Niklas Ekman, EVP of Data & AI at Inizio Evoke, attended Reuters Pharma in Barcelona in April, where one theme consistently dominated conversations across the industry: AI. Across four days of sessions, workshops, presentations, and meetings, a clear pattern emerged - while enthusiasm for AI is accelerating rapidly, many organizations are still struggling to operationalize it in ways that drive meaningful business and patient impact.
AI Was Everywhere at Reuters Pharma - But Clarity Wasn’t
I recently returned from Reuters Pharma in Barcelona after spending four days across sessions, workshops, presentations, and vendor discussions.
One thing became immediately clear: if you removed the word “AI,” you would likely eliminate half the conference agenda.
In the span of a single hour, I attended sessions titled:
Transform Impact with AI
AI Fluency That Fuels Field Results
Launch Advantage in an Era of AI
By the fourth session, it almost felt inevitable that someone would introduce:
“The AI that builds your AI strategy.”
The volume of discussion around AI reflects how important the technology has become across pharma. But beneath the momentum was a consistent theme: excitement without clarity.
The Real Takeaway on AI in Pharma
AI has significant potential to improve:
Efficiency
Speed
Decision-making
Outcomes for patients and HCPs
However, across conversations, panels, and workshops, one challenge surfaced repeatedly: organizations are still determining where AI delivers the greatest value and how to apply it effectively within existing commercial and engagement models.
To explore that challenge directly, we tested AI in practice during our workshop.
Our Workshop: Leveraging HCP Digital Behavior to Optimize Targeting & Impact
At Reuters Pharma, we led a workshop focused on how digital behavior data from HCPs and patients can improve targeting, media strategy, content prioritization, and engagement decisions.
The foundation for the discussion was a dataset we’ve developed over the past five years, tracking online HCP and patient behavior across searches, clicks, video engagement, website visits, content interaction, and email engagement - all timestamped to better understand behavioral patterns and decision-making dynamics.
We started with three questions:
Do you have more data than ever before?
Every hand went up.Do you have more channels than ever before?
Again, unanimous agreement.Is decision-making becoming faster or clearer?
Silence.
That moment highlighted the core issue:
Pharma does not have a data access problem. It has a data synthesis problem.
Pharma’s Data Challenge - And Why AI Alone Isn’t Solving It
As the workshop progressed, attendees described the range of datasets they are managing:
Salesforce notes
Claims data
Media performance
Website analytics
Segmentation data
Messaging and creative testing
And many others
The challenge was not a lack of data. It was:
Determining which data truly matters
Connecting fragmented datasets effectively
Translating insight into action quickly
In theory, this is exactly where AI should create value - simplifying complexity, identifying meaningful patterns, and accelerating decision-making.
Yet throughout the discussion, many participants described a different reality: AI was often adding another layer of complexity rather than reducing it.
Reflecting on the workshop afterward, five consistent questions emerged.
Not theoretical concerns - operational barriers organizations are actively facing today.
Five Core Challenges Pharma Faces When Implementing AI
1. How Do You Create a Single Source of Truth for AI?
The Challenge:
Given the volume of available data, what should ultimately serve as the organization’s source of truth?
Most organizations align around sales as the primary business outcome. The challenge is structuring and prioritizing data in a way that meaningfully supports AI-driven decision-making.
Technically, many datasets can already be unified through:
NPI
ZIP code
Channel + date
That is not the difficult part.
The more important question is:
“What data should actually inform decisions?”
What Matters Most:
Prioritize the moments, channels, and partnerships most strongly tied to business impact
Connect those moments to the HCP and patient journey
Focus on signals linked to measurable outcomes, whether that is prescription growth, awareness, diagnosis improvement, treatment switching, or behavior change
2. How Do You Determine What Data Actually Matters?
The Challenge:
Many organizations track everything - which often results in prioritizing nothing.
While AI benefits from large-scale datasets, more data does not automatically create better outcomes. Without clear prioritization, outputs become less actionable.
The strategic shift is straightforward:
Instead of asking:
“What data do we have?”
Organizations should ask:
“What decision are we trying to improve?”
In pharma, the commercial and engagement objectives are generally clear:
Improve prescribing outcomes
Improve patient and HCP outcomes
From there, the distinction becomes simpler:
If the data improves targeting, messaging, engagement, or conversion, it is signal
If it does not, it is noise
3. How Can AI Better Connect Offline and Online Efforts?
The Challenge:
Pharma continues to operate across two largely disconnected environments:
Field insights and rep interactions
Digital engagement and behavioral data
Organizations are already investing in efforts to bridge this divide through:
Next-Best-Action systems
Sales-note integrations
Digital twins and training environments
Dynamic segmentation updates
Digital targeting tied to prescribing behavior
But the larger challenge remains:
How do organizations connect digital behavior with the contextual understanding field teams provide? And how can AI create a continuous feedback loop that improves decisions over time?
Field insights provide human context. They explain why something may or may not be resonating.
Digital behavior provides scale. It shows what is happening across thousands of HCPs simultaneously.
Together, they create significantly stronger commercial intelligence:
More precise targeting
Reduced wasted spend
Stronger engagement and conversion
The question is no longer:
“Can these datasets be connected?”
It is:
“What connections actually drive measurable impact?”
Where AI Creates Value:
AI should not simply connect field and digital data. It should:
Identify patterns among high-performing field teams
Surface which interactions influence HCP behavior
Generate actionable recommendations for the field
Personalize engagement based on digital behavior, content preferences, engagement timing, segmentation, prescribing trends, and field insights
4. How Do You Drive Real AI Adoption Across Teams?
The Challenge:
One comment surfaced repeatedly during discussions:
“This is not a technology challenge. It’s a human one.”
Like many industries, pharma is navigating a significant change-management challenge around AI adoption.
If AI feels disruptive or overly complex, adoption slows.
What Drives Adoption:
Identify internal early adopters and champions
The individuals most willing to explore and operationalize AI
Position AI as workflow enhancement rather than transformation
Adoption increases when AI integrates naturally into existing ways of working
Prioritize usability
Teams need to clearly understand how AI improves efficiency and decision-making in practical terms
A simple test remains useful:
If non-technical teams cannot use it effectively, it is unlikely to scale successfully.
5. Can Organizations Actually Trust AI?
The Challenge:
This may be the most important question organizations are asking - even if it is discussed less openly.
Before implementing AI solutions, organizations need clarity around:
Whether outputs are grounded in reliable analysis
What data is being used
Which models are driving recommendations
Whether the solution is designed for regulated environments
How adaptable the system is across brands and markets
Because in healthcare, “directionally correct” is not sufficient.
What Builds Trust:
Organizations need clearly defined governance and evaluation frameworks for AI implementation - frameworks that establish transparency, accountability, and confidence from the outset.
Final Thoughts Coming Out of Reuters Pharma
AI is making it easier than ever to:
Aggregate data
Identify patterns
Generate insights
But none of those capabilities matter if:
Organizations do not know what to prioritize
Teams do not trust the outputs
The technology is not usable at scale
The real opportunity with AI sits at the intersection of:
Clarity
Alignment
Action
Over the past five years at Inizio Evoke, we have been building toward solutions designed specifically to address these challenges across AI, data, and commercial impact:
Identifying the most valuable signals
Unifying fragmented datasets
Establishing trusted sources of truth
Designing AI around clarity, speed, and measurable impact
Creating solutions that can be used across organizations - not only by data scientists
What was most validating at Reuters Pharma was hearing many of these same challenges echoed consistently across the industry.
Final Question
Which of these five challenges is your organization currently focused on solving?
Interested in hearing more? Connect with us here.