2026 Symposium - Sponsor

Why did Brand X lose 3 points of share in the Southeast — and how much of it is a formulary access problem versus a field execution problem? Today, that question gets split across two teams, four data sources, and a three-week turnaround. In this workshop, you'll answer it yourself — hands-on, in minutes.

Working in a live sandbox with realistic (anonymized) Rx, claims, payer, and CRM data, attendees will conduct a complete multi-source root-cause investigation using natural language: decomposing the share change, separating access-driven from execution-driven effects, reconciling sources with different grains and definitions, and tracing every number back to where it came from. Then you'll take it a step further — turning the finding into a finished, decision-ready work product, and converting the one-off investigation into a reusable app and an always-on monitoring agent, so the next occurrence is caught the morning it starts.

This workshop explores:

• Natural-language root-cause analysis across sources that don't naturally line up — Rx, claims, payer/formulary, and CRM data
• AI-augmented approaches to separating payer-driven share shifts from field execution effects
• Producing finished work products — the exposure quantified, the response drafted — rather than charts that still need assembly
• Converting a one-off investigation into a reusable analytical app and an always-on monitoring agent, hands-on
• Real-world pitfalls and learnings from applying AI analysis to pharma commercial data

The format is a mix of live demonstration and guided hands-on exercises using the Tellius platform. Whether you're in brand analytics, market access, field excellence, or commercial data science, you'll leave with practical root-cause skills, an agent you built yourself, and a concrete sense of what changes when a three-week question takes minutes.

Pharmaceutical forecasting has traditionally relied on a fragmented, multi-step process spanning epidemiology assessment, market research, assumption development, and scenario evaluation across disparate tools, making consolidation and traceability difficult. This workshop explores an agentic AI system that translates a forecasting request into a structured, reviewable plan and orchestrates specialized agents to gather evidence, evaluate epidemiology, assess market dynamics, identify analogs, and quantify market-event impacts, with every input tagged by provenance and confidence for full transparency. By separating agent-driven reasoning from deterministic forecasting engines, the system produces an integrated epidemiology-to-revenue forecast that remains reproducible, auditable, and interactively refinable while preserving human oversight.

More information coming soon.

Forecasting plays a critical role in biopharma decision-making, influencing portfolio strategy, launch planning, commercial investments, and resource allocation. This workshop provides a practical introduction to forecasting fundamentals, helping participants understand how forecasts are built, how market insights and data are translated into assumptions and decisions, and how emerging technologies, including AI, are reshaping the future of forecasting. Designed for professionals new to forecasting or those who work closely with forecasting teams, the session combines industry frameworks, real-world examples, and practical applications to build foundational forecasting knowledge.

Choosing comparable product launches is a consequential step in pharmaceutical forecasting, yet analog selection often depends on familiar examples and subjective judgment. This case study examines how a global biopharmaceutical company and Axtria developed Analog AI to bring a consistent evidence base and structured review to that process.

The session explains how curated clinical, market, access, and company attributes support expert-led filtering, LLM-assisted recommendations, and machine-learning predictions of launch performance. A narrated walkthrough using a blinded asset will show how forecasters assess candidate analogs, interpret recommendations, and review competitive sets through a governed Market Basket workflow. We will discuss how to evaluate recommendation quality, distinguish a plausible explanation from supporting evidence, and decide where human review remains essential.

The presenters will share lessons on forecaster trust, proprietary-data integration, and phased adoption, alongside the role of agent-assisted curation in keeping the evidence base current. Attendees will leave with a practical blueprint for designing an analog intelligence capability and evaluating its use in their own forecasting processes.

More information coming soon.

Most of us measure analytics success by outputs: dashboards built, reports shipped. However, the metric commercial leaders care about is time-to-decision which can have a direct and often significant impact on business outcomes. For us, that process often took days. During a business review, a leader would ask a follow-up question, discover the answer wasn't in the deck, and hear the familiar response: "Let's take that offline." The question became a ticket, joined by dozens of others in the queue, and momentum stalled while teams waited for answers.

We wanted to close that gap and put a number on it. We first designed and built a governed semantic model that translated complex business logic into a trusted, reusable analytics layer. By publishing that model in ThoughtSpot, analysts gained a self-service foundation for exploration, and business users gained access to Spotter, ThoughtSpot’s agentic search experience. Users can ask questions in plain English and receive trusted answers without SQL, ticket queues, or report rebuilds, while the agent handles the follow-up questions that previously stalled or required analyst intervention.

Two outcomes emerged from this work:

• The first is we reduced decision latency. Questions that sat in the queue for a day or two are now answered in minutes, on the spot. More than thirty people across brand, field, and leadership now explore their data independently, eliminating roughly two-thirds of that fifty-plus monthly backlog. The result is over 100 analyst hours returned each month—more than 1,000 annually—while preparation time for major business reviews has fallen to approximately one-quarter of previous levels.
• The second finding was less expected. By reducing the friction to data driven insights, the questions changed. Leaders increasingly asked "why did it happen?" and "what should we do next?" rather than “what happened?”. Our usage data offers a window into how curiosity evolves when the cost of exploration approaches zero, creating a more analytical and decision-oriented culture.

None of this was really about the software. The harder shift was what it did to our analysts. Analysts moved from answering questions one at a time to owning the governed semantic model that answers them at scale. They became stewards of trusted definitions rather than operators of a request queue. That is the part that made leaders willing to act on an answer in the moment: they knew the business logic behind the agent’s answer was owned, governed, and accountable.

Sharing our journey, we will also be candid about what it took to get there. Adoption did not happen automatically. A handful of early answers exposed gaps in the model and threatened confidence before we strengthened governance and refined the underlying business logic. The longest pole in the tent was not the technology—it was establishing trust in the data.

This is what "the convergence of data, talent, and AI" looked like in practice: not analysts replaced, but decision-makers equipped and empowered. We will share how we measured the impact, governed the semantic layer, analyzed usage patterns, and learned from missteps, providing a practical framework for organizations looking to reduce decision latency and scale trusted, AI-enabled analytics.

Oncology trigger programs combine claims-derived signals, biomarker-testing indicators, treatment-pattern information, healthcare professional (HCP) targeting, and field engagement data. Multiple alerts may be generated for the same HCP, requiring users to reconstruct the “why now” from fragmented records. We developed a context-engineered generative AI workflow to convert multi-source trigger data into concise, standardized summaries.

More information coming soon.

Every pharma commercial organization now wants an agent that can look at an account, decide it deserves more resourcing, and act with minimal human friction. Almost none are ready to build one safely, because the account the agent reasons about was assembled by a resolution method nobody ever interrogated. Two reasonable ways of mapping a provider (NPI) to a parent account can disagree on a meaningful share of a book of business, and that disagreement propagates silently into every tier, recommendation, and eventually every autonomous action built on top of it.

This session presents a full-stack account intelligence architecture in two parts. First, a proven, production-tested foundation: a dual-bridge resolution engine (calls-based and ZIP-based, unified behind a common interface) and a six-component, config-driven Center-of-Excellence scoring model, built to make resolution bias visible and auditable instead of hidden, validated through 28 passing unit tests and a synthetic tier-flip example showing how the same account lands Platinum under one method and Silver under another.

Second, built directly on that foundation, we present the design for an Account Strategy Agent: a bounded, explainable agent that monitors tier movement, drafts specific resourcing recommendations with a documented rationale tied to exactly which CoE components moved and how confident the underlying resolution is, and routes every recommendation to a human owner for approve, modify, or reject before any budget or field-force action is taken.

More information coming soon.

More information coming soon.

Commercial decisioning today is a tangle of segmentation, targeting, next-best-action, and omnichannel tools — stitched into rigid workflows that rarely flex across a brand's lifecycle. This session introduces the Adaptive Decision Intelligence Engine: a network of collaborating AI agents that reframes decisioning as continuous reasoning, not a fixed sequence of steps. Give it an objective — "accelerate adoption among high-potential cardiologists" — and it reasons through who to prioritize, what's holding them back, what to say, and how to reach them, coordinating field, medical, marketing, and access into one coherent strategy. The same architecture scales with your data: it reasons from playbooks and analogous-brand memory when signals are thin, and progressively activates propensity models, hyper-personalization, and reinforcement learning as evidence grows. Come see how one agentic framework delivers sophisticated, explainable, and compliant decisioning across the entire brand lifecycle — from launch to maturity.

Enterprise analytics runs on table sprawl. To answer each new question, teams build their own processed, use-case-specific tables, so the warehouse fills with overlapping datasets that compute the same metric under different names, definitions, and logic - and quietly disagree. Every one of these tables carries its own data-engineering pipeline to maintain. When agentic GenAI assistants are pointed at this landscape, they tap whichever use-case table a team happens to own, inheriting its fragmentation, its maintenance cost, and its contradictions. The missing piece is a shared, governed, AI-consumable data foundation the agents can reason over - not another table.

We present a foundation-first data architecture that classifies enterprise data into three explicit tiers. Raw datasets arrive from vendors untouched. Foundational datasets - claims, sales - are cleaned and conformed once into durable, enterprise-wide data products. Functional datasets hold use-case-level metrics filtered from the foundational layer. The governing principle inverts current practice: agentic systems tap the foundational layer wherever possible instead of proliferating functional tables. Foundational datasets are re-engineered for AI consumption - normalized, with every column unique and every column name and definition written to be unambiguous to both a human and a model.

Agentic AI is creating new opportunities for market intelligence teams to move beyond traditional search and standalone generative AI tools to-ward coordinated workflows that can plan, re-search, evaluate, and synthesize complex business questions. But realizing this potential re-quire more than simply adding AI to an existing process. Teams must determine how to structure tasks, assign specialized roles, validate information, and maintain human oversight while producing insights that decisionmakers can trust.

In this session, audience will be exposed to a practical framework for applying agentic AI to market intelligence. The session will walk through how a complex intelligence request can be decomposed into a structured research plan, delegated across specialized AI agents, and in-dependently evaluated before being translated into a business-ready deliverable. Particular attention will be given to source attribution, confidence assessment, cross-verification of critical claims, and safeguards designed to reduce un-supported or fabricated conclusions

As pharmaceutical organizations increasingly adopt Generative AI and Agentic AI, the challenge is no longer model capability but ensuring AI systems understand enterprise context, governance requirements, business rules, and commercial objectives. Without this context, AI-powered recommendations can be technically accurate yet commercially ineffective, reducing trust, explainability, and adoption.

This session introduces a practical Context Engineering framework that enables AI agents to operate with enterprise awareness by combining governed data foundations, business ontologies, organizational knowledge, decision policies, and human oversight. Attendees will learn how pharmaceutical organizations can build trusted, auditable, and scalable AI solutions across commercial analytics use cases including HCP engagement, next-best-action recommendations, forecasting, market access analytics, and launch planning.

Using a real-world pharmaceutical workflow, the session will demonstrate how organizations can transform fragmented data, business knowledge, and operational processes into actionable intelligence that supports faster, more informed commercial decisions. Participants will gain a practical blueprint for operationalizing agentic AI while maintaining governance, transparency, compliance, and business trust.

Key Takeaways: By the end of this session, attendees will be able to:

• Understand why context engineering is emerging as a critical success factor for enterprise AI and agentic systems.
• Learn how to bridge the gap between enterprise data, business knowledge, governance, and AI decision-making.
• Explore a practical framework for building context-aware AI solutions within pharmaceutical organizations.
• Understand how enterprise ontologies, business rules, and decision intelligence can improve trust and explainability.
• Learn best practices for deploying scalable and governed Agentic AI systems in commercial pharma use cases.
• Walk away with actionable strategies that can be applied immediately to their own AI transformation initiatives.

PRISM presents a supervised multi-agent framework designed to make AI-driven commercial analytics in life sciences more trustworthy and defensible. The session will show how governed data access, SME rules, validation, provenance, and human review can be embedded directly into the analytical workflow to improve reliability and decision confidence.

More information coming soon.

More information coming soon.

A Governed Semantic Context Layer for Trustworthy Agentic AI in Pharma Commercial Analytics

The session will outline how commercial decision intelligence can be advanced through effective forecast informed IC planning. The session will take the audience through different forecasting techniques that can help creating an informed IC Plan to ensure fairer goal allocations.

This session presents a case study in moving a global pharma marketer from siloed, channel-specific marketing mix studies to a unified, AI-driven investment system. Built on Google Meridian and tailored to the client's data, systems, and business context, the platform brings together a diverse marketing mix — spanning digital ads, TV, streaming audio, print, outdoor media, and in-person HCP engagement — into a single cross-channel measurement, scenario simulation, and budget allocation environment, replacing a fragmented, channel-by-channel view with one true omnichannel view of performance.

This poster describes an ontology-driven knowledge foundation supporting Medical Affairs and commercialization AI applications through a semantic layer.

The model defines core concepts — diseases, assets, indications, evidence, healthcare professionals, accounts, organizations, and their relationships — and maps heterogeneous structured and unstructured data into a consistent representation used for customer and account intelligence, patient journey analysis, launch and brand analytics, and competitive monitoring.

Topics include ontology design, data source mapping, governance, and trade-offs between semantic richness, implementation complexity, and the use of existing healthcare terminologies.

An Agentic, Multimodal Analytics Engine Unifying Structured Data, Dashboards, and Documents for Commercial Excellence

Market research and competitive intelligence teams in pharma don't lack external signal: trial registries, congress abstracts, news, real-world evidence, sentiment, they lack a way to turn that signal into a specific next action a brand team will actually take. This poster presents a two-layer market intelligence architecture: an operational layer (personalized daily brief, automated news/alerts pipeline, congress/trial-timeline tracker) and an analytical layer (landscape exploration, KOL mapping, evidence-gap and sentiment analysis, semantic-layer querying), grounded by a citation-backed, hallucination-monitored AI Co-Pilot that answers only from a curated, brand-approved corpus. It then presents the honestly-scoped next step: evolving that Co-Pilot from a question-answering tool into a Recommendation Agent that proposes a specific next action such as update rep messaging, flag a publication opportunity, prioritize KOL outreach, tied to a named decision owner, governed by the same raw-vs-curated human validation discipline.

In relapsed/refractory multiple myeloma (RRMM), patients approaching relapse may be underrepresented in EHR-derived cohorts because progression is often not captured as a discrete structured event, care may be fragmented across settings, and patients may not appear in a contributing EHR system until referral to a specialist or initiation of a new treatment.

EHR-identified RRMM cohorts capture patients who have already progressed through multiple lines of therapy and entered a documented care pathway, but miss the broader population of myeloma patients approaching relapse who remain invisible until they arrive at a specialist. By the time they appear in structured data, the commercial and clinical window has narrowed considerably.

This session presents a three-stage real-world data framework developed and validated in RRMM that addresses this gap directly.

Stage 1: Patient Amplification Beyond EHR. Using linked claims, pharmacy, and clinical notes data, we first establish an EHR-confirmed RRMM reference cohort that serves as a clinically enriched “gold standard.” Longitudinal treatment patterns, drug sequencing signals, comorbidity profiles, and other observable features from these patients are then used to distinguish clinically similar patients in broader claims datasets from claims-based false positives. This digital twin matching approach surfaces patients who share the clinical fingerprint of RRMM despite lacking a confirmed RRMM designation in the contributing EHR data. In a tested cohort, the approach meaningfully expanded the identifiable population while maintaining clinical plausibility against the EHR-confirmed reference cohort and established epidemiologic estimates.

Stage 2: Transportability Across Claims Datasets. Once developed and validated using the EHR-confirmed reference cohort, the amplification algorithm relies on claims-observable features—such as longitudinal treatment patterns, drug sequencing, utilization, and comorbidity profiles—to identify likely RRMM patients. The algorithm can therefore be applied to other claims datasets without requiring patient-level linkage to EHR or clinical notes in each new source, provided the required variables are available. We evaluate consistency in population estimates across claims assets and define the validation and recalibration required when data coverage, structure, or completeness differs. This transportability supports scalable payer analyses, field force sizing, and patient-finding programs without dependence on EHR availability in every target dataset.

Stage 3: Earlier Prediction. The most forward-looking application of this framework is predictive: using treatment sequencing patterns and clinical signals present in the data 6 to 18 months before a confirmed RRMM designation, we show it is possible to identify patients who are likely approaching relapse before they are formally coded as refractory. This earlier identification window has direct implications for patient support programs, clinical trial recruitment, and pre-launch commercial strategy.

Together, these three stages constitute a replicable, defensible methodology for commercial teams, market access analysts, and real-world evidence researchers working in hematologic malignancies and beyond. The session will include explicit decision criteria for when amplified populations are appropriate for decision-grade versus regulatory-grade use cases.

Learning Objectives:

• Apply a linked multi-source data amplification framework to expand RRMM patient cohorts beyond EHR-identified populations using digital twin matching logic
• Evaluate the transportability of patient amplification methodologies across real-world data sources and assess population estimate consistency
• Use longitudinal treatment sequencing signals to identify patients approaching relapse/refractory status earlier in the disease course, before formal diagnosis code confirmation

Attendee Engagement Strategy:

Open with a visual: a Sankey diagram showing patient attrition from diagnosed myeloma to EHR-visible RRMM, making the "missing patient" problem visceral before introducing the solution. Mid-session, run a structured audience prompt asking attendees to map where in their own workflows they would apply each of the three stages. Close with a Q&A anchored to practical barriers: data access, validation thresholds, and internal stakeholder communication.

A language model is fluent by construction — it sounds confident whether or not the knowledge behind it is current. In pharma, that's a real risk: marketing mix models, omnichannel targeting rules, and safety monitoring all draw from a shared knowledge layer, but each changes at a completely different speed. When governance doesn't account for that, agents produce fluent, convincing, wrong answers that pass review and propagate downstream. This poster presents a practical governance framework — calibrated to both risk and rate of change — built from real deployment experience across commercial and medical agentic AI in pharma.

Traditional launch signals such as activity volumes and prescribing trends, often provide visibility only after significant lag. However, the earliest indicators of launch success emerge across customer engagement quality, field execution, market access, patient support, and customer feedback, and are typically fragmented across multiple systems and functions.

This presentation introduces an AI-led Launch Excellence Engine that creates a unified insight-to-action capability by connecting and interpreting these early launch signals. Leveraging specialized AI agents focused on Performance Intelligence, Engagement Quality, and Customer Feedback, orchestrated through a cross-functional intelligence layer, the solution transforms disparate data into actionable insights and prioritized recommendations.

The approach enables launch teams to identify emerging opportunities and barriers earlier, optimize launch tactics, prioritize interventions, and make faster, data-driven decisions to improve execution precision and accelerate launch success.

A gross-to-net trend for the access team, a territory review for commercial operations, a contract term buried in a deck from two quarters ago - the answers already exist inside documents the organization owns. Yet the workflow is unchanged: someone files a request, it enters a data engineering queue, and the decision window closes before the answer arrives. This session asks whether that queue can be retired, and what engineering discipline is required before it safely can be.

Most teams attack this as either a retrieval problem or a generation problem. Neither framing survives contact with regulated commercial analytics. A deterministic pipeline preserves structure and provenance but cannot reason. An unconstrained language model reasons fluently but produces claims no compliance-conscious team can act on. Our contribution is not a new retrieval primitive; it is a deliberate architectural separation that assigns each concern to the layer that can actually guarantee it.

We present a ten-stage architecture in two halves. The first five stages are a deterministic data engineering substrate: batch ingestion with checksum-based deduplication, Markdown-first parsing across common office formats, near-duplicate and boilerplate removal, heading-aware chunking that preserves citation boundaries, and metadata tagging down to page, slide, and sheet. The remaining five stages are an agentic retrieval-and-generation layer: hybrid semantic and keyword retrieval over a single index, relevance reranking, citation-aware grounded prompting, per-claim source attribution with an explicit no-answer path, and end-to-end observability of every query. The design principle throughout: the language model is used only where reasoning is required, and never as the system of record.

We evaluated against a blind suite of 76 business questions curated by domain experts over proprietary commercial documents. The deterministic substrate alone, with no model reasoning, answered 12 of 76. The full grounded agentic pipeline answered 75 of 76, each answer citing an exact source location. The lift came from the separation itself - a provenance-preserving substrate is what makes the reasoning layer trustworthy enough to deploy. We will walk through the single unanswered question and what it reveals about the boundary of the approach.

We also treat the uncomfortable parts directly. An explicit no-answer path is a product decision, not a technical default, and teams must be willing to accept "not found" over a plausible guess. Chunking choices silently determine citation quality. Answer accuracy is bounded by document coverage, so gaps in what gets ingested become invisible gaps in what can be known. And self-serve access to document insight raises access-control questions that must be resolved before rollout, not after.

The approach runs on the unstructured commercial and operational documents analytics teams already hold, generalizes across formats without document-specific workflows, and remains model-agnostic as underlying models change. Attendees leave with a stage-by-stage architectural blueprint, an evaluation design they can replicate on their own document corpus, and a candid view of what shortens the analyst queue versus what merely moves it.

Pharmaceutical landscape assessments require analysts to reconcile evidence across claims, literature, trial registries, and syndicated sources. Differences in indication names, disease subtypes, and coding can change the meaning of an analysis before synthesis even begins.

This poster presents an architecture for landscape intelligence that combines a knowledge graph and semantic mapping layer with agent-assisted workflows for landscape synthesis, literature review, and patient analytics. It explains how harmonized context can connect evidence on disease burden, treatment patterns, pipeline activity, and pricing and access to downstream work such as target product profile development and forecasting.

An illustrative example will follow an indication through source mapping, evidence extraction, and synthesis. The poster will examine where deterministic rules are useful, what humans need to review, and how source references and validation checks can help identify errors before they affect subsequent outputs. Attendees will leave with a practical framework for evaluating the data foundation, review responsibilities, and evidence needed to assess an agent-assisted landscape intelligence capability.

From Periodic Readouts to Always-On Intelligence, an Agentic AI Approach to Market Mix Modeling.

Pharmaceutical commercial organizations run on fragmented systems: CRM knows interactions, IC platforms know payouts, forecasting tools know projections, and BI dashboards know past performance. But no system holds a persistent, shared understanding of who a territory, HCP, or rep is, what has happened to them over time, and where they are headed. The result is organizational amnesia: a rep who missed goal by 10% looks identical in a dashboard whether they are four months into a tough new territory or a tenured performer genuinely trending down. This session introduces a simple, three-part identity framework designed to close that gap.

Why can the same business question produce different answers across the same enterprise?

This poster will uncover how fragmented metric definitions, unresolved entities, inconsistent ontology mappings, and entitlement rules can quietly undermine the consistency, trust, and auditability of enterprise AI. It explores how a shared context layer, built on semantic governance and a pharma-native knowledge graph, can give conversational and agentic AI a common understanding of business definitions, entities, relationships, and access controls.

The poster will also show how context engineering can create a reusable foundation for more consistent, explainable, and governed AI responses across commercial use cases, while reducing reconciliation effort and incremental agent build effort.

Field teams are where life sciences strategy meets reality, yet the information they need is often distributed across CRM platforms, operational systems, analytics tools, and dashboards they rarely have time to access. AI assistants promise to close this gap by delivering insights in natural language at the moment decisions are made.

This session draws on real-world experience designing, evaluating, and operationalizing an AI-powered assistant for field-based commercial and access teams. The evidence base includes analysis of user interactions, evaluation results across multiple testing cycles, and the design decisions informed by those findings.

Analysis of user queries revealed that a significant portion focused on understanding the business environment: key accounts, stakeholder dynamics, and market developments. Another major category centered on planning and execution, including account prioritization, territory management, and progress against objectives. Remaining interactions involved performance monitoring and managing activities through to resolution. Collectively, these findings suggest that users engage AI assistants throughout their workflow, from situational awareness and planning to execution and follow-through.

One of the most important engineering lessons was that prompt optimization alone delivered limited gains. Sustained improvements came from strengthening the underlying context layer: establishing robust data models, improving metadata management, and creating clear mappings between user terminology and enterprise data assets. We also found that users routinely rely on implicit context, expecting assistants to understand concepts such as ownership, organizational scope, and reporting periods without explicitly stating them. Effective solutions therefore require operational context to be incorporated by design.

Achieving production readiness required more than generating accurate responses. Domain adaptation techniques were necessary to address specialized business terminology and use cases. Governance controls were implemented to ensure responses aligned with appropriate business roles and responsibilities. Observability capabilities enabled end-to-end traceability, while structured user testing surfaced failure modes that were not apparent in internal evaluations.

A governed Analytics Ready Data foundation allows Medical Affairs to move from fragmented reporting toward continuous, evidence-driven scientific insight generation, improving the quality, speed, and consistency of decision-making. This is what data-to-decisions demands in a scientific setting: agentic AI is only as trustworthy as the data layer beneath it, and the traceability that makes an insight auditable is what makes a medical decision defensible.

What does it take to evolve a traditional omnichannel analytics ecosystem into an agentic omnichannel decisioning engine?

This poster will uncover how a harmonized KPI taxonomy, unified measurement framework, causal experimentation, and agentic workflows can work together to move commercial teams from descriptive reporting to continuous, prescriptive decisioning. It will also explore how monitoring, diagnostic, recommendation, and orchestration agents can shorten the path from campaign signal to action, while human-in-the-loop governance helps maintain trust, control, and scalability across brands and business units.

In the pharmaceutical industry, commercial decisions rest on data that arrives continuously from external sources and degrades quietly between deliveries. For years, the most reliable safeguard against that degradation was informal: an experienced analyst looked at an output and recognized that something appeared wrong. As organizations move toward agentic AI, that safeguard disappears. An agent does not hesitate, has no sense of what a period should look like, and will act on a flawed figure and explain it fluently. Presently, most quality assurance still relies on rule-based validation, including null counts, range checks, and referential integrity. These checks only catch the failures someone anticipated well enough to write a rule for. The defects that damage commercial decisions are usually the ones no rule existed for.

This approach treats data quality management as an engineered, AI-enabled capability, addressing three failure modes that rule-based checks routinely miss:

• Detecting Incomplete Deliveries through Forecast-Based Expectation: A period lands partially delivered, is trended anyway, and reads as a business decline, sending teams to investigate a drop that never happened. Time-series forecasting of expected volume converts under-delivery and reporting lag into an explicit signal, available before any analysis runs on partial data rather than discovered weeks later.
• Catching Silent Drift through Baseline-Relative Quality Scoring: Quality erodes gradually. No single delivery breaches a fixed threshold, yet the asset is materially worse than it was a year ago. Scoring every governed dimension (completeness, uniqueness, validity, cross-field consistency, temporal ordering) against a stable baseline, as well as in absolute terms, allows degradation to be detected as a delta rather than a violation.
• Isolating Buried Defects through Multi-Level Anomaly Detection: Aggregates look normal while a small set of records carrying implausible values distorts a segment, a forecast, or a downstream payout. Unsupervised outlier detection and distributional comparison form a cascade that move from total volume, to segments such as geography, specialty, product, and payer, down to the individual records driving the deviation.

Equally important is how findings are delivered. The same quality core must serve an executive who needs a defensible ship-or-hold decision, an analyst who needs diagnostics granular enough for root cause, and a data operations team that needs evidence specific enough to take to a vendor.

This presentation will cover the design, development, and implementation of the entire capability, including real-world examples, the maturity path from reactive checks to monitored drift to predictive quality. It will also offer a candid view of what proves difficult in practice: an anomaly is not an error, and threshold design matters more than algorithm selection. In addition, it will examine the organizational constraint by clarifying who holds the authority to stop a delivery.