Digital: SaaS for Pharma Manufacturing

How is artificial intelligence, when delivered through software as a service platforms, unlocking real business outcomes for pharmaceutical manufacturers, from faster batch release and smarter investigations to smoother tech transfers and enterprise-wide compliance?

Vishal Prasad at Mareana

Pharma manufacturing is entering a new era, where competitive advantage is no longer defined by equipment, facilities or headcount, but by how intelligently you use your data. At the centre of this transformation are two powerful forces: artificial intelligence (AI) and software as a service (SaaS).

The pharmaceutical manufacturing landscape began shifting towards SaaS models in the early 2000s, with adoption accelerating over the last decade. Initially, pharma companies were cautious, hesitant to move away from on-premise solutions due to regulatory requirements and data security concerns. But over time, the benefits became too compelling to ignore.

SaaS dramatically reduced infrastructure costs, offered scalable architectures to support global operations, improved accessibility for distributed teams, accelerated time to market and ensured systems were always up to date with automatic upgrades. More importantly, SaaS revolutionised how data was managed and shared across the pharma value chain, from R&D and clinical trials to manufacturing and supply chain.

As AI continues to redefine the pharmaceutical industry, SaaS is evolving into the primary delivery model for accessing and scaling AI capabilities. Think of it as the shift from renting DVDs to streaming on Netflix – AI delivered via SaaS eliminates the need for complex, on-premise infrastructure and heavy upfront investment.

For pharma manufacturers, this means faster access to cutting-edge capabilities for drug discovery, quality assurance and production optimisation. Companies like Novo Nordisk are partnering with NVIDIA to explore AI for accelerating drug development and optimising internal operations.1,2 The common thread? Rapid experimentation – enabled by SaaS – is key to achieving measurable business outcomes with AI.

With over 70% of pharma executives viewing AI as an ‘immediate priority’, SaaS offers the fastest, most pragmatic way to begin the AI journey without being bogged down by infrastructure challenges.3

AI is no longer a futuristic concept for pharma – it’s a business-critical enabler. From drug development to manufacturing to patient outcomes, its impact is profound and growing:

• Diagnostics and disease prediction: AI enables faster and more accurate diagnostics through pattern recognition and predictive modelling4

• Accelerated Clinical Trials: algorithms streamline patient recruitment, automate documentation and reduce trial timelines5

• Regulatory-Ready Innovation: AI tools are gaining traction with regulators like the US Food and Drug Administration for drug development and health monitoring6

• Virtual Drug Discovery: lab experiments are giving way to AI-driven virtual screening and generative modelling, significantly compressing timelines and cost.7

In short, AI is speeding up every stage of the pharmaceutical life cycle, from molecule to market.

SaaS-based manufacturing intelligence platforms infused with AI unlock transformative business outcomes for pharma manufacturers. Here’s how:

Quality assurance (QA) teams have long battled the ‘data chase’ – manually combing through manufacturing execution systems (MES), laboratory information management system (LIMS), enterprise resource planning (ERP) and even paper records to ensure batch release compliance. This process is labour-intensive and error-prone. With AI co-pilots, batch records – whether digital or scanned paper – are automatically analysed. The AI flags only true anomalies, such as, ‘Potential issue on page 7, 19, and 23’, allowing QA teams to focus on decisions rather than data collection. Outcome? Faster batch release, reduced manual effort, lower operational costs and improved compliance.

APQR typically involve months of manual data gathering from disparate systems. This fragmented process often feels like ‘digital archaeology’. AI, combined with data fabric and knowledge graphs, harmonises quality data in real time. Concepts like ‘batch’, ‘deviation’, or ‘out-of-spec’ are automatically understood. As a result, APQR reports can be generated instantly, any time. Outcome? Proactive compliance oversight, improved audit readiness, and significant time and cost savings.

Once data is harmonised, genAI chatbots can become powerful root cause analysis co-pilots. Investigators can ask: ‘What conditions preceded the last three deviations on Line 2?’ or ‘Which raw material lots are linked to last month’s failed batches?’ The chatbot combs through batch records, deviations, equipment logs and environmental data to deliver actionable insights instantly. Outcome? Faster investigations, quicker deviation closures, reduced recurrence and better right-first-time performance.

“It’s not just about digitising processes; it’s about achieving tangible outcomes faster and more efficiently”

Tech transfer remains one of the most knowledge-intensive, error-prone processes in pharma. Critical process know-how is often scattered across handwritten lab notebooks, PDFs and isolated systems. AI transforms this by digitising and structuring all relevant process documents – creating searchable, audit-ready digital dossiers that can guide successful scale-up. Outcome? Faster, more reliable tech transfers, fewer failed batches and reduced documentation burden.

Building an AI-powered manufacturing intelligence platform from scratch poses steep technical and organisational hurdles. Legacy systems often resist integration, and custom-coded solutions are brittle and expensive to maintain. SaaS platforms solve this by offering ‘connected intelligence’ on top of validated systems like MES, LIMS, ERP and quality management systems, without the need for costly rip-and-replace projects. These platforms enable seamless data flow, contextualisation and insight generation without disrupting existing infrastructure.

SaaS also enhances collaboration with contract manufacturing organisations. Shared access to contextualised data streamlines investigations, accelerates batch release and aligns quality expectations across organisations, without manual back and forth. The result? A connected digital ecosystem where data flows like water, empowering teams with the intelligence needed to drive faster, smarter decisions.

The next frontier in pharma manufacturing belongs to companies that prioritise business outcomes over technology hype. Whether it’s accelerating time to market, improving quality or reducing cost of compliance, AI offers the capabilities to transform, but only if companies can experiment and adapt quickly.

SaaS makes this possible. It gives manufacturers immediate access to advanced AI without the overhead. It’s not just about digitising processes; it’s about achieving tangible outcomes faster and more efficiently. The future of pharmaceutical manufacturing will be written by those who act fast, learn faster and focus relentlessly on results.

References

1. Visit: nvidianews.nvidia.com/news/nvidia-partners-withnovo-nordisk-and-dcai-to-advance-drug-discovery
2. Visit: novonordisk.com/content/dam/nncorp/global/en/investors/irmaterial/cmd/2024/P10-Data-Science-and-AI. pdf
3. Visit: fiercepharma.com/marketing/ai-tech-immediatepriority-most-big-pharmas-many-plan-open-theirpockets-further-ai
4. Visit: youtube.com/watch?v=MCI0xEGvHx8
5. Visit: mobihealthnews.com/news/research-grid-secures-65m-ai-automation-engine-clinical-trials
6. Visit: fiercebiotech.com/medtech/fda-accepts-first-aialgorithm-drug-development-tool-pilot-deliberate-aisanxiety-and
7. Visit: youtube.com/watch?v=yIn3GECO82A&t