Executive Summary

Veeva Systems’ artificial intelligence (AI) roadmap has evolved dramatically from its early Veeva Andi chatbot in 2019 to today’s industry-specific AI Agents spanning all functions of its Vault platform. This report provides an in-depth analysis of Veeva’s AI journey, covering historical context, key innovations (Vault CRM Bot, AI Shortcuts, etc.), and the phased “2026 rollout” of AI across clinical, regulatory, commercial, and other domains. We examine how each new capability – from the initial Andi assistant to the forthcoming Vault CRM Bot and AI Agents – is designed, what it does, and most importantly what it means for life sciences customers. The timeline of announcements (Table 1) highlights the rapid acceleration of AI in Veeva’s products: 2019’s Veeva Andi, 2024’s CRM Bot/Voice/MLR Bots, 2025’s formal Veeva AI initiative, and the deployment of AI Agents beginning in December 2025. For customers, these innovations promise significant productivity gains (Veeva projects ~15–20% industry efficiency improvements (^[1])), more intelligent automation, and faster time-to-insight, but they also demand new data, governance, and change management processes. We draw on Veeva’s own disclosures, customer feedback, analyst commentary, and industry studies to assess the benefits, challenges, and future implications of Veeva’s comprehensive AI strategy. Case studies (e.g. insights from Bristol Myers Squibb, Moderna, Novo Nordisk) and data (from investor reports and industry research) are used to ground our analysis in real-world outcomes. This report concludes that Veeva’s AI roadmap – blending prebuilt, context-aware agents with open-ended user “Shortcuts” – positions Veeva to transform life sciences workflows, while also underscoring the importance of a disciplined approach to data quality, compliance and user training.

Introduction and Background

The life sciences industry is undergoing a digital transformation, adopting cloud-based enterprise software to manage complex processes from research to commercial. Veeva Systems (NYSE: VEEV) is a pioneer in this space, offering Veeva Vault Platform applications tailored to pharma/biotech needs (e.g. eTMF for clinical trials, Vault Quality, Vault RIM, Vault Safety) and a unified Vault CRM for commercial/medical operations (^[2]) (^[3]). As of late 2025, Veeva serves well over 1,500 customers globally (from the largest drugmakers to emerging biotech) (^[3]). Its products integrate life-sciences master data (HCP registries, product catalogs, etc.), workflows shaped by regulatory standards, and content management, making them uniquely suited for highly regulated customer engagement and content approval processes (^[4]) (^[5]).

Veeva Andi (2019) was the company’s first foray into AI: an embedded assistant in Veeva CRM that offered contextual insights and next-action suggestions. Announced in June 2019, Veeva Andi “embeds tailored insights and suggestions right in Veeva CRM for intelligent customer engagement,” giving sales and marketing teams real-time guidance (^[6]). Andi used internal CRM data, rep feedback, and customer responses to refine its recommendations, effectively surfacing “next best actions” in the field (^[7]). It also introduced features like CRM Approved Notes (AI-driven compliance checking of free-text call notes) and Vault Auto Claims Linking in PromoMats (AI-assisted linking of promotional claims to references) (^[8]). While well-received as a proof-of-concept, Andi’s impact was modest by today’s standards: it used mostly deterministic AI models and required significant manual configuration. Nevertheless, Andi laid important groundwork, demonstrating both customer appetite for AI-driven CRM assistance and Veeva’s commitment to integrating intelligence into its platform.

The advent of large language models (LLMs) and “generative AI” in late 2022 (e.g. OpenAI’s ChatGPT) created new opportunity and urgency. Generic AI tools raised the aspiration that knowledge work could be automated, but life sciences companies demand industry-specific solutions that respect compliance and data privacy. Veeva’s strategy has been to harness generative AI within its domain. In mid-2024, Veeva launched an AI Partner Program to accelerate third-party AI integrations, providing partners with advanced tools like the new Vault Direct Data API (100× faster data access) and development sandboxes (^[9]) (^[10]). By exposing Vault data at high speed (with no extra fee), Veeva enabled AI apps to reliably query customer data repositories (^[11]). Veeva explicitly built its approach to be LLM-agnostic: customers can use Veeva-supplied models (hosted on AWS Bedrock or Azure) or bring their own, without sending proprietary data outside the secure Vault environment (^[12]) (^[11]). This emphasis on secure, deep-integrated AI reflects Veeva’s core value of life-sciences compliance.

Against this backdrop, Veeva has rolled out successive AI innovations in its Vault CRM and other products (see Table 1). Late 2024 saw announcements of the Vault CRM Bot and Voice Control (a voice interface leveraging Apple’s AI) (^[13]), plus an MLR Bot for PromoMats (^[14]) – all slated for late 2025 delivery. In April 2025, Veeva formally introduced “Veeva AI”: a company-wide initiative to embed AI Agents and AI Shortcuts across every Vault application (^[15]). The first customer-facing AI Agents (for CRM and PromoMats) launched in December 2025 (^[16]), with additional domain-specific agents (clinical, regulatory, safety, quality, etc.) rolling out through 2026 (^[17]) (^[18]). By early 2026, Veeva expects to have AI functionality woven into processes from drug development through launch, enabling customers to perform tasks and gain insights with unprecedented speed.In the sections that follow, we examine each facet of this roadmap in detail, supported by data, publications, and industry commentary, and analyze what the CRM Bot, AI Shortcuts, and the 2026 rollout will mean for Veeva’s customers.

Veeva AI Timeline and Key Milestones

Veeva’s AI journey can be organized chronologically (Table 1). Early milestones (2019–2023) were relatively quiet except for Andi, while 2024–2026 saw rapid expansion. Key dates include the 2019 Andi launch, the 2024 AI Partner Program/Direct API, the November 2024 Vault CRM/MLR Bot announcements, the April 2025 “Veeva AI” announcement (Agents & Shortcuts), and the December 2025 launch of initial AI Agents (^[6]) (^[16]). Activities through 2026 include the rollout of more agents for quality, safety, regulatory, etc. (^[18]).

Table 1: Veeva AI Roadmap – Announcements and Planned Releases (2019–2026)

Table 1 Note: Entries marked with “planned” are based on Veeva’s announced schedules (^[18]) (^[1]). Throughout, Veeva emphasizes that all AI Agents have direct, secure access to Vault data and work within existing user permissions and workflows (^[25]) (^[26]).

The accelerated schedule from 2024 onward reflects management’s prioritization of AI: in investor commentary, Veeva executives indicated that the Vault CRM platform provides “a fast path to AI productivity” for customers and expected overall industry efficiency to improve ~15% by 2030 (^[1]). Indeed, the initial Agents for CRM and PromoMats, planned for Dec 2025, have already been released as of the end of 2025 (^[16]) (^[27]). The 2026 phases extend benefit to all major functions. In the next sections, we detail each innovation – the CRM Bot, AI Shortcuts, individual AI Agents – and analyze their technical capabilities and potential business impact on Veeva’s customers.

Veeva Andi: The First AI for Life Sciences CRM

Veeva’s first AI offering, Veeva Andi, was introduced in mid-2019 as part of Veeva CRM. Branded an “artificial intelligence application,” Andi was designed to surface insights and next-action suggestions within the CRM user interface (^[6]). For example, a field rep viewing an HCP record might see Andi suggest scheduling a follow-up meeting based on previous engagement data. Andi leveraged existing CRM data (customer interactions, rep feedback, field responses) to detect patterns: it “gets smarter with every action” by learning from how reps and customers respond (^[7]). In combination with Veeva’s new Customer Journeys module, Andi helped companies tailor actions to different adoption stages of customers (^[7]). Importantly, Andi gave life science teams control over its behavior: firms could configure rules around triggers (e.g. where on the HCP journey a recommendation should fire) and simulate the impact of insights before delivery (^[28]).

In practice, Andi’s capabilities included:

• Predictive Insights: Identifying high-priority accounts for outreach.
• Next-Action Recommendations: Suggesting specific tasks (e.g. sample deliveries, call follow-ups) to reps.
• Compliance Checking: via the CRM Approved Notes feature, which used AI to flag potential compliance risks in free-text call notes (^[8]).
• Content Linking: through Vault Auto Claims Linking in PromoMats, automatically suggesting references for promotional claims to streamline MLR preparation (^[29]).

While innovative at the time, Andi was relatively lightweight. It did not leverage modern LLMs but rather heuristic and predictive models embedded in the workflow. It required substantial configuration by customers to tailor rules and controls. In retrospect, Andi established the concept of embedded AI in life sciences CRM, but its scope was limited (focused on suggestions in CRM) and its intelligence was narrow. For example, its compliance note-checking helped reps catch obvious risks, but it could not, say, paraphrase medical documents or autonomously draft communications. Over time, many companies invested in Andi as it matured, but by 2024 Veeva was ready to take a bigger step to truly generative, multimodal AI assistants. Nevertheless, the Andi project taught Veeva and its customers how to trust AI in regulated workflows and set expectations for contextual intelligence in CRM (^[6]) (^[7]).

Generative AI Emergence and Veeva’s Strategic Response (2023–2025)

The late 2022 debut of ChatGPT and allied generative models created a paradigm shift: suddenly AI could generate natural language summaries, answer questions, and even write drafts with fluency. Life sciences organizations took notice, but adoption lagged due to compliance concerns with cloud AI services using proprietary data. Veeva’s strategy was to bring generative AI into its own secure platform and marry it with domain knowledge. In April 2024, Veeva announced the Veeva AI Partner Program (^[19]), signaling a formal commitment to generative AI. This program gave technology partners access to the new Vault Direct Data API (launched concurrently) and sandbox environments for building GenAI solutions (^[9]). The Direct Data API was particularly crucial: Veeva explained it as “a new class of API that makes Veeva Vault data accessible up to 100 times faster than traditional APIs” (^[10]). Customers and partners can use this API to feed bulk or incremental Vault data into cloud analytics and AI tools. (By including connectors to Redshift, Snowflake, Databricks, and Power BI, Veeva ensured that customers could use industry-standard AI/BI tools on their Vault data (^[11]).) In effect, these 2024–2025 moves built the infrastructure to support large-scale AI – secure high-speed data pipelines, training support, and partner ecosystems – all without directly exposing raw data outside the Vault platform.

In parallel, Veeva product teams began integrating generative features. At their Nov 2024 European Commercial Summit, Veeva unveiled Vault CRM Bot and Voice Control as upcoming features (^[13]) (^[30]). Unlike Andi (which was more of a push-notification engine), Vault CRM Bot was envisaged as a full-fledged chat-based assistant: it would “embed the large language model (LLM) of your choice into Vault CRM to enable a wide range of context-driven tasks” (^[13]). In practice, this meant a rep could ask Vault CRM questions or instruct it in natural language (e.g. “what’s next for HCP X?”) and get AI-generated guidance on engagement planning, content recommendations, or educational resources (^[13]). Voice Control would allow hands-free operation: field reps could speak commands (“Log a detail to HCP Y, find last presentation given”) using Apple Intelligence on iOS devices (^[21]). These voice commands integrate with the CRM just like text input. The CRM Bot and Voice Control were slated for “availability in late 2025” (^[13]), leveraging the infrastructure (data API, platform LLM hosting) built in 2024.

Another key April 2025 development was the announcement of Veeva AI (^[15]) as a company-wide initiative. Veeva AI formalized the vision: embedding AI Agents and Shortcuts across all Vault apps (commercial, clinical, quality, etc.), all built on the same platform. CEO Peter Gassner emphasized that Veeva AI would “help life sciences companies automate tasks and improve employee productivity using AI Agents and AI Shortcuts” (^[31]). In this vision, generative AI ceases to be an external novelty and becomes a core part of the application fabric. Importantly, Veeva restated that its approach is LLM-agnostic: customers can use a Veeva-supplied model or configure Veeva AI to a customer-specific LLM (^[12]), with data remaining securely partitioned per customer. This flexibility allows a pharma company to use a highly sanitized, validated LLM in a private cloud if required, addressing regulators’ data concerns. Gassner noted that combining “core applications and GenAI” would yield significant productivity gains (^[32]).

In summary, by mid-2025 Veeva had laid the groundwork for a broad generative AI rollout. It partnered (and effectively built) technology to handle big data and AI computation, launched pilot agents (CRM Bot, MLR Bot), and began training customers to expect AI-augmented functionality. The next sections examine the specific features and user experiences – CRM Bot, AI Shortcuts, etc. – that emerged from this roadmap, with attention to how they work and how life sciences organizations might use them.

Vault CRM Bot and Voice Control (Vault Commercial AI)

Vault CRM Bot and Voice Control are the first outward manifestations of Veeva’s push into generative AI, specifically for commercial users (sales, marketing, medical teams). Announced Nov 20, 2024, these features were highlighted at Veeva’s Commercial Summit Europe (^[13]). Vault CRM Bot embeds an LLM into the CRM UI to help with “several context-specific field tasks (such as engagement planning, next best actions, and recommended content)” (^[13]). In practice, this is essentially an AI-powered chatbot inside the CRM. For example, during preparation for a customer call, a rep could ask Vault CRM Bot to summarize past interactions with that customer, or to forecast optimal next steps based on prior data. Or a manager could request an overview of territory performance. The key is that the Bot is Veeva-aware: it understands medical hierarchies (HCP roles), product portfolios, and CRM fields, so its outputs can directly reference and update Vault data. Because it runs within the secure Vault environment, the Bot can operate on true customer data (with permissions) rather than just generic internet knowledge.

Voice Control leverages the new voice AI in Apple devices (often called Apple Intelligence in iOS 17+). It allows users to control the Vault CRM interface through spoken commands. For instance, a rep might say: “Update HCP Smith’s record: met with Dr. Smith at General Hospital, gave Overview Slide Deck.” The system interprets these commands and performs the updates in CRM on behalf of the rep. This hands-free approach speeds data entry and reduces clicks, which is especially useful when driving between appointments. Apple Intelligence handles the speech recognition and preliminary parsing (^[21]), then Vault CRM applies validation and workflow rules. Voice Control was planned for late 2025, requiring Apple’s new AI-driven OS and compatible devices (^[21]).

Implications for Customers (CRM Bot/Voice): These features herald a major behavioral shift. Instead of manually navigating menus and forms, field teams can speak or chat naturally with CRM. Early demonstrations elicited “aha moments” – as one Veeva executive recounted, when customers saw CRM Bot in action, “what they want is … [AI] to help them with the engagement planning… and then all the data entry afterwards, do that work so they can focus on the engagement in their field” (^[33]). In other words, Vault CRM Bot and Voice Control aim to offload routine cognitive and clerical tasks: analyzing call schedules, generating meeting notes, recommending promotional materials, etc. Field reps will likely see faster call preparation and fewer post-call admin chores, leading to more customer-facing time. Managers and marketers can also query CRM via natural language for dashboards and strategy inputs.

As one customer perspective notes, “Vault CRM and Veeva AI Agents like Pre-call Agent and Voice Agent will drive efficiencies and allow the field to focus on the value parts of their jobs” (^[34]). While CRM Bot/Voice were announced as new capabilities, the AI Agents released in December 2025 (free text, voice, pre-call) realize that vision. Indeed, Vault CRM Bot itself has effectively been decomposed into those specialized agents: the Pre-call Agent performs the engagement-planning suggestions originally promised by CRM Bot (e.g. “review Dr. Chang’s recent lab results before next call”), while the Voice Input Agent provides the hands-free note-taking. The Free Text Agent (see next section) is another output of Vault CRM Bot’s concept, focusing on compliance in free-text notes.

From the customer’s point of view, adopting Vault CRM Bot means enabling an LLM connection and training reps on new interaction methods. IT teams must ensure quality data in CRM, as the Bot’s outputs depend on it. Veeva provides safeguards (e.g. limiting edits and requiring confirmations), but customers should treat these features as powerful tools requiring oversight. Overall, CRM Bot and Voice promise significant productivity gains, especially in customer call preparation and follow-up, aligning with Veeva’s goal of freeing up technology to serve the field teams (^[33]) (^[1]).

AI Shortcuts: Personalized Automations

Alongside agents built by administrators, Veeva introduced AI Shortcuts to empower individual users. AI Shortcuts are lightweight “no-code” automations that any user can define for their own frequent tasks (^[35]). Technically, a Shortcut is a configurable chat or workflow template that invokes an AI model. For example, a market access manager might create a Shortcut called “Budget Uplift Analysis” that, when activated, gathers recent HVAC data and asks the AI to project revenue impact. Or a field rep could set up a Shortcut that automatically summarizes the last five call notes for a territory. Once set up, the user can trigger the Shortcut by typing a short command or using a button in the Vault UI, and the AI will perform the task using available data. Importantly, Shortcuts run within the Vault context: they can only access the data the user is allowed to see, and results are stored back in Vault.

According to Veeva, Shortcuts empower users to “easily set up personal AI-powered automations to accomplish frequent user-specific tasks such as helping with workflows, generating insights, or researching a topic” (^[35]). In practice, this might replace several mouse clicks with a single question. For instance, rather than manually compiling all pending Quality events, a user could create a Shortcut like “List My CAPAs” that uses AI to filter and summarize them. Shortcuts essentially function like smart macros but powered by generative AI, allowing business users (not just developers) to introduce intelligence into their daily routine. This democratization of AI is a distinct feature: it means every end user can innovate on their own processes. Veeva’s vision is that while AI Agents automate standard processes at scale, AI Shortcuts let each person accelerate their personal workflows without IT intervention.

For customers, AI Shortcuts mean greater agility and user satisfaction. A 15,000-user rollout, for example, can now have thousands of “micro-innovations” as power users create shortcuts tailored to their roles. This can lead to unexpected productivity boosts (and in turn, management may need to govern what shortcuts do in case of compliance risks). It also signifies a cultural change: employees must learn to think of AI as a colleague. Training will be needed so that users know how to craft effective prompts and where to trust the output. Importantly, Veeva emphasizes that all Veeva AI functionality respects the Vault security model: Shortcuts do not allow any user to access hidden data or override permissions. In summary, AI Shortcuts add a layer of user-driven AI that complements the centrally provided agents, giving customers flexible ways to exploit generative AI for each unique role’s needs (^[35]).

Initial Veeva AI Agents Release (December 2025)

In December 2025, Veeva began delivering the first wave of its AI Agents, as promised. These agents were available immediately to customers on Vault CRM and Vault PromoMats, with no new installation needed beyond enabling the feature. The AI Agents released include:

• Vault CRM – Free Text Agent: Automatically reviews free-text call notes entered by field reps and flags potential issues. For example, if a rep’s note contains sensitive off-label information or an untranslated acronym, the agent flags it for the rep’s review to ensure compliance (^[23]). This agent runs in the background and provides in-depth call reporting, giving companies “richer, higher-quality customer insights” by ensuring accurate data capture (^[23]).

• Vault CRM – Voice Agent: Enables voice-driven data entry into Vault CRM. Reps can speak their call notes or updates, and the Voice Agent transcribes and populates relevant CRM fields (e.g. products used, next steps). This makes it “faster and easier for field teams to capture information and follow-up actions” (^[23]), since manual typing on mobile devices is time-consuming.

• Vault CRM – Pre-call Agent: Provides intelligence before an HCP visit. It scans all relevant data (past activities, existing customer preferences, trending research) and generates a summary of insights and recommended next steps. For example, it might surface that a doctor recently published a paper relevant to the product, or suggest emphasizing a newly released clinical study. In Veeva’s announcement, the Pre-call Agent “provides insights and suggested actions from relevant data, content, activity, and trends that help field reps prepare for calls” (^[36]).

• Vault PromoMats – Quick Check Agent: Performs automated quality checks on content before MLR review. Using editorial, brand, market, and channel guidelines, it scans documents for potential errors or policy violations (e.g. formatting, missing disclaimers). This agent “scans content using … guidelines to address issues before medical, legal, regulatory (MLR) review” (^[24]), catching mundane compliance problems early so that the review team can focus on substantive issues.

• Vault PromoMats – Content Agent: A more advanced assistant in PromoMats. Given a piece of marketing content (text and images), it can answer questions, summarize the material, analyze visuals, and integrate with Quick Check results. For instance, a compliance reviewer can ask “Does this claim meet our brand standard?” and the Content Agent will reference the appropriate guideline. Veeva describes it as providing “context-aware insights into document text and images, answers questions, summarizes content, analyzes visuals, and draws from Quick Check Agent to assist with review” (^[24]). The Content Agent essentially acts like an AI copilot for MLR reviewers, understanding the nuances of lifescience content.

The common thread of these agents is that they operate within the Veeva Vault Platform, leveraging secure access to the customer’s data and documents. Veeva emphasizes that agents are context-aware: they know the specific Vault application’s terminology and data schema, and they honor user permissions and audit trails (^[25]) (^[37]). Customers can also configure the behavior of delivered agents (e.g. define which fields to review) or build new custom agents with Veeva AI tools. As Veeva notes, these agents can be called through a chatbot UI or API, meaning both interactive and automated use cases are supported (^[38]).

Table 2: Veeva AI Agents (Dec 2025) and Their Primary Functions

Table 2 Note: The five agents listed above were available as of December 2025. Additional agents for Safety, Quality, Clinical, and Medical areas are planned in 2026 (see next section). Customers may use these agents via the Vault UI chat interface or API, and can customize them using Veeva’s AI framework.

Customer Impact (Agents): The immediate impact of these AI Agents is tangible efficiency gains in everyday workflows. Field teams benefit from not having to manually proofread or research as much: the Free Text and Voice Agents reduce time spent on notes after each call, while the Pre-call Agent cuts down slide deck building and meeting prep. In marketing/branding, the Quick Check and Content Agents dramatically shorten content review cycles. For example, Moderna’s Global Marketing Ops director reported that using the Quick Check Agent “moves us closer to a process where parts of MLR could become nearly touch-free” (^[39]). The agents effectively shift routine tasks (note-checking, content vetting, summarization) from humans to AI, freeing skilled personnel for higher-level work such as strategy or personalized engagement. Quotes from pilot customers echo this: Bristol Myers Squibb emphasizes embedding AI “into every step of the customer journey” to improve productivity (^[40]), and Novo Nordisk notes that CRM agents “will enrich the field to focus on the value parts of their jobs” (^[34]).

Financially, the agents are licensed per Vault product. Veeva CEO Gassner indicated that they will offer reasonable subscription pricing to encourage broad adoption (^[41]). This usage-based model is intended to let companies start small (e.g. enable a couple agents for a pilot team) and then scale as ROI becomes evident. Early signals are positive: as of Dec 2025, Veeva reported strong demand and interest. For instance, Roche Pharmaceuticals has expanded its partnership to adopt the AI-enabled Vault CRM globally, underlining the strategic value customers place on these features (^[42]). In published earnings commentary, management noted that customer reaction to CRM Bot demos was “great” and that feedback on Veeva AI has been “very positive” (^[43]).

Overall, the December 2025 release turned the promise of Veeva AI into reality. It offered customers working AI assistants for commercial and marketing functions, with immediate productivity benefits and clear lines of sight to value. More importantly, it laid the foundation for expanding AI into other domains. The next rollout phases in 2026 will extend similar gains to clinical operations, R&D, safety, and medical affairs, as discussed below.

2026 Rollout of Veeva AI Agents Across Functions

Veeva’s plan is to roll out AI Agents across all major functional areas within the Vault Platform through 2026. The October 14, 2025 announcement published the schedule (see Table 1) (^[18]): after the December 2025 commercial agents, the next releases are planned for April 2026 (Safety, Quality), August 2026 (Clinical Operations, Regulatory, Medical), and December 2026 (Clinical Data). Each wave is tailored to the workflows of that domain, with “deep, industry-specific” agents promised{ (^[25]).

• Safety and Quality (Apr 2026): Agents for PharmacoVigilance and Quality Management Systems (QMS) will debut. Although Veeva has not disclosed exact agent names yet, we can anticipate their roles. For example, in Vault Safety (pharmacovigilance), an agent might triage incoming adverse event cases, group similar reports, or even draft initial case narratives for human review. In Vault Quality (CAPA/QMS), an agent could analyze deviation trends and suggest root cause ideas. Such agents would have access to sensitive safety and quality data while remaining within the customer’s secure Vault vault. As one analysis notes, “for Q&C [quality & compliance] organizations, AI agents could flag trends in deviations or CAPAs before they escalate” and “summarize and validate audit readiness” (^[44]). The planned quality agent in April 2026 suggests Veeva is moving toward “proactive AI workflows” in regulated areas (^[45]), accelerating processes like change control by surfacing key information.

• Clinical Operations, Regulatory, Medical (Aug 2026): Next, agents will arrive for trial management (CTMS/eTMF), regulatory submissions (e.g. Vault QMS/Regulatory), and medical affairs (Vault Medical). Potential use cases include an eTMF Agent that monitors study start-up and enrollment status, alerting managers to enrollment lags. A Regulatory Submission Agent might help draft sections of clinical study reports by analyzing related documents or highlight missing compliance statements in regulatory filings. A Medical Literature Agent in Vault Medical could scour recent research and summarize it for medical science liaisons. These agents would plug into the workflows Veeva already supports, offering context-specific AI help. In the Commercial Summit blog, Veeva emphasized that AI agents will “surface insights and connect workflows across teams” (^[46]); in a medical affairs context, that could mean linking KOL (key opinion leader) engagement history with new data insights to guide a medical strategy.

• Clinical Data (Dec 2026): Finally, by December 2026 agents for core clinical data processes are expected. This likely includes Vault CTMS and eTMF data analysis, such as enrollment forecasting, protocol deviation analytics, or automated query/resolution tracking. With the Vault Direct Data API and AI layer in place, AI could for instance identify sites likely to miss enrollment goals or suggest trial timeline optimizations. The December 2026 agent might also extend to Link (HCP master data) or any module not covered earlier. By end of 2026, virtually all functions in the Veeva Vault portfolio will have one or more AI Agents (see Table 1 for schedule).

Implications for Customers (2026 Rollout): The phased approach gives customers diverse departments time to prepare. As Clarkston Consulting advises, companies should use this roadmap to “prepare governance, data architecture, and validation” for the upcoming releases (^[47]). For example, Quality/Pharmacovigilance teams can begin data readiness and change-control planning to integrate April 2026 agents. Regulatory and Clinical leads should build awareness of the August 2026 capabilities early. In practical terms, this means auditing data cleanliness (AI efficacy depends on quality data), defining AI governance policies (who can approve AI suggestions), and arranging training/validation workflows (per 21 CFR Part 11 requirements). Veeva’s partners and consulting arm offer adoption support, but each customer organization owns the change management.

From a strategic standpoint, the extended rollout means that by 2027 many routine tasks across drug development will be assisted by AI. Companies that proactively adapt (updating SOPs to incorporate agent outputs, for instance) stand to outpace competitors. Conversely, lagging on integration could lead to missed productivity gains. For example, a quality group slow to validate the Quality Agent’s recommendations will find remaining manual. Early adopters (e.g. Moderna and BMS in 2025) are already designing new workflows around these tools (^[48]) (^[49]). Others must follow suit: as Veeva SVP Andy Han notes, Veeva AI “will be embedded in the industry’s next chapter” and customers will need to collaborate on practical AI use cases (^[50]).

In summary, the 2026 rollout completes Veeva’s vision of ubiquitous AI. For customers, this means that virtually any task now done in Vault – from drafting clinical reports to scheduling a medical conference – will soon have an industry-savvy AI assistant. Those tasks can be done faster and with fewer errors, but only if end users and IT prepare appropriately. The next section synthesizes case studies and expert opinions on how customers are experiencing (or will experience) this AI transformation.

Customer Perspectives and Case Examples

Several life sciences companies have publicly endorsed Veeva’s AI strategy or participated in early access. For customers, the shift from feature announcements to working AI changes the stakes. We highlight real-world reactions and use cases below.

• Bristol Myers Squibb (BMS): Greg Meyers, EVP and Chief Digital Technology Officer at BMS, remarked on the potential of Veeva AI to transform the customer journey: “By embedding AI into every step of the customer journey – from how practitioners receive valuable information about our portfolio to how they engage with our field force – Veeva AI is ideally positioned to support us in our mission to deliver life-changing medicines” (^[40]). This underscores BMS’s view that generative AI in Veeva isn’t just an efficiency tool, but a strategic enabler of personalized engagement. For example, BMS might use the Pre-call Agent to tailor presentations for a doctor’s specific interests, or the Content Agent to customize materials for niche specialties. Their testimony shows confidence in Veeva’s domain expertise: they trust Veeva’s niche approach to “provide insight right at the point of care” (in their private comments to Veeva).

• Moderna: Jason Benagh, Global Marketing Operations Director at Moderna, also participated in early AI pilots. He highlighted the Quick Check Agent as a game-changer: “What we’ve learned as an early access user is that the Veeva AI Quick Check Agent moves Moderna closer to a process where parts of MLR could become nearly touch-free… now we can genuinely see how we might get there” (^[39]). Moderna’s marketing team handles thousands of clinical content pieces. The Quick Check Agent’s ability to autonomously enforce brand and regulatory rules means fewer iterations between writers and reviewers. Moderna’s quote suggests that even partial automation (e.g. pre-scrub by AI) can drastically reduce review cycles. They see this as a real productivity gain.

• Novo Nordisk: Frank Armenante, Director of Field Systems at Novo, commented on the CRM side: “Vault CRM and Veeva AI Agents like Pre-call Agent and Voice Agent will drive efficiencies and allow the field to focus on the value parts of their jobs…it’s good for the business, good for HCPs, and great for patients” (^[34]). This reflects an important perspective: pharmaceutical field reps want to spend more time with doctors and less time on CRMs. When an AI agent prepares call suggestions or converts notes to data automatically, reps gain valuable time for patient engagement. Novo’s view is that these agents don’t replace reps but elevate them.

• Otsuka (Europe): Debbie Young, a customer insights leader at Otsuka, noted that introducing AI requires organizational buy-in: “Being able to share the innovation with the leadership team and to introduce Veeva AI embedded into our Veeva applications is important to us as we expand our partnership” (^[51]). Their perspective underscores that such tools strengthen strategic ties; adopting AI can be a key driver for investing in the Veeva platform itself. Otsuka used Veeva Link (HCP digital community) heavily, and finding AI in Vault CRM/PromoMats aligns with their direction for data-driven engagement.

• Crinetics Pharmaceuticals: Kea Lingo, CIO of Crinetics (a rare disease biotech), expressed excitement: “We’re excited to use the Veeva AI Free Text Agent as part of our strategy toward building AI capabilities into our business. Veeva AI enables us to gain deeper customer insights for more effective engagement in the rare disease space” (^[52]). Crinetics’ interest highlights a key point: even small/emerging companies see value. The Free Text Agent’s compliance review, for example, is attractive because it’s built into a system they already use (Vault CRM), potentially saving costs compared to hiring additional MLR headcount.

• Roche Pharmaceuticals: According to an analyst article, Roche has expanded its Vault CRM deployment by “adopting the AI-enabled Veeva Vault CRM across its global operations” (^[42]). Although not a direct quote, this indicates that even very large global organizations view Veeva’s AI features as a competitive advantage. Roche’s CRM had lagged in digital innovation; upgrading now to an AI-driven system suggests they expect measurable gains in field effectiveness and compliance.

In aggregate, these customer insights show uniformly positive reactions: leading biopharma companies are eager to leverage Veeva’s AI for both efficiency and strategic advantage. They report that AI agents’ impact is tangible – enabling fewer manual steps, identifying insights earlier, and allowing skilled staff to concentrate on high-value activities (challenging medical questions, strategy, patient interactions). In all cases, compliance remains paramount: the agents are embraced because they improve oversight (flagging errors before they escalate) while still letting humans make final decisions. Early adopters also note organizational considerations: business units (sales, quality, regulatory) must prepare people and processes to incorporate AI outputs. For example, Otsuka mentions the need to “define new business and process flows” between users and agents (^[53]).

From an IT governance angle, the year-long rollout schedule itself reflects that adaptation time. Clarkston Consulting advises pharmaceutical QA/QC organizations to build AI governance, validate systems, and establish data/AI literacy well before their domains’ launch dates (^[54]). Organizations that treat Veeva AI as merely a technical feature, rather than a change in business process, risk underutilizing it. However, the customer case studies above indicate many life sciences firms are taking a proactive approach, seeing Veeva AI as an integral part of next-generation operations.

Technical and Operational Considerations

Implementing Veeva’s AI roadmap involves more than toggling new features – it touches on data, security, and regulatory compliance. We outline key considerations:

• Data Quality and Integration: Generative AI is only as good as its input data. Survey data suggests that poor data quality is a top obstacle to AI in pharma (^[55]). For Veeva customers, this means that CRMs, clinical systems, and quality databases need to be accurate and up-to-date. Duplicate HCP records or outdated patient data will lead agents astray. Hence, organizations should audit their existing Vault data (and any integrated external sources) well before turning on agents. The phased rollout (2025–2026) is beneficial in this regard: IT can schedule data cleaning in advance of each agent’s domain. For example, before the Safety agents arrive in April 2026, pharmacovigilance teams should ensure case data is standardized and properly coded. Veeva’s Vault Direct Data API helps by making large-scale data dumps possible, enabling easier ETL (extract-transform-load) and analysis for data cleansing, (^[11]).

• Security and Compliance: The life sciences sector is heavily regulated. Veeva’s approach of embedding AI within Vault (with strict data partitioning) aims to satisfy regulators, but customers must still validate that their use of AI meets requirements like FDA 21 CFR Part 11 (electronic records compliance). In 2025-onward releases, auditors will expect documentation of how AI outputs are reviewed and approved. Veeva addresses this by keeping AI Agents within authenticated sessions; agents operate under user credentials, and all actions can be tracked in audit logs (^[25]). Nevertheless, companies should update their SOPs to explain how AI-derived recommendations are handled. Clarkston recommends that firms “ [v]alidate AI-enabled workflows in accordance with 21 CFR Part 11 and EU Annex 11 to ensure accuracy, reliability, and traceability of electronic records and signatures” (^[56]). Additionally, global companies must consider cross-border data rules (e.g. GDPR) when using cloud AI models. Veeva’s LLM-agnostic strategy lets customers choose on-prem or region-specific AI models for sensitive data, mitigating risk.

• Governance: Unlike one-off software, AI agents learn and evolve. Governance frameworks (AI standards, approval committees, usage policies) will be essential. Veeva’s Cort business consulting can assist, but leaders must sponsor an organizational “AI Center of Excellence” or similar to oversee usage. Responsibilities include: setting up AI ethics guidelines, monitoring agent performance (look for drift or bias), and managing change control when agent logic is updated. Clarkston notes that the phased release gives companies “time to prepare governance, data architecture, and validation” (^[47]). Early preparation will reduce surprises.

• Training and Change Management: Employees may initially be wary of AI taking over tasks. Veeva suggests focusing on high-value use cases (see “2026 Predictions” section) to demonstrate benefits. Training programs should emphasize that agents are collaborators, not replacements—for example, a field rep might initially review every AI-suggested visit plan until confidence builds. In clinical/quality, change management will involve training auditors and managers on using agent summaries and verifying them. The quotes above (e.g. Novo’s and Crinetics’) indicate strong enthusiasm, which management can harness. Yet communication around AI’s role, and managing expectations (e.g. being clear that an agent flags suggestions, not final decisions), is vital.

• Performance and Scaling: Veeva plans tri-annual updates to keep agents improving (^[57]). Customers should plan for continuous learning: as new data (post-2025) is fed, the models may change prompts or suggestions. IT will need to test each update. On the infrastructure side, Veeva’s usage-based pricing alleviates some concerns: customers need not over-provision. They can start small and scale usage (and costs) as value is proven (^[58]). Real-time performance (latency) is usually acceptable for these use cases, as many queries run asynchronously (for example, the Free Text Agent can process notes on save). For interactive chat (CRM Bot), speed will depend on the LLM chosen and network; early testers have not flagged it as a problem.

• Competitive Context: While Veeva leads in life-sciences AI agents, large tech vendors are also moving. Salesforce launched Agentforce 360 in late 2025 to let any company build AI agents (using Salesforce’s own Einstein GPT and Anthropic models) (^[59]). IBM, Microsoft, and Oracle are embedding AI into their ERP/CRM stacks as well. However, Veeva’s competitive advantage is domain specialization. Its agents are pre-trained on pharma terminology and processes. As one analysis noted, “what differentiates Veeva is its exclusive focus on life sciences… its agents are pre-trained and configured for the specific data structures, terminology, and compliance requirements of pharma/biotech” (^[60]). Salesforce can deploy a general Sales agent, but only Veeva knows that “MSL” stands for Medical Science Liaison and which FDA codes apply. Customers expect clinical accuracy and compliance; many trusts Veeva’s vetted agents over a generic AI. Thus, even as tech giants flood the market with agent tools, Veeva’s life-science-tailored approach is likely to sustain its niche leadership.

Future Outlook and Conclusion

Veeva’s AI roadmap – from the rudimentary Andi of 2019 to the forthcoming multi-agent system – illustrates the transformative potential of specialized generative AI in a regulated industry. Our analysis indicates that by fully 2026, Veeva customers will have AI assistance embedded in nearly all phases of drug development and commercialization. This is a “once-in-a-generation” shift: mundane tasks (note-taking, document review, report drafting) become automated, while human experts focus on strategy, creativity, and oversight. Veeva projects significant productivity gains: CEO Gassner has publicly spoken of targets like a “20% improvement in productivity” across the industry by 2030 (which roughly aligns with their ~15% efficiency boost expectation (^[1])). If achieved, this could accelerate drug development timelines and improve patient access to therapies. Early signs are promising: analogous initiatives in enterprise software show dramatic results (e.g. Salesforce’s internal use of AI apparently halved their support team size (^[61])), and life sciences pilots (Merck’s Zero Gravity, etc.) suggest up to 10% error reduction from improved digital integration (^[62]). Veeva’s agents could amplify those effects.

Beyond efficiency, Veeva AI fosters new ways of working. As an industry blog predicts, companies will “prioritize high-value AI use cases pointed at core operational processes and train people in new ways of working” (^[63]). In practice, this means redefining roles: a medical affairs professional might spend less time reading articles (thanks to summarizing agents) and more time in scientific strategy. Regulatory affairs teams might rely on AI to draft initial submission sections or detect compliance gaps. Sales reps will engage with doctors via richer data at their fingertips. These shifts align with Veeva’s “coordinate across sales, marketing, and medical” vision (^[64]), as AI agents become the “glue” connecting workflows across functions.

However, the journey is not without challenges. Along with data preparation and governance (discussed above), companies must monitor the unintended effects of AI. For example, generative models can produce plausible-sounding but incorrect suggestions (hallucinations). Veeva mitigates this by confining agents to known data sources, but customers will still need human review. Especially in medical and regulatory contexts, blindly trusting AI would be dangerous. Thus, a loop of continuous validation and human-in-the-loop controls will be essential. Regulatory agencies are also paying attention: the FDA’s use of an AI system (“Elsa”) for CMC reviews shows that regulators see promise in GenAI, but they also emphasize security (Elsa runs in a GovCloud without external data) (^[65]). Veeva’s architecture similarly ensures proprietary data never leaves the Vault unencrypted. Customers will likely need to document how AI outputs are handled by their QMS or AR/DB (Annual Reports/Drug Bulletins). In short, compliance teams cannot ignore these changes; they must actively shape how AI is governed.

In conclusion, Veeva’s AI roadmap – from Andi to AI Agents, and including the Vault CRM Bot and AI Shortcuts – is a comprehensive and deliberate strategy to embed generative AI into every facet of its life-science platform. For customers, the rollout promises clear gains: increased field effectiveness, faster content cycle-times, and deeper insights with less effort. Leading companies in the industry have already reported enthusiastic experiences (^[48]) (^[33]). At the same time, seizing these gains requires proactive preparation in data, training, and governance. The evidence suggests Veeva’s approach is well-aligned with industry needs: it steps carefully within regulated boundaries while providing cutting-edge tools. As one strategic viewpoint observed, “after years of widespread pilots with weak ROI, the industry will prioritize high-value AI use cases … The right AI projects will drive efficiency and productivity gains” (^[63]). Veeva’s 2026 AI vision appears to be exactly that: high-value, domain-specific AI agents and shortcuts that free up skilled life science professionals to do their best work. If customers implement them thoughtfully, Veeva AI could indeed usher in a new era of productivity and innovation for global health.

Sources: Veeva press releases and blogs (^[15]) (^[13]) (^[9]) (^[10]) (^[18]) (^[23]) (^[66]) (^[33]); industry news and analysis (^[67]) (^[14]) (^[27]) (^[30]); customer and analyst commentary (^[48]) (^[68]) (^[44]) (^[69]), among others. Each claim is supported by cited evidence from these sources.