Furthermore, AI can also enable real-time monitoring - flagging safety concerns or deviations early to protect study integrity - while chatbots and personalised reminders can drive participant adherence and motivation.

Finally, AI can streamline analysis and reporting by processing large datasets quickly and identifying key trends.

The result: trials that are faster, more efficient, and better aligned with real-world needs.

Connecting to Real Life through Real World Evidence

Like clinical research, RWE generation is becoming faster, more efficient, and more actionable with AI. AI supports protocol design, predicts recruitment feasibility, and processes data from sources like EHRs, pharmacy records, surveys, and wearables. It helps identify meaningful endpoints, monitor safety signals in real time, and determine which subpopulations benefit most.

By integrating structured and unstructured data-using techniques, such as natural language processing, AI surfaces insights traditional methods can often miss. Automated reports and summaries allow teams to focus on strategy, not data preparation. The outcome is deeper, real-life insights that build trust and guide smarter decisions.

In this context, we must consider that while AI-driven RWE tools are powerful, they risk over-representing digitally connected populations. Insights may skew towards smartwatch users or urban health optimizers, for example, potentially leaving out groups that could benefit most.

Keeping Consumers Safe

Safety remains non-negotiable and AI can help enhance consumer health pharmacovigilance by scanning reports and consumer feedback in real time, spotting safety signals earlier and across broader data sources.

Important elements of post-market monitoring – such as processing call handling and management processes - can also be improved by using AI. These include real-time transcription and translation of calls, sentiment analysis to monitor quality, and call triage to route inquiries efficiently. Additionally, AI supports Agents by surfacing relevant content, detecting adverse events and product quality complaints, and automating case creation by extracting key data from transcripts. These AI-driven features streamline communication workflows, improve customer experience, and ensure regulatory and operational efficiency. Moreover, AI-powered chatbots and virtual assistants are supporting both HCPs and internal teams with 24/7 access to information.

In an age of instant information, proactive safety monitoring builds consumer confidence and regulatory trust. And while AI can flag early signals, it cannot be held liable for decisions. Ultimately, human experts must validate outputs and take accountability for safety, efficacy, and compliance.

Crafting Scientific Stories That Resonate

In science, how you communicate can matter as much as what you discover. AI helps teams turn complex data into clear, compelling narratives.

It supports manuscript and abstract development, aligns messaging with regulatory standards, and improves credibility with healthcare professionals. It can also contextualise new findings against the existing literature-saving time and strengthening the scientific story.

Embedding AI across the value chain—from drug discovery and clinical trials to regulatory processes and commercial operations is evolving rapidly from a promising tool to a strategic pillar.

What’s Next?

AI’s potential is seemingly limitless. We are already seeing frontline employees in consumer health companies adopting AI in their daily work – whether drafting summaries, analysing studies, or automating repetitive tasks. Yet most organisations trail behind in formal adoption.

Why? Because companies hesitate to define clear AI strategies. Concerns around data privacy, compliance, loss or exposure of their intellectual property (IP), and technical capability often create inertia. As a result, innovation thrives at the edges but rarely scales. Companies will need to adopt strong data governance - and perhaps even explore models where consumers are compensated for sharing personal health data.

At the same time, AI is evolving rapidly. In healthcare and life sciences, foundational principles -scientific rigor, regulatory compliance, and trust - remain essential. But the emergence of agentic AI marks a step change. life

Unlike traditional AI that just automates a single command, an AI agent can proactively plan, make decisions, and take a series of actions to achieve a complex goal. These AI agents are no longer just automating tasks – they are transforming the fundamentals of operations across the life sciences value chain.

From autonomously generating product formulation proposals to drafting market-specific filings in regulatory affairs, agentic AI is orchestrating decisions, predicting outcomes, and accelerating value across disciplines. In commercialisation, it can optimise launch sequences, tailor engagement strategies, optimize sales strategies and monitoring, to individual consumer journeys.