2026 Veeva R&D and Quality Summit Event Brief
“I’ve been in tech for over 30 years now. AI is the biggest change we’ve got going, and it’s going to transform our industry,” said Peter Gassner, CEO of Veeva. His assessment framed the Summit’s central question: how can life sciences organizations move beyond AI experimentation and apply intelligence responsibly at enterprise scale?
Image Source: Veeva
Over the past decade, life sciences organizations have invested heavily in digitizing processes, modernizing applications, migrating to cloud platforms, and improving access to enterprise data. These initiatives established important digital foundations, but many organizations continue to operate with fragmented processes, disconnected information, inconsistent governance, and functional silos. Digital Transformation improved how work is executed, yet it did not always improve how information is understood, decisions are made, or actions are coordinated across the product lifecycle.
The industry is now confronting a more consequential challenge. Development and manufacturing organizations face increasing scientific and operational complexity. Quality teams must maintain control while improving efficiency and accelerating product release. Regulatory functions are preparing for greater interoperability, structured information exchange, and growing use of AI. Across the enterprise, leaders are under pressure to increase productivity, strengthen resilience, and improve patient access while ensuring compliance, accountability, or trust.
Against this backdrop, the 2026 Veeva R&D and Quality Summit convened industry leaders around the theme “Powerful Industry AI, Advancing Development and Quality.” AI, agents, and automation featured prominently, but the stronger signal was the industry’s shift from isolated experimentation toward enterprise deployment. Speakers consistently connected that transition to trusted data, integrated workflows, modern operating models, effective governance, and sustained human accountability.
“I’ve been in tech for over 30 years now. AI is the biggest change we’ve got going, and it’s going to transform our industry,” Peter Gassner, CEO of Veeva
Veeva’s long term vision reflected this broader shift. Gassner explained that Veeva seeks to “connect the industry” and become “the glue that binds the industry together.” The significance of this strategy extends beyond software integration. Connected applications and data create the potential for intelligence to flow across development, clinical, regulatory, safety, quality, manufacturing, sites, partners, and health authorities while retaining the context and traceability required in regulated environments.
From Axendia’s perspective, the Summit signaled a transition from Digital Transformation to Intelligence Driven Operations. Digital Transformation and cloud established the infrastructure. Intelligence Driven Operations use trusted data, connected workflows, AI enabled capabilities, and human expertise to improve how work is prioritized, governed, and executed. AI is the catalyst, but organizational readiness will determine whether companies convert digital investments into trusted decisions, measurable business value, and better outcomes for patients. (See: “AI in Life Sciences: What the Industry is Really Saying” )
AI Must Move from Experimentation to Enterprise Strategy
For several years, AI initiatives across life sciences were largely exploratory. Companies launched pilots, tested use cases, and evaluated technical feasibility while waiting for greater regulatory clarity and practical evidence of business value. The conversations at Summit suggest that experimentation alone is no longer sufficient. (See: Don’t Do AI Pilots! )
Reflecting on the industry’s early AI journey, Fernando Londoño, Vice President, Quality Business Consulting at Veeva, noted that many organizations viewed AI primarily as a technology initiative rather than a business transformation. “There was a lot of experimentation because this was new technology that people didn’t understand and were starting to work with,” he explained, adding that many efforts were driven by executive mandates to simply “Build AI.”
Image Source: Axendia
Today, the questions are fundamentally different. Organizations are increasingly focused on scale, governance, and measurable outcomes. Ravi Yalamanchili, Vice President, Global Manufacturing, Quality & Supply Innovation at Merck, observed that “the demand signal in AI has never been as high in the past.” More importantly, he noted that the conversation has shifted from asking “Can we build it?” to “Can we scale it?” and what key business outcomes it can deliver.
During the AI in Quality discussion, Robert (Rob) Famiglietti, Vice President, Quality Systems & Data at GSK, emphasized the practical demands of deploying AI in regulated environments. He connected successful deployment to process understanding, trustworthy source information, and confidence that AI supported outcomes remain reliable, explainable, and operationally relevant.
Speakers also framed adoption as a workforce and management challenge. Discussions led by Mike Jovanis, President of Veeva Quality Cloud, and industry participants underscored the need to prepare people, clarify decision rights, and align processes before expanding deployment.
Competitive advantage will belong not to organizations with the most pilots, but to those that focus on value, solve defined business problems and produce repeatable results.
Trusted Data Becomes the Foundation for Responsible AI
No theme appeared more consistently across quality, regulatory, AI, and laboratory discussions than the importance of trustworthy data.
“You cannot AI your way out of fragmented processes and poor-quality data,” Gisèle Fahmi, Director, QC Operations Digitization and Master Data (IMOQ, Pfizer
Her statement captured a central constraint: model performance cannot compensate for information that lacks quality, structure, context, or accessibility.
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The discussion reinforced that trusted and mapped master data enables responsible AI. The same observation surfaced in discussions around Agentic Quality.
Famiglietti connected this issue to process understanding and confidence in outcomes. His point was practical: the integrity and context of source information shape the reliability of every AI supported decision.
Viewed through Axendia’s Data Trustworthiness framework, organizations must establish confidence in provenance, ownership, traceability, and meaning. These attributes allow information to remain dependable as it moves into critical business processes. (See: Scaling AI Adoption in Life Sciences Manufacturing & Operations )
Data Trustworthiness is no longer simply about preparing for inspections. It is a prerequisite for enterprise AI.
Operating Model Transformation Must Precede Technology Transformation
One of the strongest messages from the Summit was that technology alone does not create transformation.
Pfizer’s experience illustrated that systems enable change, but the operating model ultimately determines whether transformation succeeds.
Digitizing a fragmented process can accelerate and expand the underlying fragmentation rather than resolve it.
These observations challenge the assumption that new technology automatically creates value. Digital tools amplify the operating environment into which they are introduced, including unclear ownership, conflicting definitions, and disconnected workflows.
The organizational component of transformation surfaced repeatedly in discussions of quality modernization, AI deployment, and enterprise harmonization. Avril England, Executive Vice President and General Manager, Vault Products at Veeva, described Vault AI as “the single biggest advancement we’ve made to Vault ever,” while emphasizing its intended contribution to “productivity, speed, and value generation” within the controlled, secure, and reliable environment regulated work requires. Her comments illustrate the scale of the technology change, but also the operational discipline required to translate new capability into enterprise value.
The implication is clear: deployment and organizational readiness must advance together. Realizing enterprise value requires organizations to redesign workflows, assign decision rights, establish ownership for data and outcomes, and prepare employees to supervise AI supported work. Without these operating model changes, new capabilities may accelerate activity without improving coordination, accountability, or results.
Humans Remain Key to the Future of Agentic Operations
While agentic systems and automation received significant attention throughout Veeva Summit, speakers consistently rejected the notion that autonomy should eliminate human involvement. Instead, they described a future where intelligent systems perform increasingly sophisticated work while people remain accountable for direction, judgment, oversight, and governance.
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Describing this vision, Jovanis explained that employees will increasingly provide oversight and strategic guidance while intelligent systems handle repetitive and information intensive activities.
“We will have that element of human accountability and human in the loop,” Mike Jovanis, President of Veeva Quality Cloud
Pfizer’s quality transformation journey reinforced the same principle, describing a smarter human in the loop approach that preserves human accountability and ownership.
Famiglietti added that confidence in outcomes must be maintained as AI enters regulated processes. Automation should enhance human effectiveness without transferring responsibility away from accountable leaders.
England provided the complementary product perspective. Vault AI, she explained, is built into the platform and applications “helping your teams do their work.” The phrasing is important. The objective is not technology operating apart from the workforce. It is intelligence embedded where regulated work is performed, with controls appropriate to that work.
These discussions align with Axendia’s Responsible AI framework. Leaders must decide where automation should act, where people should intervene, and how oversight will function as systems gain autonomy. The objective is to apply human expertise where judgment creates the greatest value.
Quality is Evolving into a Strategic Business Capability
Another significant signal from Summit was the continuing evolution of quality’s role within life sciences organizations. Historically, quality functions were primarily associated with compliance, inspections, deviations, and audits. Increasingly, quality is being evaluated through its contribution to business performance, resilience, operational effectiveness, and patient access.
Few examples illustrated this better than Pfizer’s presentation regarding its LIMS transformation “Any delays in the QC labs doesn’t stay there anymore. It travels across the supply, the business continuity, and ultimately becomes a patient access problem,” said Fahmi
The implication is profound. Quality events should no longer be viewed as isolated operational activities. They affect manufacturing schedules, patient supply, business continuity, inventory strategies, and organizational performance.
The leadership challenge is to modernize QC as part of the broader value chain rather than as an isolated laboratory initiative. Leaders must connect laboratory performance, supply continuity, business risk, and patient access so that quality data informs priorities and decisions across functions.
Other Summit discussions extended this view into patient outcomes, enterprise visibility, capacity, and business value.
This evolution aligns closely with Axendia’s Connected Quality framework. Quality is becoming an integrated enterprise capability that supports informed decision making across development, manufacturing, supply, and regulatory operations. As organizations pursue Intelligence Driven Operations, quality will increasingly serve as a source of insight, governance, and risk management, not merely compliance oversight. (See: Accelerating Modern Technology Adoption with CSA )
Connected Ecosystems Create the Foundation for Product Lifecycle Intelligence
Connectivity was another pervasive theme. Speakers described the need for information to move coherently among quality, regulatory, development, manufacturing, and laboratory functions.
This objective is central to Veeva’s long term vision of connecting the industry. The larger goal is enabling information to move across the product lifecycle while retaining context, traceability, and trust. (See: Without PLI, AI Stays a Pilot. With PLI, AI Scales)
This theme also appeared in discussions of regulatory modernization. Famiglietti described evolving attitudes of regulators, noting EMA acknowledgement that “it is reasonable and appropriate to leverage AI for GMP operations” when deployment follows a risk-based approach.
This acknowledgment shifts the regulatory question from whether AI belongs in GMP operations to how a given use should be controlled. Risk, context of use, transparency, traceability, validation, and accountability become the governing considerations.
In Brief
The 2026 Veeva R&D and Quality Summit revealed an industry moving beyond AI experimentation toward enterprise adoption. Across quality, clinical development, regulatory operations, and manufacturing laboratories, speakers emphasized that responsible deployment depends on trusted data, connected processes, effective governance, and human oversight.
The central message was not the arrival of new AI capabilities, but the need to operationalize them. Organizations that treat AI as an isolated technology initiative risk producing pilots that never deliver meaningful value. Those that align modern operating models, process harmonization, and Responsible AI with measurable business needs will be better positioned to scale intelligence across the product lifecycle.
Veeva Summit also highlighted quality’s evolution from a compliance function to a strategic business capability. Laboratory delays, fragmented information, and disconnected decisions directly affect supply continuity, operational performance, and patient access. Regulatory modernization and lifecycle connectivity are likewise becoming enablers of innovation rather than simply operational objectives.
For industry leaders, the path to Intelligence Driven Operations requires simplifying fragmented processes, modernizing operating models, and defining how intelligent systems and human expertise work together. Building these capabilities now will prepare organizations for increasingly autonomous and connected operations.
Technology alone will not determine the future of Industry AI. Competitive advantage will depend on each organization’s ability to convert digital foundations into trusted data, connected processes, accountable decisions, and measurable outcomes. The defining transition is from deploying digital systems to operating with intelligence across the product lifecycle.
Applied responsibly, AI can increase productivity, strengthen decision making, and help life sciences organizations deliver better medicines to patients faster.
To discuss how this initiative impacts your organization, contact research@axendia.com to schedule an Analyst Inquiry on this topic.
The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.