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Insurers remain cautious on AI adoption despite potential

July 23, 2025•Staff Reporter•Original Link•2 minutes
ArtificialIntelligence
Insurance
TechnologyAdoption

Only 4% of insurers fully trust AI agents, with 42% lacking a formal deployment strategy, according to Capgemini Research Institute.

  • Key Findings from Capgemini Report:

    • Only 10% of insurance organizations have partially or fully implemented AI agents.
    • 20% are running pilot projects, while 42% lack a formal deployment strategy.
    • Trust in AI agents is low, with only 4% of insurers fully trusting the technology.
    • Overall trust levels declined from 54% in 2024 to 47% in 2025.
  • Top Concerns:

    • Privacy (50%), bias (50%), and transparency (51%) are the primary risks.
    • Only 8% of insurers have fully embedded ethical AI principles, below the global average of 14%.
  • Adoption Trends:

    • Sales (52%) and customer service (57%) are the top areas for AI agent deployment.
    • 26% believe AI will augment human workers within 1-3 years.
    • 35% expect AI agents to operate autonomously under human supervision.
  • Skills in Demand:

    • Technical skills: Programming and software development (67%).
    • Soft skills: Decision-making (60%).
  • Market Opportunity:

    • Agentic AI represents part of a broader $450 billion opportunity in insurance.
  • Challenges Ahead:

    • Most insurers are in early adoption stages, lacking clear implementation roadmaps.

AI in Insurance

For more on AI in insurance, visit InsuranceAsia.

Also Read:

  • Guy Carpenter Names Hakes Global Head of Marine, Energy, and Technical Lines
  • Globally Insured Losses Highest Since 2011

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Claudionor Coelho Jr, Chief AI Officer at Zscaler, discusses AI's rapid evolution, cybersecurity challenges, and combining rule-based reasoning with generative models for enterprise transformation.

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About the Author

Dr. Sarah Chen

Dr. Sarah Chen

AI Research Expert

A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.

Expertise

Machine Learning
Natural Language Processing
Deep Learning
AI Ethics
Experience
15 years
Publications
120+
Credentials
3
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