The biggest barrier to AI adoption isn’t technical—it’s operational. Teams must be trained to trust and use new tools. We’ve seen promising platforms fail simply because SOPs weren’t updated, or change management was skipped.
Avoid enterprise-wide rollouts. Instead, target one high-volume, low-risk workflow—like feasibility assessment or adverse event monitoring. Define KPIs early, measure ROI, and scale based on real results.
Even the best AI tools will stall in QA and regulatory review if version control and validation aren’t watertight. Systems need to be inspection-ready from day one.
AI tools must plug into your existing tech ecosystem. Vendors who require duplicative data entry or proprietary platforms may slow adoption.
First-Person Insight: One sponsor formed a cross-functional AI working group (Clinical, Regulatory, IT, QA) to vet vendors and pilot tools. That team became a force multiplier—ensuring shared ownership, faster troubleshooting, and organizational buy-in.
AI is not a turnkey solution. Leaders should apply healthy scrutiny. Key questions to ask during vendor selection:
1. Is the tool validated and compliant with GxP standards?
2. How does it integrate with our current systems and SOPs?
3. Who owns the data inputs and AI-generated outputs?
4. Can we audit its use and trace decisions back to source data?
Red Flag: Any vendor claiming a "plug-and-play" AI tool with no need for training, integration, or validation is not ready for serious clinical use.
To move from interest to action, executives should:
1. Build Cross-Functional AI Working Groups
Ensure clinical, regulatory, QA, and IT stakeholders collaborate from day one. This sets the foundation for adoption and alignment.
2. Pilot with Purpose
Choose a specific, measurable use case. For example: "Reduce SAE reporting time by 25% over 6 months using NLP tools."
3. Define Ownership & IP Upfront
Ensure contracts specify that all input data, outputs, and insights belong to your organization. Avoid platforms with vague language around "shared learning."
4. Document Everything
Pilot results, change logs, SOP adjustments, and validation protocols need to be captured early—especially if AI will touch submission materials.
AI is already proving useful in clinical development—but the key to success isn’t the algorithm. It’s the leadership, strategy, and execution that surround it. At Ascendia Clinical, we help clients turn ideas into implementation—with quality systems, regulatory foresight, and operational insight built in. If you’re ready to explore how AI fits into your roadmap, we can help you do it smartly, securely, and successfully.
