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The AI inflection point
AI is transforming life sciences at pace, shifting the focus from isolated experimentation to enterprises where AI co-creates value across R&D, clinical, manufacturing, quality, and commercial functions.
Leaders envision near-real-time clinical insights, adaptive supply networks, predictive quality systems, and intelligent agents that strengthen decisions across the value chain. But how will they get there?
Genpact's Autonomy by design research includes perspectives from 60 senior life sciences executives and shows that success requires more than technology. Progress is still slowed by workflow validation, skills gaps, and fragmented ownership.
These insights show how life sciences organizations can move from siloed use cases to scalable, enterprise-wide impact.
Four levels of AI maturity
By assessing respondents across six dimensions – adoption effectiveness, decision autonomy, governance rigor, operational enablement, regulatory compliance, and technical capability – we define four distinct levels of AI maturity.
Only 12% of life sciences organizations are classified as AI leaders
AI leaders in life sciences combine strategic vision with disciplined execution. They move beyond pilots, redesign critical processes, and build scalable, validated AI architectures with clear ownership across R&D, quality, manufacturing, supply chain, and commercial operations. They also tackle legacy constraints, embed compliance and risk controls from the outset, and operate confidently in GxP-regulated environments.
Most organizations are still building foundational capabilities, either early in their AI journey or scaling through assisted, use case-based adoption, without yet achieving consistent enterprise-wide impact, with significant room to strengthen operating models, architecture, workforce readiness, and governance.
The current state of AI in life sciences
1. Technology‑led ownership with broad functional involvement
AI ownership in life sciences is largely led by technology and data leaders, reinforcing a platform- and infrastructure-led operating model rather than one driven by business units alone.
At the same time, functional engagement is broad. More than two-thirds of life sciences leaders report direct involvement in planning or implementing AI within their function, showing that AI is becoming embedded in day-to-day workflows across R&D, quality, manufacturing, supply chain, and commercial teams.
Few respondents identify as enterprise-level sponsors of AI strategy, suggesting progress is driven more by functional execution and controlled scaling than by a single top-down mandate.
2. AI investments are paying off
In our survey, 77% of life sciences respondents are satisfied with the value delivered by current AI deployments, showing that AI is already creating measurable returns.
Impact is strongest in generative AI and automation, where organizations are improving productivity, speed, and consistency, especially in knowledge-intensive environments.
The next wave of value will come from embedding AI into core processes and aligning ownership for enterprise-wide scale.
3. Human‑anchored decision models with growing AI participation
Decision authority in life sciences remains largely human-anchored, with minimal use of fully autonomous AI, especially in GxP-regulated areas. At the same time, hybrid models are becoming standard, with AI supporting prioritization, anomaly detection, and recommendations under human oversight.
The greatest value lies in scaling effective human-AI collaboration. Organizations that clarify decision ownership and evolve governance can increase impact while maintaining control and compliance.
4. Powering advanced, compliant AI deployments
While many life sciences organizations have modernized their technology infrastructure, operational AI capabilities often lag, creating friction as use cases move closer to regulated production environments.
Scaling advanced and agentic AI requires robust model lifecycle management, monitoring, and embedded controls to ensure reproducibility, traceability, and validation. By strengthening model management and integrating responsible AI into development lifecycles, life sciences organizations can turn early value into scalable, compliant, enterprise-ready deployment.
The AI of now: Barriers to scaling AI in life sciences
According to our survey, three core barriers are constraining progress toward AI autonomy in life sciences.
Skills gaps are the most significant constraint: 55% of life sciences respondents cite skills gaps as the leading barrier to scaling AI. The challenge is most acute when organizations move from experimentation to production-grade deployment in GxP environments
Regulatory and compliance challenges continue to slow progress: 48% of life sciences organizations report regulatory or compliance challenges as a major barrier. Model validation, auditability, and responsible AI requirements make it harder to move quickly from pilots to enterprise-wide deployment
Difficulty integrating AI into workflows limits scale: 38% of life sciences respondents cite difficulty integrating AI into existing systems and workflows. Legacy platforms, fragmented data, and validation requirements still limit enterprise-wide impact
The rise of the autonomous enterprise
Life sciences leaders are beginning to define what autonomy looks like in practice. The most advanced organizations are converging around four enabling themes that offer a practical blueprint for operating in a highly regulated industry.
1. A symphony of agents
As organizations deploy more AI agents, the main risk is fragmentation. Without orchestration, agents may optimize individual tasks but fail to improve end-to-end outcomes.
Fully orchestrated multiagent systems remain uncommon. Many organizations are still experimenting with isolated agents rather than coordinated execution across the enterprise.
A clear example is deductions recovery, where coordinated agents aggregate data, validate claims, prioritize recovery actions, and learn from outcomes to reduce repeat leakage.
This pattern can automate end-to-end workflows while keeping people accountable for oversight, creating a practical foundation for autonomy at scale.
2. The universal AI practitioner
Scaling AI in life sciences depends as much on people as on technology. The survey data shows active investment in workforce enablement: 63% are training teams on responsible AI use, and 45% offer personalized AI learning paths, reflecting the need for role-specific adoption in regulated environments.
Structured change enablement is also emerging. While 35% of life sciences organizations use a formal change management framework, 32% still report workforce resistance, showing that communication, skills development, and clarity around AI's role remain essential.
Together, these patterns point to the rise of the universal AI practitioner: employees remain accountable while AI augments analysis, coordination, and execution within clear guardrails.
3. Enterprise architecture redux
Technology complexity remains a major constraint on AI autonomy. Scaling advanced and agentic AI requires a data-centric, interoperable architecture, but that foundation is still incomplete for many organizations.
Only 13% of respondents in enabling functions say they have fully adopted MLOps or AIOps for scalable model management, while 37% report full adoption of cloud-native AI platforms. This highlights a clear gap between infrastructure modernization and operational AI readiness.
For life sciences, this gap matters. Without strong model lifecycle management, monitoring, and governance, AI cannot be safely embedded into validated workflows or scaled with confidence.
4. Governing at the speed of AI
Governance sets the pace, and often the limits, of AI innovation. As organizations accelerate adoption, many executives recognize that current governance models are not yet equipped for autonomous or agentic AI systems.
The responses show a strong preference for centralized and federated models, reflecting the sector's emphasis on control, consistency, and compliance. Centralized governance provides standards and oversight, while federated models add agility across functions when strong coordination is in place.
Only a small minority relies on decentralized or ad hoc approaches. As organizations move toward advanced and agentic AI, leaders are embedding controls directly into platforms, workflows, and operating models.
The leaders' playbook
Translating vision into action
Our playbook outlines practical actions for organizations at any stage of the AI journey.
1. Redefine processes to lead with AI
Redesign core processes with AI at the center, using process intelligence to enable end-to-end orchestration across the life sciences value chain. Where governance is decentralized or ad hoc, prioritize a lightweight orchestration layer that connects local innovation to enterprise standards without compromising compliance or oversight.
2. Elevate AI fluency across the organization; redesign roles to be AI‑first
Democratize AI fluency across roles and functions, positioning employees as coauthors of AI-driven change. In regulated life sciences environments, local autonomy must be matched with a clear understanding of model limits, validation requirements, guardrails, and risk.
3. Build a coherent data and integration backbone to innovate successfully
Establish a data-centric, interoperable architecture that connects data, models, and workflows across validated systems. Strengthen integration and model lifecycle management so AI can move reliably from experimentation into production, with scalability, traceability, and governance built in from the start.
4. Establish clear governance, then federate execution where appropriate
With life sciences governance centered on centralized and federated models, the priority is sequencing. Define what "safe to scale" means across data use, model behavior, validation, and risk, then federate execution where it improves speed and adoption. Embed automated monitoring, controls, and policy enforcement directly into AI platforms and workflows.
5. Anchor AI value to measurable business outcomes
Shift focus from many disconnected use cases to a small set of value-backed metrics tied to life sciences outcomes, including cycle-time reduction, quality, compliance, cost efficiency, and productivity. Use rapid test-and-scale cycles to identify models that deliver measurable performance gains.
Mastering these five actions can help your organization move toward an autonomous enterprise and turn AI into a durable source of competitive advantage in life sciences.
