Jama Software

09/02/2026

The model can write the code. It can’t write the safety case.

AI-generated code introduces new security and compliance considerations as engineering teams accelerate software development. Vulnerabilities in the implementation are only one part of the risk. In safety-critical products, teams must establish that generated code reflects approved requirements, accounts for identified hazards and threats, and traces to verification evidence.

That raises a larger question. How do teams gain the speed of AI-generated code without losing the evidence required to justify it?

Learn more in our latest blog 👉 https://ow.ly/hFAP50ZIrc3

Learn how AI-generated code risks affect safety-critical software and why a product context layer matters for traceability, verification, and compliance.

08/13/2026

Conversations around AI in medical device development have shifted from whether organizations should adopt AI to how they can use it responsibly.

Medical device manufacturers see enormous potential for AI to accelerate requirements development, improve documentation, identify inconsistencies, generate test cases, and reduce repetitive engineering work.

At the same time, quality and regulatory leaders are asking equally important questions:
- How do we validate AI-assisted outputs?
- What level of human oversight is required?
- How do we demonstrate accountability during an FDA inspection?
- Can we prove who reviewed AI-generated content and what decisions were made?

These are all valid concerns. In fact, regulatory developments suggest these are exactly the questions organizations need to be asking.

Our latest blog looks at what responsible AI adoption can mean for medical device development and why being able to trace decisions matters.

Learn More 👉 https://ow.ly/uMFm50Zz82q

08/06/2026

For years, MVP development was guided by the goal of shipping early, learning quickly, and reducing waste. Somewhere along the way, Minimum Viable Product became “What’s the least amount of work we can put in to ship something?”

This thinking optimizes for the team’s effort, not the customer’s result. Instead of validating product-market fit faster, organizations often end up in expensive cycles of rework:

- Customers don’t use the feature as expected.
- Requirements evolve after development starts.
- Engineering teams rebuild functionality multiple times.
- Product roadmaps become dominated by iteration instead of innovation.

To avoid this trap, today’s organizations need to shift their thinking from Minimum Viable Product to Minimum Valuable Product. What is the minimum they can build that delivers meaningful customer value?

That’s what needs to be asked, and what this article explores in further detail 👉 https://ow.ly/GXfV50Zxcse

MVP development should deliver value, not just speed. Learn how Spec-Driven Development, AI, and requirements reduce rework and build the right product faster.

08/05/2026

State Medicaid agencies are navigating increasing complexity while balancing modernization, compliance, and better outcomes.

In this new blog, Kirsten Moss explores why strong governance is essential to successful Medicaid modernization—and how the right approach can reduce risk while improving delivery.

Read the full article: https://ow.ly/aayF50ZwIZR

Learn how governance, traceability, and state-owned requirements support successful Medicaid Enterprise System modernization and CMS certification readiness.

08/04/2026

AI coding assistants are now ubiquitous across software engineering teams, yet relatively few organizations have transformed how engineering itself operates. Teams write code faster while continuing to struggle with bottlenecks in requirements, specifications, testing, verification, compliance, and engineering governance.

That’s because becoming an AI-native engineering organization requires changes across the entire engineering lifecycle.

This article introduces the AI adoption maturity model, a practical framework for understanding the AI maturity levels for engineering teams and the capabilities organizations develop as they progress from manual engineering to multidisciplinary AI-driven development.

To learn more about each level and see how you can scale your team responsibly, check out our latest blog 👉

Most teams use AI coding tools. Few have transformed their workflows. Learn the 5 AI maturity levels for engineering teams and what's required to advance.

07/31/2026

If your organization already standardizes on Kubernetes, your requirements management platform should fit into the same operational model.

Our new Jama Connect® Kubernetes-Native Deployment datasheet explains how to deploy Jama Connect as a Helm chart into your own Kubernetes environment while using the governance, security, monitoring, and operational processes your platform team already supports.

Learn how to:
- Deploy with Helm
- Run on EKS, AKS, GKE, OpenShift, or CNCF-conformant Kubernetes
- Support air-gapped environments
- Align with existing platform and security standards

Read the datasheet: https://www.jamasoftware.com/datasheet/jama-connect-kubernetes-native-deployment/

Deploy Jama Connect in your Kubernetes environment with Helm. Learn how Kubernetes-native deployment simplifies operations, upgrades, governance, and security.

07/24/2026

Finding and creating trace relationships across complex product development projects has traditionally been a manual, time-consuming task.

Relationship Discovery helps engineering teams accelerate that process by using AI to suggest relevant relationships between requirements and other engineering artifacts. Engineers stay in control, reviewing and approving every suggestion, while reducing manual effort and strengthening traceability.

For teams building complex, regulated products, it's another way AI can improve productivity without compromising governance.

See Relationship Discovery in action: https://ow.ly/XA1v50Zs8PH

See how AI-powered Relationship Discovery in Jama Connect Advisor identifies missing traceability links, improves requirements coverage, and accelerates development.

07/15/2026

🤔 "What makes a good requirement?"

That's the question MilliporeSigma, the U.S. and Canada Life Science business of Merck KGaA, Darmstadt, Germany, set out to answer.

By adopting Jama Connect® and becoming an early adopter of Jama Connect Advisor™, they embedded intelligent authoring guidance directly into their workflow, helping teams improve requirement quality, reduce rework, accelerate testing, and support audit-ready compliance.

Read the full customer story 👉 https://ow.ly/b19150ZnVll

07/09/2026

Most RFP processes still rely on spreadsheets, email threads, and manually tracking hundreds of requirements across multiple stakeholders. That makes it difficult to know what's complete, what's missing, and whether the final response accurately reflects your organization's capabilities.

Our latest Jama Connect® Features in Five video shows how the Jama Connect RFP Framework provides a more structured approach to RFP response management.

In just minutes, you'll see how teams can:
- Organize RFP requirements in a single system
- Link responses directly to requirements with end-to-end traceability
- Track compliance gaps and risks throughout the proposal process
- Collaborate with reviewers without relying on disconnected email chains
- Connect proposal commitments to engineering data for products with complex requirements

For organizations developing complex products, that last point is especially important. Traceability doesn't have to stop when the proposal is submitted—it can continue into engineering, helping teams maintain alignment between customer commitments and product delivery.

Watch the video here: https://ow.ly/ANRR50ZmeRP

Manage the RFP process effectively with Jama Connect's structured framework for better visibility and compliance tracking.

07/07/2026

A significant portion of automotive engineering time gets absorbed by work that is manual and repetitive: scanning standards, tracing requirements, moving content between tools.
That's the problem Matt Mickle at Jama Software tackles in his latest blog.
The solution pairs purpose-built AI agent toolkits, trained on standards like ISO 26262, ISO 21434, ASPICE, and SOTIF (ISO 21448), with Jama Connect MCP™. Together, they take AI-generated content from standards analysis all the way to auditable, traceable artifacts in your live Jama Connect project, without a manual data entry step in between.
The engineer stays in control throughout. The toolkit handles standards analysis, formatting, and first-draft generation. The subject matter expert applies judgment, domain expertise, and the accountability that safety-critical development requires.
What once took hours or days can be compressed into minutes, with engineering effort focused where it matters most.
Matt walks through a full HARA workflow example in the blog. Worth a read if your team is navigating functional safety compliance > https://ow.ly/vhbN50ZkUXu