AI ambitions vs. data reality: How to bridge the gap in pharma

AI promises big gains – but poor data foundations are holding pharma back

Artificial Intelligence (AI) is no longer a distant ambition for life sciences—it’s a present-day necessity. Commercial teams expect AI to deliver smarter targeting, faster insights, and more personalised engagement. Industry surveys reflect this optimism: most leaders anticipate measurable gains in sales and efficiency.

Yet the same research reveals a sobering truth: AI initiatives are failing because the data isn’t ready. Nearly all respondents admit their data is not structured for AI, and many have abandoned projects entirely due to poor quality and fragmentation. The message is clear: AI cannot scale on fractured foundations.

Why data readiness matters more than algorithms

AI models are only as strong as the data they ingest. When data is incomplete, inconsistent, or outdated:

Predictions lose credibility.
Field teams distrust recommendations and revert to intuition.
Analytics cycles slow under the weight of manual cleansing and reconciliation.

Conversely, when data is accurate, harmonised, and accessible, AI can deliver on its promise—driving confident decisions across launch planning, targeting, and omnichannel engagement.

What healthcare data must have to be AI-ready

To move from pilots to enterprise-scale AI, healthcare data needs:

1. High Quality, Currency & Stability

Accurate, validated records with continuous refresh to reflect real-world changes – and maintained consistency over time to ensure longitudinal reliability.

2. Standardisation and Harmonisation

Unified definitions, coding standards, and global-to-local mapping to eliminate costly remapping.

3. Connected, Longitudinal Views

Patient-level claims linked to other patient touchpoints (reimbursement HUB, copay programs, outreach programs), potential blind spots (specialty pharmacy) and treatment related influences (HCP/HCO reference) for a complete picture.

4. Accessibility and Interoperability

Frictionless delivery in ML-ready formats—no operational bottlenecks.

5. Transparency and Governance

Provenance, QA flags, and bias monitoring to sustain trust in production models.

6. Feature Engineering Readiness

Pre-derived measures and event timestamps aligned to commercial AI use cases.

The Industry consensus

Recent findings underscore the same point: building a standardised, connected data foundation is the single most important enabler of AI at scale. Without it, even the most advanced algorithms will fail to deliver meaningful impact.

Why? Because AI thrives on consistency and context. Fragmented datasets force teams into endless cycles of cleansing, mapping, and reconciliation—delays that erode speed to insight and confidence in outputs. A unified foundation eliminates these bottlenecks, enabling seamless integration of claims, HCP/HCO reference, specialty and other data. This isn’t just a technical upgrade; it’s a strategic necessity. Organisations that invest in harmonised, interoperable data today will be the ones able to operationalise AI tomorrow—turning ambition into measurable commercial advantage.

Creating an AI-ready data foundation for life sciences

Symphony Health, an ICON plc company, provides the data foundation AI needs to thrive. Transparency is not just a principle—it’s the cornerstone of scalable AI. Without clear provenance and trust in the underlying data, even the most advanced algorithms fail to deliver meaningful impact. That’s why our approach prioritises a direct connection to the data’s origin. By working closely with primary data providers, we minimise layers of intermediaries, ensuring a stable, reliable pipeline that supports AI-ready integration. In addition, Symphony’s data strategy includes intentional overlap across sources to reinforce consistency and stability over time – so insights remain dependable as your AI models scale from pilot to enterprise.

Our commitment goes beyond raw figures; we deliver clarity, context, and pre-structured insights designed for machine learning workflows. This means our clients can move faster from data to decision—confident that their AI models are powered by accurate, harmonised, and fully governed data.

Standardisation for AI-ready data

AI models demand consistency—without it, even the most advanced algorithms struggle to scale. Symphony Health ensures that every dataset adheres to industry-standard frameworks, enabling seamless interoperability across platforms and data sources. Our data is normalised using globally recognised coding systems, including ICD-10, NDC, and CPT, so your analytics and machine learning workflows start with a common language. We also maintain consistent patient and provider identifiers across claims, reference, and specialty datasets, eliminating the costly remapping and reconciliation that slows AI deployment. By delivering harmonised, standards-based data, Symphony provides the foundation for accurate feature engineering, faster model training, and scalable AI initiatives that work across brands and regions.

Ensuring Fairness and Representativeness

AI models are only as fair and accurate as the data they learn from. If patient populations are skewed or under-represented, predictions can mislead and strategies can fail. Symphony Health tackles this challenge by delivering datasets that reflect diverse patient demographics across geographies, care settings, and therapeutic areas—ensuring models capture real-world complexity rather than narrow slices of the market. Every dataset includes documented provenance and clear limitations, giving data scientists transparency into source composition and confidence in model training. Combined with continuous governance and bias monitoring, Symphony’s approach helps pharmaceutical companies build AI solutions that are not only scalable but equitable, reducing risk and improving decision quality across commercial and clinical applications.

Integrated, Privacy-Preserving Linkage through Synoma®

Unlocking AI’s full potential starts with connected data—and that’s exactly what Synoma® from Symphony Health delivers. With validated tokens achieving 95%+ patient match rates, Synoma enables secure, privacy-preserving linkage across healthcare and consumer datasets, creating longitudinal patient views essential for predictive modelling and advanced analytics. Backed by 20+ years of data integration expertise and excellence and over 40 billion transactions processed annually, Synoma transforms fragmented data into a unified, AI-ready foundation—so life sciences organisations can train models with confidence and scale commercial AI initiatives without compromise.

Cloud-Ready, ML-Friendly Delivery

Symphony Health delivers curated datasets engineered for AI from the ground up. Our data packages are cloud-ready and structured in ML-friendly formats such as Snowflake, but also in Parquet and CSV, ensuring seamless integration into any data lakes and machine learning pipelines. Each dataset includes full lineage, precise timestamps, and pre-derived features—such as treatment sequences, adherence metrics, and NBRx signals—so data scientists can accelerate model training without spending weeks on wrangling and feature engineering. By reducing operational friction and providing harmonised, interoperable data, Symphony enables organisations to move from ingestion to insight faster, powering predictive models and advanced analytics that scale across brands and geographies.

From ambition to action: Building the data backbone for AI

AI won’t scale on fractured foundations. The future of commercial AI depends on more than algorithms—it depends on the integrity, connectivity, and readiness of the data feeding those models. Without a unified, high-quality data backbone, organisations face delays, distrust, and abandoned initiatives. The path forward is clear: partner with providers who deliver consistent, harmonised, and interoperable data that is AI-ready from day one. Symphony Health offers exactly that foundation—curated, cloud-ready datasets with full lineage, timestamps, and pre-derived features designed for machine learning workflows. By eliminating fragmentation and accelerating integration, we help life sciences companies move beyond pilots to scalable AI programmes that drive measurable impact across brands, regions, and channels.

For more about how Symphony Health, an ICON plc company, can assess and support your AI readiness, connect with us today.