The rapid rise of Generative and Agentic AI is transforming not just how work gets done, but who does it. As organizations move from experimentation to enterprise-scale deployment, new roles are emerging to bridge the gap between human judgment and machine capability. These roles sit at the intersection of technology, governance, and business strategy—focused on making AI safe, reliable, and productive in real-world settings.

Every major technology wave has created new kinds of jobs—from the rise of the internet giving us web designers and cybersecurity experts, to the cloud era ushering in DevOps and data architects. Generative and Agentic AI are no different, but the pace is faster and the impact broader. We’re witnessing a reshaping of the modern workforce in real time, as companies scramble to harness the power of AI responsibly and effectively.

The new roles emerging aren’t just about technology—they’re about trust, creativity, and control. Organizations are hiring people to govern how AI is used, to teach it how to think in context, and to integrate it into business workflows in ways that augment human intelligence. In short, these roles are the scaffolding that will allow AI to scale safely and strategically across industries.

In the coming years, we’ll see a wave of job titles that didn’t exist even a few years ago. From AI governance leads who ensure compliance and ethical use, to model trainers and prompt engineers who refine how systems learn and respond, to AI workflow designers who reimagine processes around human-machine collaboration—the shape of the workforce is evolving. These new roles reflect a fundamental truth: AI isn’t replacing humans; it’s redefining the boundaries of what humans can do.

As for new roles, in the near term I expect to see positions focused on augmenting human expertise and building governance frameworks to guide responsible use of these tools.

• AI Product Manager / GenAI Product Owner – Defines AI use cases, prioritizes features, and translates business needs into AI-enabled workflows.
• Prompt Engineer / Interaction Designer – Crafts prompts and reusable frameworks to maximize AI accuracy and relevance.
• AI Trainer / Evaluator – Curates data and feedback loops to continuously improve model performance.
• AI Solution Architect – Integrates AI tools with enterprise systems, including policy administration, claims platforms, and CRM systems.
• Human-AI Collaboration Lead / AI Literacy Coach – Teaches employees how to effectively work with AI, optimizing productivity while maintaining oversight. Insurance-Specific:
• Underwriting Augmentation Specialist – Develops AI-assisted tools for risk evaluation.
• Claims Intelligence Engineer – Uses AI to summarize claims, detect anomalies, and flag potential fraud.
• Policy Analysis Agent Developer – Creates AI systems that interpret complex policy language for rapid insights.

Longer term the rise of agentic AI will shift the enterprise from human-augmented tasks to human-guided ecosystems, creating opportunities for roles that combine technical mastery, regulatory insight, and strategic foresight.

• Agent Architect / Multi-Agent Designer – Designs ecosystems of specialized AI agents that collaborate to achieve business goals.
• AI Orchestration Engineer – Builds infrastructure to monitor and manage autonomous AI agents, including human escalation when confidence is low.
• AI Ethics & Safety Lead – Defines safe boundaries, mitigates bias, and ensures regulatory compliance.
• AI Performance Auditor / Model Risk Officer – Validates agent decisions, ensuring transparency, reproducibility, and governance. Enterprise AI Governance Roles:
• Chief AI Officer (CAIO) – Sets AI strategy, investment, and ethical standards.
• AI Policy & Regulatory Liaison – Aligns AI operations with evolving regulations and engages with industry bodies. Insurance-Specific Examples:
• Digital Colleague Designer – Orchestrates workflows where human underwriters or claims adjusters collaborate with autonomous agents.
• Synthetic Data Engineer – Generates high-quality, privacy-preserving datasets to train autonomous models.
• AI Model Validator for Regulated Models – Ensures agentic systems in pricing, underwriting, or claims meet transparency and fairness requirements.

For insurers and the technology partners who support them, now is the time to prepare for this next wave of talent. Building clarity around the skills, governance frameworks, and operating models needed for these new AI-driven roles will separate early movers from laggards. Insurers should begin identifying where human expertise adds the most value—and where AI can safely take over routine tasks—while vendors must design solutions that empower, rather than replace, human decision-makers. Those who invest early in defining, attracting, and enabling these new roles will be best positioned to turn AI from an experimental tool into a true competitive advantage.