Tag Archive for: Requirements & Requirements Management Page 4
Tag Archive for: Requirements & Requirements Management
Jama Connect® Features in Five: Industrial Machinery Development Solution
Streamline Industrial Machinery Development with Jama Connect!
In this Features in Five session, Patrick Garman, Solution Lead for Industrial Automation and Machinery at Jama Software, demonstrates how Jama Connect’s Industrial Machinery Data Model empowers teams to accelerate development and maximize project success in the industrial machinery space.
Key highlights include:
Purpose-built support for complex machinery, from robotic assembly cells to heavy equipment.
Centralized systems engineering with integrated safety, cybersecurity, risk management, and testing.
Tools for improving requirements quality, identifying gaps early, and ensuring seamless traceability.
Introduction to Industrial Machinery Data Model
Hi, everyone. I’m Patrick Garman, Solution Lead for Industrial Automation and Machinery at Jama Software. Today, I’ll introduce our industrial machinery data model and why it’s so powerful for teams building sophisticated machinery. Industrial machinery includes systems like robotic assembly cells, packaging equipment, elevators, and heavy machinery. Any automated system with software, safety, or network components.
Integration of Standards and Systems Engineering
These products must comply with a wide range of standards, and our data model integrates systems engineering, safety, cybersecurity, risk management, and testing into one structure in Jama Connect.
This gives your teams a head start so you can launch products faster without reinventing processes. With predefined structures, traceability models, and workflows, Jama Connect reduces rework and recalls by exposing gaps early. Centralized traceability helps teams respond to change confidently, measure progress, and identify risks before they become problems. At the core is our traceability information model, which enforces good engineering practices, prevents invalid links, and highlights gaps automatically. Let’s see how this works and looks in the tool.
First, here’s the project explorer tree. You’ll notice that it’s organized by product architecture as well as domain. This makes it easy for project members to quickly locate relevant data. And, of course, XAML is more than just a repository for requirements. We’re actively managing those requirements based on stakeholder review and feedback.
Utilizing Live Trace Explorer™ for Traceability
Next, let’s look at Live Trace Explorer. This gives a real-time view of traceability coverage across our project. We can immediately see what’s complete, what’s missing coverage, and so on.
Identifying Gaps in Coverage
This is really one of the biggest value drivers, knowing your gaps early before they turn into late-stage redesign. So let’s drill into one of these gaps right now. So I can see that I have just shy of seventeen percent coverage at the system level.
Using Trace View™ to Add Coverage
I can click that metric in the Live Trace Explorer diagram to open Trace View and find exactly where I need to add coverage. In Trace View, you can see that Jama Connect is prompting me to add coverage where required links are missing.
Creating and Managing Test Cases
And you can take action directly from this view to add that coverage, or we can open a specific requirement for a more detailed view. Here we have a system requirement with missing test coverage. I can author test cases directly in Jama Connect using the add related feature, or I can use Jama Connect Advisor™’s test case intelligence tool to generate suggested test cases, complete with test steps based on the context I provide. But of course, traceability doesn’t end with test coverage.
Jama Connect integrates directly with Jira to track development tasks. Jama Connect also has turnkey integrations for the most commonly used digital engineering and productivity tools. For example, I’m able to link my subsystem requirements to model elements in Simulink, again, with one click, links to the source artifacts. Pulling data from your digital thread into Jama Connect is not about duplicating work. Each team works in the tool fit for their purpose, and that work is reflected in Jama Connect for traceability and in context reporting. For teams managing product lines or customer-specific customizations, we can create catalog or library projects for reusable requirements.
Reusability and Component Management
With reuse, we can easily pull a reusable component and its related requirements into any project, and we can also use sync comparison to see which products a part or component is being leveraged in and how it may vary from what we have in our library. And that concludes our tour of the Industrial Machinery data model in Jama Connect. If you’d like a deeper dive or to learn more about Jama Connect Advisor and our live integration capabilities, please let us know.
How Digitization and Traceability Are Transforming Industrial Manufacturing
Modern industrial manufacturing undergoes frequent transformation as a result of technological innovations. Digitization and traceability have emerged as critical enablers that help manufacturers enhance operational efficiency, ensure regulatory compliance, and achieve sustainable growth in an increasingly competitive global market.
The integration of these processes (and technologies that support and enable them) represents more than a simple upgrade to existing systems. It reshapes how manufacturers approach
product development, quality control, and supply chain management. Companies that successfully implement digitization and traceability solutions position themselves to respond more effectively to market demands, reduce operational risks, and accelerate innovation cycles.
This comprehensive guide explores how digitization and traceability work together to create intelligent manufacturing ecosystems, the specific benefits they deliver, and the practical
considerations for successful implementation. We’ll examine real-world applications, address common challenges, and look ahead to emerging trends that will continue to shape the future of
industrial manufacturing.
For manufacturers ready to embark on this digital transformation journey, understanding these concepts and their strategic implications becomes essential for maintaining competitive
advantage and ensuring long-term success.
Understanding Digitization in Industrial Manufacturing
Digitization in manufacturing represents the systematic conversion of analog processes, systems, and data into digital formats that enable intelligent automation and data-driven decision making. This transformation extends beyond simple computerization to create interconnected networks of smart devices, systems, and processes that communicate seamlessly throughout the manufacturing ecosystem.
At its core, manufacturing digitization involves several key technological components working in harmony. AI-powered systems analyze vast amounts of production data to identify patterns,
predict equipment failures, and optimize manufacturing processes in real time. These intelligent systems learn from historical data and continuously improve their predictive capabilities, enabling manufacturers to make more informed decisions about production scheduling, resource allocation, and quality control measures.
Internet of Things (IoT) devices serve as the sensory network of digital manufacturing environments. Embedded sensors throughout production lines collect continuous streams of data on temperature, pressure, vibration, speed, and countless other operational parameters. This constant monitoring enables manufacturers to maintain optimal operating conditions and detect anomalies before they impact production quality or efficiency.
Real-time data analytics transforms the continuous flow of information from IoT sensors into actionable insights. Advanced analytics platforms process streaming data to identify trends, detect
deviations from normal operating parameters, and generate alerts that enable immediate corrective actions. This capability allows manufacturers to maintain consistent product quality while minimizing waste and downtime.
Cloud computing infrastructure provides the scalable foundation that supports these digital capabilities. Cloud platforms enable manufacturers to store and process massive datasets,
run complex analytical models, and provide secure access to critical information across global operations. The flexibility of cloud solutions allows companies to scale their digital capabilities as their operations grow and evolve.
These components work together to create a comprehensive digital ecosystem where every aspect of the manufacturing process generates valuable data. Production equipment communicates with
quality control systems, inventory management platforms share information with supply chain partners, and maintenance systems coordinate with production schedules to minimize disruption.
The result is a manufacturing environment that operates with improved visibility, control, and efficiency. Manufacturers can track individual products through every stage of production, monitor equipment health in real time, and adjust processes dynamically to meet changing demands or conditions.
The Role of Traceability in Modern Manufacturing
Traceability establishes the ability to track and document the complete history of a product, component, or process throughout its entire lifecycle. In manufacturing contexts, this capability
provides detailed records of materials, processes, quality checks, and handling procedures that enable manufacturers to verify product authenticity, identify sources of defects, and
demonstrate compliance with regulatory requirements.
The significance of traceability extends far beyond simple record-keeping. Enhanced supply chain transparency becomes possible when manufacturers can track components and materials
from their original sources through every transformation and handling step. This visibility enables better supplier relationships, more effective quality management, and faster response
to supply chain disruptions or quality issues.
Improved quality control represents another critical benefit of comprehensive traceability systems. When manufacturers can correlate product defects with specific batches of raw
materials, particular production runs, or individual pieces of equipment, they can implement targeted corrections that prevent similar issues from recurring. This capability reduces waste,
minimizes customer complaints, and protects brand reputation.
Better risk management becomes achievable through traceability systems that provide early warning of potential problems. When manufacturers can quickly identify which products might
be affected by a defective component or problematic production batch, they can take proactive measures to prevent widespread quality issues or safety concerns.
Regulatory compliance requirements across many industries mandate detailed traceability records. Pharmaceutical manufacturers must track ingredients and production processes to
ensure drug safety and efficacy. Food producers need comprehensive records to enable rapid response to contamination issues. Aerospace and automotive manufacturers require detailed
documentation to verify that components meet safety and performance standards.
Several key technologies enable comprehensive traceability in manufacturing environments. Blockchain technology provides immutable records of transactions and processes that create tamper-proof audit trails. Each step in the manufacturing process generates a blockchain entry that cannot be altered or deleted, providing absolute confidence in the accuracy and completeness of traceability records.
Radio Frequency Identification (RFID) systems enable automatic tracking of components, products, and equipment throughout manufacturing facilities. RFID tags attached to items provide unique identification that can be read automatically as products move through production processes, eliminating manual data entry errors and ensuring complete tracking coverage.
Advanced sensor technology continuously monitors environmental conditions, process parameters, and product characteristics throughout manufacturing operations. These sensors generate detailed records of the conditions under which products are manufactured, enabling manufacturers to correlate quality outcomes with specific environmental factors or process variables.
Benefits of Integrating Digitization and Traceability
The strategic integration of digitization and traceability technologies creates benefits that exceed what either approach can achieve independently. This combination enables manufacturers to build intelligent, responsive operations that adapt quickly to changing conditions while maintaining complete visibility into every aspect of their processes.
Enhanced Efficiency and Productivity
Digital traceability systems eliminate many manual data collection and recording tasks that traditionally consumed significant labor resources. Automated data capture through IoT sensors and RFID systems ensures complete and accurate records without requiring dedicated personnel for data entry or verification activities.
Predictive maintenance capabilities emerge when digitization platforms analyze traceability data to identify patterns that indicate impending equipment failures. By correlating equipment
performance data with maintenance records, manufacturers can schedule preventive maintenance activities during planned downtime periods, avoiding unexpected production interruptions.
Process optimization becomes more precise when manufacturers can analyze complete traceability records to identify the specific conditions and procedures that produce the highest quality outcomes. This analysis enables continuous improvement initiatives that incrementally enhance efficiency and product quality over time.
Improved Quality Control
Real-time quality monitoring becomes possible when digital systems continuously track product characteristics throughout manufacturing processes. Instead of relying on periodic sampling
and testing, manufacturers can monitor every product and immediately identify deviations from quality specifications.
Root cause analysis capabilities improve dramatically when comprehensive traceability records enable manufacturers to correlate quality issues with specific materials, processes, or environmental conditions. This detailed analysis capability reduces the time required to identify and correct quality problems.
Batch tracking and recall management become more efficient and accurate when digital systems maintain complete records of which specific materials and processes contributed to each finished product. If quality issues arise, manufacturers can quickly identify all affected products and take appropriate corrective actions.
Supply Chain Optimization
End-to-end visibility throughout complex supply chains becomes achievable when digitization and traceability systems extend beyond individual manufacturing facilities to include suppliers,
logistics providers, and distribution partners. This comprehensive visibility enables more effective coordination and planning across the entire supply network.
Demand forecasting accuracy improves when manufacturers have access to real-time data about inventory levels, production capacity, and customer demand patterns throughout their supply chains. This improved forecasting enables more efficient inventory management and production planning.
Supplier performance monitoring becomes more objective and comprehensive when digital systems track delivery performance, quality metrics, and compliance with specifications. This data-driven approach to supplier management enables better supplier relationships and more effective risk management.
Risk Mitigation and Compliance
Automated compliance documentation reduces the administrative burden of maintaining regulatory records while ensuring completeness and accuracy. Digital systems can automatically
generate the reports and documentation required by regulatory agencies, reducing compliance costs and eliminating the risk of incomplete or inaccurate submissions.
Proactive risk identification becomes possible when analytical systems monitor traceability data for patterns that indicate emerging risks. Early warning systems can alert manufacturers to
potential quality issues, supply chain disruptions, or compliance concerns before they impact operations or customers.
Audit trail integrity improves when blockchain and other tamper-proof technologies ensure that compliance records cannot be altered or deleted. This capability provides regulatory agencies
and customers with complete confidence in the accuracy and authenticity of compliance documentation.
Predictive Maintenance
Equipment health monitoring through continuous sensor data collection enables manufacturers to track the condition of critical production equipment and predict when maintenance activities
will be required. This capability reduces unplanned downtime and extends equipment life.
Maintenance scheduling optimization becomes possible when digital systems analyze equipment performance data, maintenance history, and production schedules to identify the optimal timing for preventive maintenance activities. This optimization minimizes production disruptions while ensuring equipment reliability.
Spare parts inventory management improves when predictive maintenance systems provide advance notice of which components will require replacement and when. This capability enables more efficient inventory management and reduces the risk of production delays due to parts shortages.
Sustainability Benefits
Energy consumption optimization becomes achievable when digital systems monitor and analyze energy usage patterns throughout manufacturing operations. This analysis enables manufacturers to identify opportunities to reduce energy consumption and carbon emissions while maintaining production efficiency.
Waste reduction initiatives become more effective when traceability systems provide detailed information about material usage, production yields, and waste generation. This data enables targeted improvements that minimize material waste and environmental impact.
Circular economy principles become more practical to implement when comprehensive traceability systems track materials and components throughout their entire lifecycles.
This visibility enables manufacturers to identify opportunities for recycling, reuse, and remanufacturing that reduce environmental impact and material costs.
We are excited to announce that Jama Connect 9.32 has set new scalability benchmarks five times greater than legacy systems. As products across industries become multi-disciplinary engineered product lines, the scale and complexity required to manage the product development process has grown dramatically. Jama Connect 9.32 scalability benchmarks:
Scale benchmarks for on-premise or SaaS deployments:
Items per project – 10 million
Items per instance – 100 million
Concurrent users – 10,000
These new scale benchmarks are five times greater than legacy systems’ published benchmarks. Companies across industries have been hamstrung by the inability of legacy systems to scale to the levels required by their engineering teams to speed time-to-market and deliver increasing levels of quality. Systems that cannot scale to 10 million project items and 100 million items per instance force enterprise engineering teams to artificially separate workstreams leading to defects, delays, cost overruns, recalls and warranty costs. Jama Connect is the only system that scales to the levels required.
Companies rely on Jama Connect for consistent and stable performance regardless of project or instance size and with today’s release of 9.32 this capability continues Jama Software’s unparalleled leadership in managing scale. As the only provider of a true multi-tenant SaaS offering, Jama Software provides companies with the ability to offload the burden and cost of hardware, hosting, upgrades and IT management that is required with legacy tool providers.
“The system architecture required to achieve these levels of scalability is foundationally different than current legacy systems,” said Jim Davidson, CTO Jama Software. “This is the result of two years of effort to incorporate the latest technologies and architecture for scalability.”
All Jama Connect customers will be seamlessly upgraded as part of the normal upgrade process.
Jama Connect 9.32 is available today.
Frequently Asked Questions about Jama Software
Q&A Q1: What is the best requirements management software for my industry or team size? A1: The suitability of requirements management software depends on factors such as regulatory demands, system complexity, and collaboration needs. Jama Connect is highly selected by teams in regulated and systems-engineering-heavy industries due to its focus on traceability and structured requirements management.
Q2: What scalability benchmarks has Jama Connect achieved for on-premise or SaaS deployments? A2: Jama Connect 9.32 has achieved the following scalability benchmarks:
Items per project: 10 million
Items per instance: 100 million
Concurrent users: 10,000
These benchmarks are five times greater than the largest known legacy system deployments.
Q3: How does Jama Connect compare to legacy systems in terms of scalability? A3: Jama Connect 9.32 outperforms legacy systems by scaling to 10 million items per project and 100 million items per instance. Legacy systems cannot store or perform at these levels, forcing enterprise engineering teams to separate workstreams, which can lead to defects, delays, cost overruns, recalls, and warranty costs.
Q4: How does Jama Connect support the increasing complexity of modern product development? A4: Jama Connect is designed to handle the growing scale and complexity of multi-engineered product lines. By providing scalability benchmarks of 10 million items per project and 100 million items per instance, it ensures that engineering teams can manage complex product development processes without needing to artificially separate workstreams, reducing the risk of defects, delays, and cost overruns.
Q5: How does requirements management software support scaling across multiple teams and programs?
A5: As organizations scale, requirements management software must support parallel development, standardized processes, and cross-program visibility. Tools like Jama Connect provide centralized requirement repositories and role-based workflows that help maintain consistency across distributed teams and initiatives.
Q6: What makes Jama Connect’s architecture suitable for complex use cases?
A6: Jama Connect’s architecture is built on the latest technologies specifically designed for scalability. This foundationally different system architecture enables it to support 10,000 concurrent users and manage billions of items and API calls in the cloud, making it ideal for complex, large-scale engineering projects across industries.
Q7: How does Jama Connect improve the quality of requirements? A7: Jama Connect improves the quality of requirements by leveraging natural language processing (NLP) through its AI-powered Jama Connect Advisor™. This tool provides guided authoring and multi-statement analysis to optimize the clarity, accuracy, and usability of requirements. It minimizes ambiguity and contradictions, which are responsible for 70-80% of rework costs, and aligns requirements with industry-leading standards like INCOSE Rules and EARS Notation.
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.
Transforming Requirements Engineering with AI to Enhance Clarity, Consistency, and Scalability
As systems grow more complex, traditional processes struggle to keep up, ultimately impacting requirements quality. AI can assist in processing the sheer volume of data, enhancing clarity, consistency, and scalability across workflows.
Join Katie Huckett, Product Line Manager for Advisor/AI at Jama Software, for an exclusive webinar exploring how AI is becoming an essential cognitive amplifier in requirements engineering. Discover how AI is redefining the way teams detect ambiguity, surface hidden conflicts, and maintain alignment at scale.
What You’ll Learn:
Understand why requirements quality is declining under modern system complexity.
Learn the hidden costs of poor requirements and why traditional practices fall short.
Discover how AI amplifies cognitive processing and improves requirements quality.
Explore practical steps for adopting AI in your engineering workflows.
Gain insights into the future of requirements engineering with AI.
The video below is a preview of this webinar, click HERE to watch it in its entirety
WEBINAR TRANSCRIPT PREVIEW
The Collapse of Requirements Quality Under System Complexity – How AI Can Help
Katie Huckett: Welcome, and thanks for joining. Today we’re going to talk about something many engineering organizations are experiencing, but rarely say out loud. Requirements quality is collapsing under the weight of modern system complexity. This session isn’t about tools, features, or automation for automation’s sake. It’s about why this problem exists, why traditional fixes are no longer sufficient, and why AI is becoming a necessity rather than a nice to have in requirements of engineering.
My name is Katie, and I lead product strategy focused on AI-driven capabilities and requirements management. I spend most of my time working with engineering teams in highly regulated complex industries, aerospace and defense, automotive, medical devices, and other systems where requirements quality is not optional. What I’m sharing today is based on what those teams are actually struggling with in practice, not theory.
Here’s how we’ll spend our time together. We’ll start looking at why requirements quality is breaking down despite increased process maturity. We’ll talk about the hidden costs of complexity and why traditional approaches no longer scale. Then we’ll look at how AI changes what’s possible, not as a replacement for engineers, but as a cognitive amplifier. And finally, we’ll discuss what this shift means for engineering organizations moving forward. We’ll have a brief Q&A portion before we conclude today. Let’s dive in.
Here’s the paradox we’re living in. Requirements practices are more mature than they’ve ever been. Teams have invested heavily in process, tooling, standards, and governance, and yet many organizations are seeing more rework, more late stage surprises, and more friction between teams than before. What’s important here is that this isn’t happening because teams stopped caring about quality. It’s happening because the nature of the systems we’re building has changed faster than the way we manage requirements. In other words, the rules of the game changed, but most practices did not.
Modern products are no longer confined to a single domain. A single system now routinely spans software behavior, physical components, data flows, safety constraints, regulatory requirements, and operational considerations. All of these elements evolve together, often on different timelines and often with different teams responsible for each part. As systems scale and change in parallel, the number of relationships between requirements increases dramatically, not linearly. And yet, many traditional approaches still assume that these relationships can be reasoned through manually during periodic reviews or checkpoints. The challenge isn’t capability or commitment. It’s that the structure of the work itself has fundamentally changed.
Huckett: Before we go further, I want to ground this discussion in your experience. We’re going to launch a poll. Please take a moment to answer honestly. What is the biggest contributor to requirements quality issues in your organization?
Looks like we have the results in. In nearly every organization I work with, the answer is rarely just one of these. These challenges stack on top of each other, and that compounding effect is exactly what overwhelms traditional requirements practice.
Traditional requirements practices were built for a world where change was slower, and systems were more predictable. Reviews happened at defined milestones. Documents were relatively stable. Dependencies were fewer and easier to reason about. Today, however, requirements are changing continuously, often across teams working in parallel. When you apply periodic document-centric review models to this environment, gaps are almost inevitable. The process itself isn’t wrong. It’s just being asked to operate outside the conditions it was designed for.
It’s important to say this clearly. This is not a lack of skill problem. It’s not a lack of effort problem. It’s not a lack of accountability problem. It’s a structural mismatch between human cognitive limits and the complexity of modern systems.
One of the most dangerous things about requirements quality issues is that they rarely fail loudly. A single ambiguous requirement doesn’t stop a project. It quietly creates multiple interpretations. Those interpretations propagate into design decisions, test cases, and validation activities. By the time the issue is discovered, multiple teams have already invested time and effort based on different assumptions. And at that point, the cost isn’t just fixing the requirement. It’s undoing everything that was built on top of it.
Huckett: Let’s do another quick poll. Where do requirements quality issues most often surface too late in your lifecycle?
Some interesting results here. Wherever this shows up in your lifecycle, the pattern is consistent. Humans don’t see the issue until it’s already costly. That’s not a vigilance problem, that’s a visibility problem. When quality issues surface, the instinctive response is to add more safeguards. That means more reviews, more sign-offs, more documentation. The problem is that these measures increase effort without increasing visibility. Teams end up spending more time checking artifacts, but not necessarily improving quality or alignment. In highly complex systems, quality doesn’t improve by adding friction. It improves by improving signal.
This is where AI fundamentally changes the equation. AI doesn’t get tired. It doesn’t lose focus. It doesn’t skip over sections because a document is long or familiar. It can continuously scan requirements, compare them, and look for patterns or anomalies across the entire system. That doesn’t replace human expertise. It supports it by ensuring that engineers are spending their time where judgment actually matters. In that sense, AI becomes part of the engineering infrastructure rather than a separate tool.
2026 Predictions for Nuclear Energy: Innovation, Safety, and the Path to a Sustainable Future
The nuclear energy industry stands at a pivotal moment where innovation and tradition intersect to tackle the world’s most urgent challenges: decarbonization, energy security, and sustainability. From the emergence of small modular reactors (SMRs) and advanced reactor designs to the adoption of AI, automation, and digital engineering, the sector is embracing transformative technologies that are set to redefine how nuclear power is designed, operated, and perceived.
Key trends shaping the nuclear landscape include the transition from conceptual innovation to deployable solutions, the role of digitalization in enhancing safety and efficiency, and the evolution of regulatory frameworks to support next-generation technologies. Additionally, cybersecurity, workforce development, and global collaboration are becoming essential pillars of the industry’s future, ensuring that growth and innovation remain firmly grounded in the safety-first principles that define nuclear energy.
In this final blog of the 2026 prediction series, we bring these insights to life with perspectives from Jama Software’s industry expert, Patrick Garman, Solutions Manager for Energy, Industrial, and Consumer Electronics sectors. Patrick shares a forward-looking vision for 2026 and beyond, exploring the deployment of SMRs and advanced fuels, the integration of predictive analytics and real-time monitoring, and the innovations, strategies, and cultural shifts that will shape the nuclear industry’s role in a clean energy future.
Curious to read leading thought leaders’ predictions for their industries in 2026 and beyond? Dive into each blog below:
Q: What next-generation technologies (e.g., small modular reactors, advanced reactor designs, digital control systems) will have the most significant impact on the nuclear industry in the next five years? How can organizations prepare to adopt and regulate these innovations safely?
Patrick Garman: Over the next five years, the nuclear industry is likely to be shaped by a practical shift from conceptual innovation to deployable technology. Small modular reactors (SMRs) and microreactors are expected to lead this transition, moving beyond pilot projects toward early commercial use thanks to their modular construction, smaller footprints, and ability to serve diverse applications, from grid support to industrial process heat and remote operations. In parallel, advanced non-light-water reactors, such as high-temperature gas, molten salt, and fast reactors, are gaining traction as long-term solutions for high-efficiency power generation and emerging use cases like hydrogen production and industrial decarbonization. These reactor designs are closely linked to advanced fuels, including HALEU and TRISO, making fuel availability, qualification, and supply chain readiness a central factor in how quickly projects can move forward. At the same time, the industry is embracing digital instrumentation and control, automation, and data-driven operations to improve performance, reliability, and safety while also introducing new considerations around software assurance and cybersecurity. Underpinning all of this is a growing reliance on factory-based manufacturing, modularization, and robotic inspection, which promise to reduce construction risk and improve quality, provided these methods can be consistently qualified and aligned with regulatory expectations.
Safety and Risk Management
Q: Safety has always been central to the nuclear industry. How can digitalization, real-time monitoring, and predictive analytics further strengthen plant safety and reliability? What cultural or procedural shifts are needed to sustain a modern safety-first approach?
Garman: Digitalization is giving the nuclear industry new ways to reinforce its longstanding safety-first foundation by improving visibility, consistency, and foresight across plant operations. Real-time monitoring and predictive analytics allow operators to detect early signs of equipment degradation, performance drift, or abnormal conditions well before they escalate into safety or reliability concerns, while modern digital control and decision-support systems help reduce human-factor risk by delivering clearer, more contextual information during both normal and off-normal operations. To fully realize these benefits, organizations must evolve their safety culture and procedures to treat software, data, and analytics as safety-relevant assets governed with the same rigor as physical systems, while strengthening the human-automation partnership through training, validation, and clear operational boundaries. A modern safety-first approach, therefore, extends beyond traditional engineering excellence to include disciplined digital governance, cybersecurity resilience, and continuous learning, ensuring that advanced technologies enhance the conservative decision-making that defines nuclear safety.
Digital Modernization
Q: How do you see digital engineering and integrated data environments improving plant lifecycle management, from design through decommissioning? What challenges exist in migrating from legacy systems to modern digital platforms?
Garman:Digital engineering and integrated data environments are changing how nuclear plants are managed across their entire lifecycle, helping teams maintain clarity and control from early design decisions all the way through operations and eventual decommissioning. By creating a connected digital thread that links requirements, design models, safety analyses, construction records, and operational data, organizations can avoid the information loss that often happens at handoffs between phases or teams. This continuity makes it easier to manage design changes, maintain configuration control, respond to regulatory questions with confidence, and use operational insight to plan maintenance, life extensions, or decommissioning activities more effectively.
The biggest challenge is not the technology itself, but the transition. Many nuclear organizations are working with decades of legacy systems, documents, and institutional knowledge that were never designed to work together. Migrating to modern digital platforms requires careful, phased approaches that preserve trust in the data, maintain regulatory confidence, and respect the realities of long-lived assets that cannot pause operations for wholesale transformation. Success depends on strong data governance, disciplined change management, and a clear understanding that digital modernization is a long-term capability investment.
Regulatory and Compliance Evolution
Q: As global interest in nuclear energy grows, particularly across the EU, how can the industry ensure regulatory frameworks keep pace with innovation? What best practices can help organizations streamline compliance without compromising safety?
Garman: As interest in nuclear energy accelerates, the challenge is ensuring regulatory frameworks evolve alongside innovation without undermining the industry’s uncompromising safety standards. New reactor designs, fuels, and digital technologies don’t fit neatly into licensing models that were built around large, conventional plants, which means regulators and industry alike must continue shifting toward risk-informed, technology-inclusive approaches. This evolution works best when developers engage regulators early and often, clearly articulate their safety case, and align on expectations for evidence, review milestones, and decision points before designs are finalized.
Best-in-class organizations are streamlining compliance by treating it as an integrated engineering discipline rather than a late-stage documentation exercise. That means embedding regulatory requirements directly into design and development workflows, maintaining clear traceability from safety objectives to implementation and verification, and reusing proven arguments, data, and analyses wherever possible. At the same time, harmonization efforts across jurisdictions, transparent regulatory collaboration, and disciplined change control help reduce duplication without sacrificing diligence. The result is a more predictable path to licensing that supports innovation while preserving the conservative, safety-first principles that underpin public trust in nuclear energy.
Q: As nuclear facilities adopt more connected technologies, how can organizations guard against cyber threats while maintaining system integrity and safety? What proactive measures should become industry standard?
Garman: As nuclear facilities adopt more connected and digital technologies, cybersecurity is becoming inseparable from plant safety and reliability. Guarding against cyber threats starts with treating operational technology as safety-relevant infrastructure that is designed from the outset to limit the impact of any compromise through strong segmentation, controlled data flows, and isolation of critical functions. Leading organizations focus less on individual tools and more on disciplined system architecture, configuration control, and integrity protection, ensuring that digital systems behave predictably even under adverse conditions.
The industry is converging on practices such as secure-by-design engineering, rigorous access and change management, continuous monitoring tailored to OT environments, and well-rehearsed incident response that includes operations and engineering, not just IT. Ultimately, sustaining system integrity in a more connected nuclear plant depends on a cultural shift that recognizes cybersecurity as an extension of nuclear safety itself, governed with the same conservative mindset and operational rigor that public trust in the industry depends on.
AI and Automation
Q: What role will AI and automation play in improving design and manufacturing of nuclear reactors and efficiency, safety inspections, and predictive maintenance across nuclear facilities? What safeguards are needed to ensure responsible, transparent use?
Garman: AI and automation are set to play an increasingly practical role in the nuclear industry, particularly in areas where consistency, pattern recognition, and early detection matter most. In design and manufacturing, AI-assisted analysis can help engineers explore design alternatives, identify potential safety or manufacturability issues earlier, and improve quality through automated inspection, welding verification, and non-destructive evaluation. Across operating plants, automation and advanced analytics support more efficient inspections and predictive maintenance by detecting subtle equipment degradation, prioritizing risk-significant issues, and reducing unnecessary exposure of personnel to hazardous environments. Used appropriately, these technologies strengthen safety and reliability by helping teams act earlier and with better information.
Responsible AI deployment in the nuclear industry means applying the same conservative, evidence-based mindset that governs other safety-relevant systems. That means clearly defining where AI provides decision support versus where humans retain authority, validating models against real-world data, monitoring performance and drift over time, and maintaining full transparency into how recommendations are generated. Strong data governance, configuration control, and cybersecurity protections are essential, as is the ability to audit and explain outcomes to regulators and operators alike. When paired with clear safeguards and human oversight, AI and automation can become trusted tools that enhance the nuclear industry’s long-standing commitment to safety and public confidence.
Sustainability and Public Perception
Q: How can the nuclear industry strengthen public trust while positioning itself as a key player in the clean energy transition? What strategies are most effective for communicating safety, sustainability, and innovation to the public?
Garman: Strengthening public trust is ultimately about consistency between what the nuclear industry says, what it does, and what people experience over time. As nuclear positions itself as a critical enabler of a reliable, low-carbon energy system, the industry has an opportunity to connect its long-standing safety culture with today’s clean energy priorities, emphasizing not just carbon-free electricity, but resilience, energy security, and long-term environmental stewardship. Trust grows when organizations are transparent about both benefits and risks, communicate clearly how safety is engineered and governed, and demonstrate that lessons learned are actively shaping modern designs and operations.
The most effective communication strategies focus on clarity, credibility, and relevance to everyday concerns. That means moving beyond technical jargon to explain safety, waste management, and sustainability in plain language, using real data and independent validation rather than promises. Engaging early and continuously with communities, regulators, and policymakers helps demystify nuclear technology and humanize the people behind it. By pairing transparent communication with visible innovation, strong regulatory oversight, and measurable climate impact, the nuclear industry can reinforce public confidence while positioning itself as a trustworthy and essential contributor to the clean energy transition.
Q: With a generational shift in the workforce, how can the nuclear industry retain institutional knowledge while equipping new engineers with the digital and safety-focused skills needed for the next era of nuclear operations?
Garman: The challenge is to preserve institutional knowledge while preparing a new generation of engineers to operate in a far more digital, data-driven environment. Leading organizations are addressing this by deliberately capturing design intent, operating experience, and lessons learned in structured, accessible formats while pairing this with training that blends systems thinking, digital engineering tools, and a strong grounding in nuclear safety culture. Mentorship, cross-generational teams, and scenario-based training help bridge experience with innovation, reinforcing conservative decision-making even as new technologies are adopted. Ultimately, success depends on treating knowledge management and workforce development as long-term strategic investments, ensuring that the next generation has access not just to new tools, but have the mindset and discipline that have define safe nuclear operations.
Global Collaboration and Standardization
Q: How can international collaboration and harmonized safety standards support the safe expansion of nuclear energy, particularly as more nations revisit nuclear as part of their net-zero strategies?
Garman: Collaboration and harmonized safety standards are essential to the safe and timely expansion of nuclear energy as more countries turn to nuclear power to meet net-zero goals. Shared regulatory principles, common safety objectives, and mutual recognition of technical assessments help reduce duplication, improve consistency, and raise the global safety baseline – especially as new reactor technologies are deployed across multiple jurisdictions.
Collaboration among regulators, operators, and international bodies also accelerates the exchange of operating experience and lessons learned, allowing emerging nuclear programs to benefit from decades of global expertise. When paired with strong national oversight, this alignment supports innovation without compromising rigor, enabling countries to expand nuclear capacity with confidence while reinforcing public trust in nuclear safety worldwide.
Future Outlook
Q: What trends—technological, regulatory, or geopolitical—will most influence the global nuclear industry over the next decade? How can companies balance growth, innovation, and safety as nuclear energy plays a larger role in global sustainability goals?
Garman: Over the next decade, the global nuclear industry will be shaped by a convergence of technological innovation, evolving regulatory approaches, and shifting geopolitical priorities tied to energy security and decarbonization. Technologically, the progression of small modular and advanced reactors, digital engineering, and data-driven operations will expand where and how nuclear can be deployed, while fuel supply chains and cybersecurity will remain strategic constraints. Regulators are increasingly adapting frameworks to accommodate new technologies through risk-informed, technology-inclusive approaches, even as geopolitical dynamics, such as supply chain resilience, international collaboration, and regional energy independence, reshape investment and deployment decisions. To balance growth, innovation, and safety, companies will need to embed safety and compliance into their innovation processes from the outset, engage regulators and stakeholders early, and maintain disciplined governance over digital and organizational change. Those that succeed will be the ones that treat safety not as a brake on progress, but as the foundation that allows nuclear energy to scale credibly and sustainably in support of global climate and energy goals.
This achievement isn’t just a statistic; it is a direct reflection of the feedback, reviews, and success stories from our valued customers. In an industry where precision and traceability are paramount, being named the definitive leader ahead of competitors like Polarion, IBM® DOORS®, and Codebeamer validates our mission. We are here to modernize how complex products are built.
Our performance in the Winter 2026 report was exceptional. We secured a staggering 98% customer satisfaction score and expanded our recognition across multiple categories globally. This blog post explores what these accolades mean for our users and why Jama Connect continues to set the standard for modern requirements management.
Why G2 Rankings Matter
Before diving into the specific awards, it is important to understand the weight of a G2 report. Unlike traditional analyst reports that may rely heavily on theoretical positioning along with other factors, G2 is democratized. It is the voice of the actual user.
G2 rankings are based on two primary pillars:
Customer Satisfaction: This comes directly from verified user reviews, ratings, and feedback.
Market Presence: This accounts for market share, vendor size, and social impact.
To be named a Leader, a product must excel in both areas. It means the software is not only widely adopted but also deeply appreciated by the people who use it daily. When you see Jama Connect in the top right “Leader” quadrant, it signifies a tool that delivers on its promises, helping teams navigate the complexities of product development with confidence.
The headline of the Winter 2026 report is our Customer Satisfaction score of 98. In the software world, specifically in complex engineering sectors, users are often critical. They demand high performance, reliability, and ease of use. A near-perfect score is rare and hard-earned.
This score tells us that we are solving the right problems. It means that when engineering teams need live traceability, they find it intuitive. When compliance managers need to generate an audit trail, the process is seamless.
Beyond the Number
A 98% satisfaction score translates to tangible benefits for your team:
Reduced Friction: Teams spend less time fighting the tool and more time engineering.
Higher Adoption: When software is easy to use, team members actually use it, ensuring data integrity.
Trust: Users rely on Jama Connect as their single source of truth.
Leadership Across Every Business Segment
One of the most compelling aspects of the Winter 2026 report is the versatility of our leadership. Software often struggles to scale. Tools that are great for small startups often buckle under the weight of enterprise data, while enterprise tools can feel bloated and sluggish for smaller teams.
Jama Connect has proven it can handle it all. We earned Leader badges across three distinct business segments:
1. Enterprise Leader
Large organizations face unique hurdles, including massive data sets, rigorous security protocols, and thousands of concurrent users. Our leadership here confirms that Jama Connect scales effortlessly, supporting global enterprises without compromising on speed or performance.
2. Mid-Market Leader
Mid-sized companies are often in a critical growth phase. They need the rigor of enterprise compliance tools but the agility of a startup. We provide that balance, allowing mid-market teams to mature their processes without slowing down innovation.
3. Small-Business Leader
Even small teams tackle complex engineering challenges. Being a leader here shows that our platform remains accessible. You don’t need an army of administrators to get value out of Jama Connect; it is designed to be intuitive from day one.
Global Recognition: A Regional Leader
Innovation is global, and so is our user base. The Winter 2026 report highlighted our strengthening presence across international markets. We were named a Regional Leader in Asia Pacific, EMEA, and Europe.
This geographic diversity is crucial for modern product development. Many of our customers operate with distributed teams: engineering in Germany, manufacturing in China, and product management in the US. Our recognition as a global leader ensures that no matter where your team sits, Jama Connect provides a unified, high-performance experience.
Staying at #1 requires constant evolution. We are proud to be named a Momentum Leader, a badge that recognizes our continued growth and innovation trajectory.
Being in the top 25% of products for growth signals that we aren’t resting on our laurels. The landscape of systems engineering is changing rapidly with the introduction of AI, increasing regulatory pressures, and more complex hardware-software integration. Our momentum score proves that we are actively evolving the platform to meet these modern challenges head-on.
The Power of Relationships
While technical features are critical, the human element of software partnership is often overlooked. In the Winter 2026 report, Jama Connect earned the Best Relationship badge for Overall, Enterprise, and Mid-Market segments.
This recognition is perhaps the one we cherish most. It reflects the high-quality support, collaboration, and success services our customers experience. When you choose Jama Connect, you aren’t just buying a license; you are gaining a partner committed to your long-term success.
High relationship scores indicate:
Responsive Support: We are there when you need us.
Customer Success: We help you map your processes to the tool for maximum value.
Community Engagement: We listen to user feedback to drive our roadmap.
Voices from Our Community
The statistics are powerful, but the stories behind them are what truly matter. The praise and constructive feedback from our users on G2 drive our innovation. Here is what verified users are saying about their experience:
“Jama Connect is a powerful tool for requirements management and offers a wide range of features. Until now, the traceability of requirements was very difficult or even impossible. Jama Connect solves this problem 100%.”
— Verified User, Renewables & Environment, Enterprise
This review highlights a core struggle for many engineers: traceability. Without a tool like Jama Connect, tracing a requirement through to testing and validation is a manual, error-prone nightmare. We solve that.
“In the past, all requirement-related information was scattered across Jira, Confluence, Word, and Excel… With Jama Connect, we have centralized most of this information into a single, reliable source of truth.”
— Verified User in Manufacturing, Mid-Market
This user touches on the “document-centric” trap. Moving away from scattered Word docs and Excel sheets into a data-centric model is the single biggest leap a team can take toward maturity.
Driving Modern Requirements Management Forward
This G2 recognition reinforces our mission to help organizations move beyond outdated processes. The days of managing complex systems in static documents are over. Jama Connect provides a modern, Live Traceability™ platform designed for the complexities of today’s product development landscape.
With Jama Connect, teams can:
Improve Clarity: Establish a single source of truth that aligns all stakeholders.
Ensure Traceability: create live links across requirements, risks, and tests to visualize impact instantly.
Streamline Compliance: Automate the documentation needed for industry-specific regulations (ISO 26262, FDA 21 CFR Part 820, etc.).
Accelerate Cycles: Catch errors early in the process to avoid costly rework later.
Conclusion
We could not have achieved this milestone without you. Your partnership, feedback, and trust are the cornerstones of our success. These G2 awards, our eighth consecutive quarter at the top, are a shared victory for every team using Jama Connect to solve complex challenges.
Thank you for making us the #1 choice for requirements management. We are committed to continuing this journey of excellence with you.
Ready to see the full data? Download the G2 Winter 2026 Grid Report to explore why Jama Connect continues to lead the competition in Requirements Management.
Jama Connect® for Semiconductors Solution – It’s about time!
Jama Software recently announced the release of a solution tailored for the Semiconductor industry! This highly anticipated release has been eagerly awaited by both Jama Software’s Semiconductor team and customers, who are excited to see how it can address their requirements and data governance challenges.
As someone with a 24-year career in the semiconductor industry, primarily focused on Requirements Engineering for silicon and networking products, I’ve been anticipating this release for quite some time. My experience includes working with various tools, homegrown solutions, and the ubiquitous but limited Microsoft Word and Excel. Jama Connect stood out early on for its usability, ensuring engineering teams could adopt it without slowing down project cycles—a critical factor for success.
Addressing Semiconductor Complexity
The semiconductor industry’s complexity and variability have long posed challenges for requirements management tools. Fabless silicon design companies, EDA tool developers, and manufacturers all have unique data needs. A solution for this industry must be both configurable and governable. Unlike other industries, semiconductors lack a standardized information architecture, leading to locally optimized but unscalable solutions.
Jama Connect for Semiconductors addresses these challenges by offering a tailored, scalable solution. It provides a framework that balances flexibility with governance, making it easier for teams to manage requirements effectively.
In a recent video podcast (CLICK TO WATCH), Jama Software’s Semiconductor GM Neil Stroud described Jama Connect as the “incumbent standard” in the semiconductor industry. While the new solution was just launched, many semiconductor companies have already adopted Jama Connect as a step up from scattered document-based systems. Its SaaS cloud deployment, easy imports from Word or Excel, and support from Jama Connect Solutions Consultants enable teams to get up and running quickly.
Many companies have realized the need to modernize their requirements and data governance practices. Jama Connect’s usability, combined with expert consulting, has helped teams tailor the solution to their needs, even when starting with frameworks designed for other industries.
Time to Efficiency
A new Jama Connect deployment delivers value immediately. For example, a set of 100 requirements can be sent for review to 20 stakeholders in just a morning. The data architecture can be adjusted to match the current format, and documents can be imported into the tool. Reviewers, even those without Jama Connect licenses, can participate for free. This streamlined process reduces the time and effort required for reviews.
Jama Connect Advisor™ further enhances efficiency by identifying ambiguities and errors in requirements, improving clarity before reviews begin. This reduces the time reviewers spend asking clarifying questions, speeding up the entire process.
Deciding on a requirements management solution can be a lengthy process for large organizations. However, a Jama Connect for Semiconductors SaaS pilot can be configured in days, complete with SOC2 and other security features. Jama Software’s Solutions Consultants work with teams to define the scope of the pilot and establish success metrics, ensuring a focused and effective evaluation.
Time to Value
Semiconductor companies need to see tangible benefits from deploying Jama Connect. Executives and senior leaders value predictable cycle times and reduced costs from earlier defect identification. Over time, as more data is governed within Jama Connect, product definitions become clearer, and schedule volatility decreases. The tagline “Visibility leads to Accountability” highlights how Jama Connect provides leaders with progress indicators to monitor and address issues proactively.
Engineering managers benefit from a deployment path that doesn’t disrupt tight schedules. Jama Connect’s test capabilities, including a new feature in beta that generates test cases from requirements, help teams assess product quality and readiness. Integrations with common semiconductor tools and the Jama Connect Interchange API enable a comprehensive view of the development process.
For engineers, the intuitive user interface and immediate benefits of the Review Center help overcome initial skepticism. While transitioning from document-based systems to a modern requirements management solution requires effort, the long-term benefits are significant.
Time to Get Started
Looking back on my career in semiconductor requirements engineering, I often wished for a purpose-built solution like Jama Connect for Semiconductors. This solution offers the flexibility and governance needed to address the industry’s unique challenges. If you’re interested in learning more, Jama Software is ready to help. Current customers can reach out to their Customer Success Manager, while new prospects can contact their Account Executive to schedule a demo and discuss next steps.
