Strategic Business Planning for AI Adoption in Medical Device Design, Development & Manufacturing Firms

A Performance Transformation White Paper and Five Year Forecast

Introduction & Market Trends

Over the course of 2025, I had the privilege of attending MD&M West and BIO in Boston.  While in attendance, I took the opportunity to do some direct market research with business owners, senior executives and business development professionals.  I was able to speak with more than 120 industry professionals from outsourcing firms in the medical device and life science/therapeutic development sectors.

When reviewing the answers to my questions around AI, I didn’t need to run any regression analysis or standard deviations…simply because there were not any deviations in the replies.  Remarkable.  When I inquired about the coming wave of AI:

• Only four companies I spoke with were geared up for adopting and leveraging new AI tools and platforms (beyond toying with Generative AI).

• All the remaining companies recognized the AI revolution was coming, and sooner rather than later.

• Of these companies, they weren’t sure where to begin their AI journey.

The fact of the matter is, the medical device and life science industries stand at a transformative threshold.  By 2026, artificial intelligence (AI) will no longer be an emerging technology but an essential enabler of innovation in workflow optimization, design, development, asset identification, clinical validation, and regulatory compliance.  Firms specializing in medical device engineering must anticipate how AI will influence both their internal processes and their competitive environment.  Let’s take a deeper dive into the secondary research around the future of outsourced engineering and manufacturing.

First and foremost, the outsourcing of medical device design, development and manufacturing services is growing at a very healthy clip.  OEMs are increasingly outsourcing design, engineering, and manufacturing services to optimize costs, accelerate time-to-market, and navigate complex regulatory landscapes such as FDA alignment with ISO 13485 and EU MDR requirements.  Demand is boosted by the increasing complexity of devices—especially minimally invasive, wearable, and diagnostic technologies—and the need for specialized engineering partnerships.(1)  Additional research indicates:

• The overall U.S. medical device outsourcing market is projected to nearly double, rising from approximately $31 billion in 2024 to over $63 billion by 2030, a CAGR of 13%.(2)

• The U.S. medical device contract manufacturing market is projected to reach $19.51 billion in 2025 and $62.42 billion by 2034, with a 13.79% CAGR.(3)

• Adoption of AI and IoT: Outsourcing firms are leveraging AI-driven automation for product design, predictive testing, and streamlined regulatory documentation, while providing expertise in IoT-enabled devices for remote monitoring, diagnostics and smart implants.(4)

• The specific segment utilizing AI within IoT medical devices is expected to see high double-digit annual growth rates, aligned with the broader market.  AI in IoT (all markets) was estimated to be valued at $13.64 billion in 2022 and is projected to reach $127.62 billion by 2034, a CAGR of 28.20%.  Healthcare and the Life Sciences have dominated this segment and are projected to do so going forward.(5)

• AI in Medical Device was estimated to reach $10.8 billion in 2024 and is expected to grow to $34.2 billion by 2029, a CAGR of 28.9%.(6)

• The confluence of Medical Device IoT and Agentic AI solutions, between 2025 and 2030, is forecasted to grow at a CAGR of between 35% and 45%.  By 2030, Agentic AI is anticipated to become a core functionality of leading IoT medical device offerings, driving transformation in remote monitoring, real-time data analysis, patient engagement, diagnostics, and personalized treatment.(7)

• The global Agentic AI in the healthcare marketplace is projected to grow from about $538 million in 2024 to $4.96 billion by 2030, at a CAGR of 45.56%—with medical device IoT as a prime application segment.(8)

• Agentic systems are forecasted to be integrated into more than 35–45% of new IoT medical device launches by 2030.(9)

• By 2030, a significant portion of hospital and home-health monitoring IoT devices will leverage Agentic AI for autonomous interventions, predictive analytics, and adaptive care delivery.(10)

• FDA regulatory frameworks are evolving to support the approval and deployment of Agentic healthcare systems, with over 127 new Agentic-AI-enabled devices approved in 2025 alone.(11)

  
  

While the application of AI in medical device design, development and manufacturing is still early, the research, both primary and secondary, indicates AI is coming fast and it is going to be disruptive.  Which brings us back to the underlying challenge…where to begin?

I’ve been developing and executing Strategic Business Plans for medical device and life science companies since 1995.  Strategic planning tools such as SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) and the GOST framework (Goals, Objectives, Strategies, Tactics) provide highly structured approaches to navigating this shift.  It simply works.

This paper explores how medical device design, development and contract manufacturing firms can apply SWOT and GOST frameworks to create robust strategies for AI adoption, ensuring they remain competitive, compliant, and capable of delivering safe and effective innovations in a rapidly evolving healthcare ecosystem.  And without breaking the bank.

SWOT Analysis of Agentic AI Adoption in Medical Device Design & Development

Established medical device design and development firms typically provide a strong foundation for AI workflow adoptions.  These firms already excel in product development, quality systems (nimbly moving from one client’s QA system to another), and regulatory compliance. Their strong technical and compliance foundation positions these firms to integrate AI responsibly and effectively.

This paper will be referring to Agentic AI, so a quick and simple definition:  AI agents are nodes of code that perform a defined task.  These agents are connected by boundaries that keep the nodes’ tasks in sequence and focused on specified data sets (no hallucinations).  They can also iterate work, much like the develop process itself.  The agents are orchestrated by a supervising agent that monitors the network.  Agentic AI works independently, yet, at this stage, these systems often include a human-in-the-loop to ensure alignment and compliance.

The Self-Assessment part of the SWOT analysis will reveal company strengths, weaknesses and core competencies.  Traditionally, this opens the door for building upon your strengths, addressing your weaknesses and strategically leveraging your core competencies. Of note, core competencies are strengths that are unique to your company’s value proposition that are difficult for your competitors to replicate.  Core competencies drive strategic differentiation.  

Now take a look at your Self-Assessment through the lens of potential AI applications.  Do you have these foundational strengths that will help support AI adoption?  Are there weaknesses that need to be addressed?  Can Agentic AI help close competitive vulnerabilities and accelerate strategic advantages?

Some examples of what to look for in your Self-Assessment may include:

Stable, Reproducible Workflows

Are your strengths human-centric?  Process driven?  Well documented, stable and reproducible?  Are there workflow improvement opportunities that free up knowledge workers to focus on more value-add activities for your clients and the business?  (Note: Look to how Agentic AI might alleviate mundane, repetitive, often regulated processes…it’s often the low hanging fruit).

Cross-disciplinary Capabilities

How broad are your internal capabilities?  What are the capabilities of your vendor network?  Is this a strength or a weakness? When we look to embedded AI in IoT, the requisite skill sets may need to expand.  A multidisciplinary base is advantageous for developing AI-driven devices, where domain knowledge must align with algorithm design.  The quickest and most cost effective way to building out this capability can be achieved through vetted partnerships with specialty vendors.  This is especially true for mechanical engineering-centric firms.

Access to Clinical Networks and Data Sets

Long-standing partnerships with hospitals and research institutions provide access to diverse clinical data sets and validation sites, crucial for training and testing AI models.  Client OEM medical device companies may or may not have these resources well defined when they approach design and development companies with their product idea.  This is especially true with startups.  Much will depend on what the client brings to the project.

Market Reputation and Client Trust

Established firms benefit from existing brand equity in safety, reliability, and compliance. Trust is particularly important in gaining OEM client assurance in taking them through this novel journey (AI in IoT).

Data Infrastructure Gaps

Many firms lack mature data governance systems, access to high-quality datasets, and cloud-native infrastructures to support machine learning workflows.  Planning for this at the outset will help close these gaps.  The more projects you engage in the space will grow out your expertise in this area, even if you choose to outsource the infrastructure development.

Regulatory Landscape

While FDA, EMA, and other regulators are advancing frameworks for AI/ML-based medical devices, the path remains complex and resource-intensive. Smaller firms may struggle to meet evolving requirements.  Agentic AI can be leveraged to monitor the rapidly evolving, regulatory landscape and provide alerts in real time.  No surprises!

Ample and Aligned Talent (Potential Gaps)

Competition for AI talent remains fierce. Firms rooted in mechanical or traditional engineering may lack the expertise in deep learning, natural language processing, or federated learning that AI development demands.  In addition, are you currently positioned, in terms of knowledge workers and processes, to develop or deploy IoT in medical devices?  This will be foundational for the coming, customer demand for integrating AI into IoT.  If not, now is the time to qualify vendors.

Legacy Systems

Reliance on outdated development pipelines and non-interoperable tools slows the integration of AI across R&D, testing, and post-market surveillance.  This bites more in larger, less nimble companies.  Evaluating how an AI framework can be established to align these activities, in a sequence of priorities to your business, will help companies migrate to these new AI driven capabilities.  Of note, AI agents are especially cost effective for post-market surveillance.

Market Drivers & Implications & Market Assessment

Tucked inside the SWOT is the Market Drivers & Assessment section and your Market Assessment.  Market Drivers are macro issues…the tide that lifts all boats.  The Market Assessment is focused on specific trends, conditions and the competitive landscape for your business.  Both can benefit from generative AI in the planning process.

Here is an example of current Market Drivers and potential Implications that will influence the medical device and technology markets in the coming years.

Market Assessment

This is an ideal place to leverage generative AI (think ChatGTP or Perplexity.ai) to conduct your secondary market research.  The better generative tools include sources, so you can check to see if the information is from a trusted, well established source.  This also provides a baseline for your assumption set that can be validated through primary market research, when necessary.

Going forward, AI agents can be established to monitor the research landscape for potentially impactful changes to future strategies.  In particular, with your competitors…which brings us to…

Competitive Landscape

This is another area where generative AI can be a time and money saver.  Especially if your competitors are publicly traded companies (you can feed your AI engine their Annual Reports).  Long-term AI agents can monitor competitive interactions with regulatory bodies, product launches, marketing communications and more.

Opportunities - Workflows and Embedded IoT

Finally to the “O” and the “T” of the SWOT.  For design, development and manufacturing companies, it’s important to break this segment down into two sections; internal applications of AI to workflows and being positioned to embed AI into IoT enabled devices.  

Some Examples of Workflow Opportunities

Automation of Complex Quotations and Project Plans

This is most likely one of the first opportunities to save time and money, especially if you have a deep data set of quotations to train the AI.  Human-in-the-loop ensures accuracy, but the preparation time savings are significant.  I’ve worked with engineering firms where a complete project plan can take upwards of 40 hours of highly skilled labor to generate.  Well trained and positioned AI agents can cut well over 30 hours of time require by the human-in-the-loop to execute this critical task.

Documentation Harmonization

This can be especially helpful in design, development and manufacturing environments that “run to” customer Quality Systems.  Agents can move seamlessly between platforms to ensure compliance.  Again, another significant time saver.

Regulatory Harmonization

By 2026, agencies are moving toward harmonized standards for Good Machine Learning Practices (GMLP). Firms that invest early in compliant frameworks will gain a first-mover advantage.

Digital Twins and Simulations

AI can accelerate prototyping and reduce costs via predictive modeling, virtual bench testing, finite element analysis, virtual clinical trials, and digital twin simulations of devices interacting with patients.

Line Change Orchestration in Contract Manufacturing

This is one of the most fascinating applications for Agentic AI; Orchestrating line changes in contract manufacturing environments.  Documentation, tracking  and documenting fixtures and automation, leveraging machine vision systems, training, supply chains, forecasting and IQ/OQ/PQ (validation) activities can all be coordinated through Agentic AI platforms.

Some Examples of AI in IoT Emerging Demand

Personalized Healthcare

AI-driven medical devices can deliver patient-specific insights, aligning with precision medicine trends and creating new market niches.  OEMs are driving hard in this direction and the outsourced engineering and manufacturing vendors that are positioned to support this level of development will have an advantage.

AI-Driven Product Innovation

AI enables the development of novel devices: adaptive prosthetics, real-time diagnostic imaging tools, predictive monitoring systems, and robotics with learning capabilities.  OEMs will be searching for support in this area.

Global Market Expansion

Emerging markets are investing heavily in AI-enabled healthcare solutions. Firms that scale AI-driven products globally may capture untapped demand.

Threats

Along with identifying potential threats to the business, and in this particular case, threats to adopting and leveraging AI, I also like to include Contingencies.  Anticipatory actions the company can take to mitigate the risks identified.  Some possible threats that can emerge with the adoption of AI technology that you may wish to consider include:

Cybersecurity and Data Privacy Risks

AI requires massive amounts of sensitive patient data. Breaches or non-compliance with data protection laws (HIPAA, GDPR, etc.) can result in damages to a companies financials and reputation.  Best to have cybersecurity on your radar in the GOST.

Ethical and Bias Concerns

AI models trained on unrepresentative datasets may produce biased or unsafe outcomes. Missteps can lead to recalls, litigation, or loss of trust.  Working closely with clients will help curtail the selection of misaligned datasets.  The old truism, GIGO, garbage in, garbage out, still very much applies.

Intense Competition

Tech giants, startups, and cross-industry entrants are aggressively entering the medical AI space. Traditional firms also risk being outpaced by more agile competitors.  This is where protecting the company’s niche in the market space is critical.  Big tech has taken a run at digital health before…and taken a beating.  Adopting AI in a strategic manner will help level the competitive playing field while fully leveraging your core competencies and amplify your niche positioning.

Regulatory Delays

Shifts in regulatory requirements could stall product launches, increase costs, or render R&D investments obsolete.  This risk primarily falls upon the OEMs, but it does shine a light on the importance of speed-to-market intensifying going forward.

Capital Intensity

AI integration requires significant upfront investment in infrastructure, partnerships, and compliance. Firms unable to allocate funding or make even measured investments risk falling behind.  This is where integrating company strategy with AI strategy helps with prioritization, speed to ROI and budgeting.  Creating and following an integrated Strategic Business/AI Strategy maintains control (metrics) while accelerating strategic advantage.

Applying the GOST Framework

The GOST framework (Goals, Objectives, Strategies, Tactics) offers a structured approach for firms to act on insights that emerge from the SWOT analysis.  It’s also a living management document, enabling the company to delegate Tactics (and measure performance) throughout the organization in support of implementing its Strategies.  The implementation of the Strategies enables the achievement of company Objectives.  And attained Objectives, over time, drives the organization, in unison, towards its Goals.

The GOST can be distilled into an easy to digest (for both associates and AI tools) table that is battle tested and proven, time and time again, to drive profitable growth and company valuation. 

Creating actual Goals, Objectives, Strategies and Tactics will emerge through the execution of the company’s SWOT.  That said, here’s an example of the layout:

The GOST section of the Strategic Business Plan fans out from here in this format.

Integrating SWOT with GOST

The SWOT analysis identifies internal and external factors influencing AI adoption (and other business factors that effect success), while the GOST framework translates those insights into action.

Long-Term Perspective: AI-Driven Transformation by 2030

While the 2026 planning horizon focuses on integration, our experience and research indicates the long-term vision extends further:

From Devices to Platforms: Firms evolve from single-product models to integrated AI platforms supporting diagnostics, monitoring, and therapeutic guidance.

Continuous Learning Devices: With regulatory approval, devices will adapt post-market, improving performance based on real-world data.

Patient-Centric Ecosystems: Devices become nodes in personalized healthcare ecosystems, integrating wearables, hospital systems, home care and AI diagnostics.

Global Equity in Healthcare: AI-driven devices reduce disparities in access by enabling cost-effective diagnostics in underserved regions.

Conclusion

For medical device design, development and manufacturing firms, 2026 represents a pivotal year for integrating AI into core business models. By applying SWOT and GOST frameworks now, firms can balance innovation with compliance, mitigate risks, budget accordingly, and align organizational capabilities with emerging medical device OEM customer needs. Strategic planning must extend beyond technical adoption to encompass data governance, workforce transformation, and ethical leadership. Those who succeed will not only capture market share but also help define and shape the future of patient care in an AI-enabled era.

© 2025, Performance Transformation, Inc.

Sources

(1) Medical Device Outsourcing Market Size, Grand View Research, 2023.

(2) U.S. Medical Device Outsourcing Market, Market, Research and Markets, April, 2025.

(3) U.S. Medical Device Contract Manufacturing Market Size, Share, and Trends 2025 to 2034, Nova Advisors, July 2025.

(4) Medical Device Outsourcing Market , Tech Sci Research, https://www.techsciresearch.com/report/medical-device-outsourcing-market/28343.html, 2025.

(5) AI in IoT Market Research Report, Market Research Future, September, 2025.

(6) AI in Medical Devices, Global Research Report 2025, The Business Research Company, January, 2025.

(7) Agentic AI In Healthcare Market (2025 - 2030), Grand View Research, 2025, ID: GVR-4-68040-529-7.

(8) Agentic AI in Healthcare Market Size & Trends Report, Medi-Tech Insights, 2024, https://meditechinsights.com/agentic-ai-in-healthcare-market/.

(9) The Future of Healthcare 2030: How Generative and Agentic AI Will Redefine Patient Care., https://www.puretechnology.in/blog/. 

(10) Agentic AI in Healthcare: 2025 Industry Shift & Future Impact, Kokexo Labs, August, 2025, https://kodexolabs.com/agentic-ai-in-healthcare/. 

(11) Third Eye Data, https://thirdeyedata.ai/top-25-agentic-ai-use-cases-in-2025/.