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“In moments of technological change, impact belongs to those who pair bold initiative with responsible intent.”
The MedTech industry is clearly entering a new phase. AI has moved beyond hype and early experiments, and starting to show up in real, day-to-day engineering, testing, documentation and clinical workflows. The pace of change is hard to ignore. The AI-enabled imedical devices market alone is expected to grow almost twenty-fold by 2033, reaching about USD 255 billion. That kind of trajectory signals something bigger: a steady shift toward smarter products, connected ecosystems and devices that learn and adapt.
It was in this context that Cyient recently convened a closed-door roundtable on the theme “Leveraging Agentic AI to Accelerate New Product Development and Streamline Product Lifecycle Management for the MedTech Industry,” bringing together a peer group of R&D and business leaders. The discussion centered on the true potential of AI, how it must be engineered and what that means for product innovation, compliance, validation and long-term competitiveness in MedTech.
What followed was a nuanced, practical and forward-looking conversation about the real-world considerations shaping Agentic AI adoption today.
1. Agentic AI as an Engineering Discipline
A clear consensus surfaced early in the conversation: integrating Agentic AI into the product development lifecycle requires the same rigor applied to any engineered system. It calls for robust data foundations, architectural discipline, quality and traceability across workflows, and the ability to integrate seamlessly across hardware, embedded software, cloud and regulatory systems. The group’s view was that AI will only scale meaningfully when it is engineered, not appended.
2. Addressing the Testing and Documentation Bottleneck
A lot of light was shed on the PDLC activities that consume maximum time and cost. Testing and documentation often account for up to 60% of lifecycle effort, and this is where many organizations see early momentum. GenAI tools are already automating requirement mapping, test-case creation, traceability and compliance documentation with growing reliability. As a result, cycle times are shrinking, helping teams redirect their energy toward design, validation and differentiation.
3. Regulatory Automation: Valuable, but Not the Main Engine
Regulatory workflows remain a critical pillar in MedTech development, but leaders acknowledged that automating these workflows affects only a small fraction of the overall PDLC investment, often around 2% of total R&D spend. While important, regulatory automation alone will not unlock the scale of impact the industry is seeking. The real gains lie in accelerating new product development, improving engineering throughput and supporting regulatory readiness without slowing down innovation.
4. The Move Toward AI-Embedded Products
Another strong theme was the shift from using AI for internal efficiency to building it directly into products. Leaders are now focused on embedding intelligence into devices, software and connected-care ecosystems, a move from productivity gains to real value creation. This mirrors broader industry momentum, with McKinsey estimating that AI-driven product and service innovation could add more than iiUSD 50 billion in annual value for MedTech.
5. A Competitive Landscape That Is Evolving Quickly
Participants also noted the pace at which AI is being industrialized in global markets, including the rapid progress seen in China’s tertiary hospital deployments. This dynamic adds urgency for organizations still in exploratory phases. The competitive environment is no longer shaped by who experiments early, but by who operationalizes AI with speed, discipline and clarity.
Cyient’s Role and What Comes Next
Across the discussion, it was clear that industrializing AI across the MedTech stack has become a key priority, with growing attention on:
• Enabling the data and IT infrastructure for AI agents to operate
• Strengthening engineering and quality foundations
• Integrating AI with PLM and ALM systems
• Improving auditability and cross-functional traceability
• Aligning regulatory workflows with AI-driven processes
• Expanding digital value creation
As organizations shift from experimentation to scale, Cyient is helping bridge the gaps, aligning engineering and quality foundations, integrating platforms, and enabling the full technical stack required for AI-ready products. It’s this combination of domain depth and engineering discipline that allows MedTech teams to move from isolated wins to sustained, product-grade AI adoption.
Shift That Matters the Most!
The discussions at the roundtable made it evident that the real inflection point for MedTech isn’t just the arrival of Agentic AI, but the shift in how organizations are preparing themselves to use it responsibly and effectively. The opportunity ahead is less about mastering a technology and more about strengthening the decision-making, collaboration and engineering maturity required to bring AI-led products to life.
As we move into this next phase, the leaders who will shape the industry aren’t the ones who adopt AI the fastest, but those who adopt it with clarity of purpose. MedTech’s future will be defined by teams that balance ambition with accountability, pairing the imagination to pursue new possibilities with the discipline to make them real.
If you're mapping the next step in your AI journey, get in touch and we can explore how to turn this conversation into tangible progress.
References:
ihttps://www.grandviewresearch.com/industry-analysis/ai-enabled-medical-devices-market-report
About the Author
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Ram Chittoor |
Ram Chittoor is a business leader with deep expertise in scaling global engineering services, building high-performing teams and driving sustainable, profitable growth. Over his career, he has worked across the US, Europe and Asia, leading sales, solutions and product management initiatives for industries including Medtech, Life Sciences, Aerospace, Retail, Industrial, Oil & Gas and Hospitality. Ram currently heads the Healthcare and Lifesciences BU at Cyient.