Authored by Marina Hickson, Managing Director, Vivanti
Healthcare in Europe is entering a phase where artificial intelligence is moving from pilot projects to everyday clinical infrastructure. Hospitals and healthcare systems are increasingly deploying AI across diagnostics, triage, clinical documentation and decision support. NHS programmes and broader European initiatives are accelerating the development and adoption of these technologies across care settings.¹
This shift is not theoretical. Across Europe, 64% of countries report using AI-assisted diagnostics, making it one of the most widely deployed applications of AI in healthcare.²
Clinicians are also rapidly experimenting with new tools themselves. In the UK, around 30% of general practitioners report using AI tools during consultations, often for summarising information, reviewing clinical documentation or supporting diagnostic reasoning.³
In many interactions now, clinicians have already looked at the data before a medical representative even walks through the door. So it’s less about whether information is shared, and more about whether the conversation actually adds anything useful. The implications for pharmaceutical engagement are significant. For decades, the field force operated within an information gap: clinicians had limited time to search the literature, while pharmaceutical companies synthesised clinical evidence and delivered it in person. Today, that gap is closing. And it is closing quickly.
The End of the Information Advantage
Increasingly, clinicians interact with systems that analyse literature, summarise guidelines and suggest treatment pathways. In many consultations, clinical decision support systems may already have compared therapeutic options before a conversation with industry occurs.
If pharmaceutical engagement simply repeats information already available through these systems, its relevance diminishes rapidly.
Instead, engagement must move toward interpretation, contextualisation and dialogue around clinical complexity.
What Changes in the Field Interaction?
Three structural shifts are emerging.
1) From information delivery to clinical reasoning
Field engagement increasingly centres on understanding clinical decision-making rather than presenting product attributes. The discussion moves from ‘what therapy to prescribe’ toward ‘why a specific therapeutic choice may be appropriate in a particular clinical context’.
2) From data presentation to interpretation
AI systems provide large volumes of data but cannot always interpret nuance – individual patient context, emerging evidence or real-world clinical experience. The value of human interaction lies in helping clinicians interpret these signals and apply evidence within complex clinical environments.⁵
3) From longer visits to higher-value dialogue
Digital tools are gradually reducing administrative workload in healthcare systems. While this may free time, it does not increase tolerance for lengthy meetings. Instead, clinicians expect concise, highly relevant discussions grounded in credible evidence. The quality (not the duration) of the interaction becomes the defining factor. In this context, some field interactions risk becoming more about confirming what’s already known than moving the thinking forward. In many pharma companies, this already shows up as variability in field conversations – where some interactions lead to meaningful clinical discussion, while others revert to familiar messaging.
The Evolving Role of the Field Force
Contrary to common concerns, AI is unlikely to replace the field force. Rather, it will redefine the capabilities required for success. Routine informational tasks (literature searches, summarisation and content distribution) are increasingly automated. Human expertise becomes concentrated where technology remains limited: strategic interpretation, understanding clinical practice patterns and building trusted professional relationships.
The role shifts from information carrier to strategic navigator.
Representatives help clinicians interpret evidence where uncertainty remains. They provide context when algorithms present conflicting signals. And they support complex therapeutic decisions with insights drawn from clinical practice and real-world evidence. Across the industry, this transformation is already underway. Many pharmaceutical organisations are redesigning commercial roles, redistributing routine informational tasks to digital platforms while strengthening capabilities in scientific dialogue and strategic engagement. In that environment, field teams that do not evolve risk becoming progressively less influential in clinical decision-making.
A New Model of Engagement
In the near future, field interactions will increasingly resemble this model. Before the meeting, AI systems analyse previous interactions, relevant publications and practice patterns to suggest tailored discussion points. During the meeting, conversations focus on clinical interpretation rather than product presentation. After the interaction, digital systems integrate validated evidence directly into the physician’s workflow, while CRM platforms automatically capture insights and trigger personalised follow-up.The interaction becomes part of a continuous evidence-driven engagement model.
Enabling the Next Generation of Field Engagement
The direction is becoming clearer, but most companies haven’t yet equipped their field teams to work this way in practice.
To work effectively in this environment, field teams need to build new capabilities – not just knowledge, but the ability to align their approach with real practice and the needs of clinicians – whilst also considering clinical differentiation.
Approaches such as simulation-based training are beginning to support this transition. Platforms like AVA Trainer enable field teams to practise realistic clinical scenarios and clinician archetypes, helping them develop the ability to explore physician reasoning and respond to nuanced questions.
In parallel, tools such as AVA Companion can analyse field interactions to identify emerging clinical questions and evolving practice patterns, helping organisations better align future engagement with real-world needs. Together, these approaches support a more continuous and adaptive model of engagement.
Implications for Commercial Leaders
For commercial leaders, the key question is no longer whether AI will influence field engagement. That shift is already underway. The focus now is on how organisations adapt – through changes in capability building, engagement models and the integration of digital tools. Those that respond effectively are likely to improve not only efficiency, but also the relevance and quality of their scientific dialogue with healthcare professionals.
Conclusion
As clinicians increasingly rely on digital tools and AI-supported insights, expectations around engagement are changing. The Future of pharmaceutical engagement will depend on the ability to combine human expertise with technological support. The question for organisations is how quickly they can evolve to meet this new reality.
References
- NHS England
Artificial intelligence (AI) and machine learning in the NHS
https://www.england.nhs.uk/long-read/artificial-intelligence-ai-and-machine-learning/ - World Health Organization Regional Office for Europe
Is your doctor’s AI safe?
https://www.who.int/europe/news/item/19-11-2025-is-your-doctor-s-ai-safe - The Guardian
GPs using artificial intelligence tools during consultations
https://www.theguardian.com/society/2025/dec/03/gp-doctors-health-uk-artificial-intelligence-study - Sutton RT et al.
An overview of clinical decision support systems: benefits, risks, and strategies for success
npj Digital Medicine
https://www.nature.com/articles/s41746-020-0221-y - Topol EJ
High-performance medicine: the convergence of human and artificial intelligence
Nature Medicine
https://doi.org/10.1038/s41591-018-0300-7
