Part 1: How to Automate Drug Development with an AI-Native Approach?

Posted on: April 29th 2025

Despite significant medical and technological advances, drug development is still incredibly inefficient. Bringing a single drug to market costs over USD 2.6 billion and takes 10–15 years, with 90% of candidates failing post-Phase I trials. This drawn-out, high-risk procedure is unsustainable from a financial and social standpoint for a sector that relies on speed-to-therapy.

Many pharmaceutical companies have responded by using AI to address the above-mentioned challenges. But let’s be clear—most applications are skin-deep.

Retrospective analytics, document digitization, and PDF scanning may help tick the “innovation” box, but they don’t change the fundamentals. These are marginal improvements, not strategic shifts.

Pharma doesn’t require another dashboard these days. It calls for a brand-new operating system for drug development that is AI-driven from the ground up. This is what we call AI-Native drug development.

Yet, going AI-Native is easier said than done. Pharma companies must contend with intricate clinical data, long-standing processes, stringent regulations, and organizational inertia. They need more than a tool—they need a transformation partner. That’s where Straive comes in.

What AI-Native Drug Development Means

Let’s get straight to the core of what “AI-Native” means. Being “AI-Native” is more than meets the eye. It’s beyond automating a task or adding a chatbot. It means embedding AI into every layer of the drug development process—from molecule to market. In today’s world, AI is the engine that drives the entire system, not a technology on the side.

Think of it like this:

• Protocols for clinical trials are dynamically modified based on real-time data rather than being created and then forgotten.
• Patient recruitment doesn’t rely on manual screening; it’s hyper-personalized using enriched health records and historical eligibility data.
• Regulatory submissions aren’t manually compiled; they are auto-generated, validated, and uploaded using NLP-powered document intelligence.

Simply put, AI-Native development aims to reinvent the system rather than merely support it.

However, generic tech providers are unable to provide this reinvention. Pharma’s ecosystem comprises numerous interconnected systems and stakeholders; its data is disorganized, and its compliance standards are high. You need a specialist who understands data and the domain, and this is precisely where Straive’s strength lies.

At Straive, we don’t consider ourselves just another AI solutions provider but as a “blue-collar AI partner” built to overcome the challenging aspects, such as uneven data, legacy systems, teams resistant to change, and changing legal requirements. With a team of more than 600 life science SMEs and 30+ PhDs, we help global pharma companies shift from automation to transformation.

Rethinking Clinical Trials with Straive

Let’s move on to clinical trials, the most complex and asset of pharmaceutical R&D.

As mentioned above, trial management is traditionally full of inefficiencies. High patient dropouts, deviations from set protocols, underperforming sites, and delayed site activations are challenges that extend trial timelines and raise costs. Phase III trials alone can take 6–7 years and are frequently beset by problems that could have been predicted or prevented.

Straive helps clients turn this narrative upside down. With our AI-Native clinical trial framework, pharma companies can:

• Monitor trial health in real-time to track patient behavior, protocol adherence, and site progress.
• Predict risks before they become issues, such as dosing errors, delayed recruitments, or patient attrition.
• Surface insights early and visually using intelligent dashboards and dynamic alerts instead of static reports.

Real-World Impact

A global pharmaceutical company running over 100 concurrent clinical trials partnered with Straive to automate their clinical operations reporting. Here’s how they benefited:

• They saved USD 2.4 million across trial costs.
• They saw a 75% drop/reduction in open issues thanks to early detection and automated risk alerts.
• They got enhanced recruitment tracking, milestone monitoring, and site performance visibility.
• They could make faster, more confident decisions backed by real-time data rather than retrospective reviews.

This isn’t just about faster reporting but more brilliant, continuously optimized trial execution.

Straive’s Advantage: Operationalizing AI, Not Just Deploying It

Most AI models fail after proof-of-concept. They become trapped in pilot purgatory because they are too complicated to adopt and delicate to scale.

What Straive does differently is to focus on AI operationalization. We don’t stop at deploying a model—we ensure it’s integrated into your workflows, used by your teams, and continuously improved. We bring:

• Full-stack capability: From LLM Foundry for generative AI to biomedical data pipelines and embedded AI copilots.
• Expert-in-loop ops: Ensuring AI insights are validated, contextualized, and adopted by SMEs.
• Domain-driven design: Every model, dashboard, or insight is built for the realities of pharma, not just what’s technically possible.

What to expect in Part 2?

In the second part of this blog, you will read how Straive converts traditionally inflexible and error-prone domains into intelligent, adaptive systems by applying the same AI-Native transformation to patient recruitment and protocol optimization.

You will also read about how a European-based pharma firm saved €5.2 million in regulatory costs or how we used AI-based stratification to reduce recruitment timelines from months to days.

Stay tuned…

About the Author

Santosh Shevade

Santosh Shevade is a Principal Data Consultant at Gramener – A Straive Company. With deep expertise in healthcare strategy, digital health, and clinical development and operations, he has supported nearly 50 clinical development programs across all clinical phases. His experience spans advanced analytics solution design for pharmaceutical companies, mHealth implementation, and AI applications in healthcare. Previously at Novartis and Johnson & Johnson, Santosh led global clinical development teams and streamlined data review processes for major regulatory submissions. A certified MBTI trainer and leadership coach, he serves as visiting faculty at ISB Hyderabad and Welingkar Institute, focusing on healthcare technology innovation and biopharma strategy.