Blog posts

According to Gartner, a request for information, or a request for proposal, is defined as “both the process and documentation used in soliciting bids for potential business or IT solutions required by an enterprise or government agency. The RFI document typically outlines a statement of requirements (SOR) to be met by prospective respondents wishing to make a bid to deliver the required solutions. It might cover products and/or services to meet the given requirements.”

Yet, for anyone entering into a long-term business agreement, a well-written RFI can do so much more than just assess and collect vendor capabilities. 

For the last decade, Medable has been transforming the capabilities of organizations across clinical research using the latest in new technologies. In this time, we’ve learned the best RFIs are able to define what success looks like, create alignment on measurable outcomes, and establish accountability on roles and responsibilities well before a contract is ever signed. When done correctly, it becomes a decision-making framework that offers clear vision to both organizations.

Recently, Medable received two RFIs around eCOA from top pharmaceutical organizations. They stood out to us because they were structured around performance, not promises, a distinction that makes all the difference.

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eCOA’s time has come. The market is currently estimated to be worth $2.3 billion, with projections showing it reaching nearly $5 billion by 2030. Despite this, paper still plays a prominent role for some clinical trials today.  

At first glance, paper may seem simple and familiar, even economical. However, in today’s regulatory and operational environment, paper COAs are not a risk averse choice when held to the standards of what sponsors, CROs, and regulators are looking for trial data to prove.  

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In the five years since the pandemic, decentralized trial elements have solidified their status in medical product development. 

Trials with decentralized elements have moved past the “pilot” phase. The question is no longer whether we can operationalize decentralized trial components, it's whether we’re doing it thoughtfully at the pace patients deserve. Our industry is ready to optimize the elements for the trial based on available evidence.  

That’s exactly why Medable, in collaboration with and facilitated by the Tufts Center for the Study of Drug Development (Tufts CSDD), has launched the Innovation Evidence Workshop series. 

Last November, the inaugural, invitation-only workshop brought leaders from 20 pharmaceutical, biotech, and CRO organizations together in Boston, with representation from the U.S. Food and Drug Administration, Harvard MRCT Center, Tufts CSDD, and Medable.

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To many, the SCOPE Summit is the year’s “newsroom,” setting the stage for what hot topics and driving forces will dominate the coming year. 

With this year’s conference winding down, we’re once again offering a glimpse into the evolving operational and technological conversations shaping the future of trials with our recap below.  

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eCOA has moved from a supporting tool to a foundational pillar of modern clinical trials, and Everest Group agrees. In its inaugural eCOA Products PEAK Matrix Assessment, Everest named Medable a Leader, citing strong market impact, accelerated timelines, and a platform built for real-world trial complexity. As the eCOA market surges toward nearly $1B in value, this recognition underscores how speed, patient experience, and AI-driven innovation are reshaping how trials are designed, launched, and scaled globally.

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Each January, the J.P. Morgan Healthcare Conference sets the tone for the life sciences industry, serving as the year’s most influential gathering of biotech, pharma, investors, and dealmakers. 

This year was no different. 

Thus, we checked in with our conference attendees, booth visitors, and more to see what they thought were this year’s trend-setting takeaways.

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The numbers are in, and they spell change for life sciences. 

That’s because nearly three-quarters (73%) of global pharmaceutical organizations are actively planning, piloting, or deploying agentic AI initiatives. 

This widespread means agentic AI is no longer a futuristic concept, but a present-day imperative for staying competitive and delivering life-changing medicines faster.

For those not yet in the know, agentic AI is a sophisticated form of AI designed not just to analyze data, but to act autonomously, plan, reason, and execute complex, multi-step tasks. This goes far beyond traditional automation. Instead, agentic AI is about creating intelligent systems that can drive innovation, accelerate drug development, and optimize operations like never before.

So, which companies are leading the charge, and how exactly are these intelligent agents reshaping the pharma landscape? Let's dive into the specifics.

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For years now, the FDA has been making one point crystal clear to sponsors and CROs across our industry; they want the patient voice incorporated whenever possible in oncology trials.

The FDA's initiative is driven by the recognition that a patient's personal experience with a disease and its treatment is a unique and essential measure of a medical product's benefit and risk.

• Rationale: The FDA explicitly states that "patients provide a unique perspective on treatment effectiveness" and "some treatment effects are known only to the patient." Outcomes that truly matter to patients, such as functioning, quality of life, and the burden of side effects, are often best measured directly by the patient.
• Mandate: The Patient-Focused Drug Development (PFDD) effort, codified in part by the 21st Century Cures Act, requires the inclusion of such patient experience data in clinical research.
• Guidance series: To formalize this approach, the FDA has released a series of methodological guidance documents (the PFDD Guidance Series) that outline how stakeholders should collect, submit, and use patient input to inform medical product development.

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“Big corporations can’t rely on their internal speed to match the transformation that is happening in the world. As soon as I know a competitor has decided to build something itself, I know it has lost.” 

These candid sentences from Sanofi CEO, showcase one of the most common questions that’s at the forefront of every pharmaceutical company’s mind; whether to build or buy your way into the agentic and generative AI revolutions. 

In life sciences, many teams start with the same instinct. They see a capable large language model, stand up a proof of concept, and feel close to a breakthrough. For most of us, AI prototypes can look magical. A chatbot summarizes visit reports, drafts emails, or answers protocol questions in minutes. The experience is so strong that teams assume production is a short step away.

Unfortunately, the gap is much bigger than it looks. 

According to a recent MIT study, 95% of AI pilots will fail, as they note that “Only 5% of custom GenAI tools survive the pilot-to-production cliff, while generic chatbots hit 83% adoption for trivial tasks but stall the moment workflows demand context and customization.”

Like MIT’s example shows, moving from prototype to production in clinical research means building something validated, compliant, scalable, and integrated into real workflows. That takes far more than clever prompts. It requires domain grounding, continuous monitoring, retraining loops, robust tool orchestration, and evidence that the system is safe and auditable under regulations like GxP, HIPAA, and 21 CFR Part 11.

Many organizations only discover the hidden costs after they have committed. Internal teams often invest for two years, spend millions in sunk cost, and still never reach a dependable clinical grade system. The illusion comes from how easy it is to get an early demo working, and how hard it is to make that demo survive contact with trial reality. 

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It’s been said that the only constant in the world is change. 

For decades, clinical trials have been a human only endeavor, with teams of clinicians, study teams, and patients working hand in hand to bring the latest molecules to market. Now, a new central actor has entered the clinical paradigm, agentic artificial intelligence. 

Only three years after OpenAI kicked off the artificial intelligence arms race, AI has gone from requiring users to prompt it, to pre-emptively identifying bottlenecks, safety risks, and more, thanks to agentic AI. 

Agentic AI is an autonomous, goal-oriented system that uses reasoning and external tools to independently plan, execute, and adapt multi-step actions with minimal human intervention to achieve complex objectives. 

Sponsors and CROs have begun using AI agents across their workforces to improve trials in ways that humans have traditionally struggled to accomplish. For instance, organizations have been creating AI agents to analyze prior trial protocols, benefiting from lessons learned across prior trials and real-world outcomes, enabling teams to anticipate risks and automating elements of submission drafting. Anomaly detection has helped teams better identify outliers in operational metrics or safety signals, prompting early interventions. Document intelligence accelerates medical writing by grounding generative outputs in verified data, which reduces cycle time without sacrificing accuracy. 

However, in each of these use cases, humans remain squarely “in the loop.” Or rather, the decision making isn’t left entirely to AI. Instead, the objective is to augment clinical, regulatory, and legal teams with tools that surface the right information at the right time.

This core concept, keeping a human in the loop, is essential to clinical decision making and operations as agentic AIs, while powerful, are not inherently suited to fully autonomous operation in all regulated contexts.

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The 2025 ESMO AI & Digital Oncology Congress, held in Berlin from November 12 to 14, highlighted the accelerating role of artificial intelligence across the oncology care continuum. Although imaging and pathology remain the most established fields for AI adoption, this year’s programming revealed a decisive shift toward workflow-integrated AI that enhances clinical operations, supports trial efficiency, and addresses the realities of patient monitoring. 

Across three days of sessions, side-room conversations, and industry demonstrations, one theme was clear. AI is evolving from experimental add-on technology into a practical clinical teammate, but scaling its impact will require robust validation, seamless integration, and a sustained focus on clinician trust.

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Artificial intelligence continues to move from experimentation to execution across the life sciences industry. On a recent episode of the AI in Business podcast, Matthew Peruhakal, Global Head of Data Architecture, Utilization, and AI Engineering at Sanofi, offered a deep look at how the pharmaceutical giant is integrating AI to transform clinical trials. From intelligent data workflows to proactive risk detection and regulatory alignment, Peruhakal described an organization reshaping its research and development operations around a new digital core. 

His message was clear: AI cannot remain a side project. To make a meaningful impact, it must be embedded as a strategic capability that connects people, systems, and data across the enterprise.

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