Regulatory impact assessment (RIA) is an essential step for ensuring pharmaceutical product compliance, quality, and safety whenever changes occur in products or their manufacturing processes. However, this process has traditionally been complex and time-consuming, with risks of delaying product availability and increasing regulatory workload.
Today, Generative AI (GenAI) is rapidly changing this landscape, presenting new opportunities for efficiency and accuracy across global pharma operations.
The Challenge: Regulatory Impact Assessment in Focus
Whenever a change is made to a pharmaceutical product or its manufacture, whether planned or reactive, regulatory teams must swiftly evaluate the implications for compliance and supply continuity.
Key activities in this process include:
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Determining what was previously submitted to authorities.
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Reviewing current labeling and how it’s affected.
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Identifying the appropriate regulatory procedure and documentation.
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Consulting local affiliates for country-specific requirements (including translations and timelines).
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Deciding reporting order and implementation urgency (immediate vs. during next print run).
Traditional approaches involve manually searching disparate, often unstructured sources and engaging multiple stakeholders, sometimes extending assessment timelines from hours into days or weeks.
GenAI Improves the Speed and Accuracy of Regulatory Impact Assessments
Accelerated Information Discovery
GenAI tools excel at quickly searching and synthesizing information from vast, disparate data sources—including previous regulatory submissions, agency exchanges, and local regulatory updates. This substantially reduces the time spent on the initial investigative phase, empowering regulatory teams to focus on decision-making rather than on administrative burden.
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Faster document and data retrieval: AI can scan and extract key information across non-indexed or unstructured documents in multiple languages.
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Regulatory intelligence updates: GenAI monitors and summarizes relevant regulatory changes in different markets in real-time.
Parallel Process Enhancement
The regulatory impact assessment involves many actions that must happen in parallel, such as demand and supply chain planning, manufacturing site coordination, and regulatory notifications.
GenAI supports these by:
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Enabling coordinated workflows between central and local regulatory staff.
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Automating repetitive data gathering for parallel tasks.
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Reducing inter-team handovers through automated cross-referencing.
Tangible Efficiency Gains
Early applications of GenAI in regulatory processes have demonstrated:
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Processing times reduced by 50-80%.
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Handovers halve during manual reviews and regulatory intelligence tasks.
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Extraction of regulatory data in over 23 languages with 90% accuracy.
Even modest time savings for large pharma handling hundreds of submissions monthly translate into significant resource and cost benefits.
Strategic Implementation: Getting Started with GenAI
To maximize GenAI’s impact, pharmaceutical organizations should:
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Identify key pain points in the regulatory impact assessment process. Pilot GenAI in 1-2 select product lines or regions to demonstrate value and refine workflows before broader deployment.
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Assess data and technology readiness. High-quality, standardized, and accessible data assets will enhance GenAI performance.
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Build cross-functional teams to oversee AI deployment, ensuring human oversight in final decisions.
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Train and upskill regulatory staff to effectively integrate GenAI insights and automation into everyday operations.
The Human Element and Governance
While GenAI can relieve much of the administrative burden, crucial decisions such as determining applicability, urgency, and agency notification timing remain with experienced teams.
Strong governance, clear communication, and structured validation are essential to avoid over-reliance on automation and to ensure regulatory trustworthiness.
👉 Need help in planning and executing Gen AI in your pharma company? Consult our experts at Consainsights and book a free consultation call.
How can pharma companies assess their readiness to implement GenAI tools now?
Start by identifying where GenAI can create the most value in regulatory workflows
Pharmaceutical companies must first decide where Generative AI can deliver meaningful and immediate results. Regulatory Impact Assessment is a strong starting point because it involves complex decisions, large volumes of documentation, and tight turnaround times.
Tasks such as retrieving past submissions, analyzing product labels for changes, and calculating required implementation timelines are ideal candidates for automation.
Instead of applying GenAI across all regulatory functions at once, companies should begin with one or two product lines or focus on a specific region.
This smaller scope makes it easier to manage risks, test real-world effectiveness, and build internal confidence. Once GenAI proves successful in a focused pilot, the results can inform wider adoption.
Review if your regulatory data is accessible, structured, and ready for automation
The performance of GenAI depends on the quality and structure of the data it uses. Many pharmaceutical firms have large collections of regulatory documents stored in different formats, such as PDFs, emails, scanned forms, and internal systems. Much of this data is unstructured, which makes it hard for AI to process accurately.
To prepare for GenAI, companies must ensure their historical submissions, health authority responses, and product documentation are stored in machine-readable formats. Information should follow global data standards like IDMP to ensure consistency and traceability.
Because regulatory processes often span multiple countries, the AI systems must also handle multilingual data inputs. Investing time in data cleanup and organization improves accuracy and model performance.
Make sure your technology systems can support GenAI tools at an enterprise level
Implementing GenAI requires a strong technology foundation. Companies need to evaluate whether their current systems can support AI-based tools. A modern Regulatory Information Management system is necessary, especially if it can integrate directly with AI platforms.
GenAI systems also require cloud infrastructure to run models at scale, process large datasets, and store outputs securely. Regulatory systems must be connected using application interfaces so that GenAI tools can interact with databases, pharmacovigilance systems, and document authoring tools.
If a company does not have enough computing resources or storage capacity, performance will suffer. Preparing your infrastructure in advance ensures smoother GenAI deployment.
Establish governance processes that ensure compliance and maintain human control
Since the pharmaceutical industry is heavily regulated, companies must put governance frameworks in place before using GenAI. Every output created by GenAI needs to be traceable, auditable, and approved by qualified professionals. This is especially important for regulatory decisions involving patient safety or urgent product changes.
Companies must ensure that their use of GenAI aligns with compliance requirements like GxP. Systems should have clear version control, audit logs, and documented oversight steps. GenAI should assist in decision-making but must not operate without human review.
These controls give regulatory teams confidence to trust the technology while maintaining compliance with global standards.
Evaluate whether your teams understand GenAI and are ready to adopt it
Even the most advanced GenAI tools will fail if employees do not understand how to use them or do not trust their outputs. Companies should assess whether their regulatory affairs, quality, and manufacturing teams have the necessary AI awareness and digital skills.
Organizations should offer basic training in how GenAI works and explain how it supports existing workflows. They should also provide clear communication about what GenAI will do and what decisions will remain with humans.
A well-planned change management program can help build trust, reduce resistance, and increase adoption across departments.
Compare your current process timelines with proven GenAI benchmarks
To understand how much improvement GenAI can offer, companies should benchmark their current regulatory processes. Metrics such as time to complete an impact assessment, number of internal handoffs, and approval delays provide useful baselines.
Recent GenAI pilots have shown that processing times for regulatory assessments can improve by fifty to eighty percent.
Handovers between departments were also reduced by half. Comparing these benchmarks to your internal performance can help estimate return on investment and support stronger business cases for GenAI deployment.
Begin with a small pilot and expand after measuring real outcomes
Pharmaceutical companies should not try to implement GenAI across all regulatory functions at once. A more effective approach is to launch a pilot focused on a single high-priority use case, such as safety-driven label updates or change control assessments.
Once the pilot is running, companies should measure performance using specific indicators such as turnaround time, quality of output, and user satisfaction.
Teams can then use these findings to improve system performance and train models further. When GenAI shows reliable value in one area, it becomes easier to expand to related functions such as submission compilation and end-to-end change management.
Looking Forward
The adoption of GenAI for regulatory impact assessment is still in its early stages, but is expected to accelerate as:
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Regulatory teams see firsthand the time and cost savings.
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Organizations develop improved data foundations.
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Best practices and regulatory guidance evolve alongside these technologies.
Future applications will likely extend to automated cross-checks for data integrity, improved label management, and increasingly sophisticated end-to-end automation across product lifecycle management
