SPEAKERS:
- Hartmann Estruch, Commercial Excellence & Customer Centricity Iberia Director, Zambon S.A.U.
- Jonathan Vitale, Business Development Lead, Trueblue
KEY TAKEAWAYS:
• CRM investment has become infrastructure without outcome, value isn't generated in the dashboard
• Commercial impact is created in moments of interaction, technology must help reps make better decisions in real time.
• Data has become a commodity; competitive advantage now lives in field decision quality
• CRM logging friction corrupts the data foundation that analytics and AI depend on
• AI deployment risks repeating omnichannel's pattern: easy adoption without commercial impact measurement
From CRM Adoption to Commercial Impact
"The CRM does not create value for itself, for the customers, and even for the sales rep,” said Hartmann Estruch at Pharma 2026, not as a complaint, but as a diagnosis. He is the person responsible for making commercial technology work in a market where the average GP call is "usually not more than one minute in Spain and in some other countries, 30 minutes, 15 minutes in the most advanced countries." The range itself is instructive: even in the most HCP-accessible markets, the commercial window is measured in minutes. In most European markets, it's measured in seconds.
Against this operational reality, many commercial organizations are shifting their focus from data visibility to field execution, recognizing that real impact is created when insights translate into better decisions during HCP engagement. The industry has spent the better part of a decade constructing analytics infrastructure; dashboards, segmentation engines, omnichannel platforms, designed to give commercial organizations visibility into field activity. While these investments have improved visibility and coordination, many organizations are still working to translate insight into meaningful field impact.
Data Became a Commodity While Nobody Was Watching
"Having data is not an advantage anymore. I mean, the advantage is to take the better decision, to make the correct answer. Because having data is a commodity, it's not producing value for the industry."
Estruch's framing is a market structure claim, not a rhetorical flourish. When every competitor has access to equivalent data assets, the asset itself stops generating differentiation. What differentiates is what you do with it in the moment it matters.
The measurement architecture pharma has built, however, was not designed around that moment. "We are measuring a lot of activity—calls, tiers, promotional discounts in the pharmacies, the segmentation, whatever, a lot of transactional data," Estruch argued. "But we are not taking data from what the doctors and the pharmacies want; behavior, habits, business impact, or the pain points of them." The instrumentation captures what reps did. It does not capture what HCPs needed. This creates a disconnect between the metrics organizations measure and the contextual insights field teams need to drive meaningful engagement.
The scale of what's already been built makes this harder to confront, not easier. Jonathan Vitale, Business Development Lead at Trueblue, described a platform built on over 25 years of development, with "400 pre-built KPIs, 32 different dashboards." That represents genuine domain expertise. The challenge is no longer access to KPIs, but ensuring that insights are contextualized and translated into clear, actionable guidance for field teams.
Analytics teams in pharma have been incentivized, structurally, to build dashboards that satisfy management's reporting requirements. Traditional analytics environments have historically focused on visibility and retrospective reporting. Today, organizations are increasingly prioritizing tools that also support real-time decision-making in the field. What the rep needs to know is what to say in the next conversation; almost no dashboard answers that question. The result is a data estate rich in retrospective description and poor in forward-looking prescription, precisely inverted from what the last mile requires. Organizations that measure commercial technology ROI by dashboard adoption rates or data completeness scores are measuring their own reporting comfort, not their commercial effectiveness.
Why Human Engagement Still Matters
Following COVID, many organizations accelerated investments in digital engagement and omnichannel capabilities, anticipating a long-term shift in HCP interaction models. The investment followed the assumption. Omnichannel platforms, digital detailing tools, and remote engagement infrastructure absorbed significant capital on the premise that HCP behavior had permanently shifted.
Estruch is direct about what actually happened. "Five years ago, after COVID, we thought that the face-to-face was going to disappear, but it's not a reality anymore. I mean, face-to-face is still the king in the pharma industry, and we need to invest in that moment." The durability of in-person engagement is not a cultural artifact or a temporary reversion. It reflects the information density of physical interaction, the clinical nuance, the relationship signal, the ability to read and respond in real time, that digital channels have not replicated in a therapeutic context.
This evolution is leading organizations to reassess how digital investments can better enable field effectiveness and customer engagement: not that digital channels lack value, but that they may have been resourced at the expense of the interaction that still generates the most commercial impact. The corrective is not dismantling digital investment but subordinating it, treating every channel as a support mechanism for the rep's next HCP conversation rather than as an independent engagement pathway with its own optimization logic and its own success metrics.
Which returns the problem to the rep. Drawing on his own field experience, Vitale was unambiguous: "Listen to your sales reps. I can tell you, as a former sales rep, we want to be included in conversations when it comes to tools, what's important. So we want to be heard because we are the ones that are having those conversations in front of HCPs." When tool design excludes the end user, the resulting tools optimize for visibility, what managers want to see, rather than utility, what reps need at the exact moment of engagement.
The feedback loop runs deeper than user experience. "And I can tell you something else from the field: reps really don’t enjoy spending time logging interactions and activity" Vitale acknowledged, "so making that process as simple and seamless as possible is absolutely critical." If CRM logging is painful, reps minimize it. If reps minimize it, data quality degrades. When data capture becomes burdensome for field teams, data quality inevitably declines, impacting the reliability of downstream analytics, AI models, and commercial decision-making. The logging problem is not an adoption problem. It is a data integrity problem that compounds upstream through the entire commercial technology stack.
Discover more on this topic at Pharma Commercial Data & Tech Europe 2026 (4-5 November, London) Europe’s collaborative home for data and tech pioneers. Visit the website here.
AI Is About to Make the Same Mistake Twice
Pharma's current enthusiasm for AI deployment carries a structural resemblance to the omnichannel wave that preceded it: broad organizational appetite, relatively low barriers to initial implementation, and a tendency to measure success by deployment breadth rather than commercial impact. Estruch flagged the parallel explicitly. "What we have to try is to avoid what happened with the omnichannel five years ago. The barriers to experimenting with AI have significantly decreased. The real challenge now is identifying where AI can drive measurable business impact.”
The ease of deployment is not an advantage. It is a risk amplifier. When implementation friction is low, the strategic discipline required to ask "where does this actually improve a field decision?" is easy to skip. Organizations can deploy AI broadly, measure adoption, and report progress without ever demonstrating that a rep's next conversation with an HCP was materially better because of it.
The architectural corrective, Vitale argued, is domain specificity. "Having contextualization specifically for your business logic in the pharma industry as a whole, understanding what's important, the KPIs that really move the needle—this is absolutely critical here, the semantic layer." Without pharma-specific business context and semantic alignment, even the most advanced AI models risk generating recommendations that may sound convincing, but lack the relevance and precision needed to support real commercial decisions in the field.
The organizational reframe Estruch offers is the standard every AI deployment should be evaluated against. "The sales team are our internal customers, because they are in the last mile, so where the things happen." If reps are internal customers, AI deployment must pass the same test as any customer-facing product: does it solve the customer's problem at the customer's point of need? As organizations scale AI initiatives, the focus is increasingly shifting toward measurable adoption, workflow integration, and field-level impact.
Every Technology Investment Needs a Last Mile Audit
The speakers' argument, taken to its logical conclusion, implies a more radical reorientation than either explicitly stated.
Pharma's current commercial technology measurement architecture evaluates whether technology was deployed: CRM adoption rates, dashboard utilization, data completeness scores, omnichannel engagement metrics. None of these measure whether technology improved the quality of the next decision a field rep makes in front of an HCP. They measure organizational compliance with the technology, not the technology's commercial return.
The corrective is what the speakers' framework implies but stops short of naming: a last mile audit. Trace every commercial technology investment backward from the HCP conversation. Evaluate whether it accelerates, impedes, or is simply irrelevant to the rep's ability to make the right call in real time. For many organizations, the uncomfortable finding will be that their most expensive investments—the ones with the highest dashboard adoption, the most impressive data volumes, the most sophisticated segmentation models—score worst on this test, because they were designed to satisfy upstream reporting requirements rather than downstream decision speed.
This pattern has repeated across three technology generations in pharma commercial operations: CRM, omnichannel, and now AI. Each entered with the same promise of better field effectiveness and each has carried the same failure mode: deployment logic driven by headquarters capability rather than field necessity. The technology changes. The organizational incentive that produces the failure mode does not.
Estruch's prescription is the right starting point and the right ending question. "More data won't win. It doesn't matter how much data you have, it's not relevant for me. The point, the key message, is that the better decision that you are taking in the last mile, the better business you will be able to do with your customers."
Organizations that can operationalize that standard—evaluating every CRM enhancement, every analytics build, and every AI deployment against whether it improves what happens in the next sixty seconds with an HCP—have a commercial architecture built for the constraint that actually governs the business. Organizations that cannot answer that question with specificity, not aspiration, are still building infrastructure for the dashboard, not for the conversation. Given that this same gap has persisted through every technology cycle for twenty-five years, the challenge is not the technology itself, but ensuring that commercial success is measured by the quality of decisions and customer engagement it enables in the field.
To get you highlights of Pharma 2026 faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.
Discover more on this topic at Pharma Commercial Data & Tech Europe 2026 (4-5 November, London) Europe’s collaborative home for data and tech pioneers. Visit the website here.
