At the RAPS Regulatory Intelligence Conference in March 2026, regulatory and quality intelligence leaders,  including UCB, discussed how their organizations are building and operationalizing quality intelligence programs. This article is a summary of that discussion.

There’s a quiet but significant transformation in life sciences regulatory and GxP compliance programs. Quality intelligence is finding its place in life sciences decision-making intelligence.

There is a critical evolution happening in the life sciences industry with quality intelligence, where it’s no longer just about inspection preparation and readiness, but becoming vital for enterprises doing risk management and strategic decision making.

“Is it Quality Intelligence or Regulatory intelligence, it seems wherever you sit in an organization today, everyone’s kind of trying to figure this out together. Our industry is going through a lot of change and transformation and these distinctions are being analyzed for how useful or relevant they still are.”

Barbara Bovy, UCB: “Regulatory intelligence only becomes value when it travels the full distance from signal to decision –and from decision to visible change.  This was a recurring theme at the RAPS Regulatory Intelligence Conference in Baltimore this year. One message was clear: modern Regulatory Intelligence is no longer a passive activity of collecting updates, but an orchestrated capability that connects insight with ownership, implementation and learning.”

Historically, quality teams focused on inspection readiness and monitoring enforcement actions such as FDA Form 483 observations. While those activities remain important, the scope of quality intelligence is expanding. Today, organizations increasingly view it as a strategic capability that helps interpret regulatory change and maintain compliance across complex global operations.

One theme was consistent across the panel: quality intelligence is closely connected to regulatory intelligence, but it serves a different purpose.

Regulatory intelligence typically focuses on how regulatory developments affect submissions, approvals, and regulatory strategy. Quality intelligence, by contrast, interprets those same signals through the lens of GxP compliance and operational processes.

In practice, the same regulatory change may trigger different analyses depending on which function is evaluating it. Regulatory teams may ask how the change affects submission timelines or regulatory strategy. Quality teams ask how it affects the quality management system, operational controls, and compliance obligations.

Defining Quality Intelligence

Quality intelligence is evolving beyond traditional inspection readiness into a strategic function for enterprise risk management. The panel revealed that while definitions vary, the core distinction is clear: Regulatory Intelligence focuses on approvability and submissions, while Quality Intelligence examines the same regulatory signals through the lens of GxP compliance, asking whether changes will impact a company’s ability to maintain compliance across its quality management system.

 

Quality Intelligence MaturityKey Focus
Quality Intelligence as a partner to Regulatory Intelligence; hub-and-spoke model with lean (1-2 person) team.GCP, GLP, GCLP, GxP compliance; regulation-to-QMS mapping.
Formal Quality Intelligence function created 2 years ago; QMS recently integrated.End-to-end signal management; expanding beyond GxP.
End-to-end proceduralized process (CMC focus) built after a missed-signal event.Surveillance, communication, analysis, implementation; tied to inspection management.
Newer group; operating via project charters and RI champions. 
Full product lifecycle across all GxPs; cross-intelligence coordination. 

From monitoring to implementation (decision-making and outcomes)

Many organizations are still building formal quality intelligence capabilities.

Some companies have only recently created dedicated quality intelligence roles. Others are evolving existing regulatory monitoring processes into more structured intelligence programs.

Across organizations, the trajectory is similar. Teams are moving beyond simply capturing regulatory signals toward managing the full lifecycle of regulatory change.

That lifecycle typically includes:

  • Monitoring regulatory signals
  • Triage and prioritization
  • Impact or gap assessment
  • Action planning and implementation
  • Governance and oversight

This shift reflects the reality that regulatory signals only become meaningful when they are translated into operational change. Monitoring alone is not enough. Organizations must ensure that regulatory insights lead to updated procedures, training, and process changes inside the quality management system.

For quality teams, that means intelligence work increasingly intersects with operational governance and change management.

Key challenges facing quality intelligence

As organizations shape their definitions of and build their quality intelligence capabilities the panel shared they’re all facing similar challenges.

 

ChallengeDetail
Missed SignalsWithout clear ownership and upfront alignment, regulatory signals can be lost in handoffs between teams. One organization has built its current program after a missed signal caused significant organizational pain.
Action Planning OwnershipGap assessment completion often stalls at the action planning stage, where finger-pointing about ownership delays implementation. Face-to-face facilitation was cited as critical.
Resource ConstraintsSMEs are frequently overloaded, creating bottlenecks. Signals can become stuck at assessment stages without proper governance visibility to escalate.
Proving Value to LeadershipOften the strongest proof of value comes from risks identified before they turn into inspection findings. However, organizations are still maturing in how they demonstrate this proactively through risk metrics and gap-criticality reporting.
AI LimitationsAI tools are useful for surveillance and filtering, but struggle with strategic impact assessment because they lack company-specific context. Outputs often lack risk-based nuance and require heavy human validation.

Building cross-functional intelligence networks

Because regulatory change affects multiple functions, quality intelligence cannot operate in isolation.

Many organizations are building networks of subject matter experts across functions to support intelligence workflows. These networks often include regulatory affairs, quality assurance, clinical operations, manufacturing, and other operational teams.

At some companies, formal “intelligence champions” represent different functional areas and help assess regulatory signals relevant to their domain. These champions coordinate gap assessments, identify subject matter experts, and ensure operational ownership of implementation activities.

Other organizations rely on broader networks where responsibility for impact assessments can shift depending on the regulatory topic.

Regardless of the structure, panelists emphasized the importance of clear accountability. Without defined ownership, regulatory signals can easily stall during assessment or implementation.

Strong networks also help organizations scale intelligence programs. Many regulatory and quality intelligence teams remain small, often consisting of only one or two dedicated staff members. Effective networks allow those small teams to coordinate large cross-functional responses to regulatory change.

Process, roles, and cross-functional coordination

Across all four companies, the operating model relies on structured networks of subject matter experts and intelligence champions, with the QI team acting as the central coordination point. Key themes include:

  • UCB maintains a strong internal network but has not yet formalized processes into SOPs, recognizing that emerging signal types (e.g., AI regulations) require flexible structures.
  • Other organizations operate:
  • a process around four pillars: surveillance, communication, analysis, and implementation, with gap assessments managed through Veeva and approved by inspection management.
  • a lean hub-and-spoke model, working backwards from implementation deadlines and relying on business process owners to drive execution.
  • across all GxPs with intelligence champions embedded in each business area, coordinating with competitive intelligence and other functions to prevent duplication.

Governance creates visibility and accountability

Governance plays a central role in successful intelligence programs.

For many organizations, governance structures provide the forum where regulatory signals are reviewed, implementation progress is tracked, and risks are escalated.

Governance meetings often bring together intelligence teams, quality leaders, regulatory affairs representatives, and operational stakeholders. These forums support several critical functions:

  • Decision-making on regulatory implementation strategies
  • Escalation of delays or compliance risks
  • Visibility into regulatory trends and future regulatory developments
  • Alignment between quality, regulatory, and business strategy

Governance also helps leadership understand the operational implications of regulatory change. Intelligence teams can use governance forums to communicate emerging risks, resource constraints, or upcoming regulatory shifts that may affect company strategy.

Without this visibility, regulatory intelligence efforts can remain disconnected from strategic decision-making.

Demonstrating the strategic value of Quality Intelligence

The panel articulated a compelling case for why Quality Intelligence is becoming essential:

  • Risk Mitigation: QI teams translate regulatory changes into actionable compliance requirements, preventing inspection findings and enforcement actions.
  • Proactive Readiness: Moving from reactive 483-tracking to forward-looking gap assessments ensures organizations are compliant before deadlines hit.
  • Cross-Functional Alignment: QI serves as the connective tissue between regulatory affairs, quality assurance, and operational teams, ensuring no signal falls through the cracks.
  • Right to Operate: QI metrics feed directly into enterprise risk management and right-to-operate dashboards, giving leadership visibility into compliance posture.
  • Regulatory Relationship Management: Understanding health authority thinking through precedent research strengthens the organization’s ability to engage constructively with regulators.

For organizations building quality intelligence capabilities, leadership support is critical.

One effective way to demonstrate value is through risk management. Intelligence teams can highlight the potential consequences of missed regulatory signals, including compliance findings, inspection observations, or delays in product approvals.

Some organizations also track metrics across the regulatory intelligence lifecycle, such as:

  • Time required for gap assessments
  • Implementation timelines for regulatory changes
  • Bottlenecks in cross-functional workflows

These metrics help leaders understand both the workload and the operational risks associated with regulatory change.

Ultimately, quality intelligence is about protecting the organization’s ability to operate in a complex regulatory environment.

The future of quality intelligence

As regulatory complexity continues to increase, the role of quality intelligence will likely expand.

New regulatory frameworks, evolving global standards, and emerging technologies such as AI will continue to reshape how organizations monitor and interpret regulatory change.

For many companies, quality intelligence is still an emerging capability. But the direction is clear. Organizations that can translate regulatory signals into coordinated operational action will be better positioned to maintain compliance and adapt to regulatory change.

The lesson from the panel was straightforward: quality intelligence cannot succeed in isolation. It requires networks, governance, leadership support, and strong collaboration across the enterprise.

Technology and AI outlook

All panelists see AI as a tool that augments (not replaces) expert judgment. Current and near-term applications discussed:

  • Surveillance and monitoring: Automated alerts and filtering to reduce noise and surface impactful signals (One organization reported ~80% accuracy after initial training).
  • Gap assessment population: AI can pre-populate templates from source documents, reducing manual cut-and-paste work.
  • Signal routing: AI-driven distribution of signals to the right SMEs based on expertise profiles, freeing QI teams for strategic work.
  • Annex 22 compliance: Expected to go effective later this year, requiring human-in-the-loop for any AI use in GxP environments. Companies should be preparing now.
  • Key limitation: AI cannot yet perform context-aware impact assessments tailored to a company’s specific strategy, risk profile, and processes. This remains a human responsibility.

Annex 22

What does Annex 22 mean for life sciences? Artificial Intelligence (AI) is moving from pilot projects to real-world pharmaceutical manufacturing applications — but not without regulatory oversight. With the publication of the draft of Annex 22 to the European Guidelines for Good Manufacturing Practice (GMP), regulators are officially entering the conversation. This new annex, now open for public comment, represents the first structured framework for the application of AI/ML in GMP environments. Annex 22 is not just regulatory housekeeping—it’s a signal that AI is here to stay. And the message is clear: AI can be used in regulated environments, but it must be transparent, traceable, and well-controlled. Annex 22 provides additional guidance to Annex 11 for computerized systems, focusing specifically on AI and machine learning (ML) models that are used in critical GMP applications—those impacting patient safety, product quality, or data integrity. In summary, Annex 22 risk assessment governance provides a structured, risk-based framework for integrating AI/ML into GMP-critical processes, emphasizing predictability, explainability, traceability, and human oversight to safeguard product quality and patient safety.

  • AI systems in critical operations must be predictable, validated, and auditable.
  • Risk assessment must identify potential biases, limitations, and subgroup performance variations.
  • Companies may need to redesign or restrict AI models that are dynamic or generative to comply with critical GMP requirements.
  • Cross-functional collaboration and robust documentation are essential to maintain regulatory readiness and ensure patient safety.

AI is helping, but human-in-the-loop expertise must lead

Annex 22 confirms that as Artificial Intelligence begins to play a role in regulatory and quality intelligence workflows, the human-in-the-loop element is going to be critical for life sciences organizations.

Today, most organizations are using AI primarily to support monitoring and signal filtering. AI systems can help identify potentially relevant regulatory updates from large volumes of information sources, reducing the manual workload for intelligence teams.

Some organizations are also experimenting with using AI to summarize regulatory documents or assist with gap assessments.

However, all panelists agreed that AI still has limitations. The most challenging part of regulatory intelligence is not identifying regulatory updates. It is interpreting their relevance within a specific organizational context.

Impact assessments require understanding a company’s overall strategy, products, processes, regulatory strategy, and risk tolerance. That level of contextual interpretation remains difficult for AI systems.

As a result, most organizations emphasize a “human-in-the-loop” approach. AI can support intelligence workflows, but expert judgment remains essential for interpreting regulatory signals and determining how organizations should respond.

Panel key takeaways

 

PanelistKey message
UCBAlign expectations upfront across all teams to prevent misunderstandings about what QI should deliver.
Others included:Scope your deliverables clearly. Use a charter to set expectations and document what is out of scope.

Build your network of allies. Quality intelligence cannot succeed in isolation. 

Secure buy-in and accountability from upper management down through the organization as your starting point.

How Infodesk helps solve the challenges

At Infodesk we are human first, always. We use AI to elevate human judgment, not to replace it. In high-pressure environments, explainability matters as much as efficiency. Our team of 150 technical and information experts ensures that our human-first, AI-elevated solution provides complete confidence in both the quality of information and the outcomes.

Introducing Infodesk’s three-step regulatory intelligence activation framework is delivered through the main Infodesk platform connected to Infodesk’s Regulatory Workflow Solution, both of which integrate with Veeva.

Infodesk focuses on regulatory intelligence for life sciences. Using large language models and agentic systems, the platform helps scale human judgment when interpreting regulatory text. Infodesk combines large language models with structured agentic workflows to support reliable interpretation of regulatory text.

Quality and regulatory intelligence activation involves three steps:

  1. Step 1 – Observation

Continuous regulatory sensing identifies emerging signals across the regulatory landscape. Agents scan health authorities and track developments across guidance, enforcement, safety, and competitive activity.

  1. Step 2 – Interpretation 

Agentic AI with humans in the loop helps teams interpret ambiguous regulatory text more consistently.

  1. Step 3 – Activation

Turning insight into regulatory decisions and outcomes including impact assessments.

The Infodesk Regulatory Workflow Solution is purpose-built to support impact assessments by life sciences. It supports regulatory impact assessment by embedding intelligence, structure, and accountability into daily workflows, connecting regulatory intelligence with execution through a unified, auditable system.

The Infodesk solution helps QA and GxP teams:

  • Centralize regulatory updates from validated sources
  • Filter intelligence by product, region, and topic
  • Create structured impact assessment cases
  • Assign review and comment tasks to SMEs
  • Track approvals, decisions, and timelines
  • Maintain complete audit trails

By integrating regulatory intelligence directly into impact assessment workflows, teams move from surveillance to action without losing visibility.  See how Infodesk’s regulatory intelligence solution combined with the Regulatory Workflow Solution supports proactive, compliant decision-making and the RIA end-to-end process.

In addition to the technology we also provide managed professional information services. Our team of analyst, editorial and information curation experts blend human expertise with technology, so you get relevant, trusted intelligence monitored without any effort. Our analysts and curators work alongside you as part of your team to deliver timely, relevant intelligence that’s ready to use, so your teams can move faster and focus on high-value work.

As leading experts in AI-powered technology talk to us about how we can help you with your Quality-Regulatory Intelligence evolution and how we can help you make the most of AI-powered technology to achieve that.

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“At UCB we recognized that regulatory intelligence only becomes value when it travels the full distance from signal to decision –and from decision to visible change. We knew we needed to move beyond pure monitoring towards “activation” of regulatory intelligence and turn raw information into decision-ready insight. Working with Infodesk has enabled us to move from monitoring to activation. For UCB, this partnership with Infodesk is helping us transform a growing volume of unstructured updates into a single, structured stream of intelligence that flows into our digital backbone and supports our Compliance Domains. Infodesk has been identified as the key digital partner to facilitate the regulatory intelligence journey. Reporting on Implementation, as defined by the E2E regulation Intelligence project, and subsequently applying Infodesk to real signals, real decisions and real changes – is where orchestration becomes culture.”

Barbara Bovy – Head Quality Intelligence & QMS, UCB

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