What if your quality team could cut documentation errors by 70% and accelerate batch review cycles by 50% within a single year? That isn’t hypothetical—it's a real outcome reported in a 2024 by UST where Merck redesigned its batch record processes using digital automation. The project showed a 70% reduction in documentation errors and a significant decrease in “manual entries” that historically slowed release(1). More generally, as highlighted by the latest McKinsey study, an estimated 70% of processes in Pharma are agentifiable with overall potential time savings of 30% across the Quality area(2).
For Quality Directors and Regulatory Affairs leaders, the question has shifted. It’s no longer whether AI can improve GMP processes—but which AI use cases deliver measurable ROI without compromising compliance under the key frameworks FDA 21 CFR Part 11, EU Annex 11, GMP, and GAMP 5. The answer starts with structured data, document automation, predictive quality analytics and batch record transformation.
Problem: The Real Cost of Manual Quality Processes
You already know how tedious and strenuous manual quality processes can be — but the scale of the problem is rarely quantified. Across global pharma operations, documentation remains one of the most persistent bottlenecks: manual batch record management and fragmented quality data lead to extended cycle times and repeated deviations, costing companies millions annually.
A 2024 analysis found that up to 25% of quality faults and 90% of product recalls pharmaceutical manufacturing stem from human errors, including manual data entries(3). Errors compound when handwritten entries, scanned PDFs, and disconnected systems, for instance making SOPs hard to find or search.
We estimated that 60–80% of pharmaceutical operations still rely on paper batch record or scanned paper content, with some EBR processes also requiring manual checks with machine specific reports or SOPs. A study from ArisGlobal revealed that most quality professionals found the regulatory workloads to be unsustainable and 48% see AI as a part of the solution, enabling to transform routine regulatory work and considerably streamline processes(4).
The fragmented landscape of document reviews doesn’t just slow production—it limits your ability to extract meaningful insights. AI-assisted quality processes can only succeed when companies first convert unstructured quality documents into machine-understandable, structured and cleaned datasets. Without that foundation, predictive quality, automated deviation analysis, and risk modelling will remain out of reach.
For global QA leaders under pressure to accelerate releases, reduce deviations, and maintain compliance, AI isn’t optional—it’s now a required tool to optimize operations and cope with increasing demands(5).
Document Automation & Structured Data is the foundation to the Strongest ROI use-cases
AI’s highest-leverage impact begins with turning manual, unstructured quality documents into traceable, structured, compliant data.
Why this matters
Smart automation of manufacturing and quality processes depend heavily on converting human-generated content—batch records, stability reports, certificates of sterility, change control documents—into structured data that AI models can interpret and validate. Without this foundational step, automation simply cannot scale reliably.
What the data shows
- A 1% manual data entry error rate leads to more than 40% of manufacturing documents containing manual entry errors(6)
- Digitally integrated systems lead to 42% reduction in quality-related deviations, primarily attributed to the elimination of transcription errors and the implementation of real-time quality monitoring systems(7)
- McKinsey estimates that each 5 hrs spent on developing AI agents in Quality yield 25 hours of time saved(2) and that companies using advanced analytics are 23x more likely to outperform peers
Actionable Takeaway
Start by targeting the five document types consuming the highest QA hours:
- Batch production records or Certificates of Analysis
- Stability Reports
- CAPA / deviation reports
- Certificates of Sterility
- Change control documents
Use Machine Learning tools to extract fields and values, normalize terminology, and link documents to their audit trails. This creates the structured-data foundation that drives ROI across all other AI use cases, such as process automation, big data analysis or generative AI.
Use-case 1: Batch Review Automation Delivers High Cost & Time Savings
Few AI use cases deliver ROI as quickly as batch record automation—because batch release processes are repetitive, lengthy and involve a lot of tedious yet necessary checks.
What the data shows
- Average batch review time is between 4hrs and 8hrs when all the data is correct
- However, we estimate right-first-time on batch records at 5-20%
- Most paper batch reviews require 2-3 weeks total turnaround time(8)
- Assisted reading and automated checks decrease review time by 60%
- Digital, review by exception process lead to up 90%+ total turnaround time improvements(9)
- Pushing further, Roche GenAI initiatives applied to deviation management accelerated investigations and reviews by 30% to 50% as reported by Yiming Peng and Margaux Penwarden(10)
- For a $2bn Pharma company or CDMO, we estimate that shortening batch releases by 1 week liberates $8-12m in capital previously tied up in inventory
How does AI help?
Structured understanding of documents and AI models can automatically:
- Check signatures, batch numbers and dates
- Validate entries against specifications
- Flag missing or anomalous data
- Automatically draft the bill of materials
- Pre-populate QA deviation forms and review summaries
- Track sources of error over time to support improvement initiatives
How can AI solution support in a GxP environment?
1. Not all these benefits require GxP validation, as they only assist the experts in saving time, whilst the experts remain in control of the final decision and outputs.
2. Solutions based on deterministic Machine Learning models, such as Acodis Batch Review, fit the current requirements for validation in GxP environment.
It’s not about the review time, it’s about the total turnaround time or release time. Saving 2 or 3 back and forth between QA and operations is already going to lead to a big ROI.
In more advanced setups, process changes leverage these capabilities: the manufacturing team digitalises its batch record document and all subsequent changes are made on the digital versions. Custom rules and alerts can be built on the process and tailored per product to create ultimate efficiencies.
Actionable Takeaway
Batch record automation does not need to be $1m+ MES implementation project. Start with clearer, more readable Master Batch Record templates, strict data entry rules especially for handwriting and enable first automated checks based on machine learning before moving on to digital RBE. The Acodis Batch Record Review automation solution can be implemented without rewiring your entire process and deploy progressively to bring increasing ROI, quality and time savings overtime.
Use-case 2: Predictive Quality & Maintenance Shift QA From Reactive to Proactive
Once manufacturing data and batch records data are structured, companies can enable predictive quality—a major ROI accelerator. Advanced applications and core functionality typically use Machine Learning models, rather than Generative AI, given the required specificity and trainable nature of the models.
Evidence from the research
- McKinsey’s 2024 analysis shows that “smart quality control” can decrease QC lab costs by 25% to 45% through document automation and predictive maintenance(11)
- Siemens report that predictive maintenance—powered by AI and sensor data—can reduce unplanned downtime by 85% in pharma facilities(12)
- Deloitte Analytics estimates that on average, predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25%(13)
Why predictive quality is high ROI
Predictive systems can:
- Detect early signals of process drift
- Avoid extended downtime due to unforeseen maintenance
- Reduce batch failures and lost material
- Avoids the other extreme: over-maintenance
Case Example
A large multinational pharmaceutical company deployed a predictive and prescriptive maintenance program that rolled out to 30 sites(14). Here's the specific ROI they achieved:
- Predictive maintenance applied to a bead mill enabled seal replacement to be extended from every eighth to every twenty-fifth batch on average
- This resulted in an estimated saving of $10,000 per seal
- The greatest benefit came from increased production uptime due to avoided maintenance, which delayed the need for capital investment into additional production capacity
Actionable Takeaway
Start with the most ageing machines and an empirical approach: see if the variability in their process parameters increases. And if such variations correlate with planned or unplanned maintenance. Run simple correlation-based models and derive rule‑based, threshold logics (e.g., vibration limits, trend‑based rules) before rolling out complex ML models(15) and scaling up across equipment ranges and plants.
Use-case 3: AI for Regulatory Document Generation Decreases the Workload Across Quality & Reg teams
Regulatory documentation and compliance tasks continue to consume increasing QA/RA resources, across dossier preparation, dossier updates and regulatory inspections. Whilst the burden is most felt on the regulatory teams, the Quality teams need to make their output readily consumable, auditable and handle thousands of ad-hoc queries.
Concrete example where AI drives measurable value
- Automated drafting of regulatory summaries and patient narratives, supported by structured data, can reduce authoring time by 30–50%, according to Yseop(16)
- AstraZeneca RAG-based GenAI application to support Q&A with regulatory authorities reported initial success in shortening response time with a goal to win up to 6 weeks of processing time and is currently used by more than 2000 people internally
- Syngenta has structured its medical leaflet and labels database thanks to Machine Learning and structured content solutions, to enable fast localisation and ongoing content control, thanks partly to Acodis technology (see case study). Given that 20% of product recalls are linked to wrong labelling, ensuring this does not happen has a strong benefit for all teams.
Zoom on one method: How Structured Authoring works(17)
- Template Design: creating a structured, dynamic blueprint from which all documents begin and which automatically includes or excludes content based on predefined conditions
- Document Instantiation: populating the document with specific data and content
- Output Creation: The final step is about formatting and preparing the document for distribution
- Best-of-breed approach uses a combination of structured templates with GenAI content generation, to provide reliability and flexibility
Actionable Takeaway
Once the background data from R&D and Quality is structured and contextualised, software tools based on structured authoring and / or Generative AI deliver proven ROI on single content like patient narrative or entire dossiers. Given that one day of earlier approval is estimated at $1-4m depending on the drugs, entire weeks of time saved deliver very large ROI.
Conclusion
Quality teams have been cautious with AI deployment and rightly so. But the increasing regulatory and process complexity of modern Pharma manufacturing make it an absolute necessity for Quality teams to digitalise and leverage AI where possible.
Between GxP-compliant Machine Learning tools such as Acodis Batch Record Review and non-GxP generative AI use-cases in predictive maintenance for instance, Quality teams can leverage new technologies to streamline their work, accelerate batch releases, reduce deviations and costs.
In 5 years, Machine Learning and AI tools will be a key component to stay competitive in Pharma Manufacturing & Quality, those who move first—starting with targeted document automation and scaling to predictive and regulatory use cases—can start tomorrow to release products faster, better respond to regulators, and turn quality from a bottleneck into a strategic accelerator of enterprise value.
Sources and References:
(1) UST – 70% Reduction in Documentation Errors Case Study https://www.ust.com/en/insights/ust-redesigned-batch-record-system-to-acheive-70-percent-reduction-in-documentation-errors
(2) McKinsey – Reimagining life science enterprises with agentic AI https://www.mckinsey.com/industries/life-sciences/our-insights/reimagining-life-science-enterprises-with-agentic-ai
(3) Cognitive Factors Leading to Human Error: A Major Contributing Factor for Quality Deviations in Pharmaceutical Industry, Sudheer Moorkoth, Ranjani Nayak, Srinivasa BN, Shivani Kunkalienkar
(4) ArisGlobal and Censuswide – Unsustainable Regulatory Workloads Leave No Choice About AI Adoption https://www.arisglobal.com/media/press-release/survey-regulatory-workloads-ai-adoption/
(5) LifeSci Voice – The now-urgent automation agenda: out-of-control regulatory workloads necessitate AI adoption https://lifescivoice.com/the-now-urgent-automation-agenda-out-of-control-regulatory-workloads-necessitate-ai-adoption/
(6) Quality Mag – Manual data entry and its effects on quality https://www.qualitymag.com/articles/96853-manual-data-entry-and-its-effects-on-quality
(7) World Journal of Advanced Research and Reviews, The role of system integration in advanced manufacturing automation, Rishi Nareshbhai Lad
(8) CurePharma – The Pharmaceutical Product Batch Release Process https://www.curepharma.co.uk/innovation/the-pharmaceutical-product-batch-release-process-ensuring-quality-compliance-and-patient-safety-with-curepharma/
(9) ISA – Digital-transformation-of-batch-review https://www.isa.org/intech-home/2020/september-october/features/digital-transformation-of-batch-review-improves-op
(10) Bioprocess – Roche AI Breakthrough https://www.bioprocessonline.com/doc/ai-breakthroughs-revolutionizing-pharma-tech-ops-at-roche-0001
(11) McKinsey – Smart Quality Control & Online Testing https://www.mckinsey.com/industries/life-sciences/our-insights/digitization-automation-and-online-testing-embracing-smart-quality-control
(12) Siemens – Predictive Maintenance in Pharma https://resources.sw.siemens.com/en-US/white-paper-maximize-roi-scalable-predictive-maintenance-pharma/
(13) Deloitte – Predictive Maintenance in Pharma https://www.deloitte.com/content/dam/assets-zone2/de/de/docs/about/2024/Deloitte_Predictive-Maintenance_PositionPaper.pdf
(14) ISPE – Making Maintenance a True Asset in Pharma Manufacturing Through Digitalization https://ispe.org/pharmaceutical-engineering/ispeak/making-maintenance-true-asset-pharma-manufacturing-through
(16) Yseop – Content Automation https://yseop.com/case-study/eli-lilly-case-study-content-automation/
(15) I-Care – Optimizing Pharma Industry: The Role of Predictive Maintenance https://www.icareweb.com/knowledge/predictive-maintenance/predictive-maintenance-pharma-industry/
(16) Yseop – Embracing the future of Document Authoring in Pharma https://www.fontoxml.com/blog/embracing-the-future-of-document-authoring-in-pharma-a-structured-and-data-driven-approach
(17) RWS Fonto – Embracing the future of Document Authoring in Pharma https://www.fontoxml.com/blog/embracing-the-future-of-document-authoring-in-pharma-a-structured-and-data-driven-approach
