Author: Travis Smith
The Industry Challenge: Finding the ‘Hard Truths’ of Generative AI
For regulated industries, the conversation around Generative AI (GenAI) has shifted decisively from theoretical hype to the imperative of achieving measurable business outcomes. On December 2, 2025, Enginius.AI hosted a specialized technology symposium for leaders in the medical device industry.
The gathering, which included regulatory and quality executives, product development leaders, and digital health innovators, focused intently on a single, vital question: How do we responsibly and effectively integrate AI into regulated industries such as MedTech to accelerate decision-making, reduce administrative burden, and strengthen compliance?
The overwhelming consensus among attendees was clear: the medical device sector needs validated, practical AI use cases that deliver tangible results, and not mere theoretical hype.
Foundational Principles: The Importance of a Secure Ecosystem
Sandeep Mehta, PhD, Enginius.AI’s CTO, commenced the event by sharing the company’s origin story. The platform was initially envisioned for aerospace applications to accelerate regulatory approvals for aircraft airframes by better managing the numerous disparate documents that engineers handle. This early experience revealed that AI as a standalone tool was not sufficient; success required building an interconnected ecosystem of technologies designed specifically for engineering contexts where traceability, accuracy, and validation are paramount.
The Enginius.AI approach is built on these insights focussing on ‘Domain Adaptive Intelligence’ and ‘STARS framework’, prioritizing the application of lessons learned to the highly regulated MedTech industry.
Industry Leaders Demand Actionable, High-Value Solutions
The attendees, a diverse group including heads of RA/QA, digital health consultants, founders of emerging AI startups, and quality engineering specialists, shared a strong, unified interest in understanding the real, near-term use cases of AI inside medical device OEMs. The appetite was not for abstract AI theory, but for actionable solutions that reduce administrative friction, accelerate product development, and improve the quality of regulatory documentation.
Where Generative AI Delivers Measurable Value Today
Stan Rowe, founder and CEO of Nidus Biomedical and member of Enginius.AI Board of Advisors, framed his remarks around a simple question: Where can AI deliver measurable value right now?
He pointed to areas with heavy documentation and repeatable workflows, specifically Design History Files (DHF), labeling, complaints handling, and SOP management, where organizations are already seeing impressive gains in speed and output. Stan estimated that early adopters of specialized AI could potentially reduce document preparation cycles by 40–70% and achieve overall operational productivity improvements of 20–30%.
The Non-Negotiable Need for Trust and Domain Expertise
Stan emphasized that adoption success is dependent on building AI tightly around the domain. Systems must embed into existing processes, be tailored to specific roles, and deliver clear, auditable outputs. Crucially, AI should “suggest,” not decide, with humans remaining squarely in the loop.
He also addressed the critical issue of trust and security. He cautioned that public AI tools “can compromise confidentiality because queries effectively ‘teach the system’.” For regulated companies, trust is earned through privacy, transparency, and control, meaning AI must run behind the firewall and be purpose-built for RA/QA and engineering work. A one-size-fits-all model is not advisable for this sector.
Strategic Problem Selection: Maximizing Near-Term ROI
During group discussion, attendees converged on the idea that AI is most effective when tasks are well-defined and data rich.
- High ROI: For relatively common or “me-too” products where documentation patterns are consistent across regulatory submissions, AI can automate significant portions of DHF development, DMR creation, or early-stage risk analysis. This allows us to initiate tasks quicker and focus on the strategic ‘how’ and ‘why’ much faster, without humans walking away from the process altogether.
- Managing Expectations: By contrast, novel or highly innovative tasks offer less historical data for an AI model to pull from, resulting in smaller gains. However, this still represents a gain, provided expectations are properly managed.
The overwhelming consensus was that the highest near-term ROI comes from automating the administrative burden and not the expert decision-making tasks. AI’s role is to generate first drafts, organize evidence, and maintain traceability, freeing engineers and SMEs to focus on complex technical thinking instead of paperwork.
Tackling System Integration: AI as an Agnostic Layer
Todd Abraham, MedTech COO and member of Enginius.AI Board of Advisors, addressed one of the biggest pain points for large organizations: integrating data across different systems (ERPs, QMS platforms, PLM systems, and homegrown databases) during M&A or after years of incremental tool adoption. Pulling the right information for CAPAs, verification & validation (V&V) work, or product submissions is a massive challenge.
Todd described a vision where AI acts as an agnostic layer above all these systems, aggregating data without requiring an overhaul of existing infrastructure. He provided CAPAs as an example, stating that in complex CAPAs, especially those with field implications, this kind of integrated AI layer could significantly accelerate evidence gathering and investigator alignment, reducing risk and improving regulatory outcomes.
He cautioned that organizations must only assign structured problems with clear inputs and outputs to AI; assigning highly ambiguous tasks that humans still struggle to define will not produce meaningful ROI.
The Competitive and Regulatory Edge
The conversation concluded with a vital look outward. Kenny Dang, Chief of Staff – Neuro Global R&D and Manufacturing Operations at Terumo Neurovascular, raised an important point: regulators may eventually use AI to perform ‘antagonistic searches’ on submissions, identifying gaps at a speed no human reviewer could match. This possibility ensures AI will become even more essential for manufacturers to maintain watertight submissions.
As Julian Husbands, Enginius.AI’s Chief Revenue Officer, posed to the attendees, “How efficient can you be? The reality is we are our own competition.” Companies that adopt AI early will move faster, build cleaner documentation, and maintain tighter alignment across RA, QA, clinical, and product development. Those who wait risk falling behind.
A Defining Moment for AI in MedTech
The window for early advantage in GenAI adoption is accelerating. The future will be defined by trust, validation, workflow integration, and the balance between automation and human oversight. The mission ahead is to build AI tools that are not just functional, but dependable, secure, and designed for the realities of regulated product development.
Ready to deliver verifiable results? Book a demo with Enginius.AI to have detailed discussion on implementing Domain Adaptive Intelligence (DAI) and learn how the STARS Framework can secure and validate your product development pipeline.
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Ready to eliminate the pain of endless searching and manual documentation? Book a demo with Enginius.AI to have a detailed discussion on implementing our purpose-built AI stack, powered by Domain Adaptive Intelligence (DAI), to optimize critical elements of your product development process.
