All Insights Report Reimagining Data Platforms in the Agentic AI Era
Reimagining Data Platforms in the Agentic AI Era
Reimagining Data Platforms in the Agentic AI Era
Building Pharma’s Next-Gen Commercial Ecosystems for Autonomous Decision-Making

Only 23% of pharma and healthcare companies have adopted agentic AI, according to a recent survey. This statistic reveals a massive untapped opportunity—but also a warning: rushing into AI-driven automation without modernizing data platforms could create compliance, trust, and operational risks.
At Axtria Ignite 2025, senior executives from leading life sciences organizations joined Amanjeet Singh, Head of Strategy, Operations and SBU Leader, Axtria, to discuss what it will take for pharma’s commercial data ecosystems to fully harness agentic AI. This white paper captures their candid insights and offers a pragmatic roadmap to transform legacy platforms into intelligent ecosystems where agents act, learn, and deliver insights autonomously.
The Agentic AI Shift in Pharma
Pharma’s digital transformation has moved from data warehouses to lakehouses, and dashboards to data products. Now, agentic AI represents the next frontier:
- From insight delivery to autonomous action: Agents can interpret data, act independently, and learn over time.
- From insight delivery to autonomous action: Agents can interpret data, act independently, and learn over time.
- From siloed platforms to orchestrated ecosystems: AI agents thrive in interconnected data environments optimized for speed, compliance, and personalization.
The agentic future is no longer theoretical—it’s being piloted today. Organizations that act now will define pharma’s next era of commercial operations:
- Agents as teammates, not tools: Supporting field teams and executives with proactive insights.
- Compliance-first automation: Built on transparent governance frameworks.
- Scalable innovation: Real-time, contextual, autonomous platforms at the enterprise level.
FAQs
Agentic AI goes beyond traditional AI models by acting autonomously. Unlike rule-based or predictive models that require human intervention, agentic AI systems can interpret data, make decisions, and take action while learning from feedback. In pharma, this means agents can manage workflows like personalized HCP outreach, real-time market analysis, and medical content creation—dramatically reducing latency and manual oversight.
Most pharma data ecosystems were designed for human interpretation, focusing on dashboards and reports. Agentic AI, however, requires machine-readable semantics, real-time processing, and autonomous reasoning capabilities. Without upgrading to semantic knowledge layers, standardized data formats, and streaming architectures, organizations cannot safely deploy agents at scale.
The paper identifies six major challenges:
- Lack of semantic context and knowledge graphs
- Inconsistent data quality and standards
- Latency in data availability and limited use of unstructured data
- Governance and auditability gaps in regulated environments
- Cultural resistance and fear of automation
- Siloed organizational structures and fragmented data ownership
The white paper outlines a phased roadmap:
- Immediate (6–12 months): Identify 2–3 agent-ready use cases, launch governance councils, and establish initial semantic layers.
- Mid-term (2 years): Expand real-time processing, integrate unstructured data, and align with industry standards.
- Long-term (3–5 years): Deploy orchestration frameworks and fully autonomous agent systems across commercial functions.
Clear KPIs are essential to ensure trust and ROI. Recommended metrics include:
- Data Quality: Error reduction, accuracy of third-party data, anomaly detection time.
- Business Impact: Campaign ROI, HCP engagement, sales productivity.
- Governance: % of actions with audit trails, number of overrides, compliance scores.
- Platform Maturity: Real-time data volume, semantic coverage, interoperability rates.

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