AI workflows for the performance reviews, forecasts, launch cases and brand decisions your team already supports.
Built for country, regional and global pharma Insights & Analytics teams working on monthly and quarterly business reviews, annual planning and forecasting, launch business cases, competitor impact estimates, market research synthesis and brand strategy support.
Use enterprise-approved AI to prepare, challenge and communicate the work — while analysts retain ownership of data quality, assumptions, models, forecasts and final recommendations.
Works with enterprise-approved AI environments
- Microsoft Copilot
- ChatGPT
- Claude
- Gemini
- Internal & other approved AI
The same workflow method works across platforms. Your company’s tool, data-use and review policies remain the authority.
- MBR / QBR
- Annual planning
- Forecast updates
- Launch business cases
- Competitor impact
- ATU and segmentation
- Brand strategy
- Leadership readouts
Same decision cycles. Different level of responsibility.
The cycles are shared. What a country partner owns and what an above-market partner owns are not the same job.
Diagnose one brand or market, own the local performance narrative and the action implications for the brand team.
Compare and challenge markets on common definitions and prepare the regional or global leadership narrative.
Build and update local forecast assumptions, gap-to-target logic and the local plan narrative.
Consolidate forecasts, harmonise assumptions and definitions and run the affiliate challenge.
Prepare the local launch case, uptake assumptions and readiness view.
Consolidate launch cases across markets and support sequencing, portfolio and investment decisions.
Estimate local impact on patient flow, share and volume, and the brand response.
Separate transferable patterns from local exceptions and align the cross-market response view.
Translate ATU, segmentation and qualitative research into local brand choices and tactics.
Harmonise research read-across, challenge country interpretation and protect comparability.
Support GM and cross-functional brand-team decisions with evidence-backed options.
Support portfolio, market prioritisation and resource-allocation decisions.
The team is not short of analyses. It is short of time to prepare, challenge and explain the decisions behind them.
Review cycles rebuilt each month
Sales, market, patient, access, execution and field inputs need to be reconciled before the team can explain performance.
Assumptions are difficult to challenge consistently
Forecast, launch and competitor assumptions sit across models, decks, emails and local narratives.
Uncertainty is compressed too quickly
Leadership wants one answer even when uptake, access, competitor timing or patient-flow impact remains uncertain.
Research does not automatically become strategy
ATU, segmentation and qualitative findings often stop at findings rather than changing positioning, targeting, tactics or forecast assumptions.
Above-market teams struggle to compare like with like
Definitions, KPIs, assumptions and market contexts differ, making affiliate challenge and consolidation harder than the final deck suggests.
PharmAI Pro helps analysts prepare the first structured view faster, make assumptions and uncertainties visible, and spend more time challenging the recommendation with the business.
Who this track is built for
- Country and affiliate Insights & Analytics business partners
- Regional, international and above-market I&A business partners
- Performance analytics and forecasting leads
- Market research, ATU and competitive-intelligence professionals
- Commercial Excellence and brand analytics teams
- Data-capable analysts building Excel, SQL, Python, automation or dashboards
Six recurring decision cycles
One core body of work: the commercial Insights & Analytics cycles a team already owns. Supporting methods sit inside each cycle, never above it.
| Workflow chain | Named workflows included | What it helps teams do |
|---|---|---|
| Monthly and quarterly performance reviews | Monthly / Quarterly Business Review Pack · Performance Driver and Variance Narrative · Risk, Opportunity and Action Register · Leadership Readout and Anticipated Q&A | What changed versus forecast, target and prior period, which factors explain it, whether the variance is temporary or structural, and what the brand team or GM should do. |
| Annual planning and forecasting | Forecast Assumption Book · Base / Upside / Downside Scenario Pack · Risk and Opportunity Register · Gap-to-Target Narrative · Affiliate Challenge Pack | Which assumptions changed, what explains the gap to target, what the scenarios imply and which risks or opportunities require action or investment. |
| Launch business cases and launch readiness | Launch Market-Sizing and Patient-Funnel Model · Uptake and Peak-Sales Assumption Pack · Launch Scenario Business Case · Launch KPI and Decision-Trigger Framework · Leadership Investment Readout | What the addressable patient opportunity is, which uptake curve is credible, which access, channel, capacity and competitor assumptions matter and which milestones show the launch is on track. |
| Competitor impact estimation | Competitor Event Impact Assessment · Patient-Flow and Market-Share Scenarios · Impact Range and Uncertainty Register · Monitoring Triggers · Brand Response Recommendation | What a new entrant, label change, trial readout, guideline update, access decision or price move means for the brand, which patients are affected, when impact appears and what response is justified now. |
| Market research synthesis and brand strategy support | Market Research Synthesis · Segment and Customer Implication Map · Brand Strategy Implication Readout · Forecast and Positioning Assumption Updates · Evidence-Gap and Further-Research Questions | What ATU, segmentation, qualitative research and advisory-board findings mean for the brand, what changes in positioning, targeting, tactics or forecast assumptions, and what remains unanswered. |
| Above-market cross-market challenge and portfolio support | Cross-Market Performance Comparison · Affiliate Assumption Challenge Pack · Market Archetype and Outlier View · Portfolio Prioritisation Readout · Regional / Global Leadership Narrative | Which differences reflect true market conditions and which reflect inconsistent definitions, which patterns transfer, which markets are outliers and where leadership should focus resources. |
What changed versus forecast, target and prior period, which factors explain it, whether the variance is temporary or structural, and what the brand team or GM should do.
Which assumptions changed, what explains the gap to target, what the scenarios imply and which risks or opportunities require action or investment.
What the addressable patient opportunity is, which uptake curve is credible, which access, channel, capacity and competitor assumptions matter and which milestones show the launch is on track.
What a new entrant, label change, trial readout, guideline update, access decision or price move means for the brand, which patients are affected, when impact appears and what response is justified now.
What ATU, segmentation, qualitative research and advisory-board findings mean for the brand, what changes in positioning, targeting, tactics or forecast assumptions, and what remains unanswered.
Which differences reflect true market conditions and which reflect inconsistent definitions, which patterns transfer, which markets are outliers and where leadership should focus resources.
From scattered monthly inputs to a brand-team performance narrative.
- 01Reconcile inputs
Sales, market, patient, access, execution and field inputs brought to one comparable view with stated definitions.
- 02Quantify the variance
Performance against forecast, target and prior period, split by the components that actually moved.
- 03Diagnose drivers
Driver tree decomposition with plausible explanations and the check that would confirm or reject each one.
- 04Risks, opportunities, actions
What is at risk, what is recoverable and which action belongs to which function.
- 05Leadership narrative
A short performance story for the GM and brand team, with remaining uncertainty stated.
The analyst validates the inputs, the driver logic and the final narrative.
Where AI helps. What the analyst still owns.
The boundary is set before the workflow runs, not after the output is produced.
AI can help the analyst
- Structure mixed inputs before a review
- Compare assumptions and identify inconsistencies
- Draft first-pass variance explanations and challenge questions
- Generate scenario logic and sensitivity questions
- Synthesise market research across sources
- Convert analysis into alternative leadership narratives
- Draft documentation, caveats and anticipated Q&A
- Accelerate a first analytical prototype in one agreed environment
The analyst still owns
- Source quality and data definitions
- Calculations, joins, formulas and model logic
- Forecast assumptions and scenario selection
- Interpretation of causality
- Market and brand judgement
- Reconciliation and QA
- Recommendation and decision framing
- Forecast governance and formal approval
- Deployment and production controls
What changes for the team
Shorter cycle preparation
Less time consolidating recurring inputs and rebuilding the same first drafts each month, quarter and planning round.
Reusable cycle assets
Assumption books, driver logic, registers and narrative structures carried from one cycle to the next.
More consistent QA
Evidence, caveats, test results and ownership stay visible in every cycle output.
Stronger business partnership
Analysts spend more time explaining performance, challenging assumptions and advising the brand team.
What participants produce
Reconciled inputs, variance view and the review narrative for the brand team and GM.
Structured decomposition of what moved, why it moved and what remains unexplained.
Assumptions written down so they can be challenged, with base, upside and downside logic.
Patient-funnel logic, uptake and peak assumptions and the investment narrative behind them.
KPI tree, milestones and the triggers that would change the launch plan.
Impact mechanism, scenario ranges, trigger points and the recommended brand response.
ATU, segmentation and qualitative evidence converted into segment, positioning, tactical and forecast implications, with the open questions kept visible.
Comparable affiliate view on common definitions, with patterns, outliers, prioritisation input and the leadership decision ask.
Advanced Analytics Builder Lab
A specialist module for data-capable analysts who want to use enterprise-approved AI to accelerate one defined analytical build while retaining full responsibility for testing, reconciliation, documentation and deployment controls.
Best for: Commercial analytics, business intelligence, forecasting and data-capable analysts who can read, test and validate what they build.
- Excel and Power Query
- SQL
- Python analysis and QC
- Automation / recurring analytical routines
- HTML dashboard or executive data view
| Workflow chain | Named workflows included | What it helps teams do |
|---|---|---|
| Frame the analytical question and asset design | Business Question Brief · Input & Grain Specification · Analytical Design · Acceptance Criteria | Define what the build must answer, at what granularity and how it will be tested. |
| Excel and Power Query model development | Model Architecture · Formula Development · Power Query Transformation · Scenario Tool Prototype | Accelerate a first structured build while keeping model logic visible and testable. |
| SQL development, debugging and documentation | Query Plan · SQL Draft · Debug & Optimisation Pass · Data-Lineage Notes | Move faster from question to reviewable query without hiding joins, filters or assumptions. |
| Python analysis, QC and recurring automation | Analysis Plan · Python Prototype · QC Routine · Reproducible Runbook | Create testable analytical routines with explicit validation and operating instructions. |
| HTML dashboards and executive data views | Dashboard Specification · HTML Prototype · Interaction Logic · Executive Summary View | Turn analytical output into a clear, portable decision interface. |
| Analytical testing, reconciliation and technical documentation | Test Plan · Reconciliation Log · Edge-Case Register · Technical Documentation | Make errors, caveats and ownership visible before an asset is relied on. |
Define what the build must answer, at what granularity and how it will be tested.
Accelerate a first structured build while keeping model logic visible and testable.
Move faster from question to reviewable query without hiding joins, filters or assumptions.
Create testable analytical routines with explicit validation and operating instructions.
Turn analytical output into a clear, portable decision interface.
Make errors, caveats and ownership visible before an asset is relied on.
Module outputs
What the build must answer, with sources, joins, filters and output grain.
A first reviewable build in one agreed environment — not a production asset.
Defined checks, expected values and the reconciliation record behind them.
Known limitations, exceptions and dependencies kept visible.
How the asset is run, maintained, reviewed and handed over.
Not a coding bootcamp. One primary environment is selected before the session. Prototypes are not production assets until normal company validation, review, ownership and deployment controls are complete.
Reusable support assets
Support assets are the reusable scaffolding behind the outputs — provided with the masterclass format.
- Decision-Cycle Workflow Architecture
- Cycle Selection Guide
- Evidence & Traceability Checklist
- Forecast Assumption and Scenario Templates
- Launch Business Case Template
- Competitor Event Impact Template
- Leadership Readout and Q&A Template
- Worked Example Pack
- Two-Week Implementation Path
Choose the depth that fits the team.
The same operating model at three depths. Format and scope are agreed before anything is booked.
30-minute Insights & Analytics Workflow Briefing
How AI can help the analyst answer what changed, why it changed and what the business should do next — without weakening evidence, QA or accountability.
- One decision cycle explained in analytics context
- One anonymised example
- Priority cycle discussion
- A recommended next step
Boundary: Not training. No workflow architecture or analytical asset is provided.
2-hour Insights & Analytics Workflow Inspiration & Prioritisation Session
Worked examples across the recurring cycles so the team can choose where to go deeper.
- Three to five worked examples
- Enterprise-safe application discussion
- Opportunity mapping against the team's real cycle calendar
- A prioritised cycle and workflow shortlist
Boundary: No complete workflow architecture, participant pack or production asset.
4-hour Insights & Analytics Masterclass + application follow-up
One decision-cycle masterclass, chosen before the session.
- One decision cycle selected before the session
- Selected workflows practised live
- Reusable cycle architecture and support assets
- Frame → Generate → Validate → Apply → Communicate applied throughout
- Anonymised case practice with QA checkpoints
- Two-Week Implementation Path and one shared application clinic
Monthly and quarterly reviews, driver diagnosis, annual planning, forecast assumptions and scenarios.
Market sizing, patient funnel, uptake assumptions, launch KPI logic and competitor event impact scenarios.
ATU, segmentation and qualitative synthesis, implications, strategic options and leadership readouts.
A 4-hour session focuses on one masterclass. The Advanced Analytics Builder Lab is scoped separately to one primary build environment; it does not cover Excel, SQL, Python and dashboards together.
Boundary: No live production database connection, confidential-file review, deployment, formal model validation or ongoing development support.
Private team delivery is available. Public Analytics cohorts are scheduled selectively.
Private sessions use standard anonymised PharmAI Pro material with bounded adaptation to the cycle, workflow priority and approved-tool context. Public dates appear only when a future session is confirmed.
Built for governed pharma environments
- Public sessions use synthetic or anonymised PharmAI Pro material and do not connect to live company databases.
- No patient-level data, restricted commercial data, confidential production credentials or uncontrolled data extracts.
- Internal application only in company-approved tools and environments, under company data classification and access controls.
- Analysts remain accountable for source quality, query and model logic, reconciliation, forecast governance, deployment and final recommendations.
- Prototypes are not production assets until normal validation, documentation, review and deployment controls are complete.
Questions teams ask
Built by a pharma commercial and analytics operator — not an AI generalist.
Michał Lubas is a senior pharma commercial, marketing analytics, forecasting and insights leader with global and regional experience. PharmAI Pro is built from recurring planning, synthesis, analytical and leadership workflows commercial teams already run.
