AI-Driven HCP Targeting in Veeva CRM: Predictive Field Intelligence with Pre-call Agent and ODAIA

Executive Summary

In recent years, pharmaceutical field teams have increasingly turned to artificial intelligence (AI) to enhance how they target and engage healthcare professionals (HCPs). This report examines two cutting-edge solutions integrated with Veeva’s life sciences CRM platform – Veeva’s own Pre-call Agent and the ODAIA Engagement Intelligence (now embedded in Veeva CRM) – which bring AI-driven predictive field intelligence to HCP targeting. We analyze the background of HCP segmentation, describe how these AI tools function, and evaluate the early evidence of their impact.

Traditional “reach and frequency” tactics (e.g. static segments by past prescriptions) are giving way to dynamic, data-rich models. As Axtria notes, conventional segmentation based on historical Rx volumes “lacks a few key elements” such as predicting future behavior or identifying why some HCPs differ from others (^[1]). Modern AI approaches attempt to address these gaps by ingesting a broader set of data (e.g. recent Rx trends, digital/online behaviors, patient journeys) and continuously updating target lists. For example, a recent Pharmaceutical Commerce report explains that ODAIA’s AI leverages Veeva CRM data plus external sources (website visits, benefit verifications, patient journey signals, event attendance, etc.) to deliver “predictive intelligence on HCP behavioral and prescription trends” (^[2]).

The two AI solutions we focus on are complementary but different. Veeva’s Pre-call Agent (part of Veeva Vault CRM) is an AI chat assistant that summarizes key account information – recent interactions, open tasks, call objectives, performance against goals – into concise “Cliff Notes” before each HCP visit (^[3]) (^[4]). It is effectively a generative AI summarizer built on top of the rep’s own CRM data. In contrast, ODAIA Engagement Intelligence (integrated into Veeva CRM) uses advanced machine learning (including large language models) to predict and prioritize. It automatically generates personalized call lists for each rep’s territory, produces pre-call insights (AI-generated one‐page prep briefs), and offers dynamic route planning. The ODAIA agent runs continuously, updating lists in real time as new data arrives (^[5]) (^[6]). Early marketing case studies from ODAIA report striking results: in one six-month pilot, ODAIA’s platform delivered an 8× return on the CRM software investment and a 6–9% lift in new prescriptions (NBRx) for a top-10 pharma brand (^[7]). Another case found that 39% of AI-flagged high-value HCPs had previously been classified as low-priority (^[8]). Sales leaders at GSK report that “our reps are 100% sold on ODAIA’s solution,” enabling “more relevant and timely engagements, rather than just reach and frequency” (^[9]).

These AI tools matter because the field still drives the large majority of pharma sales. An Accenture benchmark underscores that up to 70% of impactable sales come from direct field engagement – 5–10× more than any digital channel (^[10]). Yet many reps feel ill-equipped: only 1 in 4 field teams say they are fully supported by their CRM (^[11]). Accordingly, thought leaders from Veeva, ZS Associates, Salesforce and others emphasize that an “AI-powered CRM is changing the game,” enabling smarter, more personalized field interactions and stronger ROI (^[12]) (^[13]).

This report is organized as follows. We begin with historical context on HCP targeting and Veeva’s CRM platform. We then detail the technical capabilities of Veeva’s Pre-call Agent and ODAIA’s Engagement and Targeting Intelligence solutions, including the data sources they use and how they are integrated into the CRM. We compare these approaches (see Table 1) and cite evidence from case studies and industry experts on their value. We also discuss practical challenges – data readiness, change management, and ensuring explainability – which analysts and Veeva itself highlight as critical for successful AI in pharma (^[14]) (^[15]). Finally, we explore future implications: Veeva’s own roadmap for “ AI Agents” across Vault CRM (launched Dec 2025) (^[16]), expanding use of generative AI, and evolving standards for deploying AI safely and effectively in life sciences. Throughout, we provide extensive citations from trade publications, vendor and consultant materials, and regulatory guidelines to support our analysis.

The bottom line: AI-driven HCP targeting in Veeva CRM – via tools like Pre-call Agent and ODAIA – promises to make reps’ call planning far more data-driven and predictive. By harnessing machine learning and LLMs, companies can prioritize the right doctors at the right time, tailor messages with contextual insights, and ultimately accelerate patient access to therapies. Early adopters in biopharma are already seeing productivity gains and prescription lifts. However, realizing this “predictive field intelligence” at scale will require thoughtful data integration, rigorous validation of AI outputs, and change management to ensure reps trust and use these new insights. With AI innovations rapidly evolving, companies that master these tools may gain a significant competitive edge in a market that values timely, relevant HCP engagements above all (^[17]) (^[12]).

Introduction and Background

The Evolving Role of the Pharma Field Force

Pharmaceutical sales organizations have long relied on field teams (account managers and medical liaisons) to build and maintain relationships with prescribers, the primary drivers of drug utilization. Industry analyses consistently show that in-person sales calls remain the most impactful channel: for example, Accenture reports that “up to 70% of impactable sales are driven by field engagement, 5–10× more than direct-to-consumer or non-personal promotion” (^[10]). This is why, despite multiple digital channels, life sciences companies continue to invest heavily in their sales forces. A typical global pharma may employ thousands of field reps, with each rep costing well over six figures annually (salaries, travel, etc.). Maximizing the productivity of this “last mile” force is thus a strategic imperative (^[18]) (^[12]).

Traditionally, field reps have been guided by fairly static HCP segmentation and call frequency models. The old mantra was “reach and frequency” – ensure high-frequency visits to a limited number of top prescribers (the famous Pareto rule that roughly 20% of HCPs generate 80% of a brand’s scripts was often cited).These plans were often devised manually or with basic analytics, refreshed only quarterly or annually. However, the modern environment is far more dynamic. HCPs’ prescribing patterns can change quickly (e.g. due to formulary shifts or new indications), new physicians enter a disease space, and customer access constraints (time, regulation) vary. As Axtria observes, robust segmentation must be “aligned with the brand strategy”, updated frequently to track changes, and easily understandable and actionable by field teams (^[19]). Conventional approaches based solely on past prescription data fall short; they answer “who prescribed what” but not “who will prescribe next and under what circumstances.” Indeed, as one analytics firm notes, traditional Rx-based segmentation “lacks a few key elements”: it does not answer questions like “what is the expected behavior in the future?” or “what causes some HCPs to behave differently than others?” (^[1]).

Compounding the challenge is the explosion of available HCP data. In addition to historical sales and market data, companies now have digital touchpoint data (email clicks, web visits, online event attendance), patient-level information (e.g. insurance claims, lab results), and even behavioral signals (web surfing, news sentiment) that can hint at a physician’s interests and upcoming needs. In theory, these diverse signals should make targeting more precise, but they also raise the question of how to efficiently harness them. Sales reps cannot manually parse dozens of sources before every call. This is where AI and machine learning enter the picture, promising to digest vast datasets and surface the most relevant insights.

CRM Platforms and the Rise of Intelligent Engagement

Customer Relationship Management (CRM) systems have become the operational backbone of pharma commercial teams. By the late 2010s, Veeva Systems had emerged as a market leader: its Veeva CRM platform (built atop Salesforce) is used by the majority of the world’s largest drugmakers to schedule calls, track interactions, and manage call objectives (^[20]). Veeva’s CRM ensures all HCP data – past calls, sample orders, marketing responses, notes – is centralized. A field rep logging into Veeva CRM can see the entire history of a physician account: previous visit outcomes, outstanding tasks, key opinion leader status, and planned activities. This unlocks visibility and compliance, but by itself still requires manual analysis to spot triggers or craft next steps.

Recognizing the potential for AI, Veeva has long integrated some data-driven suggestions. In 2019 it introduced Veeva Andi, an embedded AI assistant that provided tailored insights and action suggestions in CRM (^[21]). Yet these early tools were mainly analytic supplements (e.g. highlighting top customers or suggested calls) rather than full-fledged autonomous intelligence. Similarly, Veeva’s classic Suggestions tool could highlight customers based on preset rules (e.g. highest share-of-voice or low engagement). But as Veeva executives acknowledge, this approach lacks the real-time predictive power of advanced AI: the old suggestions only used internal CRM data (like past interactions and sales numbers) (^[2]).

Today, the industry is on the cusp of a new paradigm. The term “predictive field intelligence” has emerged to describe systems that use AI to forecast and prescribe field actions. Instead of static call plans, these tools continuously learn from the latest data. They can answer questions like “Which HCPs in this territory have suddenly become poised to try our product?” or “Is Dr. Smith likely to switch formularies soon, based on similar physicians’ actions?” In effect, AI aims to ferment a constantly updating “next best action” strategy for each rep. As a Pulse Health blog puts it, predictive analytics uplifts the concept of the “next best message” (traditional targeting) to the “next best moment” – anticipating when an HCP will be most receptive and triggering outreach at that precise time (^[22]).

Implementing this vision requires two key breakthroughs. First, data unification: breaking the silos between sales, marketing, medical, and external data sources so that an AI model sees the full picture of each HCP and patient journey. Second, intelligent automation: embedding AI-generated insights directly into the rep’s workflow, rather than forcing them to switch to separate tools. To quote Veeva’s commercial strategy chief Paul Shawah in 2019, the goal is “the right customers in the right channels at the right time” driven by AI (^[21]). Industry experts now agree: AI-powered CRM is “changing the game,” enabling “smarter engagement for customers, stronger collaboration and measurable business impact” (^[12]).

Challenges in HCP Targeting and Why AI Matters

Despite the promise, several challenges remain when bringing AI to pharma field operations. Analysts warn that success hinges on data readiness and problem definition. A recent Veeva blog cautions that an AI-generated “next best action” must consider a wide array of factors – from a rep’s call plan to HCP access constraints – that are not easily modeled (^[23]). Similarly, Accenture emphasizes that most AI initiatives only succeed when companies dedicate time to structuring the data and training users upfront (^[14]). If the inputs are incomplete or the models inexplicable, reps may distrust the AI. (Indeed, one Accenture survey found only 25% of field teams feel their CRM fully supports them (^[11]).)

Data privacy and compliance also loom large. While AI can ingest external D;octor-reminder data like publicly available web activity and claims, any HCP-targeting tool must strictly adhere to privacy regulations (HIPAA, GDPR, PIPEDA, etc.) and promotional compliance. Vendors must ensure that packaged AI outputs do not inadvertently include sensitive patient health information or off-label promotion. Notably, Veeva’s AI Agents are built on platforms (Anthropic LLMs, AWS Bedrock) within secure cloud environments, and can be configured with access controls to company data (^[24]). Strict audit trails and “guardrails” (e.g. preventing above-label suggestions) are part of the design (^[25]).

Finally, change management is critical. Reps are used to certain rhythms and may resist new tools. If an AI “co-pilot” requires a steep learning curve or interrupts current workflows, adoption will suffer. Thus, current AI solutions emphasize seamless integration. For example, ODAIA’s product is available “within the existing Veeva CRM workflows” so that reps get insights without a lengthy tech rollout (^[26]) (^[17]). Veeva itself recognizes that early users need time to trust AI outputs. ODAIA’s CEO Philip Poulidis stresses that their guided AI doesn’t rely on reps to craft prompts; it “automatically delivers insights users wouldn’t even think to ask about” (^[27]).

In summary, the next sections will delve deeper into two representative solutions at the forefront of this trend: Veeva’s own Pre-call Agent (a generative AI assistant in Vault CRM) and ODAIA’s Engagement/Targeting Intelligence (an external AI engine integrated into Veeva CRM). We will examine how each tool works, how they differ, and what early evidence exists for their effectiveness in improving HCP targeting and field productivity.

Veeva’s AI Capabilities: Pre-Call Agent and CRM Intelligence

Veeva CRM and Vault CRM: Platform Overview

Veeva Systems is a leading provider of cloud-based software for life sciences. Its Veeva CRM (built on the Salesforce platform) has been the industry standard for global field teams to manage accounts, call plans, and content delivery. In recent years, Veeva has introduced Vault CRM, a next-generation CRM built on Veeva’s Vault cloud architecture. Vault CRM is gradually replacing the old system for new customers, offering deeper integration with Veeva’s content management (PromoMats) and data products (OpenData, Nitro). Notably, Vault CRM comes with embedded AI agents (the “Inbox” icon) that leverage generative models for user assistance (^[3]) (^[4]).

Both Veeva CRM and Vault CRM aim to give reps a “single pane of glass” view of each HCP and organization. Inside the platform, users can see an HCP’s profile, call history, key contacts, attachments (e.g. detention reports), compliance data checklist, and more. Veeva also offers advanced features like Account Hierarchies and Multiple HCP Profiles to capture a physician’s affiliations (hospitals, clinics, subsidiaries) and roles. All of this data is the raw material for AI: the more comprehensive the CRM data, the richer the insights a generative agent can provide.

Pre-Call Agent: Contextual Summaries and Chat Assistance

The Pre-Call Agent is Veeva’s first-party AI assistant in Vault CRM. It was introduced in 2023-24 to help reps prepare for each HCP interaction. The agent uses natural language generation (NLG) to summarize account details and answer questions. Specifically, the Pre-Call Agent “intelligently assist [s] users in planning and preparing for interactions with HCPs” by consolidating complex data into concise responses (^[3]). When a rep opens an HCP’s account record (or related objects like call objectives or planning sections), they can click the AI icon to invoke the chat interface. The agent will analyze the account’s recent interactions, outstanding tasks (“open suggestions”), cycle plan performance, and other relevant CRM data (^[4]). It can then generate a “Cliff Notes” summary in seconds, replacing what might otherwise be 10–20 minutes of manual review.

Key functionalities of the Pre-Call Agent include:

• Account Summary Generation: It can produce a natural-language paragraph summarizing the account’s recent activity, top objectives, and pipeline status. For example, it may say: “Dr. Smith has had three visits by your team this quarter, with primary interest in [therapy area]. The last call objective was met, and there is an open suggestion to discuss new safety data. The account’s plan is 80% complete.”
• Interactive Q&A: Reps can ask follow-up questions in free text, such as “What is the HCP’s Rx volume trend?” or “List any open action items for this account.” The agent pulls the answers from CRM fields. (Veeva’s documentation emphasizes that it is a “context-aware” chat, meaning it only answers with information from the account’s data (^[28]).)
• Personalized Call Prep: The agent can consolidate all relevant inputs (past meeting notes, CRM activities, email engagement, etc.) into actionable guidance. This reduces cognitive load and helps reps quickly focus on what’s most important for each HCP.

The user experience of the Pre-Call Agent is designed to fit into the rep’s existing workflow. The agent populates as a panel or module within the Vault CRM interface, and the rep can scroll through or copy the summary. Importantly, it uses the rep’s own CRM data only; it does not incorporate external data sources. The benefits to the rep are unquestionable: instead of manually filtering through charts and tables, the rep gets an AI-generated briefing. Veeva claims that this leads to “more focused preparation, targeted outreach, and enhanced productivity” (^[3]). One imagined scenario: a rep whose territory covers 100 doctors might use the Pre-Call Agent to quickly get up to speed on each HCP before a busy day.

While the Pre-Call Agent is compelling, it is limited by the scope of data it has. It primarily digests information already in Vault CRM (call logs, cycle plans, open suggestions, etc.) (^[4]). It summarizes what has happened and answers questions about the account’s status. It does not generate new targeting lists or incorporate real-time predictive signals from outside the CRM. For example, it might note that Dr. Jones had two on-site visits last quarter, but it won’t surface that Dr. Jones is now receiving many new patients or attending an upcoming conference unless those data are entered into CRM. In other words, Pre-Call Agent excels at synthesis and summarization of existing internal data, but it is not a system for discovering new HCP opportunities beyond the current call list.

Veeva’s Broader AI Vision: AI Agents and Vault Intelligence

Beyond the Pre-Call Agent, Veeva is rapidly expanding its AI portfolio. In April 2024, Veeva launched an AI Partner Program and invited vendors (like ODAIA) to integrate generative AI into the Veeva Vault platform (^[29]). In parallel, Veeva announced that it is building its own suite of “Veeva AI Agents,” which are industry-specific copilots embedded in various applications. In October 2025, Veeva revealed that its first AI Agents (for Vault CRM and Vault PromoMats) would become available in December 2025 (^[16]). These agents, powered by Anthropic and Amazon LLMs, will have “direct, secure access to Veeva application data, documents, and workflows” (^[25]). They are planned to cover not just field CRM but also clinical, regulatory, safety, and quality domains by 2026.

In practical terms, by December 2025 a rep using Vault CRM will likely have multiple AI assistants. The Pre-Call Agent (launched earlier) will provide account summaries, while the new “Vault CRM Agent” will likely offer a broader set of insights (though details are limited as of writing). Veeva’s vision is that these Veeva-branded agents will complement partner solutions: reps could eventually ask the Vault CRM Agent to “generate a call list for next week prioritizing high-value HCPs in COPD,” or have it analyse detachment slide content on the fly. Importantly, Veeva emphasizes control and compliance: customers will be able to configure or even train their own custom agents in the Veeva environment, ensuring data governance (^[25]).

In summary, Veeva’s own AI initiatives focus on enhancing the rep’s workflow through automation and summarization (Pre-Call Agent and upcoming AI Agents) with internal data. These built-in tools raise the baseline for what “intelligent CRM” can do. However, to maximize HCP targeting, many companies also look to third-party AI platforms that can operate on a wider dataset. This leads us to the case of ODAIA, a firm that has developed an AI engine connected to Veeva.

ODAIA and the Customer Science Approach

ODAIA’s MAPTUAL Platform and Customer Science

ODAIA is a Toronto-based analytics startup that has branded itself around the concept of Customer Science. As ODAIA’s co-founders explain, Customer Science is a multidisciplinary approach combining data science, behavioral science, and AI to deeply understand the journey of healthcare customers (HCPs) and their patients (^[30]). The aim is to use predictive models not just on prescription data, but across the myriad signals that reflect how a physician practices and how patients flow through the system.

ODAIA’s core product is called MAPTUAL (pronounced “matched you all”), described as a “Customer Science cloud for pharma”. MAPTUAL ingests large amounts of HCP and patient data – ranging from prescribing history and CRM calls to specialty referrals, patient diagnoses, and more – and applies machine learning to predict future behaviors. Their press material states that MAPTUAL uses “data science and behavioral science with AI and machine learning to gain a deep understanding of HCPs and their patients’ journeys” (^[31]). The platform claims, for example, to predict which HCPs are likely to start a new therapy (“precursor patients” model) and to surface untapped influencers (e.g. an interventional cardiologist who is starting to see more patients with diabetes).

Companies use ODAIA’s Customer Science engine to augment their targeting strategies. Instead of relying solely on recency/frequency, reps can see probabilistic “PowerScores” for each HCP – a dynamic value indicating how likely that doctor is to generate new prescriptions in the near future for a given brand. These scores are territory-specific and update in real time as new data arrives. ODAIA integrates with the company’s own sales strategy: for example, if a brand’s strategy is to focus on immunologists this quarter, the AI will tune its outputs accordingly.

Importantly, ODAIA does not require uprooting the existing CRM. Instead, it connects “through the Vault Direct Data API” to Veeva, pulling HCP master records and relevant activities (^[32]). Its user-facing components are delivered via Veeva MyInsights or embedded AI chat. This means a rep can stay within Veeva CRM, but see tiles or charts generated by ODAIA’s engine, or ask ODAIA’s agent questions.

Engagement Intelligence: Dynamic Call Lists and GenAI Insights

ODAIA markets two main modules: Engagement Intelligence and Targeting Intelligence. In practice, these are often used together.

Engagement Intelligence is focused on empowering the individual rep’s day-to-day activities. Its key features (now integrated into Veeva CRM) include (^[5]) (^[6]):

• AI-Powered Call Lists: Instead of static quarterly plans, the AI continually ranks and reprioritizes HCPs in a rep’s territory. ODAIA’s machine learning creates a personalized call list by combining the company’s segmentation and targeting data with real-time indicators such as recent website visits by the HCP, benefits eligibility checks, and event attendance. These call lists refresh automatically as new data comes in, ensuring reps are always focusing on the highest-value opportunities. (As ODAIA claims: “updated in real time as customer data refreshes, ensuring reps always have the latest actionable insights tailored to their unique territory” (^[5]).)
• GenAI Pre-Call Insights: Before each call, the rep can access an AI-generated account brief. Unlike the Pre-Call Agent, ODAIA’s GenAI pulls from external and internal data. It “autonomously pulls and processes data from multiple sources” to deliver plain-language summaries and recommendations (^[33]). The rep gets concise talking points and context (e.g. “Dr. Lee’s prescriptions for your brand were down 5% last month, possibly due to Clinics X… You might review the new study results on [topic] which align with his interests.”). Crucially, ODAIA’s agent is designed to work without any rep prompting: the AI autonomously surfaces context it deems relevant, rather than waiting for a user-composed query (^[33]).
• Dynamic Route Planning: A map-based interface shows the location of high-priority HCPs. Reps can see on an interactive map the value of each HCP in real time and adjust their daily routes to seize unexpected opportunities. For instance, if a high-priority target is identified in an adjacent town, the rep can reroute to include that visit. ODAIA claims this helps “maximize productivity” by ensuring the rep visits the “most relevant physicians” in their territory given current insights (^[6]).

Conditioned on a company’s brand strategy, the Engagement Intelligence adapts its output. The AI agent can communicate to the rep why each HCP was chosen (e.g. “HCP X is featuring as a key near-term adopter due to rising patient triggers in his area”). This transparency is intended to build trust and help the rep understand the AI’s reasoning.

Targeting Intelligence: AI-Enhanced Segmentation

While Engagement Intelligence empowers the rep with daily work lists, Targeting Intelligence helps the marketing and brand teams retune the overall segmentation strategy. ODAIA’s marketing materials describe the traditional problem: segmentation and targeting often take months of work across multiple teams. In contrast, the AI-driven solution “automates segmentation and targeting, reducing lag time from months to hours” (^[34]).

Features of Targeting Intelligence include:

• ML-Driven Territory Targeting: ODAIA computes a PowerScore for each HCP that factors in not just historical prescriptions but also predictive elements. Influencers are scored on propensity to adopt (e.g. “Identify HCPs likely to adopt new products pretty soon”), patient volume projections, and brand affinity. This yields a ranked list of all HCPs in a territory, differentiated by contextual criteria (such as local disease prevalence or emerging prescription trends) (^[35]). The system can highlight, for example, that a physician treating many early-stage asthma patients (a “precursor therapy” indicator) should move from Tier 4 to Tier 1 because they’re likely to prescribe a new asthma biologic soon.
• Predictive Behavioral Segmentation: Going beyond static demographics, the AI clusters HCPs by predicted behavioral traits. As ODAIA puts it, this examines “HCP behaviors to understand who is more likely to try a new product, who is just entering the therapeutic space, and who loves your brand” (^[36]). In practice, this might reveal that one segment of cardiologists is “conservative but loyal” and another is “tech-savvy early adopters,” allowing tailored outreach strategies (e.g. webinars vs dinner meetings).
• Individual Channel Assessment: The AI identifies which channels each HCP is most likely to engage with effectively. By analyzing past responses to emails, literature distribution, meetings, etc., it can tell reps “Dr. A prefers email alerts for clinical updates, while Dr. B responds best to face-to-face office calls” (^[37]). This ensures marketing and field efforts align with the HCP’s behavior instead of assuming a one-size-fits-all channel mix.

A key point: ODAIA’s solutions are no-code from the user’s perspective. The marketing team uploads the company’s brand segmentation logic (top-tier physicians, specialties of interest, etc.), and ODAIA augments it. The AI then continuously adjusts those segments as new data flows in, but will always respect the underlying brand mandates and compliance guardrails. This dynamic segmentation yields two immediate benefits: it uncovers “hidden gem” HCPs that planners had overlooked, and it justifies resource allocation by showing measurable ROI. For example, in one Emerging Pharma case study, 18,000 HCPs were analyzed via ODAIA’s Targeting Intelligence and 7,000 were newly flagged as high-value despite having been previously categorized as low/mid-tier (^[8]). This kind of insight alone can dramatically change a campaign focus.

Integration and Workflow

Both Pre-Call Agent (from Veeva) and ODAIA’s intelligence tools integrate with the Veeva CRM workflow, but at different layers:

• Pre-Call Agent is natively part of Vault CRM, so it appears directly on account pages or call planning pages. It uses Veeva’s own data models. Reps invoke it by clicking the AI icon in Vault.
• ODAIA Engagement Intelligence is embedded through Veeva MyInsights dashboard widgets and chat. Once enabled, a rep opening their CRM will see an ODAIA “AI agent panel” or MyInsights component. It may auto-run overnight to update call lists and insights. When the rep logs in, they see ODAIA-generated call lists prioritized at the top, and can drill into an AI “Call Insight” or talk to the ODAIA virtual agent for pre-call summaries.

Because ODAIA operates via the Vault Direct Data API (^[32]), the integration is real-time and secure. No data is moved to an external system; the AI processing happens either in ODAIA’s cloud or in a protected environment and the results are piped back into the rep’s CRM screen. Veeva’s own AI partner program was created to enable precisely this kind of plug-in; ODAIA was the first vendor announced in that program (^[38]).

Example Workflow

To illustrate, consider a rep planning a Monday with five doctors to visit. Without AI, the rep might review last quarter’s prescription reports and playbook to decide which visits to focus on. With ODAIA Engagement Intelligence:

1. On Friday evening the AI evaluates all 200 doctors in her territory. It combines this company’s target segments with recent data. It notices Dr. Nguyen has an influx of special diagnostics claims (meaning new patients) and Dr. Patel just lost a formulary addition (meaning an opportunity), so it bumps both up. It pushes Dr. Reyes and Dr. Morales down due to recent extended MLR restrictions.
2. The next morning the rep opens Veeva CRM. On the dashboard, the ODAIA call list prioritizes Dr. Nguyen and Dr. Patel first, followed by the usual top targets.
3. She clicks on Dr. Nguyen’s record and the ODAIA Pre-Call Insights panel loads a one-page summary: “Dr. Nguyen’s prescriptions for [brand] are up 15% this month, driven by a new patient cluster X. Discuss how [brand] addresses [patient segment]. Note: recent competitors’ claim X may be at risk.” Meanwhile, Veeva’s Pre-Call Agent (built-in) provides a CRM-focused recap: “Last interaction was a clinic visit on 9/10, call objective achieved. No open tasks. Cycle plan 75% complete.” The rep thus has a rich pre-call briefing combining both CRM data and external behavioral insight.
4. Off she goes: the ODAIA map shows her an optimized route through town, grouping Dr. Nguyen and Dr. Patel geographically. Mid-route, `the ODAIA app suggests an impromptu stop at a conference being held nearby where another high-value doctor is presenting.

In practice, no live case studies have been published in academic journals yet, but ODAIA’s preliminary data (discussed below) and customer testimonials (e.g. from GSK) suggest these capabilities can significantly sharpen the rep’s day-to-day decisions. Table 1 (below) summarizes key differences between the solutions.

Table 1: Comparison of Veeva’s Pre-Call Agent and ODAIA’s AI-driven HCP targeting tools.

Evidence and Case Studies

While fully independent studies of these new AI tools are not yet available, trade press and vendor publications provide useful data points on their early performance:

• Case Study – Top-10 Global Pharma (Immunology Brand): ODAIA reports that one of the world’s largest pharma companies deployed Engagement Intelligence for a leading immunology product facing new competition. In 6 months, ODAIA’s AI-driven platform “transformed their HCP engagement strategy, delivering faster, personalized insights and more efficient execution” (^[39]). Key results from this pilot included: an 85% average utilization rate among reps (meaning most reps used the AI tool actively) and ~70 minutes saved per rep per day due to automation (^[40]). Critically, the brand saw a 6–9% lift in new prescriptions (NBRx) attributed to the AI intervention, and an overall 8× ROI on the software investment (^[41]). (Exact calculation periods and control comparisons are not disclosed, but these figures indicate substantial gains over business-as-usual.)

• Emerging Specialty Pharma Case: In another published ODAIA case (^[42]), a smaller specialty pharma company (with a limited sales team) used Targeting Intelligence to analyze 18,000 HCPs. The AI identified 7,000 high-value HCPs, a remarkable adjustment given the company’s original database. Intriguingly, 39% of those 7,000 had been previously classified only as mid- or low-tier by the legacy segmentation (^[8]). This suggests the AI uncovered many overlooked but important targets. The company’s objectives were to find new growth opportunities, maximize limited resources, and validate AI’s ROI (^[43]). The reported outcome was a significant reshaping of who the sales team would engage, enabling them to focus on physicians they “hadn’t been prioritizing before” (^[44]).

• Customer Testimonials: Beyond the numbers, qualitative feedback is strong. For instance, Geoff Nocar (Lead of HCP Experiences at GSK) states: “Our reps are 100% sold on ODAIA’s solution and its value to provide insights about their customers to have more relevant and timely engagements, rather than just reach and frequency.” (^[9]). This highlights improved rep buy-in and perception of relevance. U.S. pitch materials also note that three of the top 15 biopharma companies are “already using” the ODAIA engine (^[45]), implying significant early adoption at the industry’s leading firms.

• Efficiency Gains: In general, AI-targeted tools promise to cut waste in planning. Traditional segmentation work could take brand teams many weeks. ODAIA claims their automated targeting can refresh lists in hours, not months (^[34]). Veeva’s Pre-Call Agent similarly touts time savings: a rep can turn “20 minutes of prep into a 20-second review”, according to Veeva’s field enablement materials (^[46]). While quantitative benchmarks (like minutes saved) depend on user behavior, these categories of time savings are credible given that reps otherwise must sift through reports, notes, and market data manually.

• External Analyst Views: Beyond the vendors’ claims, independent consultants underscore the potential. An Accenture survey (quoted above) found that current CRM tools leave many reps dissatisfied (^[11]), implying significant headroom for improvement. ZS Associates similarly notes that “agentic AI [for] pharma CRM is reshaping how customer engagement... is defined” (^[13]). Industry media (e.g. Pharmaceutical Commerce) has recognized the immediate availability: ODAIA’s AI “isn’t a vision… sales reps can start getting AI-driven insights in Veeva CRM within weeks” (^[47]).

Collectively, these indicators point to real-world benefits, though much of the evidence is currently proprietary or anecdotal. Notably, academic publications or government reports on these specific tools have not yet appeared (likely due to the recency of these offerings). Thus, most “data” are self-reported or from trade sources. Readers should interpret the precise metrics (like ROI multiples) with healthy skepticism, as they depend on many assumptions. However, the consistency of the qualitative message – namely, that reps save time and focus on higher-value HCPs – lends weight to the claims. We also note parallels in related domains: for instance, IQVIA’s work on field alerts has shown that predictive models can reliably identify when a physician is likely to see a patient who could benefit from a therapy (^[48]). If such patient-clinician triggers are detectable, it stands to reason that machine-learning could likewise detect surrogate signals for rep visits.

Table 2 below summarizes selected reported metrics from the examples above.

Table 2: Reported results from AI-driven HCP targeting. Outcome metrics are drawn from ODAIA case studies (^[40]) (^[8]) and industry surveys (^[10]) (^[11]).

Discussion: Implications and Future Directions

Benefits and Competitive Advantage

The adoption of AI-driven HCP targeting within Veeva CRM offers several strategic advantages for life sciences companies:

• Increased Productivity: By automating data analysis and call planning, reps spend more time talking to the right HCPs and less time on admin. Even a modest 15–30 minute per rep per day improvement (as claimed by some pilots (^[40])) can translate to dozens of extra calls per rep per quarter, potentially leading to significant incremental sales.

• Better Customer-Centricity: These tools make engagements more relevant. ODAIA’s CEO emphasizes focusing reps on “engaging HCPs, not prompt engineering” (^[27]), meaning reps can concentrate on value-adding activities (dialogue, providing samples) rather than document review. Personalized pre-call insights help reps speak to what matters most to each HCP, aligning with expert calls for more customer-centric, data-driven engagement (^[12]).

• Resource Optimization: Forecasting which HCPs are likely to grow prescriptions allows brands to shift their reach from declining doctors to emerging ones. This can yield direct cost savings (fewer visits to low-potential accounts) and opportunity gains (capturing new share-of-voice). The ODAIA case with 39% previously-missed high-value HCPs (^[8]) illustrates how traditional filters can overlook valuable doctors.

• Rapid Response to Change: Because AI models update in near real-time, pharma companies can adjust tactics much faster than before. A sudden spike in a competitor’s marketing might be detected by the AI within days (via changed prescribing patterns or physician digital activity), prompting an immediate counter-engagement. This agility was virtually impossible with static planning cycles.

• Scalable Insights: Perhaps most importantly, these AI agents make insights accessible at scale to every rep. Larger organizations cannot feasibly employ data scientists for each region; embedding AI in the CRM democratizes analytics. It turns every rep into an “experienced analyst”, as one vendor puts it (^[27]), without specialized training.

Collectively, these benefits can create a competitive moat. In markets where many brands vie for the same specialists, being the first to pinpoint an on-the-fence doctor can deliver a weeks-long head start in the sales process. Early adopters planting AI-driven targeting tools may thus achieve better market penetration than peers who rely on slower, traditional methods.

Challenges and Cautions

Despite the promise, the path to fully AI-driven engagement has pitfalls:

• Data Quality and Integration: All AI models are only as good as their inputs. Varying data latency (e.g. prescription claims may lag by weeks) and missing fields can impair precision. Companies must invest in data integration (often via warehouses like Veeva Nitro or third-party platforms) to feed these models in a timely, high-quality manner. Veeva’s own experts warn that advanced AI use cases demand “structuring data” carefully as a precondition for success (^[14]).

• Model Trust and Explainability: Field reps and managers may be skeptical of AI suggestions, especially if they conflict with their gut instincts. The complexity of a prescriptive AI (an opaque LLM with many variables) can make replication or explanation difficult. Therefore, vendors and companies emphasize the need for transparency. ODAIA’s approach, for instance, is to provide explicit “why” statements (the trigger behind each suggestion) and to avoid requiring reps to craft their own prompts (^[50]) (^[33]). Nevertheless, ensuring reps trust and understand AI output will require training and evidence (as Accenture suggests on “training users to act on insights” (^[14])).

• Change Management: Even well-intentioned AI features can falter without buy-in. Sales organizations must actively include the field in testing and deciding how to use AI. Performance incentives, KPIs and coaching also need updating to align with AI-enhanced metrics. For example, if AI recommends a previously low-tier HCP, will the rep be rewarded for making that call even if it falls outside pre-defined plans? As Accenture points out, even “if the problems and data are right, success depends on building a change management culture” to address skill gaps (^[14]).

• Over-Reliance on AI: There is a risk that marketers might become lazy and over-trust the AI. As Veeva’s recent predictions warn, companies must be mindful of “AI’s limits”. A blog piece notes that an AI next-best-action tool must juggle so many factors that it can’t capture everything (^[23]). Real-world uncertainties (like sudden hospital policy change) may defy pure data-driven plans. Leading companies can avoid this by treating AI as augmentation, not replacement, and by maintaining human oversight for final decisions.

• Regulatory Scrutiny: The use of AI in healthcare settings is receiving increasing regulatory attention. While current guidelines (e.g. FDA’s Good Machine Learning Principles, HCLS privacy regs) are more about product development than marketing, pharma companies should stay ahead of ethics/privacy expectations. For instance, any AI model using patient data must meet HIPAA standards; even de-identified claims use must be handled carefully. As AI recommendations begin to shape medical discussions, pharma legal teams will want to validate that outputs do not stray into off-label suggestions or inappropriate influence. Thus, building compliance checks into the AI pipeline (a feature of Veeva’s new Vault AI architecture (^[25])) is crucial.

Future Directions

Looking ahead, the capabilities of AI in CRM will only grow. Several developments to watch include:

• Expanded Veeva AI Agents: As noted, Veeva is rolling out deep AI agents across its platform. By late 2025, Vault CRM will have a dedicated AI agent (beyond Pre-Call) that likely integrates vendor and internal AI. (^[16]) (^[17]). These Veeva-native agents will be tuned to life sciences compliance by design, possibly offering even tighter integration (e.g. directly in CLM presentations, medical inquiry resolution, email generation). Customers can extend or train additional agents on Amazon/Azure LLMs (^[24]), including potentially building their own fine-tuned models for proprietary data.

• Stronger Data Lakes and Feature Stores: To feed these AI tools effectively, companies will invest in consolidated data platforms (possibly using Veeva Nitro or Snowflake). Adoption of industry standards (like HL7 FHIR for patient data) could also enable new signals. We may see models that incorporate genomic data or electronic medical record (EMR) insights, making HCP predictions even more precise. The Maptual concept hints at patient-level forecasting; if feasible, reps might someday get alerts like “Patient X of Dr. Y, who could benefit from our drug, has just had a lab result — consider arranging a treatment discussion.”

• Agentic AI for Workflow Automation: The idea of “agentic AI” – autonomous software that not only advises but acts – is emerging. ZS and Salesforce discuss “agentforce” where an AI could schedule calls or send follow-up emails without rep intervention (^[13]). More sensibly for pharma, an agent might auto-generate a compliant message to be reviewed by a medical lead, or auto-assign MSL outreach for complex questions. Veeva’s agentic roadmap and ODAIA’s “GenAI” hints suggest we are moving towards more proactive automations.

• Cross-Function Integration: Today, we focus on sales reps. In the future, integrated AI could link field data with marketing campaigns, MSL insights, and even payor outcomes. For example, ODAIA’s 2025 roadmap includes AI-powered marketing orchestration (targeted omnichannel campaigns) (^[51]). A well-coordinated AI suite might ensure that if a rep visits a physician, that doctor also receives tailored digital content and reimbursement support around the same time, amplifying impact.

• AI Transparency and Monitoring: As these tools mature, companies will likely implement rigorous monitors ("model dashboards") and feedback loops. Explainable AI techniques will be applied to ensure that when AI is “surprising,” it can be checked. We may see new industry external audits or guidelines for “AI in pharma” similar to checking DTP advertising compliance.

• Field Training and Roles: Ultimately, the adoption of these systems may change the role of sales reps. The most routine analytical tasks will be automated, potentially allowing reps to be more consultative (like mini-KOLs) or to cross-train (medical or digital marketing skills). Companies may begin emphasizing data interpretation skills in rep training, or create hybrid roles (AI analyst for the field). Cultural shifts will be needed to fully realize the AI’s potential.

• Metrics and Validation: Over time, independent studies may emerge. For now, we rely on vendor-reported statistics. But the life sciences community will want robust validation: e.g. controlled field trials comparing AI-assisted vs control territories, to measure lift in script share or physicians added. Journals or conferences may see papers on “AI in field engagement” with quantitative findings. When that happens, it will further clarify best practices.

In summary, AI-driven HCP targeting is not a distant vision but a present reality entering mainstream use. The integration of ODAIA’s AI and Veeva’s agents into core CRM workflows means commercial teams will soon operate in a firmly data-augmented way. While careful implementation is required, the potential upside – faster decision cycles, more personalized interactions, and accelerated patient access to therapies – is enormous. As one consulting partner puts it, achieving this will “open new frontiers of engagement” that traditional tools could never reach (^[13]) (^[12]).

Conclusion

AI-driven tools like Veeva’s Pre-Call Agent and ODAIA’s Engagement Intelligence represent a significant leap forward in pharmaceutical field operations. By combining natural language generation and machine learning with vast datasets, these systems transform the way reps plan and execute HCP visits. Our review shows that:

• Contextual Prep (Pre-Call Agent): Veeva’s in-CRM agent can synthesize an account’s key data into actionable summaries, saving reps time. It represents an important step in making AI a seamless part of the rep’s workflow (^[3]) (^[4]).

• Predictive Targeting (ODAIA): ODAIA’s platform leverages AI to continuously identify which HCPs are “hot” and ready for engagement, generating customized call lists and conversation prompts (^[5]) (^[33]). Early user feedback and case metrics suggest this can significantly boost outcomes (e.g. multi-percent lifts in new scripts (^[41])) and rep confidence (^[9]).

• Integration with Veeva: Both Veeva’s own AI and partner solutions are delivered within the familiar Veeva CRM interface. This minimizes disruption and suggests rapid adoption: as one report noted, reps could “start getting AI-driven insights in Veeva CRM within weeks” of deployment (^[47]).

• Industry Momentum: These developments are consistent with broader pharma trends. Accenture and ZS analysts note that “AI-powered CRM is changing the game” and that unified data plus intelligent applications are now a must-have (^[12]) (^[13]). Veeva’s announced roadmaps (AI Partner Program, Vault CRM AI Agents) indicate this is a strategic priority going forward (^[38]) (^[16]).

However, realizing the full potential will require addressing practical challenges: ensuring clean, integrated data; managing user change; and validating models. The literature cautions that companies should front-load data prep and define clear objectives to see returns (^[14]) (^[15]). Additionally, companies should watch for emerging regulatory guidance on AI in marketing and maintain human oversight.

In the coming years, “predictive field intelligence” will likely become the new baseline for pharma CRM. We expect to see broader adoption of features like dynamic prioritization and generative call briefs. As Veeva and its partners continue to refine their AI agents, and as more companies document performance outcomes, the art of HCP targeting will become ever more scientific.

In conclusion, the integration of AI in Veeva CRM – exemplified by Pre-call Agent and ODAIA – represents a fundamental shift in how pharmaceutical companies engage with HCPs. The early evidence, while mostly from vendor and case sources, signals meaningful improvements in relevance, efficiency, and ROI. For a sales-focused industry facing shrinking access to doctors and rising complexity, these tools offer a way to stay ahead. If widely adopted, AI-driven HCP targeting could accelerate the industry’s ability to get the right therapies to patients faster, which ultimately benefits both business and public health.