Keeping pace with regulatory change is becoming increasingly difficult for pharma, Medtech, and other life sciences organizations, as the pace and volume of regulatory changes has increased drastically over the past two decades.
Regulatory intelligence together with effective change management can help organizations identify and adapt to regulatory and guidance changes and automation can be used to improve efficiency.
In this blog post, we’ll explore how regulatory intelligence works, where automation and AI can support the process, and why human expertise remains essential for turning regulatory information into effective compliance decisions.
Key takeaways
What is regulatory intelligence and why is it becoming more important?
Regulatory intelligence is a process guided by three main questions or principles:
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What changed?
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What does it mean to your organisation?
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What do we need to do about it?
This process cannot be dependent on the work of one person. It must be systematic, active, continuous, consistent and repeatable. So, why does regulatory intelligence matter more now than it has done before?
The life sciences industry has long been a heavily regulated industry. However, the rate of change of regulations has increased substantially in recent decades, and in the last year there were more than 1300 global updates, over 500 new guidance documents and upwards of 100 new regulations in the EU alone.
This equates to over 100 impact assessments per year! This increased rate of regulatory changes is largely because authorities have faster publishing processes, more streamlined digitisation and cover more countries than ever before.
With regulatory updates now produced in constant waves, organisations are being affected faster than ever before, and need to stay on top of regulations they need to comply with and actions they’ve taken to do so.
How does the regulatory intelligence process work?
There are four key elements that are required for a regulatory intelligence process to flow effectively:
Access to high-quality global data that provides full visibility and awareness of any and all regulatory changes and updates (including to guidance).
It is absolutely crucial to keep an eye on the horizon and be aware of any new regulations, updates to existing regulations, revised technical standards or new decisions from a notified body or authority.
Effective impact assessment and planning, including:
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A thorough and well managed review process and impact assessment.
An impact assessment should be made outlining which departments will be affected by the regulatory changes and how.
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An action and implementation plan.
Using the impact assessment, organizations can plan how to update their compliance and governance processes and decide what needs to change. Actions that need to be taken can be identified and an implementation plan can be created. Failing to plan effectively at this point can lead to noncompliance and catastrophic consequences.
When outlining an implementation plan, decisions can be split into two groups; database decisions involving a simple factual lookup of information, or knowledge-based decisions that require proper judgement and a thorough contextual understanding of how the regulations are applied through lived experience. This part of the process should never be left to AI as the principal decision maker, as the risk of noncompliance is too great.
Well-organized management of actions and implementation.
Once an action and implementation plan has been made, actions and tasks can be assigned and tracked with full traceability and associated deadlines.
Thorough upkeep of documentation and data repositories.
Any changes to documentation requirements should feed into internal repositories, communications, reports, technical documentation or your RIMS (regulatory information management system) to manage the process in a traceable manner.
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Where can automation improve regulatory intelligence?
To keep up with the vast pace of change of regulations in the life sciences industry, automation is becoming increasingly attractive for organizations yet to implement it and indispensable for those already leveraging it.
Much of the process of adapting to regulatory change can be automated without the need for AI because regulatory changes involve clear inputs and outputs and a fixed logic, making it testable and robust.
The key to a robust regulatory intelligence process is capturing high quality clean and structured data with regulatory updates captured fully, formatted consistently and categorised correctly by country/regulatory area. Without high quality data, the automation process falls apart.
Here’s where automation can and should be used during each step:
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Awareness: For crawling, classifying, formatting, cleaning and translating data.
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Filtering: For screening and to quickly filter out irrelevant changes such as irrelevant countries or markets, product categories, or regulatory areas.
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Workflow notifications and accountability: For managing the workflow and to trigger notifications that help maintain accountability throughout the workforce.
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Data management: For updating dashboards, generating reports and audit trails and to manage access across the workforce.
Effective automation depends on the quality of the regulatory data behind it. That data should meet six core principles:
How AI is changing regulatory intelligence
AI has a place in regulatory intelligence, and it can add value to your organisation above and beyond what standard automation processes can. For example, AI can help structure information from documents, make screening suggestions, and perform a broad search across databases and extract requirements.
However, it is crucial that at this point in time, AI is not used inappropriately where it could create compliance risk. Instead should be used to accelerate the more simple and laborious parts of the regulatory intelligence process, it cannot and should not be used to replace human decision making that requires judgement and experience.
When using AI as an efficiency tool to analyse regulatory changes, all AI outputs must be cited and sourced so that compliance professionals can properly assess it and reduce the risk of noncompliance.
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What Annex 22 means for AI governance in GMP-regulated environments
Regulatory changes and change control using a Quality Management System (QMS)
Even with excellent regulatory intelligence, change management is needed to ensure compliance and accountability. This is where an effective eQMS such as Scilife can help.
According to Scilife’s Knowledge Manager, Angel Buendía, regulatory intelligence on its own doesn’t result in compliance, because awareness isn’t the problem, but rather, execution. Buendía goes on to describe how some organizations receive updates, establish company-wide knowledge, and hold meetings to discuss the changes, yet follow up with a distinct lack of action.
“Being compliant is more than just reading the update,” says Buendía. “It means action. It must be tracked and documented so an inspector can easily visualise what’s been done.”
This means the regulatory intelligence and change management process together becomes:
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Identify the regulatory change and initiate a change request within your QMS: Identify and document the proposed change including a rationale for impact on your processes and systems.
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Impact assessment by subject matter experts (SMEs): Experts assess the effect on processes, resources, and timelines, all documented and traceable within the QMS system.
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Planning and approval: If a change is deemed necessary, production of a detailed implementation plan including steps, resources required, and timelines to be presented to decision makers for sign off.
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Execution: Carrying out the proposed changes becomes much simpler using a centralized QMS, including changes to SOPs, protocols, and policies.
- Monitoring, reviewing and reporting: A digital QMS can track KPIs and metrics, collect feedback from SMEs, and help determine whether the change achieved its intended outcome and met regulatory requirements.
What should and shouldn’t you automate?
So where does all of this information leave you? To summarise, here is what should and shouldn’t be automated and where AI can help streamline processes:
It should be considered that AI’s strengths lie in speed and volume but human decision making cannot be outsourced. Anything procedural, that can be software engineered, can be automated.
The future of regulatory intelligence
When it comes to regulatory intelligence in the life sciences industry, AI and automation adoption is inevitable and can make things more efficient. Also, regulatory bodies and auditors are aware of the need to use it. In fact, when executed properly with minimal risk, leveraging AI is actually viewed favourably.
So, what does the future hold for this sector?
Watch Scilife’s webinar “The Future of Regulatory Intelligence: Transforming Compliance with Automation and AI” where Ivan Perez Chamorro, Founder and CEO at MedBoard and Angel Buendía, Scilife’s Knowledge Manager, discuss this very topic and explore how structured Regulatory Intelligence combined with AI is helping professionals efficiently manage change, reduce risk, and stay compliant.
Conclusion
Regulatory intelligence is becoming increasingly important as life sciences organizations face a growing volume and pace of regulatory change. Automation and AI can make the process more efficient, while human expertise remains essential for impact assessment, interpretation and compliance decision making.
A digital QMS such as Scilife can help turn regulatory intelligence into controlled, traceable action by enabling organizations to:
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Initiate and document regulatory change requests
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Coordinate SME impact assessments and approvals
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Assign actions, owners and deadlines
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Manage updates to controlled documentation
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Track implementation and maintain audit-ready records
By combining automation, AI and expert human oversight within a structured quality management process, life sciences organizations can respond to regulatory change more efficiently while reducing compliance risk.
