91制片厂

Analyst Perspective

Align AI initiatives with critical business challenges.

In today鈥檚 rapidly evolving health insurance landscape, payers are under increasing pressure to optimize operations, enhance member experiences, and improve cost efficiencies. With rising healthcare costs, growing member expectations, and regulatory demands, traditional health insurance processes are proving to be insufficient. To stay competitive and meet these challenges, payers must adopt innovative technologies that enable them to remain ahead of the AI trends.

AI-driven applications in health insurance have the potential to transform the industry by automating processes, personalizing member interactions, and improving decision-making through data-driven insights. These solutions empower payers to detect and prevent fraud, optimize resource allocation, accurately assess risk, and provide tailored product offerings. By leveraging predictive analytics and real-time processing capabilities, AI reduces inefficiencies, enhances member satisfaction, and improves profitability.

While many health insurance payers recognize the potential of AI, they often lack clarity on how to begin, what to prioritize, and how to scale AI initiatives. To fully capitalize on AI's benefits, payers need a clear roadmap to identify high-impact use cases, align technology investments with business objectives, and ensure ethical and effective AI implementation.

This research provides actionable insights into AI use cases for health insurance, examples of successful implementations, and a step-by-step guide to building a robust and practical AI use case library that helps payers unlock the full potential of AI in their operations.

Sharon Auma-Ebanyat
91制片厂 Director, Healthcare Industry
91制片厂

Executive summary

Your Challenge

The health insurance industry faces pressure to streamline operations while maintaining compliance with complex regulations.

Customers expect personalized, efficient, and responsive interactions, which requires leveraging AI for improved engagement.

Insurers need scalable AI solutions to enhance risk management, detect fraud, and create innovative products that cater to evolving market demands.

Common Obstacles

Fragmented and incomplete data across legacy systems creates significant challenges for effective AI implementation.

Navigating complex regulations and addressing ethical concerns like bias and transparency complicates adoption.

Internal resistance to change and a lack of AI expertise hinder the successful execution of initiatives.

Info-Tech鈥檚 Approach

Introduce an approach to build your AI roadmap rapidly and responsibly via seven-step practical framework to accelerate adoption.

Help your organization to discover and understand a variety of AI use cases that can address your business challenges as well as support organizational strategic goals.

Guide your organization on their AI journey by identifying and prioritizing AI use cases for business capabilities through a benefits realization model.

Info-Tech Insight

In the health insurance industry, fragmented systems and disjointed data often hinder AI adoption. By establishing foundational readiness through an AI roadmap and identifying and prioritizing use cases aligned with strategic goals, organizations can uncover AI opportunities, address business challenges, and maximize benefits using a clear benefits realization model.

AI is transforming the health insurance industry

• Accurately Detect Fraud

  62%of the time required to identify fraudulent claims can be reduced by NLP AI models ensuring faster and more accurate fraud detection. (Source: Journal of Computer Science and Technology Studies, 2025.)

• Operational Cost Reduction

  20% Cost reduction through AI-driven automation and environmentally aligned cost-efficiency strategies. (Source: 鈥淭he AI Opportunity,鈥� McKinsey, 2024.)

• Improve Customer Service

  55% of health insurance customers believe AI is key to improving efficiency in their health insurance experience. (Source: Talkdesk, 2024.)

• Increase in Revenue

  12% Revenue increase from using AI to analyze member data, payers are offering personalized plans and proactive care management, leading to better member retention. (Source: 鈥淭he AI Opportunity,鈥� McKinsey, 2024)

The health insurance industry is experiencing growth in AI adoption

Fraud detection: AI algorithms analyze vast data sets to identify patterns indicative of fraudulent activities, enhancing the accuracy and speed of fraud detection.

Claims processing: By automating claims management, AI reduces processing times and minimizes errors, leading to increased efficiency and cost savings.

Customer service: AI-powered chatbots and virtual assistants handle routine inquiries, providing 24/7 support and improving customer satisfaction.

Underwriting and pricing: Machine learning models assess risk more accurately, allowing for more precise pricing and personalized policy offerings.

Health insurance executives are committed to investing in AI

73% of health insurance executives are increasing their AI investments. (Source: Fierce Healthcare, 2024.)

Health insurance companies are implementing AI

75% of health insurers have prioritized implementing AI in their customer services areas. (Source: Statista, 2025.)

Health Insurance is prioritizing customer service and claims management

The health insurance industry is leveraging AI to analyze large data sets to improve risk predictions and set competitive premiums, attracting more customers, which enhances underwriting and risk assessment. This precision optimizes profit margins while managing risks effectively.

Health insurance companies are also using AI to improve customer service and retention by using AI-powered tools like chatbots and virtual assistants provide 24/7 support and personalized services, enhancing customer satisfaction. These innovations reduce churn and strengthen loyalty through tailored interactions.

The health insurance industry is also using AI to improve operational efficiency and cost reduction by automating claims processing and fraud detection. These systems reduce losses from fraud and free staff for strategic initiatives.

Distribution of AI technology adoption in health insurance worldwide 2024, by area

Very few (5%) are not yet implementing AI in customer service and claims management

(Source: Statista, 2025 N=100 (Managers and Board-level Executives))

Lead with value by aligning AI initiatives to business needs

"There鈥檚 currently a real delta between the numerous proofs of concept and full-blown AI strategies.鈥� - Paul McDonagh-Smith, Senior Lecturer at MIT Sloan School of Management. (Source: MIT Sloan School of Management, 2024.)

• Prioritize AI investments

  53% Of health insurance payors are prioritizing AI as an immediate business need. (Source: Define Ventures, 2024.)

• Develop AI governance committees

  73% Of health insurance payors have established AI governance committees to ensure that AI initiatives align with organizational values and objectives. (Source: Define Ventures, 2024.)

• Identify strategic use cases

  76% of health insurance payors and providers are establishing AI pilot programs (Source: Define Ventures, 2024.)

AI is an innovation in machine learning

Artificial Intelligence (AI)

An artificial intelligence (AI) system that can make predictions, recommendations, or decisions influencing real or virtual environments.

Machine Learning (ML)

Machine learning (ML) is a subset of AI algorithms that parse data, learn from data, and then decide or make predictions.

Generative AI (Gen AI)

A subset of artificial intelligence systems that generate new outputs based on the data the system has been trained on using modalities such as text, audio, visual, and code.

What makes AI different

• Traditional programming

  

• Machine learning

  

Info-Tech鈥檚 approach and team can help, irrespective of where you are in your digital journey

Where are you in the journey?	Establish your digital North Star	Quantify the value of digital use cases	Create the digital roadmap	Deliver digital use cases and realize impact	Create the infrastructure to drive and sustain change
	Set aspiration: Vision setting with key business unit stakeholders to discuss and align on digital aspiration (e.g. Big T vs. little T transformation, self-funded and slow burn vs. investments)	Assess opportunity: Comprehensive E2E understanding of the digital opportunity across BU/functions (e.g. data analysis, process walks, and interviews)	Design and plan: Bottom-up initiative design and planning (e.g. opportunity to initiatives, financials, phasing, design principles)	Execute: Detailed initiative builds and implementation; execution with rigor and transparency (e.g. process optimization then automation, test, measure, scale)	Enable: Set up the transformation infrastructure, operating model, and culture to drive value capture and sustain change.
Examples of how Info-Tech can help	Digital North Star placemat (e.g. industry trends, top-down opportunity, high-level planning)	Digital maturity assessments (e.g. current-state digital adoption and transformation readiness)	Initiative bottom-up design (e.g. initiative ideation and business case creation, workplan, investments)	Initiative building (e.g. zero-based process redesign with technology)	Transformation infrastructure (e.g. transformation program design, transformation office)
		Opportunity assessments (e.g. BUs/function value creation diagnostics, opportunity levers)	Holistic initiative planning (e.g. phasing, interdependencies, investments)	Initiative implementation (e.g. testing & pilot, scale-up roadmap, performance tracking)	IT modernization (e.g. technology infrastructure required to execute digital levers)
				Value assurance assessment (e.g. course correcting and accelerating initiatives underway)	Change management (e.g. org-wide change program and stories, comms, governance)
	The following content will provide an overview of AI/ML use cases in utilities. This will support opportunity assessments across the organization鈥檚 value chain. Note: This does not provide the value/ROI specific to your organization. To do that, detailed current-state assessments and opportunity assessments need to be executed.				Performance management (e.g. KPIs 鈥� leading and lagging, people mgmt. for continuous imp.)
					Workforce management (e.g. upskilling, right people, right place, right time)

Applicable framework element for this document

How to use this report

Use this map to determine where to use this research material.

This report is intended to act as both a standalone report on the AI opportunities within the health insurance industry while also serving as a research-based accelerated input to the 91制片厂 Your AI Strategy Roadmap blueprint and associated activities. It uses research-based data for 鈥淎CME Corp鈥� to demonstrate AI use case opportunities for use in a holistic AI strategy and roadmap.

Realize all teams are unique and you may feel that some sample information may not be relevant to or represent your organization well due to the different type of products and services you are engaged in, geographic area your located in, etc. We recommend that you adjust and customize the template as needed to be organization-specific and to create the most valuable AI strategy for your organization.

If using this report as a research-based accelerant input to the 91制片厂 Your AI Strategy Roadmap blueprint, please use it in phases 2 and 3 and activities 2.1 and 3.1 specifically:

2.1 Map your candidate AI use cases 3.1 Prioritize candidate AI use cases

AI Strategy Roadmap Activities

Act. X.X

When you see these symbols in this document, they represent the research based on AI use case opportunities for use in the corresponding (X.X) AI Strategy blueprint activity.

Visit Info-Tech鈥檚 91制片厂 Your AI Strategy Roadmap blueprint for full activity details

Measure the value of this document

Document your objective

Highlight best-in-class use cases to spur the initiative-planning and ideation process.

Measure your success against that objective

There are multiple qualitative, quantitative, direct, and indirect metrics by which you can measure the progress of your initiative pipeline鈥檚 development. Some examples are:

• Increased initiative pipeline value
• Number of capabilities impacted by initiative pipeline
• Enhanced understanding of the initiatives鈥� impact aligned to the organization鈥檚 capability map
• Better understanding of which sources of value are being addressed or under-addressed in the organization鈥檚 initiative pipeline

See Establish Your Transformation Infrastructure in the Digital Transformation Center for more details

AI in the health insurance industry should produce measurable results

Consider the risks of AI

There are more than the usual number of risks with AI technology.

MITIGATION FACTORS

• Trust

  • Transparency: Can the system explain its decision in an understandable way to users?
  • Control: Are there procedures for detecting and responding to errors, as well as mechanisms for human oversight?
  • Trainable: Can the AI system be retrained using a diverse data set to identify and remove bias from the data?

• Continuous improvement

  • Institutions should continuously monitor the use of AI-enabled technologies to ensure they meet the needs of their users and are used safely and ethically.

RISKS

• Bias

  • Many large language models (LLMs) are trained on data from the internet, adopting its biases as well as those of their trainers.

• Accountability

  • Ultimately, the institution will be accountable for the decisions of the AI tool, including the issues around copyright. The systems are often opaque, thwarting mitigation techniques.

• Technology

  • Accuracy: The models are often inaccurate and have 鈥渉allucinations," where responses are not based on observation.
  • Shadow IT: There is likely uncontrolled implementation and use of AI among constituents.
  • Vendors: AI is a new landscape, and the suppliers lack maturity.

• Privacy and security

  • Concerns around data privacy and security are both typical of technology and novel to the strangeness of AI.

91制片厂ing your AI strategy use case library for Health Insurance

What is known

• AI has the power to transform operations in the health insurance industry.
• Leveraging AI can reduce fraud and waste in the health insurance industry presenting opportunities for revenue growth.

Actions to take

1. Identify areas within the health insurance operations where AI can provide the most impact 鈥� Step 4 of 91制片厂 Your AI Strategy Roadmap.
2. Systemically build your AI use case library for health insurance based on the value proposition and feasibility of each use case 鈥� Step 5 of 91制片厂 Your AI Strategy Roadmap.
3. Prioritize and build a structured path toward adopting AI in health insurance 鈥� Step 6 of 91制片厂 Your AI Strategy Roadmap.
4. Develop your AI strategy roadmap with timelines. Step 7 of 91制片厂 Your AI Strategy Roadmap.

AI Use Case Library Methodology

SECTION 1