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How pharma teams are using AI: 10 practical use cases

AI is gaining real traction in pharma, but not every use case lives up to the hype. As teams explore where it can genuinely add value, a clearer picture is emerging of what works in practice. This article highlights 10 examples where AI is already making a measurable difference.

by Graphite Digital
26 June 25

AI is everywhere in pharma right now—but many organisations are still figuring out how best to harness it. One of the biggest strategic debates happening across the industry is whether to build AI tools in-house or buy off-the-shelf solutions from external providers. Right now, we’re seeing teams doing both: piloting internal experiments while also testing partnerships. But it’s becoming increasingly clear that many internal tools won’t survive long term.

Why? Because the pace of innovation is accelerating. Large platform providers have been slow to release AI solutions that go beyond the basics, but that’s beginning to change. As their offerings mature, many of the scrappy, custom-built tools being developed today are likely to be retired in favour of more robust, integrated platforms that can scale and comply with regulatory demands.

At Graphite, we wanted to explore where AI is already delivering tangible value in pharma. So we recently hosted a roundtable with our partners at Digitalya, joined by guest speaker Manuel Mitola from ctcHealth. The session combined expert insight with hands-on experimentation, surfacing some of the most practical and promising use cases currently in play.

Below, we’ve summarised 10 of the most impactful examples we’ve seen—spanning patient support, regulatory review, content generation and more.

1. AI-powered patient chatbots

Chatbots are being used to support patients by answering questions, offering education and improving medication adherence. One example is the Therakey chatbot by Berlin Chemie, which is part of a patient support portal designed for the German market.

Available 24/7, it delivers relevant information in real time, helping to reduce pressure on human support teams and improve patient confidence. With further training and integration, tools like this have the potential to become even more empathetic and responsive.

2. Personalised patient support platforms

AI is being used to build more intelligent, responsive patient support services. For example, Novo Nordisk developed “Sophia”, an AI-powered chatbot available on their digital platforms for diabetes patients. It provides empathetic, multilingual support and answers common patient queries outside of office hours.

Similarly, Johnson & Johnson created "Andy”, a virtual assistant for users of their ACUVUE contact lenses. Andy offers tailored guidance and habit-building suggestions throughout the product journey, helping to drive better adherence and brand loyalty.

3. Internal knowledge assistants

Many pharma organisations are adopting AI tools like Microsoft Copilot to help employees quickly find and interpret internal information. These assistants can retrieve data from research documents, standard operating procedures or training repositories, responding to natural language queries.

This improves internal decision-making, helps break down silos between departments, and gives teams faster access to the knowledge they need to work effectively.

4. MLR review acceleration

AI can support the medical, legal and regulatory (MLR) review process by flagging potential compliance issues before human review. The MLR Acceleration Engine by Viseven scans marketing content, applies customisable rules, and predicts the likelihood of approval. It also suggests edits and improves its accuracy based on reviewer feedback. This helps reduce manual workloads, streamline compliance and speed up time-to-market for new materials.

5. Generative AI for marketing content creation

AI is being used to automate content generation for marketing teams. Anthill has built an email builder tool that uses generative AI to create promotional copy based on clinical data and campaign goals. The tool drafts emails in real time, reducing the need for manual writing and helping teams deliver personalised campaigns at speed. By combining creativity with compliance guardrails, generative AI offers a practical way to accelerate content workflows.

6. AI for HCP engagement optimisation

AI is improving how pharma companies connect with healthcare professionals (HCPs). By analysing data on when and how HCPs engage—such as email opens, portal visits or response patterns—AI tools can recommend the best time and channel to reach each individual.

IQVIA offers orchestrated engagement tools that support this kind of precision targeting. The result is more relevant communication, improved HCP satisfaction and better campaign performance.

7. Training and role‑play simulations for sales teams

Sales success in pharma depends on reps being able to confidently navigate complex clinical conversations. PLATO, developed by ctcHealth, is an AI-powered platform designed to support pharma sales professionals through realistic, scenario-based training. Reps engage with custom simulations tailored to specific therapeutic areas, practising objection handling, compliance-sensitive messaging and adaptive communication.

The platform provides real-time AI feedback on performance and includes post-call evaluations to reinforce learning. By mimicking real-world pressure and personalising development, PLATO helps teams strengthen skills and improve field performance with measurable outcomes.

8. AI for clinical trial design and recruitment

AI models can analyse patient databases to identify trial-eligible individuals, simulate recruitment strategies, and optimise inclusion criteria. Tools like TrialGPT and ACTES help accelerate recruitment while maintaining diversity and trial integrity. This has the potential to shorten development timelines and improve the success rates of clinical studies by ensuring protocols are more realistic and patient-centric from the start.

9. AI-assisted regulatory review

Even regulators are now using AI to improve their own workflows. In June 2025, the US Food and Drug Administration (FDA) announced the launch of “Elsa”, an AI tool designed to support scientific review teams. Elsa can summarise adverse event data, compare product labelling and assist in drafting database code.

By speeding up these time-consuming processes, the FDA is aiming to reduce review timelines without compromising on rigour—a promising development for both industry and patients.

10. Medical writing and documentation automation

AI tools are being used to automate the creation of clinical and regulatory documents, including study protocols, investigator brochures and safety reports. While still requiring human oversight, these tools can generate accurate first drafts based on structured data, saving time and reducing inconsistencies.

Platforms like Scibite and Sorcero are leading this shift, helping pharma teams meet tight deadlines while maintaining compliance and scientific integrity.

Balancing creativity with compliance will be key for success

As AI adoption accelerates across pharma, the opportunities for efficiency, personalisation and innovation are clear, but so are the risks. Ensuring compliance with regulatory standards remains non-negotiable, especially when deploying tools that generate or automate content.

Whether building or buying, organisations must strike the right balance between experimentation and governance. The most successful teams will be those who embrace AI’s potential while embedding robust compliance checks at every stage.