This blog overviews our Customer Story, “SPAN Electrifies Its Product Development and Safety with Jama Connect” – Download the entire story HERE.
SPAN Electrifies Its Product Development and Safety with Jama Connect
“By implementing Jama Connect, our teams are able to maintain transparency across stakeholders, streamline communication, and ensure alignment on project goals. This integrated approach reduces redundant efforts and helps accelerate product development cycles while maintaining compliance and quality standards,” – Arnaldo Arancibia, Senior Staff Systems Architect, SPAN
ABOUT SPAN
SPAN is an innovative company revolutionizing the home energy market with smart electrical panels, EV chargers, and energy storage systems. Headquartered in San Francisco, SPAN emphasizes sustainability and cutting-edge technology to deliver smarter energy solutions for homes, advancing how people interact with energy systems.
CUSTOMER STORY OVERVIEW
SPAN needed to improve traceability of requirements across product ideation, systems, hardware, and software development, while ensuring compliance with critical safety standards such as UL 916, UL 60730, UL 1998, UL 3141, UL 1741, and UL 9540, among others. As requirements grew more complex and teams scaled across functions, the startup recognized the need to replace its manual process for managing traceability in spreadsheets based on product requirements documents (PRDs) in Confluence.
To address these challenges, SPAN selected Jama Connect for its centralized platform that enables cross-functional collaboration and alignment using an easy-to-use, single source of truth for managing its systems, hardware, and firmware requirements, tests plans, and compliance documentation.
Streamlined system validation, reducing timelines by up to 25% through effortless tracing and organized requirements management
Reviews with feedback and questions were reduced to two cycles, which expedited the way each new feature started being implemented
Fewer delays and improved efficiency through automatic syncing of tasks in Jira and requirements using Jama Connect Interchange™
Efficient reuse of requirements and tests for shared components between existing and next-generation products using Jama Connect’s Reuse and Sync capabilities
“Using Jama Connect for test reporting has increased my team’s visibility significantly. The ability to add custom cycles and show test progress is a huge help in getting clarity on the stability of our system.” – Paloma Fautley, Systems Integration Manager, SPAN
CHALLENGES
SPAN had various priorities during its growth phase, matched by equally pressing challenges that defined the criteria for a new solution, including:
Difficulty finding information in Confluence and maintaining traceability for complex requirements across projects in spreadsheets
Ineffective cross-team communication and collaboration due to siloed hardware and software departments and their workflows
Struggling with a highly iterative development process involving increasingly complex requirements, while scaling startup operations
EVALUATION
Key stakeholders with experience with Polarion and IBM® DOORS® recognized that Jama Connect was the right solution because of its intuitiveness, flexibility, interoperability, and structured collaboration.
Quick configuration and launch of a customized project structure that encouraged team collaboration and communication
Centralized system providing a single source for tracking changes and ensuring alignment with product safety standards
End-to-end traceability across hardware and software requirements and tests with connectivity to other development tools
“Jama Software’s core principles of collaboration, innovation, and customer focus have created a ‘Jama Connect culture’ at SPAN that encourages engineers to think systematically about requirements from development to testing, which are now central to their operations.” –Arnaldo Arancibia, Senior Staff Systems Architect, SPAN
Since implementing Jama Connect, SPAN has realized significant benefits from the solution that have contributed to greater confidence and speed in its product development process.
Savings of about three months of system validation due to ease of tracing and organizing requirements
Reviews with feedback and questions were reduced to two cycles which expedited the way each new feature started being implemented
Fewer delays and improved efficiency through automatic syncing of tasks in Jira and requirements in Jama Connect using Jama Connect Interchange
Efficient reuse of requirements and tests for shared components between existing and next-generation products using Jama Connect’s Reuse and Sync capabilities
Jama Software is always looking for news that would benefit and inform our industry partners. As such, we’ve curated a series of customer and industry spotlight articles that we found insightful. In this blog post, we share an article from AMA, titled “Augmented Intelligence in Medicine” and originally published on October 21, 2025.
Augmented intelligence in medicine
Artificial intelligence vs. augmented intelligence
The AMA House of Delegates uses the term augmented intelligence (AI) as a conceptualization of artificial intelligence that focuses on AI’s assistive role, emphasizing that its design enhances human intelligence rather than replaces it.
AMA policy on AI development, deployment and use
The AMA is committed to ensuring that AI can meet its full potential to advance clinical care and improve clinician well-being. As the number of AI-enabled health care tools continue to grow, it is critical they are designed, developed and deployed in a manner that is ethical, equitable and responsible. The use of AI in health care must be transparent to both physicians and patients.
In addition to medical devices, AI is increasingly used in health care administration or to reduce physician burden, and policy and guidance for both device and non-device use of health care AI is necessary. Recognizing this, the AMA has developed new policy (PDF) that addresses the development, deployment and use of health care AI, with particular emphasis on:
Health care AI oversight
When and what to disclose to advance AI transparency
Generative AI policies and governance
Physician liability for use of AI-enabled technologies
AI data privacy and cybersecurity
Payor use of AI and automated decision-making systems
Physician sentiments on AI
In 2023, the AMA conducted a comprehensive study of over 1,000 physicians’ sentiments towards the use of AI in health care including current use and future motivations for use, key concerns, areas of greatest opportunity and requirements for adoption. Given the rapidly evolving AI landscape across health care, the AMA repeated the study in late 2024 (PDF). The objectives of this research remain:
Capturing the sentiment among practicing physicians regarding the increased usage of AI in health care
Evaluating AI use cases based on their familiarity, relevance, and usefulness
Identifying key resources and areas of need for physicians to consider implementation of AI tools to their practice
Physicians largely remain enthusiastic about the potential of AI in health care, with 68% seeing at least some advantage to the use of AI in their practice, up from 65% in 2023. We also saw use of AI increase from 38% in 2023 to 66% of physicians reporting they use some type of AI tool in practice in 2024.
However, there are still key concerns as physicians continue to explore how these tools will impact their practices. Implementation guidance and research, including clinical evidence, remain critical to helping physicians adopt AI tools.
Physician sentiments study on AI: AMA’s latest study on physician sentiments around the use of AI in heath care: motivations, opportunities, risks and use cases. Read Now (PDF)
AI is playing an increasingly important role at all stages of the medical education continuum, both as a tool for educators and learners and as a subject of study in and of itself. AI has the potential to transform the educational experience as a part of precision education and transform patient care as a part of precision health. Learn more about how AI can impact medical education.
In October 2025, AMA launched the Center for Digital Health and AI to put physicians at the center of shaping, guiding and implementing AI tools and other technologies that are transforming medicine.
AMA welcomes the federal government’s new 2025 action plan on AI and the opportunity to work with the administration to address key areas in shaping AI regulation, policy and implementation. Learn more.
An AMA issue brief (PDF) provides a brief overview of recent state legislative activity and discusses three key AI policy areas for state legislative/regulatory activity: health plan use of AI, transparency and physician liability.
To develop actionable guidance for AI in health care, the AMA reviewed literature on the challenges health care AI poses and reflected on existing guidance. These findings are published in a paper in Journal of Medical Systems:Trustworthy Augmented Intelligence in Health Care.
The current CPT® code set drives communication across health care by enabling the seamless processing and advanced analytics for medical procedures and services.
AMA offers several resources to provide guidance on the updated CPT® code set for classifying various AI applications as well as advisory expertise through the Digital Medicine Payment Advisory Group (DMPAG). DMPAG identifies barriers to digital medicine adoption and proposes comprehensive solutions on coding, payment, coverage and more. Stay up-to-date on the criteria for CPT® codes, access applications and read frequently asked questions.
Stop Scrambling for Submissions. Build Readiness Into Your Process With AI.
Regulatory submissions often become a stressful, last-minute rush, increasing risk, rework, and frustration. But what if you could embed submission readiness into your process from the start? Artificial Intelligence (AI) is making this a reality by connecting requirements, regulatory guidance, and ongoing monitoring seamlessly throughout the product lifecycle.
From Requirements to Regulatory: How AI Is Transforming Submission Readiness
Tom Rish: Thank you to everyone for being here today. We have a very exciting webinar about AI, a hot topic, of course, as always, and so I’m excited to dive into it. Before we do, I just want to talk very briefly about Jama Software and what we do. I know some of you have watched previous webinars, and you know all about this, but I want to give a high-level overview and talk just a little bit about how we are looking to incorporate AI to make your life easier when it comes to requirements management. So first, Jama Connect ®. As you all know, when it comes to launching a product, you have to keep track of all your requirements, all of your risk items, all of your testing, and everything like that. It can be a lot of work, especially on spreadsheets or disjointed systems, whatever it is you use.
And at Jama Software, what we’re trying to do is make it simple for you. We want you to focus on designing. We want you to focus on testing. We want you to focus on important things like the safety of the patient and not worry as much about paperwork and organizing everything. A lot of times, as you know, that’s done at the end, and it’s a checkbox activity. But we have a system, as you can see there on the left. I know many of you are used to a lot of documentation and everything. We want to bring that into a very organized V model that you’ve all seen there. Start with user needs. Enter those right into the system, build as you go. We can connect all of the systems you use, whether it’s software products, and you’re using a lot of things like Jira, GitHub, things like that, all your test systems, but we want to keep things organized.
Rish: What’s cool about Jama Connect is that we work with all industries, but we have frameworks specifically for medical devices. So out of the box, we’re able to build a framework where you can match it to your processes to track your user needs, design controls, risk management, and all of your tests. We have real-time collaboration so that you can do all of your reviews and comments in the software, create libraries, and release things. And finally, we have the AI guidance that I’m here to talk about today.
A couple of things here on this slide. This is mostly focused on requirements management. One of them is there today and available for use. Some of our customers are using it, and we’ve gotten some good feedback. Some of these here are things that are coming in the future. First thing that we have here today, though, is a scoring system. So when you enter your requirements into Jama Connect, we have AI that scans through INCOSE and EARS guidance and tells you how well this requirement is written. So it gives you a scoring system to tell you, “Hey, this one looks pretty good,” or, “This one doesn’t, and here’s why this is the rule or the guidance that it doesn’t quite meet.” So that’s ready to use today. I’ve talked to a few customers already who have said how helpful it has been for downstream operations like testing to create better testing and things like that.
We’re also working on some things where we will help rewrite requirements if needed. So not only does it give you scores, but help you rewrite them so that they can match the guidances better. So if you give it an initial draft of a requirement, we’ll go through, we’ll score it, but we’ll also give you some recommendations for changing it.
I think ideally everyone’s probably wondering how you just create them for us. So we are looking into some ways that we can enter some project inputs into the software, and then it will give you some requirements for you. So that will be in the future, along with PDF parsing. A lot of you come with existing documentation already. You might have requirements documents, software specification documents, things like that. We’re working on some AI features that will take those and create requirements automatically for you in the structure that they are.
Rish: A couple of other things. One thing that is new now, again, is test case generation. When you have your requirements in there, what we want to do is help you create good testing and guidance for creating the right acceptance criteria and things like that for your testing. Also, looking at an AI assistant, I think everyone is used to AI assistance these days, but a more conversational workflow where you can enter information into the software, and we’ll give you some guidance and feedback on that. Also, looking into ways that we can take your requirements and give you tips on how to link them together better, create better relationships, and finally help with reviews to detect areas that maybe are high risk.
I think later on, what we’re going to talk about is how the FDA and other regulatory bodies are starting to incorporate AI. So what we want to do is help you get it right up front so that when it’s sent over there, you feel good about everything. So that’s a little bit about Jama and how we’re using AI today. Now for the main event, I’m excited to pass it over to Adam. I met Adam at the MedTech conference in San Diego. And when I went up to his booth, I was instantly impressed. I think as a product development engineer, I spent a lot of time searching through the FDA databases.
And there are a lot of them, as I’m sure you all know, and there is excellent information in those databases. The challenging part is that it’s hard to go to each one every time and find what you need. The interfaces are a little outdated at times as well. You can find everything, but it’s just not easy. And what I always thought is, why can’t anybody scrape this information or pull this information and use it in a better format and make our lives easier? And that’s exactly what Adam and his team are doing. And so I’m excited to hand it over to him, and he will tell you more about Agent Astro and give some practical tips about how to better use AI throughout your process.
Navigating FDA AI Guidance for Medical Devices: A Practical Guide
For medical device professionals, the integration of Artificial Intelligence (AI) and Machine Learning (ML) represents a monumental leap forward in innovation. However, this progress comes with significant regulatory hurdles. As AI algorithms evolve, so do the rules that govern them, leaving many development, quality, and regulatory teams struggling to keep pace. Failing to understand and adapt to the latest FDA AI guidance can lead to submission delays, compliance issues, and costly rework.
This guide delivers a practical overview of the evolving FDA regulatory framework for AI and ML-based medical devices, drawing on both recent draft guidance and the agency’s longer-term action plans. We highlight essential concepts including the Predetermined Change Control Plan (PCCP), Good Machine Learning Practices (GMLP), and Real-World Performance (RWP) monitoring and show how these shape the compliance landscape for manufacturers.
TL;DR: The FDA is moving toward a holistic Total Product Lifecycle (TPLC) regulatory approach for AI/ML-enabled medical devices, emphasizing continuous monitoring, clear GMLP, and mechanisms for pre-planned algorithm updates. Robust, traceable documentation, and proactive lifecycle risk management are now essential for compliance and product success.
The FDA’s Evolving AI/ML Regulatory Framework
The FDA has signaled its commitment to adapting device oversight in response to rapid advances in AI/ML. Traditionally, regulatory submissions were point-in-time events. Now, regulators recognize that adaptive, learning systems require ongoing oversight, especially as software “learns” from real-world experience.
Key foundational documents illustrate this evolution:
FDA’s 2021 AI/ML-Based Software as a Medical Device (SaMD) Action Plan: This action plan lays out five pillars to modernize oversight including development of a tailored regulatory framework, advancement of GMLP, fostering transparency with users, promoting methodologies for bias/robustness, and supporting real-world performance pilots.
Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations (Draft Guidance, 2025): This draft guidance details expectations for managing AI within medical devices throughout the entire product lifecycle, including design, labeling, bias mitigation, cybersecurity, postmarket surveillance, and the importance of the Predetermined Change Control Plan.
Clinical Decision Support Software Guidance (2026): Clarifies FDA’s criteria for Clinical Decision Support (CDS) software functions, offering practical examples to distinguish between Non-Device CDS such as software functions excluded from device regulation and those that remain under device oversight.
FDA AI/ML-Enabled Medical Devices List: Provides a current catalog of FDA-authorized devices using AI/ML technologies, helping manufacturers benchmark their projects and understand regulatory precedent.
In summary: The FDA’s approach now encompasses both initial submissions and ongoing, risk-based management, aligning regulatory expectations with the unique characteristics of AI/ML-driven technologies.
Introduced in both the 2021 action plan and expanded in the draft 2025 guidance, a PCCP enables manufacturers to define anticipated modifications to an AI/ML algorithm upfront. The plan specifies “what” may be changed (pre-specifications) and “how” changes are managed (an algorithm change protocol). This approach recognizes the evolving nature of AI/ML models, especially those learning from real-world use.
2. Good Machine Learning Practices (GMLP)
The FDA calls for GMLP, which are best practices covering data management, training procedures, documentation, interpretability, and bias mitigation, all aligned with consensus standards. GMLP underpins both product quality and regulator confidence, reducing the risk of unexpected outcomes or patient harm (See Action Plan Pillar 2).
3. Transparency and User Trust
Both guidance documents emphasize transparency for end users including clinicians, patients, and caregivers. Clear labeling, robust documentation, and transparency about model logic, data sources, and limitations are expected to build trust in AI/ML-powered devices.
4. Real-World Performance (RWP) Monitoring
Unlike static software devices, AI/ML-based products must demonstrate ongoing safety and efficacy. The FDA encourages collection and review of real-world data as part of postmarket surveillance. Manufacturers should implement plans for ongoing performance monitoring by adapting both processes and documentation to ensure device quality over time.
5. Bias Mitigation and Robustness
AI/ML algorithms can inadvertently encode biases from historical datasets. The FDA expects proactive identification and management of bias through diverse, representative training data, ongoing performance validation, and transparent reporting on limitations and subgroup analysis.
Your design history, risk management, GMLP adherence, model versions, data sets, and algorithm updates should all be auditable and linked. Use digital solutions for traceability and compliance, making audit preparation seamless.
Step 3: Prepare and Maintain a PCCP
If your product uses adaptive algorithms, develop a comprehensive Predetermined Change Control Plan. Detail the types of future modifications, associated risk controls, and your process for validating postmarket changes.
Step 4: Embrace Ongoing RWP Monitoring
Postmarket surveillance now means real-world performance tracking including collecting user feedback, monitoring for data drift, bias, and managing field updates in a proactive, traceable way.
Step 5: Differentiate Wellness from Medical Claims
Consult the Wellness Policy to determine if any features of your device are exempt from device regulation and document your rationale.
Frequently Asked Questions
Q: What’s the difference between Software as a Medical Device (SaMD) and AI in Medical Devices (AiMD)? A: SaMD refers to software that is itself a medical device. AiMD is software that is integrated into a physical device. Both fall under the FDA’s AI/ML regulatory frameworks.
Q: Is a PCCP mandatory for all AI-enabled devices? A: PCCPs are expected for devices with adaptive/evolving algorithms. Rigid, non-learning AI products may not need a PCCP, but processes for documenting and justifying updates are still required (draft guidance, 2025).
Q: How should we implement GMLP? A: Follow best practices outlined by the FDA and consensus standards. Ensure your team manages data, training processes, versioning, and labeling in a repeatable, controlled, and demonstrable manner.
Master the Complexity of AI Medical Device Development
The regulatory landscape for AI medical devices is complex, but it shouldn’t stifle innovation. By adopting an integrated approach with a live digital thread, you can manage the intricate web of requirements, risks, and data that define modern device development. This not only prepares you to pass audits with confidence but also empowers your teams to build safer, more effective products faster.
Jama Connect®, enhanced with AI-powered features in Jama Connect Advisor™, provides the end-to-end traceability needed to manage the development of complex AI-enabled systems. Streamline your documentation, automate traceability, and ensure your team is always audit-ready.
Note: This article was drafted with the aid of AI. Additional content, edits for accuracy, and industry expertise by Tom Rish.
In this blog, we’ll recap a section of our recent Expert Perspectives video, “A Method to Assess Benefit-Risk More Objectively for Healthcare Applications” – Click HERE to watch it in it entirety.
Expert Perspectives: A Method to Assess Benefit-Risk More Objectively for Healthcare Applications
Welcome to our Expert Perspectives Series, where we showcase insights from leading experts in complex product, systems, and software development. Covering industries from medical devices to aerospace and defense, we feature thought leaders who are shaping the future of their fields.
Assessing benefit‑risk is a foundational requirement for medical device manufacturers, yet it has long been one of the most challenging aspects of risk management. While risks are analyzed with rigor and precision, benefits are often described qualitatively, making objective comparisons difficult and slowing decision‑making across the product lifecycle.
A new, revolutionary method for assessing benefit‑risk changes that dynamic by unifying benefit and risk into a single, objective framework. Our expert perspectives video, “A Method to Assess Benefit-Risk More Objectively for Healthcare Applications,” offers actionable insights for healthcare innovators aiming to meet rigorous regulatory requirements while ensuring patient safety and efficacy.
In this episode of Expert Perspectives, Richard Matt explains how his method, dubbed the “Grand Unified Theory of Risk Management”, enables medical device companies to perform benefit-risk analyses with unprecedented speed and precision, delivering definitive determinations within minutes. This efficiency allows for multiple assessments throughout a project, unlocking opportunities to refine patient populations, expand product indications, and even use a benefit-risk assessment as a design parameter during development. Beyond product development, this method also provides a robust framework for addressing regulatory requirements, post-market analysis, and quality management system evaluations.
By transforming a traditionally subjective process into a data-driven, objective methodology, Richard Matt’s approach empowers healthcare innovators to bring safer, more effective solutions to market. For a deeper dive into this method and its implications, download the whitepaper from Aspen Medical Risk Consulting.
Below is a preview of our interview. Click HERE to watch it in its entirety.
Kenzie Jonsson: Welcome to our expert perspective series where we showcase insights from leading experts in complex product, systems, and software development. Covering industries from medical devices to aerospace and defense, we feature thought leaders who are shaping the future in their fields. I’m Kenzie, your host, and today, I’m excited to welcome Richard Matt. Formerly educated in mechanical, electrical, and software engineering and mathematics, Richard has more than thirty years of experience in product development and product remediation. Richard has worked with everyone from Honeywell to Pfizer and is now a renowned risk management consultant. Today, Richard will be speaking with us about his patent pending method to assess benefit-risk more objectively in health care. Without further ado, I’d like to welcome Richard Matt.
Richard Matt: Hello. My name is Richard Matt, and I’m delighted to be speaking with you about our general solution to the problem of assessing whether the benefit of a medical action will outweigh its risk. I’ll start my presentation by saying a few words about my background and how this background led to the benefit-risk method you’ll be seeing in the presentation.
To understand my background, it really helps to go back to the first job I got out of undergraduate school. I graduated with a degree in mechanical engineering and an emphasis in fluid flow. And my first job was in the aerospace industry at Arnold Engineering Development Center, at a wind tunnel that Baron von Braun designed. I worked there as a project manager, coordinating various departments with the needs of a client who brought models to be tested. These are pictures of the ADC’s transonic wind tunnel with its twenty-foot by forty-foot long test section that consumes over a quarter million horsepower when running flat out. Those dots in the walls are holes, and a slight suction would pull the out on the outside of the wall to suck the air’s boundary layer through the holes. So a flight vehicle appeared more closely to match its flight air characteristics in free air. It was amazing place to work.
We could talk about aerodynamic issues and thermodynamic issues like why nitrogen condenses out of the air at mach speeds above six or why every jet fighter in every country’s air force has a maximum speed of about mach three and a half. But to stay on the topic of benefit-risk, the reason or my intro to this, the reason I was brought this up was that I saw here firsthand the long looping iterations that came from different technical specialties, each approaching the same problem from the respective of their technical specialty. I found it very frustrating and the, following analogy very apt, after getting, so each of our technical specialties would look at the same problem, the elephant from their own view. And I found myself getting frustrated with my electrical and software engineering coworkers, that they didn’t understand what I was talking about, but I knew realized soon I didn’t understand what they were talking about either.
So I decided I wanted to become part of the solution to that problem by going back to graduate school and getting myself rounded out and my education so I could talk to these folks from their perspective also. So I went back to grad after mechanical and undergraduate, went back to graduate school in electrical and mathematics and picked up enough software. I started teaching, programming also in college. I developed there a solution for the robot arms in those wind tunnels to to control a robot arm for every possible one, two, or three rotational degree of freedom arm, and that was my graduate thesis. After I completed my thesis, I felt empowered to start, my work doing going wherever I wanted doing whatever I wanted to do and realized that if I wanted to do anything significant, it would take many years, and I decided to focus on teamwork. Does that sound pretty good?
Matt: My ability to work across technical boundaries enabled me to bring exceptional products to the market. For instance, I brought an Internet of Things (IoT) device to the market during the 1990s before Internet of Things was a thing. My leadership in product development advanced rapidly, culminating in as a VP of Engineering at a boutique design firm in the Silicon Valley.
And, the combination of the breadth of my formal training and my system perspective for solving problems has really helped me work across continue to work across boundaries, so that I’ve worked for companies to help them establish their pro product requirements, trace requirements, do V and V work. I’ve done a lot of post-market surveillance work. I established internal audit programs. I’ve been the lead auditee when my firm is audited. Done had significant success accelerating product development and has been on work on. So mixed in with all of these works, I special I started specializing into risk management as consulting focus versus something I just did normally during development.
And since the defense of a patent requires notice, I’ll mention that the material here is being pursued on the patent, and, would like to talk with anyone who finds this interesting to pursue after you’ve learned about it. So let me start my presentation on benefit risk analysis by talking about how important it is to all branches of medicine and the many problems we have implementing it. The solution I’m gonna come up with, I’ll just outline here briefly so you can follow as we’re going through the presentation. I’m gonna first establish a single and much more objective metric to measure benefit and risk than people traditionally use. I’ll be accumulating overall benefit and risk with sets of metric values from this first metric. And finally, we’ll show how to draw a conclusion from the overall benefits and risk measurements of which is bigger benefit or risk.
So in terms of importance, historically, benefit-risk has been with medicine for millennia. It’s a basic tenant to all of medicine. The first do no harm goes all the way back to the quarter of Hammurabi 2,000 BC, and it legally required physicians to think not just about how they can help patients with treatment or what harm they might cause to treatment and making sure that the balance of those two favor the patient is very much the benefit-risk balance that we look at today. The result we’re gonna talk about is gonna be used everywhere throughout medicine with devices, with drugs, with biologics, even with clinical trials.
So is that fundamental cross medicine? How it’s used currently?
If you are in one of the ways developing new products, benefit-risk determinations have to be used in clinical trials to show that they’re ethical to perform, that we’re not putting people in danger needlessly. Benefit-risk determinations are the final gate before a new product is released for use to patients. And I have a quote here from a paper put out by AstraZeneca saying the benefit-risk determination is the Apex deliverable of any r and d organization. There’s a lot of truth to that. It’s the final thing that’s being put together to justify a product’s release. And so it has a very important role here for FDA and has a very important role for pretty much the regulatory structure of every country, including the EU.
Matt: In terms of creating a quality system, every medical company is required to have one. Benefit-risk determinations are used to assess a company’s quality system. This is per the FDA notice about factors on benefit-risk analysis. When regulators are evaluating company’s quality system, they’ll use benefit-risk to determine if nothing should be done, if a product should be redesigned, if they should take legal actions against a company of a range of possibilities from replacing things in the field to stopping products from being shipped. It’s also a key in favorite target for product liability lawsuits, because of how subjective it is, and we’ll get to that in a moment. It can also be used for legal actions against officers. So benefit risk is a really foundational concept for getting products out and keeping products out and keeping companies running well. Just a bit of historical perspective of medical documentation and development. We have here, I cited four different provisions of the laws, regarding medical devices in the United States. This is a small sampling.
The point I’m trying to make here is that each of these summaries of the laws discuss continually evolving, continually growing, more rigorous standards for evidence, more detailed requests for information from the regulators to the instrumentation development companies to the product development companies. So first, medical products are heavily regulated. We have the trend of increasing analysis and rigor. Per ISO 142471, and this is an application standard that is highly respected in the medical device field. A decision as to whether risks are outweighed with benefits is essentially a matter of judgment by experienced and knowledgeable individuals.
And this is our current state of the art.
Not that everybody does it this way, but this is the most common method of performing benefit-risk analysis. And benefit-risk analysis by this method, has a lot of problems because it’s based on the judgment and it’s based on individuals, and both of those can change with different settings. That’s why it’s a favorite point of attack for product liability lawsuits.
This quote was true in 1976, when medical devices were put under FDA regulation, but significantly remains unchanged nearly fifty years laters. Benefit-risk determinations are an aberration and that unlike the rest of medicine, they have not improved over time. They’ve remained a judgment by a group of individuals. In, twenty eighteen, FDA was, approached by congress to set a goal for itself of increasing the clarity, transparency, and consistency of benefit risk assessments from the FDA.
This was in human drug review as the subject, and the issue was that various drug companies had gotten very frustrated with the FDA for disagreeing with their assessments of what benefit-risk should look like. And to repeat again, when you have a group of individuals making a judgment, that’s gonna lead to inconsistencies because both the group and their own individual judgment will vary from one situation to the next. I have another, quote here from the article from AstraZeneca. The field of formal and structured benefit-risk assessments is relatively new.
Matt: Over the last twenty years, there’s still a lack of consistent operating detail in terms of best practice by sponsors and health authorities. So this is an understatement, but a true statement. We have had a lot of increasing effort over the last few years because if people are dissatisfied with the state of benefit-risk assessments, they want to do better than this judgment approach. And so there have been a plethora of new methods developed. I’ve found one survey here that summarize fifty different methods just to give you an idea of how many attempts there are. And I went through those fifty methods.
The other thing that’s interesting to see is the FDA’s attempt to clarify benefit-risk assessments. I have here five guidance documents from the FTA, and I would put forth the proposition that anytime you need five temps five attempts to explain something, it means you didn’t understand the thing well in the first place or failing about a bit trying to get it done right. I think this is also held up by the drug companies, pressure on congress to get FDA to improve their clarity and consistency of benefit-risk assessments.
So here’s the, fifty methods that I found in one study of benefit-risk assessments. They have them grouped into, a framework, metrics, estimate techniques, and utility surveys. These are the fifty different methods, and I’ve gone through each one of them. And they all have fundamental problems. They, I’m going through them a bit slowly. Like, here’s one, from the FDA, another benefit risk assessment. Health-adjusted life years are one of the few that uses the same metric for benefit and risk. Number needed to treat is a very popular indication for a single characteristic, but you can’t integrate that across the many factors that needed to do benefit-risk assessment.
And so we’ve gone down the rest of these, methods. If I group these fifty methods by how they accumulate risk, I get a rather useful collection. Most of the methods do not consider all the risk-benefit factors for benefit-risk situation. They will pick on just one factor. And you can’t combine the factors with themselves or with others. It’s simply looking at one factor by itself. So it’s an extremely narrow view of benefit-risk for most of these. The few methods that do look at all the risk-benefit factors, most of them start with what I call the judgment method, where you’re forced to distill all the factors down to the most significant few, only four maybe four to seven methods, four to seven factors.
So either the methods consider only one type of, one factor at a time, or they force you to throw away most of the methods and consider maybe four or seven factors is the second method. The third method is they assign numbers to the factors, they’ll add the factors together, and they’ll divide the benefit sum by the risk sum. And if the division is bigger than one, they’ll say the benefit’s bigger than the risk. And if the division is less than one, they’ll say the risk is bigger than the benefit.
Next Generation Nuclear: Reactor Innovations Shaping 2025
The nuclear energy industry is about to undergo a significant change. A new generation of reactor technologies is emerging to offer safer, more economical, and efficient solutions as the world’s power demands rise. These cutting-edge concepts will transform our understanding of nuclear power, going beyond conventional models to provide clean and adaptable energy.
The main advancements in nuclear reactor technology that are anticipated to gain traction will be examined in this post. We will examine innovative designs such as Fast Reactors, High-Temperature Gas Reactors, and Molten Salt Reactors and talk about how they could transform energy production for a sustainable future.
The Evolution of Reactor Design
For decades, traditional nuclear power plants have been reliable sources of carbon-free electricity. However, the industry has moved to developing advanced reactors that improve upon these foundational designs. These next-generation technologies focus on passive safety systems, modular construction, and enhanced efficiency. This evolution allows them to not only generate electricity but also provide industrial heat, support renewable energy grids, and even address nuclear waste.
In addition to the advancements in modular construction and passive safety systems, the development of microreactors is gaining momentum. For instance, NANO Nuclear Energy’s KRONOS Micro Modular Reactor (MMR) represents a significant leap in reactor design. This high-temperature gas-cooled microreactor is designed to deliver 15 MWe (45 MWt) and can operate autonomously during grid outages. Its use of TRISO fuel and passive helium cooling ensures safety and resilience, making it a promising solution for energy resilience in urban and military settings.
We expect to see significant progress in regulatory approvals and pilot projects for these cutting-edge designs. This progress will bring us closer to commercial demonstrations that could reshape the global energy mix.
Innovations to Watch: MSRs, HTGRs, and Fast Reactors
Several advanced reactor types are leading the charge. Each offers unique benefits that make them suitable for different applications, from powering data centers to decarbonizing heavy industry.
Molten Salt Reactors (MSRs)
Molten Salt Reactors represent a significant departure from conventional water-cooled reactors. Instead of solid fuel rods, MSRs use nuclear fuel dissolved in a molten fluoride or chloride salt. This liquid fuel also acts as the primary coolant, operating at low pressure and high temperatures.
This design has inherent safety advantages. If the reactor overheats, a freeze plug melts, and the liquid fuel automatically drains into a secure containment tank where the reaction stops. While commercial applications are anticipated by the mid-2030s, important developmental milestones are expected in the coming year.
High-Temperature Gas Reactors (HTGRs)
High-Temperature Gas Reactors use gas, such as helium, as a coolant and operate at very high temperatures. The high temperature allows them to generate electricity with great efficiency and also makes them ideal for providing industrial process heat for applications like hydrogen production and chemical manufacturing.
The KRONOS MMR, developed by NANO Nuclear Energy, exemplifies the potential of HTGRs. This microreactor is not only designed for multi-decade use but also incorporates features like autonomous operation and resistance to cyber and physical threats. Its modular nature allows for scalability, making it suitable for diverse applications, including military installations and industrial use.
Fast Reactors
“Fast” neutrons are used in fast reactors to maintain the nuclear chain reaction. Compared to conventional reactors, this enables them to extract a notably greater amount of energy from uranium. This technology’s capacity to “breed” its own fuel and consume nuclear waste from other reactors, converting long-lived waste into a useful energy source, is one of its main advantages.
These advanced reactor technologies promise to have a profound impact on the global energy landscape. Their key benefits extend beyond simple electricity generation.
Enhanced Safety and Cost-Effectiveness
New reactor designs incorporate passive safety systems, which safely shut down the reactor using gravity and convection without the need for external power or human intervention. This greatly improves the safety profile of nuclear energy.
These designs frequently incorporate modular construction. By producing smaller, standardized parts in a factory and drastically reducing construction schedules and costs, nuclear power can become a more affordable option, assembling them on-site.
The KRONOS MMR’s ability to operate independently of the main grid and its reliance on passive safety mechanisms highlight the strides being made in reactor safety. These features ensure that critical operations can continue uninterrupted, even in the face of external disruptions.
Integration with Renewable Energy
The operational flexibility of advanced reactors, like TerraPower’s Natrium, makes them ideal partners for renewable energy. They can ramp their power output up or down to balance the variable nature of wind and solar power, providing the grid with a consistent and reliable backbone of clean energy. This ability to integrate seamlessly with renewables is critical for building a stable, zero-carbon energy system.
Decarbonizing Industry
The high temperatures produced by reactors like HTGRs and MSRs can be used to provide process heat for heavy industries such as steel, cement, and chemical production. These sectors are historically difficult to decarbonize. By replacing fossil fuels with clean nuclear heat, advanced reactors can play a key role in helping these industries achieve climate goals.
Challenges and the Road Ahead
Advanced reactors have enormous potential, but there are obstacles in the way of their widespread deployment. Significant challenges that need to be addressed include managing early development costs, gaining public acceptance, and navigating complex regulatory environments.
But things are gathering steam as as investment in these technologies rises. For a number of innovative designs, we expect regulatory approvals to advance, opening the door for additional pilot projects and commercial demonstrations. These projects will provide essential real-world data on performance, safety, and economic viability.
As countries around the world expand their nuclear programs, the ongoing refinement of these technologies will continue. With a keen focus on digital engineering and operational efficiency, advanced reactors are poised to become a cornerstone of a clean, secure, and sustainable energy future.
Summary and Conclusion
The potential of nuclear energy is being transformed by advancements in nuclear reactor technology. The industry is moving toward safer, more adaptable, and more efficient power generation with designs like Molten Salt Reactors, High-Temperature Gas Reactors, and Fast Reactors setting the standard. Despite obstacles, these advancements will move us closer to a time when modern nuclear power and renewable energy sources coexist to meet the world’s energy demands without significant risk to the climate.
Empowering Complex Development with Responsible AI
Streamlining Efficiency and Compliance with Scalable Solutions
Product and system development is entering a new era, driven by AI innovation. Highly regulated industries like aerospace, automotive, medical devices, and financial services are facing unprecedented challenges such as escalating regulatory scrutiny in some cases, rising product complexity, and the relentless demand to accelerate time-to-market. Navigating these challenges requires a balance of innovation, compliance, and efficiency.
Artificial intelligence is beginning to demonstrate its potential in requirements management by automating manual processes, enhancing decision-making, and streamlining compliance. However, harnessing AI’s full potential requires a commitment to responsible AI practices, ensuring transparency, fairness, and security.
This whitepaper explores how AI is shaping the future of product development, offering insights into its applications, best practices for governance, and the role of Jama Software and AWS in delivering scalable, secure, and responsible AI solutions.
A System of Systems (SoS) is a collection of independent systems, integrated into a larger system that delivers unique capabilities
It is difficult to produce accurate predictive models of all emergent behaviors, so global SoS performance is difficult to design
Testing and verifying upgrades to a SoS is difficult and expensive (sometimes prohibitively) due to scale, complexity, and constant evolution
AI Applications in Complex Product Development
1. Challenges in Product Development
Complex product development demands businesses to manage an increasing number of variables, such as system interconnectivity, regulatory requirements, and shorter development
cycles. This intensifies the need for precise requirements management tools.
Modern systems, such as self-driving cars, embody system of systems architectures, integrating hardware, software, AI functionality, and cybersecurity. While this creates immense innovation opportunities, the complexity of these systems presents significant challenges:
Predicting behaviors accurately
Designing test frameworks for integration
Scaling verification and validation processes efficiently
These challenges are amplified as the systems grow in complexity and sophistication. Accurately predicting behaviors becomes increasingly critical as interconnected components interact
in unpredictable ways, potentially leading to performance issues, safety concerns, or unintended outcomes. Addressing this requires advanced modeling and simulation techniques capable
of capturing the intricate relationships across subsystems.
Designing effective test frameworks for integration presents its own hurdles. Comprehensive testing must account for the diverse interfaces, software dependencies, and hardware configurations found in modern systems. Without a robust plan, teams risk delays, inefficiencies, and gaps in system validation that can lead to compliance failures or product recalls.
Scaling verification and validation processes to match the demands of high-complexity systems also requires significant innovation. Traditional, manual methods are often unable to keep pace,
resulting in slowed time-to-market and increased resource consumption. Automated solutions offer a scalable pathway, providing traceability, consistency, and efficiency needed to manage
these complex operations effectively.
Ultimately, organizations must balance innovation with rigorous oversight to address these challenges while ensuring safety, reliability, and compliance. Adopting tools designed for enhanced requirements management, streamlined traceability, and automated testing is paramount for achieving these goals in an evolving technological landscape.
AI-driven solutions are addressing these challenges in profound ways:
Automating Requirements Validation
AI uses natural language processing (NLP) to verify that project requirements are complete, precise, and testable
By identifying ambiguous requirements early, businesses reduce the risk of failures
Automated test case generation cuts time and ensures that all requirements are tested
AI-driven solutions are fundamentally transforming the way businesses address traditional challenges in requirements management and validation. Through the use of natural language processing (NLP), AI automates the validation of project requirements by ensuring they are complete, precise, and testable. This advanced capability allows ambiguities or inconsistencies within requirements to be identified early in the development process. By addressing potential issues proactively, businesses can significantly reduce the risks associated with failures, enhancing overall project efficiency and success.
Ensuring Regulatory Compliance
AI tools can help map requirements to stringent regulatory standards in sectors such as aerospace, defense, automotive, and medical devices
Automated monitoring ensures continuous compliance throughout the product lifecycle, minimizing risks
Ensuring regulatory compliance is critical for organizations operating in highly regulated industries such as aerospace, defense, and medical devices. AI tools can play a pivotal role in this process by mapping requirements to stringent regulatory standards, ensuring that all necessary conditions are met without manual oversight. These tools offer automated monitoring, which enables continuous compliance throughout the product lifecycle. By reducing the likelihood of human error and streamlining the regulatory process, businesses can minimize risks and maintain adherence to evolving standards, ultimately supporting the success and longevity of their projects.
Accelerating Development Cycles
Predictive analytics can enable immediate impact assessments of change requests, minimizing rework and speeding up delivery timelines
Predictive analytics play a crucial role in accelerating development cycles by enabling immediate impact assessments of change requests. This capability minimizes rework, allowing teams to address potential issues swiftly and efficiently. By streamlining workflows and reducing delays, organizations can significantly speed up delivery timelines, ensuring that projects are completed on schedule while maintaining high-quality standards.
Enhancing Collaboration
Distributed teams benefit from AI-powered traceability that links requirements, tests, and design components in real time
Efficient collaboration is critical for success, especially for distributed teams. Jama Connect enhances collaboration by providing AI-powered traceability that seamlessly links requirements, tests, and design components in real time. By fostering better communication and streamlining the sharing of critical project information, Jama Connect empowers teams to work more cohesively, reducing misunderstandings and improving overall productivity.
Mergers and Acquisitions in MedTech: Positioning Your Company for Success
The MedTech Mergers and Acquisitions (M&A) scene is more active than ever. As global healthcare needs grow and regulatory landscapes shift, strategic acquisitions are becoming a key route to innovation, growth, and market expansion. For emerging MedTech companies, understanding this environment and positioning themselves smartly within it can shape their future.
Recent activity shows that opportunity is abundant for companies that are prepared. Strategic buyers are on the lookout for innovative technologies, strong product pipelines, and solid regulatory foundations. But having a breakthrough product is not enough. Success in M&A hinges on preparation, documentation, and systems that can stand up to intense due diligence.
The Current M&A Landscape: A Shifting Landscape
The MedTech M&A scene has evolved dramatically over the past year, marked by fewer deals but significantly larger transactions. While deal volume dropped by over 40%, the average deal size surged to $636 million, driven by strategic acquisitions like Stryker’s $4.9B purchase of Inari Medical and Thermo Fisher’s $4.1B buyout of Solventum’s filtration business.
Strategic Focus: Bigger Bets, Sharper Targets
Major players like Johnson & Johnson and Medtronic continue to lead the charge, but their strategies are shifting. J&J, for example, has spent over $30B on acquisitions since 2022, including Shockwave Medical ($13.1B) and Abiomed ($16.6B). These deals weren’t just about scale as they targeted technologies that redefine standards of care, particularly in cardiovascular intervention.
Medtronic, meanwhile, is leaning into tuck-in acquisitions to transform slower-growth units into innovation hubs. With 11+ deals in the past five years, the company is focused on strategic adjacencies and long-term portfolio optimization.
Trends Driving the Market
Portfolio Shifts: Companies are divesting non-core assets and doubling down on high-growth areas like robotics, diabetes, and structural heart technologies.
Private Equity’s Role: PE firms are increasingly active, both as buyers and partners in divestitures, helping streamline portfolios and unlock value.
Cultural Fit Matters: Executives emphasize that successful deals go beyond financials. Shared values and aligned visions for patient impact are now critical to integration success.
What Strategic Buyers Seek: The Acquisition Criteria That Matter
To attract the right buyer, emerging MedTech companies need to align with what strategic acquirers value most. Here is what consistently matters:
Technology Differentiation & Market Position
Buyers want technologies that offer real clinical advantages such as better outcomes, simpler procedures, or cost savings. Proprietary tech backed by strong patents is especially attractive.
The best targets complement the buyer’s existing portfolio and address unmet clinical needs. Think AI diagnostics that plug into existing imaging platforms or minimally invasive tools that expand surgical options.
Regulatory Clarity
A clear regulatory strategy is a major plus. Companies that have engaged with the FDA, gathered solid clinical data, and understand approval pathways stand out.
Detailed documentation like pre-submission notes, trial protocols, and quality systems reduces risk and speeds up integration. It also boosts valuation.
Commercial Potential
Buyers assess market size, competition, and go-to-market strategy. Companies with clinical relationships, distribution channels, or early traction are more appealing.
Technologies that target large markets with clear reimbursement paths, and show signs of physician adoption, are especially valuable.
Financial Performance and Scalability
Even early-stage companies need to show a viable business model. Efficient use of capital, clear milestones, and scalable operations build credibility.
Detailed financials like cost breakdowns, revenue forecasts, and funding needs help buyers model ROI and integration scenarios.
Getting Acquisition-Ready: Preparing for Due Diligence
Due diligence represents the most critical phase of any acquisition process. Companies that invest in comprehensive documentation and systematic organization significantly improve their chances of successful transactions and favorable valuations.
Product Documentation
Keep everything up to date: design requirements, risk files, and testing protocols. Use document control systems to manage versions and changes.
Requirements management is key. Acquirers want to see how products were developed, validated, and maintained. Full traceability from concept to release builds trust.
Risk & Quality Systems
Risk documentation such as hazard analyses, mitigation controls, and post-market surveillance plans is essential. Quality systems should be fully implemented and certified (ISO 13485 is a big plus).
Design controls should be complete and easy to navigate: planning, inputs/outputs, reviews, V&V protocols, and change logs. A well-organized design and development file makes due diligence smoother and less risky.
Testing and Validation Evidence
Strong testing documentation is essential to prove your product’s safety and performance. This includes everything from software testing protocols and biocompatibility studies to electrical safety tests and clinical evaluations.
To stand out, companies should maintain:
Detailed test plans
Clear procedures
Organized results
This shows a systematic approach to validating product performance. When testing documentation is thorough and easy to navigate, acquirers can quickly assess technical risks and regulatory readiness.
Validation should not stop at product launch. Ongoing monitoring, post-market studies, and performance tracking signal a commitment to continuous improvement — something buyers value highly.
Your IP and regulatory documentation are more than just paperwork — they are strategic assets. Patent portfolios, FDA submissions, and clinical data all play a key role in valuation and deal structure.
To prepare:
Keep patent files current
Document freedom-to-operate analyses
Develop a clear IP strategy
On the regulatory side, maintain organized records of:
FDA correspondence
Clinical trial data
Post-market surveillance reports
Well-managed documentation shows a strong compliance history and gives acquirers confidence in your ability to navigate future regulatory hurdles.
How Jama Connect® Supports M&A Readiness
Requirements management and traceability are critical for M&A success and that’s where Jama Connect shines.
The platform helps companies maintain acquisition-ready documentation throughout the product lifecycle by:
Connecting requirements to design decisions, tests, and regulatory submissions — giving acquirers full visibility into development processes.
Organizing documents with version control — making it easy for due diligence teams to trace product history and compliance.
Generating detailed reports — showcasing the maturity of your quality management system and development discipline.
Supporting collaboration across teams and locations — ensuring documentation integrity even in distributed environments.
With Jama Connect, medical device and life sciences companies can confidently present their development story and proof of compliance, a major advantage during acquisition discussions.
Building Long-Term Value Through Strategic Preparation
The MedTech M&A landscape is evolving fast. As healthcare needs grow and technologies advance, new opportunities are emerging for companies that are ready.
Success isn’t just about having a great product. It’s about:
Operational excellence
Regulatory sophistication
Systematic development processes
Investing early in documentation, requirements management, and quality systems pays off. These capabilities lead to faster development, lower regulatory risk, and better product quality.
If you’re looking to strengthen your M&A readiness, start by evaluating your documentation systems. Book a demo with Jama Software to see how structured requirements management can streamline your development and boost acquisition appeal.
Note: This article was drafted with the aid of AI. Additional content, edits for accuracy, and industry expertise by Tom Rish and Decoteau Wilkerson.
An Inside Look at the Airborne Fire Control Radar Market: The Sky’s AI
These days, air superiority isn’t just about speed and firepower; it’s also about data and information. At the center of this data-driven battlespace is the Airborne Fire Control Radar (AFCR), a cutting-edge system that gives pilots unparalleled situational awareness. The AFCR systems on an aircraft act as its eyes and brain, enabling it to track, detect, and engage targets with remarkable accuracy from a considerable distance. They have a significant impact on the outcome of aerial engagements and the effectiveness of combat aircraft, making them vital to military aviation.
This blog will examine the ever-changing AFCR market. We’ll look at the current developments that are fueling its expansion, such as evolving geopolitical environments and technological advancements. The main participants in the industry, their difficulties, and the prospects for this crucial defense technology will also be discussed.
What is an Airborne Fire Control Radar?
Military fighters, bombers, and attack helicopters are the main aircraft equipped with the advanced sensor system known as an Airborne Fire Control Radar. An AFCR offers the high-resolution information required to direct weapons to a target, in contrast to conventional surveillance radar, which merely detects objects. It provides the aircraft’s fire control computer with the target’s range, altitude, speed, and trajectory. This enables the pilot or system to fire cannons or launch missiles with a high chance of hitting a target directly, even if the target is moving quickly or evasively.
It is impossible to exaggerate the significance of these systems. They enable a single aircraft to engage multiple threats at once, monitor large areas of airspace, and discriminate between friendly and hostile forces. To put it simply, an air force that has a better AFCR system has a clear combat advantage.
Current Drivers and Trends in the Market
A number of important factors are propelling the global AFCR market’s steady growth. The main drivers are global air force modernization and geopolitical tensions. Countries are investing in new-generation fighters with cutting-edge technology and updating their current fleets of aircraft with more sophisticated radar systems.
The primary force behind change in the AFCR market is technology. There are two noteworthy developments:
AESA Radar Dominance: The industry standard today is Active Electronically Scanned Array (AESA) radars. Because AESA systems can electronically steer their beams, they can track multiple targets in different directions simultaneously, unlike older mechanically scanned radars. They are essential for contemporary air forces because they are more dependable, more difficult to detect, and more resilient to electronic jamming.
AI and Cognitive Radar: “Cognitive” radars are being produced by combining machine learning and artificial intelligence. These systems have the ability to learn from their surroundings, adjust in real time to new threats, and more accurately separate targets from clutter. By lessening the pilot’s workload and accelerating decision-making, this technology has the potential to completely transform air combat.
Increasing Need for Unmanned Systems
A new area for AFCR systems has been made possible by the widespread use of Unmanned Aerial Vehicles (UAVs), also known as drones. Sophisticated, portable radars are necessary for advanced combat drones to conduct autonomous missions and surveillance. Compact and effective AFCR solutions designed for UAVs will become more and more necessary as their use in military operations grows.
Obstacles in the Market
The AFCR sector still faces many obstacles in spite of its expansion. These difficulties may affect development schedules, expenses, and the general growth of the market.
High Costs of Development and Production
The complexity of AFCR systems necessitates years of study and billions of dollars in funding. They are costly to manufacture and maintain because they require sophisticated electronics and exotic materials. The potential market size may be constrained by these exorbitant expenses, which may act as a deterrent for smaller countries seeking to update their air forces.
The export of sophisticated AFCR systems is strictly regulated since it is a vital military technology. To keep sensitive technology out of the wrong hands, governments enforce stringent regulations. Market expansion may be slowed by these export restrictions and international arms control laws, which can make international trade and cooperation more difficult.
Complexity of System Integration
One of the biggest engineering challenges is integrating a new radar system into an existing aircraft. Aircraft hardware types and avionics interfaces differ from manufacturer to manufacturer, creating interoperability challenges. For the radar to function flawlessly with the aircraft’s other avionics, mission computers, and weapon systems, significant hardware and software adjustments are needed. Program upgrades take longer and cost more because of this complexity.
Prospects for the Future and New Technologies
With ongoing innovation poised to unlock new capabilities, the AFCR market appears to have a bright future.
The shift to multifunction RF systems is among the most exciting developments. Future aircraft will use a single, integrated aperture that can do all of these tasks at once, rather than having distinct systems for communications, radar, and electronic warfare. This will significantly increase an aircraft’s capabilities while decreasing its size, weight, and power consumption.
The creation of distributed and networked radar is another expanding field. This idea uses real-time radar data sharing between various platforms, including fighters, drones, and satellites, to produce a single, complete image of the battlespace. This networked strategy increases the effectiveness and survivability of all friendly assets and makes it nearly impossible for an adversary to hide.
In conclusion, a market ready for innovation
A key component of the contemporary defense sector is the market for airborne fire control radars. The need for more capable and intelligent radar systems will only increase due to technological advancements and the ongoing requirement for air superiority. Despite ongoing regulatory obstacles and exorbitant costs, the industry is progressing. The sky’s eye is growing more potent than before with the introduction of AI-driven cognitive radars, multifunction systems, and networked capabilities, giving pilots the advantage they need to manage the air.
Note: This article was drafted with the aid of AI. Additional content, edits for accuracy, and industry expertise by Mario Maldari, Cary Bryczek, and Decoteau Wilkerson.
Engineering Governance: The Engine Behind Automotive Excellence
The automotive industry is a world of constant change. Engineering governance is the invisible engine that keeps everything humming smoothly.
Innovation to support AI-powered software-defined vehicles, electric and hybrid drivetrains, and sustainability initiatives are navigating where the automotive sector is going. But while technology makes headlines, what happens behind the scenes to manage the complexity of automotive development to stay on the right course is just as critical.
What Is Engineering Governance?
Engineering governance is a system of policies, processes, and standards that guides everything from vehicle design to production. It serves as the GPS for automotive engineering teams to ensure that they are building the right vehicles in the right way, so that every decision aligns with regulatory, safety, and quality standards and the broader goals of the company. It helps with the unification of the hardware and software siloes to not only avoid defects resulting in costly recalls and accelerate product development, but also provide an holistic operational overview of the complete engineering organization reducing risks and driving efficiency.
When designing a new hybrid, for example, engineering governance ensures that the final product adheres to emissions laws, passes stringent safety tests, and meets customer expectations. It
touches every stage of a vehicle’s lifecycle — from initial design to when it rolls off the assembly line and beyond.
AI is accelerating development of systems to enhance safety, enable convenience, and make maintenance more effective. Engineering governance will ensure that concerns about cybersecurity risks and ethical decision-making are addressed. OEMs face the significant challenge of seamlessly integrating diverse software from hundreds of suppliers. Modern premium vehicles have more than 100 million lines of software code — far exceeding the 14 million lines of code in the Boeing 787 Dreamliner. Dealing with safe-critical, non-safe, secure, open-source, and proprietary software, each with its own requirements and standards, necessitates robust engineering governance, efficient collaboration, and cutting-edge tools to ensure that the systems coexist harmoniously.
For automotive companies, failure to follow strong engineering governance risks expensive recalls, lawsuits, and fines, as well as harm to customer health and property, leading to significant negative brand impact. Here’s why getting it right matters so much:
Ensuring Compliance with Regulations: Automotive companies operate within a tightly regulated environment. Engineering governance provides a structured approach to ensure compliance with all relevant regulations across markets where vehicles are sold.
Managing Risks Proactively: Helps identify and mitigate risks early before they escalate. Without comprehensive safety and quality testing, defects might surface after vehicles are delivered to customers, rather than during development when fixes are much less costly.
Maintaining Quality Standards: A robust framework ensures that vehicles meet or exceed performance and reliability specifications without cutting corners during design, manufacturing, or testing. It enables a comprehensive view of all aspects of vehicle safety by unifying hardware and software development siloes.
Driving Innovation Responsibly: Innovation without governance can spiral into impractical or unsafe ideas. Engineering governance ensures innovation is balanced with feasibility, compliance, and cost control. Companies racing ahead with autonomous vehicle technology, for example, need engineering governance to ensure that these vehicles undergo rigorous safety tests, align with evolving regulations, and deliver innovations responsibly.
Achieving Sustainability Goals: Sustainability has become a business imperative for automakers in response to demands from governments and consumers. Engineering governance helps automakers achieve sustainability goals by embedding eco-friendly practices into every stage of development and production.
Here’s how engineering governance plays a role at every step in the development of any new vehicle or system:
Design Phase: Ensures vehicles comply with emissions standards and fuel efficiency standards in the United States, the European Union, and other regions.
Testing and Validation: Frameworks ensure rigorous testing of every component — from the engine to the suspension system and software. Engineers follow defined processes to simulate driving conditions to ensure safety and durability.
Supply Chain Oversight: Identifies system or parts suppliers whose products and processes meet quality and sustainability standards.
Post-Market Monitoring: Even after vehicles are sold, engineering governance mechanisms monitor performance through data collection to identify recurring issues and develop structured response plans to ensure quick fixes that reduce customer dissatisfaction.
For automotive executives, engineering governance is more than a technical concern; it’s a business strategy driving competitive advantage by ensuring that reliable, innovative, and compliant vehicles hit the market faster than the competition. It builds customer trust, bolsters investor confidence, and helps companies stay ahead of the constant changes in regulations.
How Jama Software Helps
Jama Software empowers automotive companies to excel in their engineering governance initiatives by providing a centralized platform called Jama Connect® that ensures traceability, compliance, and collaboration across complex development projects. By facilitating real-time alignment between teams and maintaining a clear connection between regulatory requirements, development processes, and business goals, Jama Connect helps streamline decision-making and reduce risk. This enables organizations to confidently manage innovation, comply with regulatory standards, and accelerate time to market — all while maintaining high levels of quality and accountability.
