For many years, insurance companies have recognized the need for a meaningful enterprise staffing and talent strategy. The fundamentals have not changed: organizations need the right people, skills, capabilities and leadership to execute the business strategy. What has changed is the environment in which that strategy must operate.
Insurance companies continue to face an aging workforce, competition for specialized skills, changing customer expectations and technology-driven changes to work. AI introduces another dimension. It can change the amount of work required, the skills required to perform that work, the speed at which work is completed and the way work is organized across the enterprise.
Why the Enterprise Staffing Strategy Still Matters
A staffing strategy is more than a succession plan, performance review process or hiring budget. It is an ongoing business discipline that connects business objectives to the people and capabilities required to achieve them.
The original four-part model remains relevant:
Hiring
Determine when capabilities should be developed internally and when critical skills must be acquired externally.
Retention
Protect critical knowledge, retain high-value employees and identify people capable of transitioning into emerging roles.
Training
Build the skills and capabilities required by the future operating model, including role-specific AI literacy.
Exit
Plan retirement, attrition, redeployment and separation while protecting institutional knowledge.
AI Changes the Staffing Equation
AI is likely to change jobs before it eliminates jobs. Many insurance roles combine judgment, analysis, communication, research, documentation and repetitive administrative activity. AI can increasingly assist with portions of that work while people retain accountability and higher-order judgment.
- Underwriting: AI can assist with submission intake, document extraction, risk summaries, data enrichment and portfolio analysis while underwriters remain responsible for complex risk judgment and exceptions.
- Claims: AI can support FNOL intake, document review, correspondence, triage, fraud indicators and claim summarization while adjusters focus on investigation, negotiation, coverage and customer situations requiring judgment.
- Customer service: AI can handle routine questions and transactions while employees concentrate on complex service issues, retention and relationships.
- Actuarial and analytics: AI can accelerate data preparation, exploratory analysis, coding and model development while professionals provide validation, interpretation, governance and business judgment.
- Operations: AI can automate repetitive workflows and knowledge work, increasing the span of responsibility for remaining employees.
- Technology and data: Demand may shift toward data engineering, AI engineering, architecture, integration, cybersecurity, model governance and business-facing technology capabilities.
The practical implication is important: a position that appears to be “automatable” may instead become a different position. Staffing plans need to distinguish among work that should be automated, work that should be augmented, work that requires human judgment and work that may emerge because AI creates new capabilities.
The Fifth Discipline: Redesign the Work
AI adds a discipline that traditional staffing models did not explicitly require: work redesign. Staffing cannot be determined accurately until the organization understands how work will be performed after automation and AI are introduced.
Map the work
Understand current work at the task level rather than relying only on job titles and organization charts.
Classify the work
Determine which tasks should be automated, augmented, retained as human work or redesigned.
Define human accountability
Identify where human review, approval, escalation and judgment remain necessary.
Recalculate capacity
Redesign roles and workflows, then recalculate staffing requirements using the new AI and automation assumptions.
Reskill Before You Recruit
AI makes the original training principle more urgent. Before assuming a capability must be purchased in the labor market, companies should know which existing employees can acquire the capabilities required by the future operating model.
- Build an inventory of current skills and compare it with future-state requirements.
- Provide role-specific AI literacy rather than generic AI awareness.
- Train employees to use AI safely, effectively and within defined governance standards.
- Develop deeper capabilities in data, analytics, automation, process design and AI oversight where appropriate.
- Measure training through demonstrated capability and changed work outcomes—not simply course completion.
A Practical Three-Year Planning Approach
Inventory jobs, tasks, skills and technology. Identify processes most likely to change through AI. Establish baseline productivity, quality and staffing measures.
Pilot AI and automation in targeted processes. Redesign jobs around the new division of work between people and technology. Begin systematic reskilling and selective hiring.
Reassess staffing requirements based on actual results. Expand successful AI-enabled processes, formalize new roles and adjust the workforce model as business volumes and capabilities change.
Conclusion
The central principle of enterprise staffing remains the same: staffing and talent strategy should receive the same focus, rigor and application as the core business strategy.
AI makes the relationship between work, technology and people far more dynamic. Insurance companies should not wait for AI to tell them what their workforce will look like. They should define the future operating model, understand how work will change, identify the capabilities that will be required and deliberately move their people and organization toward that future.
Enterprise Staffing Strategy in the Age of AI
The updated AIA perspective extends the original staffing strategy to address AI, work redesign, reskilling and the future operating model.
Read the 2026 AI Strategy →