AI is offering new ways to improve efficiency, enhance decision-making, and deliver better experiences to policyholders. But an important question is: how do you measure the real impact of AI initiatives?
Understanding and measuring Return on Investment (ROI) is essential for answering that question. ROI doesn’t just reveal whether an AI project is “worth it” – it provides the data and insights needed to prioritize initiatives, secure stakeholder buy-in, and guide future investments.
At its simplest, ROI can be expressed as:
ROI = Benefits ÷ Investments
While the formula is straightforward, truly capturing ROI for AI projects requires a deeper, more strategic perspective. AI delivers value in ways that can be both direct and indirect, immediate and long-term.
Two Core Drivers of ROI in AI Initiatives
AI delivers value in two primary ways that are especially impactful in insurance:
1. Automation: Driving Efficiency
AI can take over repetitive, rules-based tasks that once consumed significant staff time, such as data entry, document processing, policy issuance, or routine claims triage. By streamlining these operations, carriers can reduce labor costs, accelerate cycle times, and minimize human error. This operational efficiency not only lowers costs but also frees employees to focus on higher-value tasks like customer engagement, strategic underwriting, and complex claims handling.
2. Augmentation: Enhancing Effectiveness
AI doesn’t just automate – it enhances human expertise. With advanced analytics and predictive modeling, underwriters can make more precise risk assessments, adjusters can prioritize claims more effectively, and customer service teams can provide faster, more tailored support. This augmentation leads to better decision quality, increased productivity, and improved outcomes that drive measurable business impact and higher customer satisfaction.
When implemented together, automation and augmentation amplify one another, enabling insurers to achieve both operational efficiency and strategic agility – two key levers for sustainable ROI.
Measuring ROI in AI Automation Projects
A practical approach to calculating ROI involves comparing the documented benefits against the total costs of the AI initiative. Below is a simplified framework:
| Benefit | Definition | Pre-AI Metrics | Post-AI Metrics | AI Costs Type | Costs Amounts |
| Human Task Volume | Tasks completed in a given time period | Development | |||
| Time Per Task | Average time to complete each task | Integration | |||
| Wage Rate | Average hourly wage | Operational | |||
| Total |
Detailed ROI Example: AI Automation
Task Volume:
- Employees: 50
- Tasks per hour: 10
- Work schedule: 40 hours/week × 52 weeks (about 12 months)
- Annual task volume: 1,040,000 tasks
Time Saved:
- Human time per task: 30 minutes
- AI time per task: 1 minute
- Time Saved per Task = 29 minutes
- Total time saved: 502,666 hours/year
- Calculation: (29 Minutes x 1,040,000 tasks ÷ 60)
Automation Benefits:
- Wage rate: $30/hour
- Total benefit: $15,080,000
- Calculation: (502,666 hours x $30 /hour)
Costs:
- Development & Integration: $60,000 (amortized)
- Operational: $24,000/year
- Total cost: $84,000
ROI Calculation:
- ROI = $15,080,000÷ $84,000 ≈ 179.52
- For every dollar spent, $179.52 is returned in value.
Why ROI Matters in AI Insurance Projects
Beyond the numbers, a measurable ROI provides strategic clarity and operational discipline. Here is why it is critical:
- Justifies resource allocation: Demonstrates that the initiative delivers tangible financial benefits that outweigh the investment.
- Aligns with strategic goals: Ensures AI projects support broader objectives like profitability, efficiency, and growth.
- Enhances accountability: Establishes performance benchmarks and enables tracking of outcomes.
- Facilitates stakeholder buy-in: Provides a compelling value narrative for executives and decision-makers.
- Improves decision-making: Enables comparison across initiatives to prioritize those with the highest impact.
- Supports post-implementation review: Validates whether projected benefits were realized, informing future investments.
Best Practices for Successfully Implementing Insurance AI and Obtaining a Great ROI
Implementing AI in insurance isn’t just about deploying advanced technology, it’s about ensuring those investments generate meaningful and sustainable value. Achieving a strong ROI requires more than a good use case or the right algorithms; it depends on having the right strategy, people, and processes in place to support success.
These best practices will help to maximize returns on AI initiatives. By focusing on these foundational elements, organizations can turn AI from a promising tool into a true competitive advantage.
- Develop a balanced team with acknowledged AI insurance leaders
- Develop a balanced team with business, technical, and analytical expertise.
- Consider whether to build internal capabilities or partner with external specialists.
- Invest in training and upskilling your teams.
- Create centers of excellence to share knowledge and best practices.
- Start with use cases demonstrating rapid time-to-value and feedback loops to capture learnings from first implementations and scale from there
- Celebrate early wins to build momentum and organizational support.
- Focus on change management and training
- Communicate the vision and benefits across the organization.
- Involve end-users in design and testing phases.
- Provide comprehensive training and support during transition.
- Address concerns about job displacement honestly and constructively.
- Maintain ethical standards
- Develop AI governance principles that align with organizational values.
- Ensure algorithms are transparent and decisions can be explained.
- Regularly test for unintended biases in AI systems.
- Create oversight mechanisms to ensure ethical implementation.
- Measure and communicate results
- Establish clear metrics to track performance improvements.
- Create dashboards that provide visibility into AI impact.
- Regularly communicate successes and lessons learned to stakeholders.
- Use results to inform and refine ongoing AI strategy.
- Plan for continuous improvement
- Establish processes for regular model monitoring and refinement.
- Stay current with advancing AI capabilities and emerging use cases.
- Create feedback channels for end-users to suggest improvements.
- Develop a long-term roadmap for AI capability evolution.
Realizing AI’s potential requires a disciplined approach to measuring, managing, and maximizing ROI.
By focusing on clear metrics, building the right teams, and maintaining strong governance, insurers can ensure their AI investments deliver not just operational gains, but strategic, long-term value. Automation and augmentation work best when combined, allowing organizations to achieve both cost savings and enhanced effectiveness.
In the end, ROI isn’t just a financial calculation – it’s a roadmap for making smarter, more confident decisions about where and how to deploy AI.
Learn more about getting the most from AI and where humans add the greatest value by downloading our infographic on AI vs. Human: Who Handles What in Insurance?