Generative AI (GenAI) is rapidly moving from hype to hard impact in health care. With an estimated CAGR of 85% and a projected market size of $22 billion by 2027, it is on track to outpace every other sector in adoption speed and value creation. What sets health care apart is the combination of complex processes, strict regulatory oversight, and massive data volumes — all of which create fertile ground for GenAI’s unique capabilities.

Yet, the industry stands at a crossroads. About 25% of biopharma firms and 10% of medtech players already report tangible business value from AI, including cost reductions and revenue increases of at least 5%. These early adopters are also seeing measurable improvements in speed, compliance, and agility. In contrast, nearly 60% of manufacturers remain cautious, with no clear GenAI strategy or at best, a wait-and-see stance. This divide underscores an urgent reality: health care organizations that hesitate risk falling behind in efficiency, quality, and competitiveness.

Why Health Care Operations Are Ripe for GenAI

Health care operations — spanning procurement, supply chain, manufacturing, quality, regulatory compliance, and workforce training — are complex, resource-heavy, and prone to inefficiencies.

Traditional automation and analytics have delivered incremental gains, but GenAI goes further by:

  • Understanding context and generating insights beyond structured data.
  • Automating knowledge-intensive tasks like deviation analysis, regulatory reporting, and training.
  • Creating new workflows that were previously unimaginable, such as end-to-end automated tech transfers.

This leap means health care leaders can shift from doing digital to being digital: embedding intelligence into the DNA of their operations.

The Three Value Plays: Deploy, Reshape, Invent

BCG identifies three complementary ways GenAI can be applied in health care operations:

1. Deploy – Boost Productivity with Off-the-Shelf Tools

This is the fastest path to value. Off-the-shelf GenAI solutions can be integrated into existing workflows to streamline repetitive tasks:

  • Shop-Floor Agents: Provide real-time troubleshooting based on SOPs, reducing downtime and boosting throughput.
  • Logistics Execution: Automate documentation and scheduling to accelerate supply chains.
  • Procurement Analytics: Optimize spending by analyzing supplier contracts and historical data.

2. Reshape – Reinvent Critical Workflows End-to-End

Here, GenAI is used to reengineer complex processes, delivering exponential improvements:

  • Quality & Regulatory Affairs: Automating deviation investigations, generating root-cause analyses, and harmonizing compliance reporting.
  • Risk Management: Using GenAI to flag anomalies across assets and processes before they escalate.
  • Regulatory Submissions: Drafting technical documents and stability reports at scale, reducing cycle times.

Case in point: GenAI-assisted deviation handling (Exhibit 2) can clarify trends, classify issues by severity, and propose corrective actions, cutting investigation timelines from weeks to days.

3. Invent – Create New Business Models and Experiences

The most transformative play is using GenAI to invent entirely new operating models:

  • Tech Transfer Reinvention: AI agents can automate planning, predict risks, and recommend optimal process shifts across sites. This minimizes variability and accelerates transfers from months to weeks.
  • AI-Powered Learning Ecosystems: Personalized assistants can curate training, track learning progress, and assess readiness for regulatory audits.
  • Embedded Insurance Models (for medtech): Using GenAI to create bundled offerings, e.g., device + predictive maintenance + risk coverage.

The most successful organizations will not choose between these plays — they will run all three simultaneously, balancing short-term wins with long-term transformation.

High-Impact Applications Across Operations

GenAI’s versatility means it touches nearly every link in the health care value chain:

  • Procurement: AI models optimize supplier contracts, ensuring cost-effective spending.
  • Supply Chain: Demand forecasting improves accuracy, while GenAI agents automate logistics execution and track anomalies.
  • Manufacturing: Shop-floor AI agents monitor KPIs, flag process deviations, and optimize yield.
  • Quality & Regulatory: GenAI accelerates deviation investigations, creates technical documents, and ensures continuous compliance.
  • Learning: AI-driven training assistants personalize workforce upskilling, bridging capability gaps faster.

By 2025, health care manufacturers deploying GenAI across these domains may realize more operational value in a single year than in the previous three combined.

The Deviation-Management Breakthrough

Deviation management — historically a time-consuming, manual process — is a standout use case for GenAI. Today, resolving deviations involves multiple steps: logging the event, classifying its scope, conducting root-cause analysis (RCA), planning corrective and preventive actions (CAPA), and closing with QA approval. Each step consumes hours of skilled labor.

GenAI can transform this process:

  • Event Reporting: Auto-transcription tools improve accuracy in logging.
  • Classification: Assisted classification avoids unnecessary escalations.
  • RCA: Automated searches highlight similar historical deviations, generating root-cause summaries.
  • Remediation: AI suggests corrective actions based on past effectiveness.
  • QA & Closure: Automates text reviews and retrieves regulatory assessments.
  • Monitoring: Detects trends across assets and process steps to preempt future issues.

This shift creates two key advantages:

  1. Efficiency — time saved in deviation handling, leading to faster product release.
  2. Effectiveness — improved compliance, reduced cycle times, and higher confidence in audit readiness.

Six Enablers for Scaling GenAI

Many pilots fail because organizations underestimate what it takes to scale GenAI. Six enablers separate leaders from laggards:

  1. Set a Bold Ambition
  2. Prioritize Value-Based Use Cases
  3. Strengthen Data Foundations
  4. Leverage Platforms & Partnerships
  5. Align People, Processes, and Change
  6. Establish Responsible AI Policies

What This Means for CXOs

For senior leaders, the message is clear: GenAI is no longer experimental — it is existential.

  • Revenue Growth: GenAI can boost top-line performance by improving time-to-market and creating new offerings.
  • Cost Efficiency: Automating regulatory and operational tasks reduces overheads and rework.
  • Agility: Faster deviation handling and predictive risk management make organizations more resilient.
  • Talent Transformation: GenAI enables employees to focus on higher-value work, while AI-driven learning bridges skills gaps.

Leaders must shift their perspective from “Should we use GenAI?” to “Where can GenAI create the most impact, and how fast can we scale it?”

The Road Ahead

Health care organizations that act decisively today will set the benchmarks for speed, compliance, and patient outcomes tomorrow. With competitors already embedding GenAI into their operations, the cost of inaction is not neutrality — it is obsolescence.

The winning formula is simple yet demanding:

  • Deploy GenAI for immediate productivity gains.
  • Reshape complex workflows to drive exponential efficiencies.
  • Invent new experiences and models that redefine patient care and operational excellence.

For CXOs, the path forward is less about technology alone and more about strategic courage, cultural alignment, and disciplined execution. GenAI in health care is not a future story — it is unfolding now. The question is: will you lead the transformation, or watch others define it?