A practical look at the barriers slowing AI adoption in life sciences and how to build the foundation for sustainable impact.
For many organizations, the greatest challenge is no longer proving that AI can work; it is building the strategy, data, governance, and workforce foundations required to make AI work at scale.
The Reality
- Nearly 5x Growth by 2035
- Life sciences AI investment is projected to grow from $3.3B in 2026 to $15.9B by 2035, increasing the urgency to scale responsibly.
- Only 5% Realize Meaningful Value
- 5% of life sciences organizations report achieving significant financial value from generative AI.
- 75% of organizations lack a comprehensive AI vision or roadmap tied to business outcomes.
Five Key Barriers
The Enterprise Value Gap
Siloed use cases lead to scattered deployments, weak ROI, and technology-first decisions with only localized benefits.
The Data Debt Gap
Poorly governed, fragmented, or incomplete data incurs compounding technical debt instead of scaled transformation.
The Trust Gap
AI that isn’t explainable, validated, or governed will not be trusted or adopted within regulated workflows.
The Workforce Readiness Gap
Employees lacking AI usage guidance, training, and career development will fail to deliver to their potential and lose engagement with AI augmented workflows.
The Operationalization Gap
AI installed as a ‘stand-alone’ capability will not transform legacy workflows that continue operating unchanged.
The Path Forward:
3D Intelligence (3Di)
Syner-G’s 3Di™ Transformation Services help life sciences organizations align People, Data, and Technology to build trusted, scalable, and human-centered AI capabilities.
Define
Identify where AI creates measurable business value by assessing organizational readiness, data maturity, governance needs, deployment models, and workflow challenges, to create high-impact use cases.
Design
Design embedded AI solutions with an operating model, governance framework, workforce strategy, and human-centered workflows for trusted, scalable adoption.
Deliver
Implement and scale AI capabilities through validated pilots, measurable outcomes, workforce enablement, and continuous improvement.
