping a generic chatbot can keep up. (Spoiler alert: it can’t.)
You’ve heard how Agentic AI is transforming pharma operations, now see it for yourself: Watch the on-demand webinar to explore real demos and hear how leading pharma teams are already transforming clinical workflows with Agentic AI.
And if you’d like to experience it firsthand, schedule a personalized demo at [email protected] to see how it can work for your team.
