Observability told you what happened.

Now meet its partner evaluation.

As AI moves from pilots to production in regulated environments, one question matters more than speed: can you prove control? For GxP teams, that proof comes from two complementary disciplines—observability and evaluation.

Observability provides execution traceability: what the model saw, what sources were retrieved, what tools were called, which prompt/model/version ran, and what safety or policy flags were triggered. Evaluation provides acceptability evidence: explicit criteria, pass/fail thresholds, rubric scores, SME outcomes, trend lines, and regression results that demonstrate outputs remain fit for intended use—even as systems change.

This is how you run AI as a controlled capability – not an experiment.

Observability tells you why it behaved that way and how to control it.

Evaluation tells you whether it’s acceptable.

Together, they give the controlled speed to move faster with operational traceability and defencible output.

In our full white paper, we break down how these controls work together to enable controlled speed: faster iteration without sacrificing inspection defensibility. We also include a practical GxP example (AI-assisted deviation triage) and sample scorecards and evidence artifacts you can use to design your own program. 

We’re kicking off Genari AI’s AI Governance Series, starting with AI observability and evaluation.   Observability and Evaluation are a fundamental necessity in regulated environments.  This series aims to demystify these topics and provide clear and practical approaches to implementing in GxP environments.

Black-box AI is not acceptable in regulated environments where AI-enabled processes influence compliance or quality decisions.

We need to answer three fundamental questions when assessing AI solutions:

What happened, why did it happen, and how risky was it?

Observability helps to answer these questions by detecting anomalies and failures, diagnosing root causes across systems, and governing AI behavior.  

This is why observability matters so much. By assessing and mitigating operational risk, we can assert some control over the process.

But, AI observability is not just an IT troubleshooting function; it is a foundational capability for controlled AI adoption.

Observability provides the operational transparency needed to support trust and control:

• Tracing helps us see latency, errors, tool failures, and token usage.
• Context tracking helps us trace inputs, content, and tool usage.
• Safety flags help us monitor for policy violations.
• Change monitoring helps detect prompt updates, model swaps, retrieval index changes, and drift.

Without observability, teams are left to react with limited investigative control, hindering proactive improvement.

Observability does not make an AI compliant on its own, but it provides the visibility required to govern it responsibly.

As compliance leaders, we should partner with IT, Quality, and business teams to embed observability into the AI operating model from the start. If AI is going to support regulated processes, then visibility, traceability, and risk monitoring must be designed in from the beginning.

AI adoption is moving fast in 2026. In life sciences, the advantage will go to organizations that move fast with control.

Next up in the tiny bites series –> AI Observability vs. Evaluation

Two complementary approaches for governing AI systems. Observability is used to monitor operations, while LLM-as-a-Judge offers automated quality measurement for outputs.

#AI #Pharma #Compliance #GxP #Quality #AIGovernance #MedDevice #AIObservability #RiskManagement #DigitalTransformation #LifeSciences #DigitalValidation #gxpgenie #genariai

Download our white paper to learn how a unified assurance model enables CSA in practice.

A CSA-Aligned Approach to Inspection-Ready Digital Systems

As regulated organizations continue to modernize their digital ecosystems, software assurance has become increasingly complex. Systems change more frequently, configurations evolve continuously, and responsibility for outcomes spans multiple functions. In this environment, traditional validation models, often built around static documentation and function-specific ownership, struggle to scale.

The FDA’s Computer Software Assurance (CSA) guidance reflects this reality. Rather than prescribing new compliance requirements, CSA emphasizes risk-based assurance grounded in intended use and meaningful evidence. For many organizations, however, the challenge is not understanding CSA in principle, but operationalizing it across Business, IT, and Quality in a consistent and inspection-ready way.

This white paper explores how aligning these functions around a single view of software assurance enables CSA principles to be applied in practice. By shifting from siloed validation activities to shared assurance visibility, organizations can support faster change while maintaining regulatory confidence

Download the white paper to learn how a unified assurance model enables CSA in practice.

Why Governance Matters for AI in GxP

—and How to Build a Framework That Works

As artificial intelligence (AI) becomes increasingly integrated into pharmaceutical manufacturing and quality systems, it holds tremendous promise for efficiency, prediction, and insight. But in GxP-regulated environments—where data integrity, product quality, and patient safety are non-negotiable—the use of AI cannot be approached casually.

Without a formal governance model, AI can introduce compliance risks, obscure decision-making, and undermine regulatory readiness. That’s why AI governance is not optional in life sciences—it’s essential.

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Why AI Governance Matters in GxP Environments

AI systems differ fundamentally from traditional software. They are adaptive, data-dependent, and often opaque. These characteristics challenge conventional validation approaches and require enhanced oversight to meet FDA, EMA, and other global regulatory expectations.

Here are five reasons governance is critical when deploying AI in GxP systems:

1. Regulatory Compliance:
   AI features must comply with regulations like regional AI Acts, 21 CFR Part 11, Annex 11, and ICH Q9. Governance ensures validated, documented, and audit-ready implementations.

2. Data Integrity:
   AI relies on vast datasets that must meet ALCOA+ principles—ensuring data is attributable, legible, contemporaneous, original, accurate, and reliable. Governance enforces proper controls around training and operational data.

3. Risk Management:
   AI introduces new risks: model drift, bias, and unpredictable behaviors. A governance model enables structured risk assessments and assigns accountability for outcomes.

4. Transparency & Explainability:
   In regulated settings, black-box decisions are not acceptable. Governance ensures decisions made by AI are explainable, traceable, and subject to human oversight.

5. Lifecycle Control:
   AI models evolve—through retraining, tuning, or updates. Governance ensures changes are controlled, documented, and, when needed, revalidated.

Bottom line: AI governance ensures that AI-powered systems in GxP environments are compliant, transparent, and controlled to protect patient safety and maintain regulatory readiness.

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A Governance Framework for GxP AI

To manage these challenges, life sciences companies need a structured, fit-for-purpose AI governance framework that aligns with existing quality and compliance processes while addressing AI-specific concerns.

Key Components of a GxP AI Governance Framework

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1. Governance Structure

• Define clear roles and responsibilities across IT, QA, data science, and business.

• Establish a cross-functional AI oversight board to guide risk classification, system approval, and lifecycle decisions.

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2. Policy and Standards

• Develop SOPs for AI use in regulated systems, including:

  • Acceptable AI use cases

  • Model transparency requirements

  • Documentation expectations for training and outputs

  • Human review or intervention protocols

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3. Lifecycle Management

• Apply System Development Life Cycle (SDLC) and GAMP 5 principles to AI:

  • Define intended use

  • Validate training data and model performance

  • Control versioning and model updates

  • Document retraining and revalidation triggers

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4. Data Governance

• Ensure AI input and output data meet ALCOA+ standards.

• Control access, maintain lineage, and ensure secure, validated infrastructure.

• Use qualified datasets for training and testing.

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5. Risk Management

• Conduct AI-specific risk assessments to evaluate:

  • Bias

  • Algorithm limitations

  • Impact on product quality or patient outcomes

• Define mitigation strategies and continuous monitoring processes.

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6. Change Control

• Integrate AI model changes into your validated change management process.

• Require documented rationale, risk analysis, and (if applicable) revalidation for:

  • Model retraining

  • Algorithm updates

  • Changes in intended use

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7. Monitoring and Continuous Compliance

• Establish KPIs and thresholds for AI performance.

• Perform periodic reviews and model audits.

• Implement exception tracking and escalation paths.

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8. Training and Competency

• Ensure personnel involved with AI systems are trained in:

  • AI fundamentals

  • Regulatory expectations

  • GxP requirements for computerized systems

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Final Thoughts

AI has the potential to transform pharmaceutical operations—but only if implemented with rigor and discipline. By establishing a robust AI governance framework aligned with GxP principles, companies can embrace innovation without compromising compliance.

The future of AI in life sciences depends on trust—and trust starts with governance.

FDA ESG NextGen: What It Means for the Industry — Large and Small

The U.S. Food and Drug Administration (FDA) is scheduled to launch its critical modernization effort called ESG NextGen, a next-generation upgrade of its Electronic Submissions Gateway (ESG). The official Go-Live date is April 14,2025. For stakeholders across the life sciences spectrum—from pharmaceutical giants to emerging biotech startups—this evolution represents both a technical leap and a strategic shift in how the industry will handle regulatory submissions in the future.

What Is FDA ESG NextGen?

The ESG has long been the backbone of the FDA’s digital submission process. It serves as the secure conduit for transmitting millions of regulatory documents annually, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), Biologics License Applications (BLAs), Medical Device Reports (MDRs), and more.

Key goals of ESG NextGen include:

• Cloud-native infrastructure to improve scalability and uptime.
• Modern APIs and standards (e.g., REST, FHIR) to simplify integration and automate submissions.
• Improved security via updated protocols, identity management, and encryption.
• Streamlined onboarding and support processes for submitters.
• Better tracking and visibility into submission statuses.

Why It Matters: Impact on the Industry

1. For Large Organizations: Efficiency, Automation, and Compliance

Big pharma and large biotechs process hundreds or thousands of submissions yearly. ESG NextGen will:

• Enable greater automation through modern APIs, reducing manual steps.
• Enhance compliance tracking by offering better submission validation and real-time status visibility.
• Integrate with enterprise systems (e.g., regulatory information and quality management systems) more easily.
• Reduce operational risk, thanks to a more stable, secure infrastructure.

In short, ESG NextGen will help large organizations streamline their regulatory operations, reduce overhead, and more effectively manage their global submission portfolios.

2. For Small Organizations: Accessibility and Scalability

Startups and smaller firms often lack dedicated regulatory tech infrastructure. ESG NextGen can help level the playing field by:

• Offering more user-friendly submission interfaces, reducing the need for deep technical expertise.
• Facilitating third-party integration allows CROs and regulatory consultants to assist small companies more seamlessly.
• Lowering barriers to entry for electronic submissions with simplified onboarding and better documentation.

This modernization could be incredibly transformative for small and mid-sized companies that are navigating FDA processes for the first time.

Challenges and Considerations

While ESG NextGen brings exciting improvements, it also requires preparation:

• Legacy system compatibility: Organizations will need to update submission tools and processes to align with new APIs and formats.
• Training and onboarding: Teams will need to learn the new platform and any revised submission protocols.
• Regulatory strategy alignment: Companies should consider optimizing their submission timing, format, and structure based on ESG NextGen’s capabilities.

The FDA is working closely with industry to support the transition, but proactive planning is essential to avoid disruption.

Final Thoughts

FDA ESG NextGen is more than a technology upgrade—it’s a shift in how life sciences organizations will interact with the regulator in the digital age. It’s a chance for larger firms to automate and scale regulatory operations. For smaller companies, it represents a more accessible, transparent path to compliance and innovation.

As the FDA continues to roll out details and pilot programs, staying informed and engaged will be key. The future of regulatory submissions is coming—and it’s smarter, faster, and more connected.

The Importance of a Robust AI Validation Strategy

Regulatory agencies such as the FDA mandate the validation of computer systems to ensure they meet defined requirements, perform as intended, and maintain data integrity. With AI solutions introducing dynamic and complex functionalities, the need for robust validation strategies becomes even more critical. Implementing effective validation strategies, procedures, and lifecycle assessments tailored for AI will mitigate risks, ensure compliance, and optimize operations.

Despite the growing focus on AI, many organizations still need help addressing the associated risks. There needs to be a better understanding of regulatory expectations for AI, expertise in AI-specific risks such as model drift and bias, and standardized practices to validate dynamic AI systems. Addressing these gaps is essential to leveraging AI effectively while maintaining compliance and operational efficiency.

This white paper discusses the necessity of creating a validation strategy for AI solutions, developing robust validation procedures, and conducting a validation lifecycle assessment. It also explores how these activities help organizations identify and address gaps in processes, systems, and skills, particularly in the context of AI technologies.

The Need for an AI Validation Strategy

A well-defined AI validation strategy ensures alignment with regulatory requirements such as 21 CFR Part 11, EU Annex 11, and emerging AI-specific guidelines. Regulatory bodies increasingly focus on AI models’ transparency, explainability, and accountability. With a cohesive strategy, organizations can avoid non-compliance, which can lead to penalties, product recalls, or reputational damage, all of which can significantly impact the bottom line and the company’s standing in the industry.

One critical gap is the need for more clarity around how traditional validation approaches apply to AI, particularly for systems that evolve through machine learning. Organizations often need help demonstrating explainability and traceability for AI models, which are critical for regulatory compliance.

Risk Mitigation

AI solutions introduce unique risks like model drift, data bias, and lack of interpretability. For instance, model drift can lead to a decrease in the accuracy of predictions over time, data bias can result in unfair or discriminatory outcomes, and lack of interpretability can make it difficult to understand how the AI system arrived at a particular decision. Validation activities assess and mitigate these risks to ensure the AI system consistently delivers reliable and accurate results. A proactive strategy identifies potential risks early, reducing the likelihood of costly errors or disruptions.

However, a known gap in AI risk management is the underestimation of data-related risks, such as the impact of poor-quality training data or undetected shifts in input data over time. Organizations must continuously develop frameworks to monitor and mitigate these risks throughout the AI lifecycle.

Operational Efficiency

A clear strategy minimizes redundant activities, accelerates project timelines, and reduces overall costs by standardizing AI validation efforts across the organization. It also provides a roadmap for achieving consistent results and supporting continuous improvement initiatives.

A common operational efficiency gap is the lack of automated tools and processes to handle the iterative nature of AI validation. Manual approaches often lead to inefficiencies and increased chances of human error. For example, manual testing of AI models can be time-consuming and prone to oversight, while automated tools can perform these tasks more quickly and accurately, freeing up human resources for more complex validation activities.

Typical AI Validation Procedures

1. Validation Planning: Define objectives, scope, and responsibilities for AI systems.
2. Risk Assessment: Evaluate and prioritize AI-specific risks, including bias, data integrity, and cybersecurity.
3. Requirements Management: Document and trace functional and non-functional requirements, focusing on AI model accuracy, reproducibility, and robustness.
4. Test Protocols: Develop AI-specific protocols, such as testing model performance on representative datasets and monitoring for model drift.
5. Change Control: Manage and validate AI model updates or retraining cycles to maintain compliance.
6. Documentation: Maintain comprehensive records to demonstrate compliance and traceability during audits.

AI Validation Lifecycle Assessment

A validation lifecycle assessment for AI systematically evaluates the organization’s validation practices throughout the AI system’s lifecycle, from planning and design to retirement. It provides insights into gaps and opportunities for improvement.

• Gap Analysis: Identify discrepancies between current practices and compliance expectations for AI.
• Process Optimization: Highlight inefficiencies and recommend improvements tailored to AI workflows.
• Skill Assessment: Evaluate the competency of personnel involved in AI validation activities.

              Steps:

1. Define Scope: Outline AI systems, processes, and areas to be assessed.
2. Evaluate Current State: Review existing validation documents, procedures, and training records.
3. Benchmark Against Best Practices: Compare practices to industry standards and emerging AI guidelines.
4. Develop Action Plans: Address identified gaps with actionable recommendations.
5. Monitor and Review: Establish metrics to measure AI system performance and compliance over time.

 Known Gaps

• AI Expertise: A significant gap exists in understanding AI-specific risks and validation requirements among traditional validation teams.
• Tool Availability: Many organizations lack access to advanced tools for automating AI validation tasks, leading to resource-intensive processes.
• Model Explainability: Many organizations lack robust procedures to validate and document the interpretability of AI models, which is critical for both internal understanding and regulatory compliance.
• Continuous Validation: Unlike static systems, AI models require ongoing validation as they evolve. This necessitates a well-defined and continuous process to address AI’s dynamic nature, demonstrating the ongoing commitment required.

Addressing Gaps

                Skill Development

                Effective AI validation requires specialized knowledge and skills. Organizations should:

• Provide regular training on AI-specific regulatory requirements and emerging trends.
• Foster a culture of quality, compliance, and innovation.
• Invest in certification programs for AI validation professionals.

               Resource Optimization

• Allocate sufficient budget and personnel for AI validation efforts.
• Utilize third-party experts for niche areas such as AI explainability and bias analysis.
• Implement scalable validation tools that address the unique challenges of AI systems.

               Best Practices

• Standardize templates and processes to ensure consistency across AI solutions.
• To incorporate diverse expertise, involve cross-functional teams, including data scientists, IT, and quality assurance.
• Where appropriate, leverage automation tools for AI model testing, monitoring, and documentation.

Conclusion

Creating a robust validation strategy, comprehensive procedures, and lifecycle assessments is crucial for ensuring compliance, operational efficiency, and risk mitigation in the context of AI solutions in the life sciences industry. By proactively addressing gaps in processes, systems, and skills, organizations can maintain the integrity of their AI solutions and uphold the highest standards of quality and compliance. A strategic approach to AI validation safeguards regulatory compliance and enhances organizational resilience, enabling life sciences companies to adapt to evolving regulatory landscapes and technological advancements. As AI revolutionizes the industry, addressing known gaps and implementing robust validation practices will be critical to success.

Clients trust Genaru AI for our strategic vision and expertise. Our consultants are highly qualified with C-level experience and the necessary skills to take on the challenges of being in the trenches. For this reason, we often play an essential role in leading the transformation journey.

Digital transformation is like embarking on a long, challenging cycling journey through varied landscapes, each representing the different terrains of the digital landscape. Just as cyclists must choose the right bike and gear for their journey, organizations must select the appropriate technologies and tools that align with their strategic goals and operational needs.

Preparing for the Ride

Akin to training and planning for a cycling tour, we assess our client’s capabilities, help set clear objectives, and prepare their teams with the necessary skills and knowledge, ensuring that the journey ahead is manageable and effective in reaching the desired destination.

The Uphill Climb

As the journey begins, you may face an uphill climb, representing the initial challenges of implementing new digital strategies and technologies. This phase requires significant effort and endurance as teams navigate resistance to change, technical complexities, and integrating new systems with existing processes; just like cyclists pushing through the steep inclines, determination, and resilience are required to overcome these early obstacles.

The Summit 

Reaching the summit offers a moment of triumph, akin to successfully integrating a new digital initiative that starts delivering tangible benefits. However, the journey continues. Just as cyclists must then navigate the descent, which requires control, precision, and the ability to adapt to changing conditions, we ensure that our clients can sustain and scale their digital transformations, adapting to new challenges and opportunities that arise.

Flat Stretches 

Just as in cycling, there are flat stretches where you can enjoy the rhythm of steady progress and stability, where digital practices become embedded in operations, delivering consistent value and allowing you to assess progress and plan for the subsequent phases of transformation.

Teamwork

The journey is not a solitary endeavor. Just as cyclists often ride in groups to share the load, offer support, and enjoy camaraderie, successful digital transformation requires collaboration across departments, leveraging collective skills, experiences, and insights to navigate the journey together.

Agility

Finally, just as no two cycling journeys are the same, each organization’s path to digital transformation is unique and shaped by its own goals, challenges, and context. The key to success lies in staying agile, continuously learning, and being willing to adjust the route as new landscapes emerge on the horizon. Like cycling, digital transformation is an ongoing journey of exploration, endurance, and evolution, with each stage offering new opportunities for growth and innovation.

If you want to learn more about how Main and Mission can help you ride to victory, reach out using the form below to request our case study or contact us to begin your digital transformation journey.