Gartner recently hosted a webinar about the rise of autonomous business. That led to the thought: “What would an autonomous health insurer look like?”
Read on to learn more about autonomous businesses, what an autonomous health insurance business may look like, and how to get there.
What are Autonomous Businesses?
An autonomous business is a strategy that utilizes self-improving, adaptable technology to make decisions, take action, and create value. It represents the next wave of transformation, fundamentally changing how organizations operate and compete. This model is primarily driven by agentic AI, characterized by being self-configuring, self-optimizing, and resilient at scale.
Note that there is a difference between autonomy and traditional automation. While automation relies on predefined rules and human supervision to manage exceptions, autonomy involves systems that operate independently and self-correct to handle failures. Despite this independence, an autonomous business does not mean an enterprise without people. Instead, human roles evolve to focus on setting intent, providing context, and maintaining oversight while machines handle routine tasks.
According to Gartner, autonomous businesses have five key attributes:
Autonomous Operations
These are business processes capable of observing their situation, making independent decisions, taking actions, and continuously learning to improve execution with reduced human intervention.
Augmented Workforce
This practice combines human experience with machine efficiency to amplify decision-making, shifting human focus to overseeing machine alignment with the enterprise.
Auto-adapting Products
These products and services incorporate connected software that allows them to evolve and reconfigure themselves based on real-time user needs and environmental changes while in use.
Machine Customers
These are nonhuman economic actors, such as AI agents, that participate in the economy by making independent purchasing decisions based on facts, rules, and context.
Programmable Economy
This serves as the foundation for autonomous business, utilizing programmable money and smart contracts to facilitate high-speed machine-to-machine (M2M) and human-to-machine (H2M) transactions.
To succeed in this new era, organizations must address flaws in their existing digital business approach, as autonomy builds directly upon those prior digital investments.
To understand the difference between traditional automation and an autonomous business, consider the difference between a standard thermostat and a smart home ecosystem. A standard thermostat is automated. It follows a predefined rule to turn on the heat when the temperature drops. An autonomous system would instead observe the family’s habits, learn their schedule, adjust settings to optimize for both comfort and energy costs, and self-correct if it detects an open window, all while the humans simply set the high-level goal of “staying comfortable and efficient.”
Health Insurance as an Autonomous Business
An autonomous health insurance business would change from an AI-enabled model (applying AI to existing processes) to an AI-first model, where every process is reengineered to center on the effective use of agentic AI.
Drawing from the components of autonomous business, a health insurance provider might look like the following:
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Autonomous Operations and Claims
Instead of manual reviews, the business would utilize autonomous operations that possess situational awareness to observe, decide, and act on health claims independently. Key components may be:
- High-Speed Processing: Decisions on standard medical claims could be addressed in nanoseconds or microseconds with significantly reduced human intervention.
- Self-Correction: Unlike traditional automation that requires a human to handle exceptions, an autonomous insurer uses systems that self-correct to handle failures or data anomalies independently.
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Auto-adapting Health Products
Insurance products would evolve from static annual contracts into Auto-adapting Products that incorporate connected software.- Real-Time Tailoring: Plans could reconfigure themselves while in use based on a customer’s real-time health data from IoT devices (like wearables) or environmental changes.
- Contextual Value: If a customer’s lifestyle or health needs change, the product adjusts its coverage or premiums dynamically to ensure sustained value and personalization.
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The Augmented Workforce and “Human-in-the-Loop.”
The role of human employees would shift from routine administration to high-level oversight and strategy.- Focus on Empathy: Because health insurance is an emotionally sensitive industry, the business would prioritize “human-in-the-loop” moments. While AI handles the logic of a claim, a human would be triggered for cases where a claimant values engagement and reassurance.
- Decision Hierarchies: Decision rights would shift from traditional organizational charts to AI agent hierarchies, with human leaders overseeing machine alignment with enterprise values.
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Machine Customers and the Programmable Economy
The insurer would interact with machine customers, which are AI agents acting on behalf of patients.
- Just-in-Time Purchasing: A patient’s personal AI assistant might act as a machine customer, purchasing supplemental “just-in-time” coverage for a specific high-risk activity (like a ski trip) based on the user’s context.
- Instant Settlements: Utilizing a Programmable Economy foundation, the insurer could use smart contracts and programmable money to facilitate instant, autonomous machine-to-machine (M2M) payments to healthcare providers once treatment is verified.
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Data and Risk Evolution
The data approach would shift from historic reporting to continuous learning.
- Synthetic Data and LLMs: The insurer would use Large Language Models (LLMs) and synthetic data to enhance risk modeling and personalize customer engagement.
- Continuous Improvement: Systems would continuously learn from every transaction and health outcome to improve future execution and pricing
How Soon Might Autonomous Health Insurers Arrive?
We can expect to see the emergence and maturation of autonomous health insurance businesses in phases over the next several years. Gartner sees 2030 serving as the target for a full industry-wide shift.
The timeline for this transformation is driven by several milestones:
2026: The Efficiency Breakthrough
By 2026, Gartner predicts that insurers adopting autonomous processing will achieve a 30% reduction in cost per transaction. This period marks a turning point where leading organizations progress beyond basic automation toward independent systems.
2028: Scaling Highly Autonomous Systems
By 2028, it is projected that 15% of agentic AI deployments will be “highly autonomous,” a significant increase from less than 5% in 2025. Additionally, 40% of services are expected to be AI-augmented by this time.
2030: The Era of the AI-First Insurer
By 2030, industry leaders are expected to transition to AI-first business models. This era will be characterized by:
- Pervasive AI agents that lay the foundation for fully autonomous business operations.
- The widespread use of auto-adapting products. Insurers that fail to adopt these strategies by 2030 may lose an average of 25% of their market share.
- An economy where machine customers influence $18 trillion in purchases and programmable money handles 20-22% of transactions.
The Current State of Readiness
Gartner noted that most insurers remain stuck between Stage 1 and Stage 2 of data maturity.
- Stage 1/2: Focuses on internal data and historic decision-making or augmenting insights with some external data.
- Stage 3 (The Goal): Involves continuous learning models and decision automation, which is the current “innovator” cusp.
To prepare for this timeline, Gartner suggests that CIOs must begin responding today by shifting from being “AI-enabled” (using AI for existing processes) to “AI-first” (reengineering processes specifically for AI).
How Can Health Insurers Prepare to Become Autonomous Businesses?
To prepare for the transition to an autonomous business, insurers must move beyond simply layering AI onto existing workflows and instead focus on fundamental structural and strategic changes.
Here is what insurers should do today to prepare:
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Shift to an “AI-First” Business Model
Insurers must transition from an AI-enabled model —where they apply AI to existing processes—to AI-first, where they reengineer business processes to center around the effective use of AI. This involves a strategic rethinking of the entire vision, product line, and operating model. CIOs should lead this by educating business partners to take ownership of AI strategies within their own units.
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Build Data Maturity and “AI Muscle.”
Autonomy requires a high level of data maturity. Insurers should focus on moving from Stage 1 (historic, human-driven decisions) to Stage 3 maturity, which involves:
- Using LLMs and synthetic data to enhance data sets.
- Developing models that continuously learn from data to achieve decision automation.
- Completing AI maturity assessments to identify weaknesses in AI-ready data and culture.
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Enhance IT and Strategic Agility
The path to autonomy requires an IT roadmap that can handle rapid change. Key actions include:
- Avoiding vendor lock-in to maintain flexibility as technology evolves.
- Building expertise in process mapping and implementing new methods for selecting AI use cases.
- Modernizing legacy systems while ensuring prioritization of discretionary spending for AI investments that match the CEO’s goals.
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Adopt Adaptable Governance
Traditional governance can hinder the speed required for autonomy. Insurers should:
- Implement an adaptable governance style that balances IT discipline (quality gates and testing) with the freedom for business units to innovate.
- Foster C-suite collaboration across all functions (CFO, COO, CHRO, etc.) to integrate autonomous concepts into every aspect of the organization.
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Prepare the Augmented Workforce
Autonomy does not mean removing humans. It means changing their roles. Insurers should:
- Design an augmented workforce that combines human experience with machine efficiency to amplify decision-making.
- Incorporate “human-in-the-loop” moments, particularly for emotionally sensitive processes like claims, where a customer may value empathy over speed.
- Engage employees early in the process to gain buy-in for process reinvention and avoid frustration.
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Re-envision Products and Customers
Insurers must look ahead to how they sell products and how consumers use them by 2030:
- Auto-adapting Products: Begin diving into the core of insurance products to determine how they can evolve and reconfigure themselves in real-time while in use.
- Machine Customers: Redesign sales, marketing, and service processes to accommodate nonhuman economic actors—AI agents that will soon be making purchasing decisions on behalf of humans.
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