Question 1

What is agentic analytics and how is it different from the BI dashboards we already use?

Accepted Answer

Traditional BI dashboards are passive tools that wait for someone to open them, filter views manually, and figure out what changed through manual exploration. Agentic analytics uses AI agents that work continuously without human intervention: they monitor hundreds of metrics automatically, detect meaningful shifts using statistical methods, automatically drill into the data to understand why changes occurred, and deliver explanations to the right stakeholders before the next meeting even happens. The technical difference is goal-oriented autonomy where agents operate with specific objectives like keep brand X NBRx within expected range or flag any payer with denial rate increase over 15 percent. They pursue those goals 24/7 through continuous sensing, automated analysis, decision-making based on configured rules, and action including alerts or automated responses. Dashboards don't do any of this; they only display what you manually request when you remember to check them. This shift from passive reporting to active intelligence is fundamental because it means problems are detected in days, not discovered weeks later in quarterly reviews when response options have narrowed significantly.

Question 2

How does AI agent development help automate routine analytics tasks in pharma companies?

Accepted Answer

AI agents automate routine analytics by using intelligent systems to handle repetitive processes like variance analysis, weekly brand performance reports, territory comparisons, and payer access monitoring that currently consume 60-70 percent of analyst time each week. These agents learn patterns over time through machine learning, improving efficiency by detecting which metrics matter most for decision-making, which investigation paths typically yield actionable insights, and which stakeholders need which types of alerts based on their roles. This learning reduces manual report production from 40 hours per week to maybe 10-15 hours of oversight and validation. Instead of analysts rebuilding the same TRx decomposition analysis every Monday morning, pulling data and creating slides manually, the agent runs it automatically overnight, identifies the key drivers using statistical decomposition, and has a complete narrative ready when teams arrive at the office. This frees analysts to focus on strategic questions that require creativity, business judgment, and cross-functional collaboration rather than repetitive data pulling and formatting work that machines handle better.

Question 3

Can you give a concrete example of an agentic workflow in a commercial pharma context?

Accepted Answer

An agent monitoring weekly NBRx by payer detects that starts from a major regional health plan dropped 18 percent compared to expected baseline, automatically triggers root cause analysis without waiting for human direction by checking prior authorization volumes for unusual patterns, queries payer policy databases and identifies that the plan added new step therapy requirements on a specific date three weeks ago, quantifies business impact by modeling expected versus actual NBRx over the next 90 days if the restriction persists, and generates a narrative summarizing the change with specific next actions tagged to the right stakeholders including market access team and regional sales leadership, all within 48 hours of the anomaly first appearing in data. Tellius automates this exact workflow where once you configure the NBRx monitoring parameters and investigation logic, the system executes all these investigation steps without human intervention and delivers the final brief automatically through email or embedded in your BI tool. Compare this to 2-3 weeks in a manual process where someone first notices the dip in a weekly review, requests a deep dive from analytics, waits for analyst bandwidth to become available, and schedules cross-functional meetings to decide what to do by which time the problem has compounded.

Question 4

What does 24/7 monitoring actually mean in technical terms?

Accepted Answer

It means scheduled or event-driven execution of monitoring workflows that run without human initiation, where an agentic platform continuously executes background jobs every few hours or in real-time, checking 200 or more predefined metrics against historical baselines and expected ranges calculated from statistical models. When a metric moves outside its normal band accounting for seasonality and known patterns, the system triggers investigation workflows that decompose the metric by relevant dimensions like geography and payer, correlate changes with known events like formulary changes or promotional campaigns, rank potential drivers by statistical significance, and assemble summaries with supporting data. Tellius implements this through two workflow types: automated workflows where Tellius Kaiya, the conversational AI, auto-plans the multi-step, multi-join analysis and executes it step-by-step without any pre-configuration beyond defining monitoring objectives; and user-defined workflows where you describe in natural language what analysis steps you need and it executes based on your instructions and trigger prompts. The 24/7 aspect means these workflows happen whether you're in meetings, on vacation, or asleep at night, with no human actively looking at dashboards and deciding what to investigate, because the agent has predefined objectives and logic that execute continuously.

Question 5

How is this different from just setting up automated alerts in our current BI tool?

Accepted Answer

Traditional BI alerts trigger when a single metric crosses a manually-set threshold like email me if TRx falls below 10,000 prescriptions, without investigating why the change occurred, adjusting for seasonality or weekly cycles, or distinguishing meaningful changes from random noise. This is why IT teams handle 4,484 alerts daily on average with 67 percent ignored due to false positives that erode trust. Agentic analytics uses contextual anomaly detection that learns normal patterns including weekly cycles, seasonal effects like holiday impacts, and correlations with other metrics, only alerting when something is statistically unusual given full historical context. Agents don't stop at detection but automatically drill into which segments drive the change geographically or by payer, test multiple potential explanations using data, and rank likely causes by contribution before sending anything to humans. The output transforms from TRx is down 8 percent, go look at a dashboard yourself, to TRx declined 8 percent driven by 23 percent Mid-Atlantic drop coinciding with prior auth denial increases from two major payers in that geography, unrelated to field activity which remained stable, recommended action: escalate to payer team to investigate policy changes immediately.

Question 6

What types of business questions can agentic analytics answer without me asking?

Accepted Answer

Agents detect and explain changes, risks, and opportunities across core commercial metrics without waiting for questions. They can show which payers are tightening access compared to 60-plus days ago with projected TRx impact quantified by plan and product. Whether high-value HCP segments are slowing their prescribing velocity and how that correlates with competitor activity or field coverage gaps. If recent formulary wins actually translated into NBRx growth or if pull-through is still below expectations despite improved coverage. Which territories underperform relative to opportunity and whether the drag comes from access barriers, execution gaps, or data-quality issues that need fixing. Whether patient drop-off rates are increasing at specific journey steps such as benefits verification or prior authorization where intervention could help. If the current quarter is tracking off budget across regions with segment-level explanations of where and why performance is off-plan. The agent constantly monitors for deviations from expected performance across these dimensions, investigates root causes when it finds them using predefined analytical logic, and packages the results into decision-ready summaries without waiting for someone to notice problems in a weekly review when response time has already been lost.

Question 7

Do I still need analysts if agents handle investigation work?

Accepted Answer

Yes, but their role shifts from spending 60-70 percent of time on repetitive production work including pulling data, building variance reports, creating monthly decks, and answering one-off ad-hoc questions, to focusing on higher-value work like designing new metrics that capture strategic priorities, building causal models that explain relationships, validating agent findings to ensure accuracy, and solving complex strategic questions that require business context, creativity, and cross-functional negotiation that machines can't replicate. A good rule is that if you've asked the same analytical question three or more times using similar logic, it should become an automated agent workflow freeing analyst capacity. While questions you've never asked before requiring synthesis with qualitative field inputs or competitive intelligence that isn't in data still need human analysts to frame and answer. Data scientists also tune agents by setting confidence thresholds that balance false positives and negatives, validating anomaly detection models against known events, reviewing false positive and negative rates to improve precision, and encoding business logic so agents make decisions aligned with how the organization actually operates rather than optimizing in ways that violate business constraints.

Question 8

How does anomaly detection work in pharmaceutical data with all its seasonality and market noise?

Accepted Answer

Anomaly detection in pharma accounts for weekly cycles where prescriptions typically drop on weekends, holiday volume dips during Thanksgiving and Christmas, and specialty-specific seasonal behaviors like allergy medications peaking in spring, using time-series models that learn these patterns from historical data automatically. So a system knows a 12 percent TRx drop during Thanksgiving week is normal expected behavior but the same drop in mid-March probably isn't and requires investigation. The technical approach combines statistical models like ARIMA or exponential smoothing that forecast what metrics should be based on history, machine learning models detecting subtler patterns by considering metric interactions across dimensions, and incorporation of known events like formulary changes or territory realignments so expected changes aren't treated as anomalies requiring investigation. The output is confidence scores where if TRx drops 6 percent when the model expected 5 percent decline, that's probably noise within normal variation, but if TRx drops 15 percent when the model expected 2 percent increase, that's high-confidence anomaly triggering investigation. Systems use configurable thresholds aiming for low false positive rates because alert fatigue destroys trust faster than missing a few true anomalies.

Question 9

What is alert fatigue and how do agentic systems avoid it?

Accepted Answer

Alert fatigue is what happens when monitoring systems generate so many notifications that people stop paying attention to any of them. In many enterprise setups, 67 percent of alerts get ignored because they're mostly noise rather than signal, and 90 percent of security operations teams report being overwhelmed by false-positive backlogs that consume time without value. Agentic systems reduce alert fatigue by fundamentally changing how alerts are created and delivered through three mechanisms. First, contextual anomaly detection not static thresholds means alerts fire only when something is statistically unusual given full history and patterns, not just crossing arbitrary numbers. Second, impact filtering where only issues that materially affect business outcomes get escalated; a 15 percent swing in a tiny segment might be real statistically but financially irrelevant, so the agent logs it without interrupting anyone. Third, narrative consolidation where related signals get combined into a single brief instead of separate alerts for every correlated metric that moved. The outcome is fewer, higher-quality alerts shifting from approximately 50 alerts per week that nobody reads to 3-5 investigated briefs that explain what changed, why it likely changed, and what actions to consider, delivered only when confidence and impact thresholds are met.

Question 10

Can agentic analytics distinguish between data quality issues and real business changes?

Accepted Answer

Yes, good agentic systems are designed to do this because pharma data often contains false signals from reporting lags, incomplete pharmacy coverage, territory mapping errors, and vendor restatements that change historical numbers. To avoid misclassifying noise as business movement, agents run data quality checks as part of the monitoring workflow before concluding a real change occurred. Example: if NBRx drops 20 percent in a territory, the agent should verify is Rx data still flowing from the usual pharmacy sources or are feeds missing or late, were there territory alignment changes that reassigned accounts and made it look like volume moved artificially, and did the data vendor issue restatements that changed historical values making comparisons invalid. If those checks fail, the system flags a data issue not a business issue and routes it to data operations instead of escalating to the brand manager who can't fix data problems. Technically, this requires bringing data lineage and quality metadata into the analytics layer so agents can reference freshness timestamps, row counts by source, join match rates, and historical restatement patterns. When confidence in the data is low, the agent either holds the alert until data stabilizes or explicitly notes quality concerns in the narrative.

Question 11

How granular should monitoring be? Should agents watch every HCP, payer, and territory individually?

Accepted Answer

It's a tradeoff between coverage and noise. Monitoring 10,000 HCPs one-by-one creates a flood of mostly meaningless signals where random variation dominates, but watching only national totals hides localized problems until they're huge and harder to fix. The practical approach is hierarchical monitoring that keeps alerts manageable while still catching local issues early. Agents watch high-level aggregates continuously at national or regional level and only drill into finer segments when something looks off at the higher level, avoiding unnecessary granular monitoring when things are normal. Example flow: monitor national NBRx for anomalies, then decompose to region when detected, then to territory within that region, then to HCP decile to find the specific segment causing it. If overall metrics are stable, nothing escalates and no one is bothered. If national NBRx drops 8 percent, the agent automatically decomposes to find the driving region, then the specific territories within that region, then the HCP segments causing it, delivering a targeted explanation instead of overwhelming users with thousands of individual HCP alerts most of which are noise.

Question 12

Can agents detect unexpected opportunities, not just problems?

Accepted Answer

Yes, and it's one of the most underused applications of agentic analytics. Most monitoring is built to catch negatives like TRx drops, denials rise, or quota misses, but agents can also flag positive anomalies such as regions where NBRx accelerates faster than expected without obvious explanation, HCP segments showing unusual uptake that could be amplified, and payers with better pull-through than historical norms suggesting successful coverage or education. The value is spotting what's working so you can amplify it before the window closes. If one territory grows 30 percent while similar territories stay flat, the agent can investigate why using the same decomposition logic, and you can scale that insight by applying learnings to similar situations before the competitive landscape changes. Technically, it's the same anomaly detection logic just applied to upside deviations not only downside risk, with thresholds tuned to flag statistically significant positive movements. Platforms like Tellius can be configured to flag both and generate narratives that clearly separate here's a problem to fix versus here's a success to replicate, giving commercial teams a more balanced view.

Question 13

What happens when an agent detects something ambiguous or cannot determine root cause with high confidence?

Accepted Answer

This is where graduated autonomy and human-in-the-loop design matter critically. If an agent detects an anomaly but root-cause analysis produces multiple plausible explanations without clear statistical evidence favoring one, it should escalate to a human instead of guessing or picking randomly. Example: an agent sees a 10 percent TRx drop, but field activity, payer policy shifts, and competitive dynamics all look normal in the data, so none of the standard drivers explain it confidently. Since it can't attribute the drop to one cause with high confidence, it should generate a diagnostic report summarizing what it checked and found normal, explicitly flag the ambiguity and low confidence, and suggest next steps like verify data quality for missing feeds, ask field teams for qualitative context about what they're seeing, or wait another week to see if the pattern persists or reverses. This transparency about uncertainty prevents agents from delivering confident-sounding but wrong explanations that damage trust when leaders act on them. The system should clearly distinguish high-confidence findings that can drive action from low-confidence findings that need investigation.

Question 14

What is a multi-agent architecture and why does it matter for pharma analytics?

Accepted Answer

A multi-agent architecture breaks a complex analytics workflow into specialized agents that each handle one part well, then coordinate to produce a complete answer instead of one monolithic system trying to do everything. Instead of a single agent, you get an agent team with defined roles such as data retrieval agent that pulls and stages data, anomaly detection agent that flags unusual patterns, root cause analysis agent that investigates drivers, visualization agent that creates supporting charts, and narrative generation agent that produces plain-language summaries. Commercial pharma analytics is inherently complex requiring multiple data sources, statistical methods, and business logic rules. Multi-agent setups can parallelize work so if the payer-data agent hits a delay, the rest of the workflow can still run using available data while clearly flagging what's missing instead of breaking silently. The technical advantage is modularity where you can improve or swap one agent without rebuilding the entire system, making the platform easier to maintain and extend as new requirements appear or better models become available, compared to monolithic systems where everything is tightly coupled.

Question 15

How do agents validate their findings before alerting humans?

Accepted Answer

Validation usually happens in layers so weak inputs or shaky conclusions don't get pushed to stakeholders. First, data validation before analysis runs checks that inputs meet quality standards including row counts matching expectations, missing values in key fields below thresholds, freshness timestamps within acceptable ranges, and join match rates above minimums, and if data quality is poor the agent doesn't proceed with analysis. Second, statistical validation tests whether an anomaly is significant by using confidence intervals around the change, comparing the change magnitude to historical volatility to distinguish signal from noise, and running multiple detection methods where if three models flag the same anomaly confidence is high but if only one flags it confidence is low. Third, business logic validation checks for logical contradictions where a validation agent reviews the logic chain to ensure conclusions make sense, for example flagging if the system claims TRx dropped due to access but approval rates actually improved. Platforms like Tellius implement these as separate agents in the workflow so conclusions pass multiple checks before reaching humans, and validation results are logged so users can inspect what was tested and how the conclusion was reached.

Question 16

What agent roles are typically needed in a pharmaceutical commercial analytics workflow?

Accepted Answer

A robust system uses specialized agents working together in orchestrated workflows. Tellius agentic analytics architecture follows this multi-agent approach, typically including planning agent that receives a query or monitoring trigger, breaks it into sub-tasks, decides sequence and dependencies, and coordinates other agents; data prep agent that pulls data from source systems, performs joins across tables, handles missing data, and stages data for analysis; analysis agent that decomposes metrics by dimensions like region, payer, and specialty, tests correlations with known events, ranks likely drivers, and applies statistical and machine learning models to detect unusual metric patterns; validation agent that checks data quality, statistical significance, and logical consistency before escalating findings to humans; visualization agent that generates supporting charts and graphs and selects the right chart types for the data and message; summarization agent that produces plain-language summaries explaining impact and suggesting next actions for business users; and optional action agent that executes pre-approved actions like updating forecasts, triggering field alerts, or creating CRM tasks when confidence thresholds are met. This division of labor lets each agent be optimized for its specific task and makes the system easier to test, maintain, and explain compared to monolithic approaches.

Question 17

How do agents handle pharma-specific business logic like calculating adherence or categorizing patient journeys?

Accepted Answer

Agents use a semantic layer and agent instructions that encode domain knowledge. The semantic layer is governed business metadata that defines pharma metrics, hierarchies, and relationships centrally, for example NBRx excludes refills and samples, adherence uses PDC calculation with specific rules, and payer hierarchies map plans to parent organizations. This ensures consistency where every agent uses the same definition so answers don't vary by which agent answered. It also improves maintainability because if the business changes how it defines NBRx or adherence, you update the semantic layer once instead of rewriting agent code everywhere. Adherence calculation example: when an agent calculates adherence, it calls the semantic definition such as adherence equals days covered by prescription fills divided by days in measurement period times 100, using the proportion of days covered PDC method. Patient journey categorization example: the semantic layer defines journey stages like prescription written, benefits verified, prior authorization submitted, approved, first fill, second fill, abandonment, and defines which data fields signal each transition so agents categorize consistently. Tellius implements this through its semantic layer that agents query for definitions rather than hardcoding business logic.

Question 18

Can agents execute actions automatically or do they only generate recommendations?

Accepted Answer

It depends on the confidence level and the risk profile of the action being considered. Low-risk, high-confidence actions can be automated such as updating forecast models when actuals arrive using standard refresh logic, flagging accounts in CRM for rep follow-up when prescribing drops, generating and sending routine performance summaries to field teams on schedule, triggering data quality alerts to the analytics team when feeds are late, and logging issues in ticketing systems for tracking. High-risk or ambiguous actions should require human approval such as changing quota assignments that affect compensation, adjusting incentive compensation payout calculations, recommending budget reallocations across brands, and suggesting payer contract terms that have financial implications. Tellius supports both modes through automated workflows where the system decides and executes actions based on business rules and confidence thresholds you configure, and user-defined workflows where you describe the step-by-step actions and triggers in natural language to Kaiya which builds and runs the workflow accordingly. The implementation uses graduated autonomy with explicit approval gates where some actions auto-run if confidence is high enough, while others require a manager to click approve before executing.

Question 19

How does an agentic analytics platform integrate with existing pharma data infrastructure?

Accepted Answer

Integration typically happens at two layers: the data layer for reading and the action layer for writing outcomes. For the data layer, the platform connects to sources using standard methods like SQL connections to data warehouses, APIs to SaaS tools like Veeva CRM, and file imports for specialty data feeds. The key architectural principle is the platform sits on top of your existing infrastructure not replacing it, so your warehouse or lakehouse remains the system of record and single source of truth. The platform queries that data, may cache common aggregations for performance, and stores its own metadata including agent configs, validation logs, and alert history in a separate schema, so deployment usually doesn't require migrating data or rebuilding pipelines. For the action layer, agents can write back via APIs to operational tools like CRM for flagging accounts, marketing automation for triggering campaigns, and ticketing systems for creating tasks. Write-back is configured per use case and not every agent needs write access; many agents are read-only and just generate insights. Architecturally, Tellius follows this pattern where it sits as a semantic plus intelligence layer over your existing warehouse or lakehouse, federates queries to source systems, applies analytics and AI models, and delivers outputs via conversational Q&A, scheduled reports, and proactive alerts.

Always-On Intelligence for Pharma Growth

What is AI analytics in Pharma?

Instant answers + proactive alerts for pharma commercial teams

Field Force Effectiveness: Overview

The ROI Behind Faster TRx Decisions

How AI AnalyticsChanges the Game

What’s inside this guide

Navigate Your Questions

Part 1: Understanding AI Analytics

1. What is AI analytics for pharma commercial teams?

2. What’s the difference between AI analytics, conversational analytics, and agentic analytics?

3. How is AI analytics different from traditional BI tools like Tableau or Power BI?

4. What types of questions can AI analytics answer for pharma commercial teams?

5. Can non-technical users actually use AI analytics without SQL or analyst support?

Part 2: Strategic Evaluation & Alternatives

1. When does it make sense to keep using traditional BI instead of adopting AI analytics?

2. How does AI analytics compare to Databricks, SageMaker, or DataRobot?

3. Should we build custom AI analytics on open source tools or buy a commercial platform?

4. Should we consolidate on one AI analytics platform or use best-of-breed tools across functions?

5. What should we evaluate during an AI analytics platform proof-of-concept?

6. What foundation is required before AI analytics works reliably?

7. What’s the right way to run a pharma AI analytics pilot?

Part 3: Org Transformation & Value Realization

1. What are the most common reasons AI analytics implementations fail in pharma?

2. How long before AI analytics delivers measurable value and positive ROI?

3: What internal resources and time commitment do we need for successful deployment?

4. How do we ensure AI analytics continues delivering value after deployment?

5. What does a realistic three-year AI analytics transformation roadmap look like?

AI Analytics built for every commercial pharma team

Brand Performance

Field Force Effectiveness

Patient Journey

Incentive Compensation

Market Access

⚡ Two Powerful Approaches to Analytics Transformation

Conversational for Questions You Know, Agentic for Questions You Don't

Conversational AnalyticsAsk → Answer → Drill Down

Foundational AI Capabilities:

Agentic AnalyticsDetects → Investigates → Briefs

Agentic Capabilities:

How Tellius Transforms Field Force Effectiveness

Unified HCP Intelligence

Agentic RCA

AI Agents for Pre-Call Planning

Next-Best-Action Engine

Always-On Territory Monitoring

Automated Business Reviews

Pharma-Specific Semantic Layer

Explore Related Resources

Brand Performance Analytics

Market Access Performance

HCP Targeting & Segmentation

AI Analytics for Pharma

Agentic Analytics

Turn Field Insights Into Action—Faster Than Ever

Navigate Your Questions

The Challenge Every Pharma Commercial Leader Faces

The Problem Isn't Lack of Data — It's Lack of Insights

Agentic Analytics Changes Everything

Introducing Pharma Intelligence

📊 Analytics Solutions for Every Commercial Function

📊 Analytics Solutions for Every Commercial Function

Field Force Sales Effectiveness

Critical Capabilities:

Brand Performance & KPIs

Critical Capabilities:

Market Access Performance

Critical Capabilities:

Contracting & Payer Strategy

Critical Capabilities:

HCP Targeting & Segmentation

Critical Capabilities:

⚡ Two Powerful Approaches to Analytics Transformation

⚡ Two Powerful Approaches to Analytics Transformation

Conversational Analytics & AI Foundations

Foundational AI Capabilities:

Agentic Analytics: AI Agents That Work 24/7

Agentic Capabilities:

Platform & Technology Questions

How Agentic Analytics Transforms Your Workflow

How AI Analytics
Changes the Game

⚡
Two Powerful Approaches to Analytics Transformation

Conversational Analytics
Ask → Answer → Drill Down

Agentic Analytics
Detects → Investigates → Briefs

📊
Analytics Solutions for Every Commercial Function

⚡
Two Powerful Approaches to Analytics Transformation

🎯
Understanding Challenges & Best Practices

🤖
AI Agents & Agentic Capabilities

💰
Platform Evaluation & Business Impact