Introduction

SPEAKER: Prasant Vijayakumar, Chief Strategy Officer, Viseven

KEY TAKEAWAYS:

• Compliance training establishes foundation for viable pharma AI deployment
• Orchestration engines connect fragmented systems through Model Context Protocol architecture
• Localization timelines compress from months to weeks across multinational markets
• Engagement data integration enables evidence-based content selection for HCP segments
• Maturity roadmap progresses from content creation toward autonomous project management

The Compliance Paradox: Why Pharma AI Must Solve Regulation Before Innovation

Pharma companies face an existential constraint that consumer industries deploying artificial intelligence never encounter: non-compliant content creates liability no efficiency gain can offset. While technology sectors race toward autonomous AI systems, pharma organizations confront a different calculus where regulatory risk determines viability before innovation delivers value. "One of the biggest blockers for all of us is MLR and compliance," Prasant Vijayakumar explained.

"The risk is greater when things are not compliant. So this is something where eVa is also getting trained on all the compliance regulations." This design priority reflects an industry-specific reality that separates viable pharma AI from vaporware.

"For us to survive—and for AI to survive—one of the key components of agentic AI for our industry is about compliance," Vijayakumar emphasized. "That's the first thing—it's the foundation."

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From Point Solutions to Orchestration Engines

The architectural shift transforming pharma AI moves beyond isolated task automation toward cross-system coordination layers that address enterprise workflow complexity. Traditional approaches deployed separate AI instances for email generation, translation services, and content assembly—creating new silos rather than solving existing integration challenges. Viseven's alternative demonstrates a fundamentally different model where AI functions as infrastructure connecting fragmented technology landscapes.

"eVa will soon become more of a super-agent because it's not just AI on its own, but it is orchestrating from different systems, pulling information," Vijayakumar described. This orchestration capability relies on Model Context Protocol, an emerging standard that eliminates custom integration burdens across Digital Asset Management platforms, CRM systems, content repositories, and analytics tools. "With MCP it's now possible to connect to any systems as long as they have an MCP connection," he explained. "And eVa as an orchestration engine is able to read across and orchestrate your content."

The strategic advantage emerges from simultaneous awareness across domains that traditionally operate independently. When brand managers develop content, the system understands approved brand templates, compliance guidelines for specific markets, customer segmentation from CRM data, and historical performance metrics—eliminating sequential handoffs between creative, regulatory, and commercial teams. This closed-loop intelligence addresses pharma's persistent challenge where engagement insights live in analytics systems while content decisions happen in creative workflows disconnected from evidence.

Vijayakumar highlighted the performance feedback mechanism: "We are working with companies who have the data on customers, who have the engagement data, and we are able to then bring that data back into the system. So when you're assembling your content that may have already been used, we will be able to highlight that this particular piece of content has had X engagement with certain customer segments." This capability shifts content selection from intuition-based decisions to evidence-based recommendations surfacing which assets drove results with specific HCP populations.

The competitive implication favors organizations treating AI as enterprise infrastructure rather than departmental tools. Companies deploying isolated point solutions risk creating tomorrow's technical debt—systems that solve narrow problems while adding integration complexity. The procurement question becomes whether platforms can absorb expanding responsibilities as AI capabilities mature, or whether current investments lock organizations into task-automation limitations.

The Localization Breakthrough and Global Brand Economics

Timeline compression in multinational content adaptation represents more than operational efficiency—it transforms the fundamental economics of global brand management. "In the old days, creating content meant months of work that could take a long time," Vijayakumar noted, describing the sequential workflows that forced staggered regional launches. The contrast with AI-enabled localization reveals structural competitive advantages for organizations achieving regional parity faster than competitors.

Vijayakumar presented concrete evidence from customer deployments:

"One of our customers was able to create variations in eight different markets which would normally take three to six months, and reducing that to merely weeks or days is a huge move forward with agentic AI."

This 90-plus percent efficiency gain changes strategic planning assumptions for multinational pharma companies where localization delays historically dictated sequential launch strategies—priority markets receiving content first while secondary regions waited months.

The P&L implications extend beyond speed. Faster local market entry accelerates revenue realization across geographies simultaneously rather than sequentially. Reduced agency and translation costs improve campaign ROI when internal systems handle adaptation workflows. Synchronized global rollouts eliminate competitive disadvantages in regional markets where delayed launches cede first-mover advantages to rivals with faster localization infrastructure.

For global brand teams, this capability challenges current planning frameworks. Does existing localization infrastructure support simultaneous worldwide campaigns, or are organizations losing market share to competitors achieving regional launches faster? The strategic advantage shifts from "how much content can we create" to "how quickly can we achieve global market presence"—a fundamentally different constraint that favors infrastructure investments enabling parallel rather than sequential execution.

The US-centric conference audience may underestimate this capability's significance, but for organizations managing international portfolios, localization speed represents a structural competitive advantage in launch execution. Vijayakumar's vision that global content should be "immediately available in local markets" reflects a shift from staggered regional waves to synchronized worldwide campaigns—changing the timeline from quarters to weeks.

Human-in-the-Loop Design for Risk-Averse Organizations

Viseven's explicit design philosophy prioritizes augmentation over automation, reflecting pharma risk profiles that demand different AI principles than consumer industries. The eVa system pre-checks materials to highlight potential compliance issues rather than making autonomous approval decisions—a strategic choice addressing organizational dynamics in compliance-critical workflows.

"We are planning to train eVa with those past approvals so that certain nuances of how approvals work will get better and better and make it far easier to approve something," Vijayakumar explained. This approach captures subjective decision patterns embedded in previously approved materials within Digital Asset Management systems, surfacing relevant compliance precedents rather than replacing human judgment. The system flags potential reference gaps and highlights areas requiring MLR attention, but humans retain final authority over approval decisions.

This design addresses pharma's adoption paradox: legal and regulatory stakeholders who hold veto power over commercial technology investments resist systems threatening their authority. "We are a company that's built on the idea that we are able to build products that are built on augmented AI, which are built on traditional systems to help engage customers," Vijayakumar noted, positioning the technology as enhancing rather than replacing human expertise.

The strategic reasoning recognizes that "auto-approve" systems create unacceptable liability exposure in pharma environments. MLR teams become AI advocates when systems reduce their workload without eliminating their oversight role—transforming potential organizational antibodies into deployment champions. This human-centered approach accelerates adoption by aligning incentives with stakeholder trust rather than triggering resistance through replacement narratives.

The measurement framework shift reflects this philosophy. Success metrics should emphasize "time to approval" and "reviewer satisfaction" rather than "approvals without human review"—focusing on efficiency gains that preserve human authority. For organizations evaluating AI investments, this suggests procurement criteria should assess whether systems reduce friction in existing workflows or attempt to eliminate human roles that regulatory environments require.

The Maturity Curve from Content Creator to Campaign Orchestrator

Vijayakumar's evolution roadmap maps a progression from current content generation capabilities toward autonomous project management coordinating multi-stakeholder workflows. Current deployments focus on email creation, eDetailer assembly, and localization—discrete content tasks with measurable efficiency gains. The next phase adds cross-platform awareness and ecosystem integration, enabling AI to understand where content should be deployed based on customer journey orchestration and channel strategy.

The future state envisions AI functioning as project manager: understanding dependencies across creative, regulatory, and commercial teams; managing stakeholder workflows; optimizing resource allocation; and flagging blockers before they cascade into delays. "Eventually it will act like a project manager which coordinates all the different tasks and flags blockers," Vijayakumar described, emphasizing the trajectory from tool to agent to orchestrator.

This progression requires breadth of perspective spanning technology capabilities, commercial operations, regulatory constraints, and stakeholder dynamics across the pharma value chain. Vijayakumar's background provides relevant context: "I've been in the industry for about 20 years. I started as a pharmacist, I was a med rep, then I moved into brand and communications, and more recently, because of this transformation, I've started working with technology teams." This cross-functional experience informs the orchestration vision—understanding not just what AI can do, but how pharma organizations actually operate.

The strategic planning question for executives becomes whether current AI evaluations account for maturity trajectories or focus solely on today's capabilities. As agentic AI advances toward autonomous workflow coordination, competitive advantage accrues to organizations whose infrastructure can absorb expanding autonomy. Procurement decisions should assess vendor platforms for extensibility beyond current use cases—can systems evolve from task automation to project orchestration, or do they solve today's problems while creating tomorrow's technical debt?

This favors multi-year vendor partnerships aligned with maturity curve progression over point-solution procurements that address immediate needs without architectural flexibility. The architectural decision framework should evaluate whether platforms enable progressive capability expansion as AI technology matures, preserving investments while absorbing new functionality.

The organizations that win aren't necessarily those deploying AI fastest, but those architecting systems that evolve from tools to agents to orchestrators as the technology matures. Vijayakumar's presentation suggests the competitive question isn't whether to adopt pharma AI, but whether current infrastructure decisions position organizations to capitalize on capabilities emerging over the next three to five years. For executives evaluating investments today, the challenge involves balancing immediate operational needs against strategic flexibility for tomorrow's orchestration requirements.

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"My team tells me, oh, but we don't have a seat at the table. My advice is to be proactive, go find the table and get a chair and share how our expertise can support shared goals. Maybe they were not aware or did not understand. Maybe you have to make it easier for them."

This partnership model extends beyond technology. "Our customers don't see the different roles that we all represent," Kelly reminds. "Our customers see the company that we represent and the products." Internal functional distinctions become invisible to customers experiencing unified company touchpoints. When medical, commercial, and market access messages arrive uncoordinated, customers perceive organizational dysfunction regardless of the sophisticated rationales justifying each department's independent optimization.

Building Capability for Digital-First Engagement

The shift toward increasingly digital go-to-market models requires systematic capability building across three dimensions. Novo Nordisk started with foundational work: creating a glossary ensuring consistent terminology. "Can we all talk about the same thing in the same manner to ensure that when we're meeting a customer journey means a customer journey and not a pathway, not a digital asset," Gala explains. Without shared vocabulary, cross-functional collaboration stumbles on semantic confusion.

Training extends beyond front-line commercial teams to back-office functions. "This needs to be much more a company mindset," Gala argues. Technology upskilling ensures infrastructure readiness for emerging capabilities. Change management acknowledges the emotional reality of transformation: "I haven't met anyone that says that. Oh, I love not knowing what is going to come. I love not knowing what is going to be my job or how to do it."

Perez Casesmeiro adds perspective on navigating constant change: "The only constant is change. So I don't think there's anything new. The difference here is that it's happening very fast."

From Aspiration to Operation

The transformation journey requires patience and persistence. Organizational change messages need approximately eight repetitions before achieving stickiness. "Someone said that for people, for a message to stick, repeat eight times. So probably do 16," Gala advises. Starting with believers before scaling broadly allows proof points to accumulate. Simple tools, Excel-based editorial calendars, transparent customer journey maps, can drive meaningful coordination improvements without waiting for sophisticated technology solutions.

Hergli shares an example of cross-company collaboration that demonstrates possibilities beyond internal silos. Working with a partner holding commercialization rights in Japan for a Chiesi product, the companies coordinated regulatory filings for a new indication. "We have been helping them from a medical perspective with discussing with the healthcare professional and key opinion leaders, but also from regulatory perspective, from marketing perspective," she explains. The cross-functional, cross-company collaboration succeeded because shared purpose transcended organizational boundaries.

Kelly synthesizes the discussion into actionable principles: have one clear goal, communicate prolifically across functions, ensure everyone has voice in strategic conversations, and use meetings to hold up mirrors when customer-first principles are violated. "I think there are some practical things that we can be doing on a daily basis to achieve this vision," she notes. "We're trying new things and we're having to break down the silos global to regional, regional to local, but also within our functions, within our organization."

The imperative is clear: move from customer-first language to customer-first operations, one coordinated touchpoint at a time. As Perez Casesmeiro reminds, overlapping functions create organizational resilience: "No one operates alone. Shared responsibility is crucial in ensuring we don’t fail. There is too much at the stake for patients and healthcare professionals." The challenge isn't eliminating complexity but learning to navigate it effectively, transforming bureaucratic friction into collaborative strength that ultimately serves patients and healthcare professionals better.

To get you highlights of Pharma Customer Engagement EU 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.

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