Executive case study, AI sales transformation
AI-Powered Commercial & Sales Decision Infrastructure
How a Top-20 Global Pharmaceutical Company moved field-force decisions from static territory lists to causal, real-time HCP prioritisation.
A top-20 global pharmaceutical company with a significant Emerging Markets commercial footprint, dozens of brands, multiple field forces, and a large HCP universe spread across multiple EM countries.
CommercialAt this scale, small improvements in field-force prioritisation and commercial decision-making translate directly into top-line share and revenue outcomes, making Sales Force Effectiveness (SFE) a board-level priority, not just a field operations concern.
CommercialPartnered with DecisionX, an AI decision-infrastructure company for CPG & Pharma, trusted for 10,000+ monthly enterprise decisions.
Engagement Scope, Consultatively Partnering for Adoption
Where the platform is live
Analytics, Business & CoE teams engaged on Margin RCA, Sales Excellence, and HCP Intelligence, all validated.
Part of the multi-country EM footprint the platform now extends into, on the same shared infrastructure.
Commercial use cases run on the same infrastructure already proven on the marketing side of the business.
How We Engaged
Multi-stakeholder, consultative model from day one: Data & Analytics, the India Business Team, and CoE each had different definitions of value.
Co-build and iterative feedback loops: commercial and analytics stakeholders validated opportunity-scoring and HCP-prioritisation logic before it reached the field.
End users made stakeholders in the co-building process itself, a deliberate choice to drive adoption, not mandate it.
Trust built methodically: validation, explainable and traceable answers, and feedback loops, non-negotiable where "the AI said so" never satisfies a field manager or compliance officer.
As-Is Scenario, A Familiar Pattern Across Pharma Field Organisations
- No mechanism to surface leading indicators of underperformance, margin and share erosion visible only after quarter-close.
- Root-cause on margin/share misses required an analyst manually stitching data across systems for days.
- HCP targeting relied on static, once-a-year territory lists.
- Commercial data siloed across CRM, call logs, HCP databases, and external signals, no unified HCP profile.
- Which HCPs matter most, which reps need coaching, tribal knowledge held by field managers, not systems.
- Sales coaching leaned on activity counts, not the signals that predict prescribing outcomes.
- Generic LLMs hallucinate confidently without business and compliance context.
- A margin dashboard shows a number moved, it can't show which SKU or cost line caused it.
- Intelligence must compound across every RCA and coaching cycle, not restart from scratch.
Commercial Reasoning, Opportunity Finder / Root-Cause Analysis
A forward-looking opportunity identifier that drills from Brand–SKU down through Geography to product-level metrics, benchmarked against Plan, Competition, Last Year, and internal best-in-class performance.
Underperforming, high opportunity, focus here first.
In line, moderate opportunity, monitor & improve.
Outperforming, low opportunity, maintain & scale.
Sub-Functional Ontologies Underneath
Prescribing & Market Performance
TRx/NRx, market share, brand performance by territory/SKU vs. Plan, Competition, Last Year.
Margin & Commercial P&L
Pricing, discounting, mix, and cost modelled as a margin waterfall by SKU and geography.
Competitive Intelligence
Competitor volumes, pricing moves, share shifts, sizing the opportunity gap, not just the results gap.
Territory & Geography Hierarchy
Consistent roll-up/drill-down from national to regional to territory level.
Adoption, Real Usage Telemetry, Not Anecdote
Depth and Breadth, Two Different Adoption Stories
Active querying proves trust in the reasoning tool. Signal distribution at 6,000+ users proves the platform scales insight to an entire field force without training every user to query it directly.
Commercial leaders act on RCA and HCP recommendations without a data science briefing. Explainability and traceability were built in from day one, essential for adoption in a compliance-sensitive commercial function.
Why This Is Infrastructure, Not a Chatbot
Unified Ontology
Data + Domain + Process + Decision layers modelled as one connected graph.
Causal, Not Just Semantic
Answers "why," not just "what," a causal state graph, not keyword matching.
Self-Learning
Compounds intelligence from past decisions automatically, no manual retraining.
Governable
Business teams build and maintain their own ontology, not locked to a vendor.
Multi-Agent Reasoning
Orchestrated agents investigate in parallel, then synthesise one answer.
Structured + Unstructured
PDFs, decks, scans, emails, DB exports, and roughly 20 languages.
Because the ontology and causal layer are shared infrastructure, this same platform is also the foundation for the company's parallel AI Marketing Transformation work. See our companion case study on media planning and digital optimisation.
Questions, answered
What teams ask before they start.
The essentials on scope, validation, data, and time to first value.