A global biopharma leader had no shortage of Generative AI ideas, but they remained stuck in disconnected pilots. Across research, manufacturing, and medical affairs, small wins failed to convert into strategic momentum. GenAI was delivering local value but not driving enterprise momentum.
That’s where Persistent stepped in.
Instead of delivering one-off applications, Persistent created a reusable, enterprise-wide GenAI fabric, anchored on a domain-specific biomedical knowledge graph that transformed isolated pilots into scalable value. The results? Faster insights, safer workflows, and a strategic capability that reshaped how the enterprise solved problems.
The Bottleneck: Innovation Without Integration
Despite enthusiastic experimentation, GenAI wasn’t scaling. Medical writers, data scientists, and business teams were trying out models and copilots but none of it added up to systemic change.
The core issue? No framework to unify, govern, or scale what worked.
Persistent identified the challenge clearly: GenAI didn’t fail in innovation; it failed in integration. Teams weren’t short on ideas, they lacked orchestration, trust, and the technical rails to scale responsibly. It wasn’t a capability gap; it was a coordination gap.
To unlock momentum, the enterprise needed more than isolated wins, it needed connective tissue.
Strategic Shift: Building a Reusable GenAI Layer
Persistent’s approach started with a mindset shift: treat GenAI not as an application, but as infrastructure. That shift shaped every decision from governance to deployment.
Rather than stitching together isolated use cases, Persistent built a unified foundation. This included reusable GenAI blueprints, a centralized orchestration layer to manage prompts and APIs, and built-in compliance aligned to industry standards like HIPAA, GDPR, and FDA 21 CFR Part 11.
“At the core of this architecture was a biomedical knowledge graph- Pi-OmniKG – that connected regulatory, clinical, and scientific content into a contextual intelligence layer”.
At the core of this architecture was a biomedical knowledge graph- Pi-OmniKG – that connected regulatory, clinical, and scientific content into a contextual intelligence layer. All of this ran on scalable, cloud-native infrastructure ensuring high availability without performance trade-offs.
Instead of reinventing the wheel each time, teams could plug new use cases into a shared foundation, accelerating development while minimizing risk. But architecture is only as good as its outcomes. The first domain to validate this vision? Medical Affairs.
First Win: Transforming Medical Affairs
The most compelling early proof came in Medical Affairs. Scientific liaisons needed faster ways to respond to complex HCP queries. Their legacy process — searching PDFs and emailing SMEs — was slow, error-prone, and tough to audit. Persistent deployed a GenAI-powered assistant that parsed medical literature, synthesized citations, flagged risks, and provided explainable answers. all within seconds. Regulatory compliance was built in. Field teams finally had a dependable knowledge partner.
The Impact: Real Metrics, Not Just Hype
The success in medical affairs unlocked a new rhythm across the organization. Teams were spending less time scouring for documents, and more time engaging with physicians.
With the architecture in place, impact followed—rapidly and measurably. New GenAI use cases launched across functions including R&D, manufacturing, and data quality operations.
Enterprise-wide impact included:
- 60% reduction in time spent on medical content retrieval
- 45% improvement in speed-to-response for HCP queries
- Cross-functional reuse across five departments
- Full traceability for audits and regulatory compliance
- Millions saved in manual effort reallocation
BCG estimates the healthcare GenAI market will grow from $1B to $22B by 2027. With Persistent’s platform approach, this client is already on that path. Impact validated the model. The next challenge was to sustain this at scale.
Scaling Smart: Sustaining Velocity Without Overhead
What mattered most after early wins wasn’t speed, it was sustainability. With a strong GenAI foundation in place, the client didn’t need to reinvent solutions each time. Its internal AI Center of Excellence could tap into shared components, streamline governance, and accelerate deployment without operational sprawl. Each new use case didn’t add overhead, it added momentum.
Each new use case didn’t add overhead, it added momentum.
Why Persistent?
The journey wasn’t powered by technology alone, it was anchored in trust, process, and partnership with Persistent, bringing in more than engineering firepower.
- Deep domain fluency in regulated industries
- Delivery models built for observability and explainability
- A co-creation model that integrated client SMEs throughout
- A strategy-led mindset to scale beyond proofs-of-concept
From Experimentation to Enterprise Confidence
The GenAI pilots that once sputtered in silos are now stitched into a living, learning system. The scattered experiments of yesterday gave way to a unified capability layer, powered by knowledge graphs and governed by design. Today, GenAI supports real-time decisions, accelerates scientific impact, and builds confidence with every audit.
With Persistent, GenAI moved from theoretical to transformational, and from siloed attempts to enterprise-wide execution.
Want to explore how GenAI can power your enterprise operating model? Talk to Persistent’s AI Transformation Team
