Authors: Audrey Gent, Client Strategy Director & Trevor Lambert, Senior Strategist & AI Lead
Empathetic customer experiences (CX) may deeply rely on the human-touch, but AI can help us scale the great experiences so that they feel timely, personalised and supportive when guided by the right strategy.
AI is now central to pharma customer experience, powering everything from personalised engagement to smarter strategy and content delivery.“We’re some way beyond asking whether AI works. The question is if and how it is genuinely helping people do their work better, faster and more accurately. Often that’s about taking the friction out of everyday jobs and decisions rather than groundbreaking outputs.”
Customer experience in healthcare has long focused on improving engagement through insight and refinement. With AI, we can now go further, anticipating needs, personalising interactions in real time, and enabling more proactive support across HCP, Payor, carer, and patient journeys.
But while the opportunity is clear, the path to getting it right is not. The value of AI lies in how well it is embedded into an intentional, human-first customer experience strategy.
From Reactive to Predictive CX
Many pharma organisations still operate within campaign-based CX models. Content is published, promoted, and reviewed based on past engagement. While that approach has served brands well in regulated environments, it is no longer enough to meet today’s customer expectations.
When applied thoughtfully, AI can help teams:
- Anticipate disengagement or drop-off points in customer journeys
- Automatically recommend timely, relevant content to keep engagement flowing
- Identify patients who may benefit from early intervention based on behavior patterns
- Deliver real-time insights at the point of HCP interaction to guide next-best actions
- Strengthen coordination across brand, medical, and field teams by highlighting emerging needs and engagement opportunities
These use cases are no longer aspirational. They are being powered through enterprise platforms such as Adobe Experience Manager, Optimizely and Veeva CRM. These systems now include intelligent tools that support experience delivery, content sequencing, and next-best-action triggers across channels.
“But often teams are more held back by process rather than the technology: getting the workflow right and finding a way through the sign-off culture. The impact of predictive AI’s will be limited by reactive process, so I recommend reviewing process before selecting tools. Otherwise you’re in danger of automating chaos.”
First, Get the Foundations Right
Before we look at predictive journeys, many organisations need to ask a more immediate question: is the data ready?
The truth is that most teams are not lacking in technology. They are facing challenges with fragmented insight, siloed data sets and limited alignment between journey touchpoints and data signals. AI is only as useful as the foundation it stands on.
That means investing in:
- Clear data mapping across the customer lifecycle
- Creating accessible data sets – do you know where and how your data is currently stored and accessed?
- Structures that allow signals to surface without compliance risk
- Cross-functional agreement on what the data means, what success looks like, and how it will be measured
Without this clarity, AI will create complexity, not value.
Scenario: Post-Congress Relevance in Action
An HCP interacts with your booth, attends a scientific session and downloads two resources focused on trial data.
A traditional follow-up might offer a general thank-you email and a brochure. An AI-supported follow-up builds a journey. It:
- Surfaces further data aligned to the topics of interest
- Suggests a related on-demand session led by a medical expert
- Alerts the field team to relevant follow-up opportunities, including insights on content engagement and HCP preferences to enable more personalised interactions
The difference is not just in the automation of the response. It is in how customer signals are connected to the strategy, enabling a more fluid, personalised follow-up experience.
Responsible Innovation: Ethics and Governance Matter
With more advanced AI capabilities comes greater responsibility. Consent, governance and ethical use must be embedded in every AI conversation.
Experience design leaders must ask:
- What data is being used, and do we have permission to act on it?
- Will this content or trigger feel helpful or intrusive to the recipient?
How are we ensuring clear visibility into data usage, ownership, and access across the customer experience – for both internal teams and external audiences? These aren’t just regulatory concerns, they’re trust considerations. For example, if an AI-powered doctor appointment is poorly signposted or lacks clarity about how decisions are made, it can leave patients feeling misled or uneasy. These moments increasingly shape how healthcare brands are perceived across digital channels.
“Trust isn’t only at risk with customers. It’s an internal issue too. The AI sceptics and the nervous adopters will be waiting for the chance to pounce when AI gets it wrong. One badly timed trigger and suddenly half the organisation wants to shut down the project – forgetting how many times human error has necessitated a lot of back tracking.”
Where Human Insight Still Leads
Even the most advanced AI models can’t truly understand emotional nuance or clinical hesitation like a human can. While AI can simulate empathy convincingly, it doesn’t experience it – and that distinction matters. This raises important questions about authenticity, trust, and the potential for unintentional manipulation in emotionally sensitive interactions. This is where CX strategy remains essential.
AI should support a strategy that starts with human needs. It should never replace the intention or empathy behind those needs. Technology enables scale, but empathy builds trust.
“AI can spot changes in behaviour but it’s currently less adept in recognising changes in emotions. For now, that’s still a job for humans. What matters is putting AI in the background supporting people doing the things only people can do.”
Tools That Support Smarter Journeys
Many platforms already used across the industry now offer advanced AI features designed to enhance customer experience. While this is just a small subset focused on pharma-related applications, there’s a wide and growing ecosystem of tools supporting different aspects of CX, from content delivery to field-force insights. For a broader view, resources like the Gartner Magic Quadrant or Forrester Wave can provide detailed comparisons and evaluations of leading platforms.
Examples include:
- Adobe Experience Manager and Sensei, which help automate content delivery and targeting
- Optimizely, enabling experimentation and journey optimisation through real-time learning
- Veeva CRM, Vault and Link, which capture and analyse HCP engagement to trigger coordinated follow-up
- Salesforce Einstein, offering intelligent segmentation and next-best-action recommendations for field teams
Measuring What Matters in AI-Enhanced CX
A shift toward proactive, AI-supported CX also means evolving the way success is measured.
“Click rates tell us if someone opened an email. But they don’t tell us if they found it helpful. Or whether they feel reassured, confused or ignored. If you’re not linking your CX metrics to decisions made, actions taken, time saved or confidence built, you’re missing the point.”
Pharma teams are beginning to look at:
- Where and why journeys drop off
- Customer sentiment over time
- Changes in adherence or confidence linked to digital interventions
- Quality and timing of field interactions following digital engagement
- Impact on time to treatment, or on sustained re-engagement
These are more meaningful than engagement data related to your digital content. They reflect real outcomes.
What Comes Next
Over the coming year, we expect to see AI move beyond optimisation and into orchestration. Brands will increasingly use it to guide entire experience pathways that adapt based on real-time behaviour and contextual data.
But the real opportunity belongs to teams who pair these tools with a strong strategic foundation: shared and well-managed, synched data structures, ethical design principles and cross-functional alignment. That is how predictive becomes personal. And that is how CX becomes a differentiator, not just a department.
“The organisations making real progress aren’t necessarily the ones chasing the latest features or the new tech. They’re the ones reviewing their processes, sorting out their content, cleaning and prioritising data, and getting teams trained up and aligned. Without all that in place, you’re in danger of putting fairy lights on a dying tree.”
Ready to Explore AI?
AI is already reshaping pharma CX – but only if the strategy, data and processes are in place to support it. Getting from reactive to predictive journeys means knowing how to guide AI effectively and responsibly.
That’s where our upcoming webinar comes in.
In just one session, you’ll learn a clear, easy-to-use prompting framework designed for pharma – so your AI outputs become reliable, relevant and ready to use.
