I have spent the last few years trying to noodle about and then apply the ideas of design thinking, autopoiesis, and applied physics to commercial insurance and the business. Well those are some big words and kind of unusual overlaps. We don’t spend a lot of time talking about physics when we’re deploying sales automation systems in insurance companies but I think we should.
I think with the shifts of software technologies and the adjustments happening with legacy systems there’s some opportunities to think about deploying new systems that will be helpful based on these ideas in insurance. The impact of new AI systems and the systems and software that it helps to generate now is affecting the way in which large companies build customer systems. This is evident in the major e-commerce companies like Amazon and the search/and companies like Google. In the last 3 years they have been making software and features differently than they made in the past. This indicates that there should be a change in banking and insurance systems creation, and legacy modernization as they follow the trends in their area of business.
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Designing Next-Generation Insurance Systems: Autopoiesis, Applied Physics, and Design Thinking in Commercial Insurance
1. Contextual Overview
The commercial insurance industry is undergoing rapid transformation, driven by technological advancements, evolving customer expectations, and competitive pressure. Carriers are now shifting towards platform-based systems that support brokers in providing seamless, personalized products to business customers.
To build effective systems that serve both carriers and brokers, it’s essential to incorporate:
Design Thinking: Ensuring systems are intuitive, customer-centric, and adaptive.
Autopoiesis: Creating self-sustaining, self-evolving insurance ecosystems that respond dynamically to market and environmental changes.
Applied Physics Principles: Establishing reliable processes, feedback loops, and system structures that align with operational efficiency and real-world constraints.
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2. Design Thinking as a Framework for Insurance System Innovation
Design thinking offers a way to bridge carrier operations with broker and customer needs, ensuring a human-centered approach to complex business challenges. This framework ensures that carriers build insurance platforms that solve real problems for brokers, improving engagement and retention.
Empathy with Brokers and Commercial Customers:
Brokers need platforms that streamline underwriting, quoting, and policy issuance processes. Understanding their workflow and pain points allows carriers to build systems that fit naturally into the broker's day-to-day operations.
Similarly, businesses seeking insurance expect tailored policies that match their unique needs. Systems that incorporate empathy allow carriers to offer dynamic pricing and personalized recommendations.
Rapid Prototyping for Insurance Products:
Prototypes allow carriers to test new digital tools with brokers. For example, a quoting tool that predicts policy adjustments based on customer input can be refined iteratively. We can build these rapid prototypes using design ideas and generative AI to do architecture and design and generate code and modify it based on feedback from subject matter experts instantaneously decreasing the cycle time it takes to do design iterations, and eliminating drawing tools from the cycle and focused on methods and means to create software that is modern and new.
Feedback loops ensure continuous improvement, where real-world insights help build systems that balance automation with manual control for complex cases. All conversations in design and and creation of software will be captured and change control logs instantaneously and subject to the controls and administration of insurance companies. There won’t be anyone to enter things into things like Jira that will be automatic and the monitoring will be autonomous as well as systems change.
End-to-End Integration:
Platforms must provide seamless workflows for broker engagement, underwriting, compliance, and claims management. Design thinking encourages collaboration across these functions, eliminating silos, while bridging and orchestrating across core systems. Customer journeys and broker journeys go across systems from marketing and sales, to financial needs analysis on the web, across into quoting and then risk information that is bound into policies which trigger publication of deck pages and policy contracts that are stored in document Management Systems and shared with a broker as well as the insured. This is complicated stuff across multiple systems that follow Conway’s law. They are centralized or decentralized depending on the nature of the organization developing them. They are not orchestrated and they’re not designed to be orchestrated. The worst case scenario is that they have business logic embedded in user interfaces and the only way to enter information to the systems is through those interfaces. New systems will have the forethought that they will do end-to-end immigration and headless orchestration systems will enable journeys across existing systems and ease the migration to new systems in the back end as well as front ends. This isn’t really lipstick on the pig it’s crawling things properly until everything can get the market together.
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3. Autopoiesis in Insurance Systems: Building Self-Sustaining Ecosystems
Autopoiesis, in the context of commercial insurance, reflects systems that adapt and sustain themselves through feedback and environmental responses. The aim is to build insurance ecosystems that reduce friction, evolve automatically, and respond to emerging risks without manual intervention. The idea is based on biological organisms that start as simple and evolve into complex things and establish homeostasis and an ability to live and be stable across time meeting the mission of the organism whatever it is.
Examples of Autopoietic Insurance Systems:
Dynamic Risk Adjustment:
A system that analyzes market trends, policy data, and environmental factors (e.g., weather patterns for property insurance) to automatically adjust risk assessments. This ensures premiums reflect real-time risk levels without requiring frequent manual recalibration.
Self-Optimizing Platforms:
Platforms could learn from broker behavior and customer interactions, fine-tuning underwriting rules and recommendations. Over time, these systems improve efficiency and accuracy without external intervention, embodying the principles of autopoiesis.
Embedded Insurance for Brokers and Clients:
Carriers can offer embedded insurance options, automatically suggesting policy updates based on client behavior and external events (e.g., adding cyber coverage after a reported security breach). Such systems become self-maintaining, ensuring customers always have relevant coverage.
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4. The Role of Applied Physics Principles in Insurance Systems
Applied physics provides essential tools to build robust, scalable insurance systems by designing feedback loops and managing complexities. In this context, physics-based thinking helps align platform behaviors with real-world constraints such as regulatory compliance, processing delays, and operational bottlenecks.
Feedback Loops and Control Systems:
Insurance systems benefit from continuous feedback between brokers, carriers, and customers. Applied physics helps structure these loops for stability, ensuring the system maintains equilibrium under changing conditions (e.g., sudden regulatory shifts or economic disruptions).
Predictive Analytics as a Control Model:
Just as physics-based models predict outcomes in dynamic systems, predictive analytics can anticipate claims patterns, renewal likelihoods, and broker performance. This allows carriers to adjust policies and offers proactively.
Energy Efficiency Applied to Business Processes:
In physical systems, energy management is crucial to maintaining performance. In insurance systems, efficiency in processes—from policy issuance to claims settlement—ensures resources (time, personnel, and computing power) are optimized. It sure should be seeking to remove friction by either adding lubrication, or aerodynamics in their knowledge-based systems. Streamlined, frictionless workflows reduce broker frustration and increase productivity.
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5. Key Overlaps and Convergences: Autopoiesis, Applied Physics, and Design Thinking
Self-Evolving Systems with Embedded Feedback:
Platforms need to learn and adapt with minimal human intervention. For example, AI-driven underwriting systems can adjust risk appetite based on broker feedback and real-world outcomes, embodying both autopoietic and design principles.
Grounding Abstract Concepts with Physics-Based Models:
While design thinking encourages experimentation, applied physics provides the structure and stability necessary to avoid chaotic development. A physics-inspired approach ensures platforms behave predictably under stress, such as a surge in claims during a natural disaster.
Adaptive, Human-Centered Interfaces:
Systems that merge intuitive design with real-time feedback become powerful tools for brokers. For instance, platforms that learn from broker behavior and adjust dashboards or product recommendations support both user engagement and system efficiency.
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6. Ideas based on this approach: — Next Generation Insurance Systems
💡Develop Autonomous Insurance Ecosystems:
Build platforms that dynamically adjust policies and automate claims management without human intervention. Leverage autopoietic principles to ensure self-sustaining processes.
💡Implement Predictive Feedback Loops:
Use predictive analytics to monitor market trends, broker behavior, and policy performance. Integrate these insights into automated systems that adjust pricing, risk assessments, and recommendations.
💡Create Broker-Focused User Interfaces:
Ensure that platform tools match the workflow needs of brokers. Use design thinking workshops to co-create features with brokers and prototype continuously.
💡 Build Resilient Platforms with Applied Physics Principles:
Ensure systems remain stable under stress through feedback-driven load management and resource optimization. Use simulation environments to test new features under real-world conditions.
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7. Repeating Things to Make them Stick
Building effective commercial insurance systems requires the convergence of design thinking, autopoiesis, and applied physics. These frameworks work together to create adaptive, human-centered, and efficient platforms that empower brokers and improve customer engagement.
Design thinking ensures systems remain intuitive and focused on real-world needs.
Autopoiesis provides the ability to evolve and maintain relevance without constant intervention.
Applied physics offers stability, ensuring systems are robust under dynamic conditions.
These principles together offer a blueprint for next-generation insurance platforms, positioning carriers to meet the challenges of a competitive, rapidly changing market while enabling brokers to deliver better outcomes for commercial clients.
Here are five experiment-based approaches for a CTO to adopt design thinking, autopoiesis, and applied physics when building next-generation insurance platforms to support brokers and commercial customers.
All these experiments are based on using new front end tools from anthropic, open Ai, Microsoft, Amazon, Perplexity, Make.com, TypeDream, Bardeen, FreedomGPT, DataVysta and Cove.ai etc
Assume that these all are near or fully implemented agentic systems as defined by Dr Andrew Ng.
In all cases the environments that people are using our team environments from these companies in which the subject matter experts and the technologists are all working together and observing each other work in these generative AI systems learning from one another and about the reasoning and planning it is required to create new software this way.
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1. 🚀 Launch Rapid Prototyping Sprints with Brokers
Objective: Use design thinking to co-create platform features with brokers.
How to Start:
Organize two-week design sprints with small groups of brokers.
Focus each sprint on solving a specific workflow problem (e.g., simplifying quote generation).
Create low-fidelity prototypes (mockups or clickable demos) and gather immediate feedback from participants.
Expected Outcome:
This experiment helps identify key user pain points and co-develop features that brokers are more likely to adopt. Iterative feedback ensures alignment with real needs.
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2. 🔄 Implement a Self-Learning Risk Adjustment Engine
Objective: Explore autopoietic principles by developing a system that adjusts risk models without manual updates. It does so absolutely transparently under the control of humans in the loop that back test the changes and allow for adjustments as the new automated updates are getting ready for production. Systems like this would be useful in risk that are affected by extreme weather or fire.
How to Start:
Use historical policy data to train an AI-based risk model.
Deploy the model in a sandbox environment and allow it to recalibrate based on live inputs (e.g., new claims or market trends).
Monitor the model for emergent behaviors that optimize pricing or risk appetite.
Expected Outcome:
A system that evolves based on real-world inputs will require less intervention and provide more accurate risk assessments over time.
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3. ⚙️ Set Up a Broker-Centric A/B Testing Environment
Objective: Utilize applied physics principles by experimenting with feedback loops and performance adjustments. It is expensive to set up 5 or 10 systems at once and then do testing across a through J… using generative techniques and software it will be easier to have 10 tests running at once and then be prepared with modern statistics for optimization purposes and selection of the best practices. It’s important to know that sometimes it’s better to run localized views of software purpose built for different personas in different places… imagine you are an international company and you’ve got operations going on in the Southern and Northern hemispheres... On the given day it is the hottest day of summer and the coldest day of winter for your customers… one size does not fit all. We need to take advantage of software to change that idea.
How to Start:
Deploy two variations of a broker-facing feature (e.g., a dashboard layout or quote workflow).
Use A/B testing to track metrics like user engagement, task completion time, and error rates.
Optimize based on real-time broker behavior and feedback.
Expected Outcome:
The experiment will reveal which design features enhance productivity and which need refinement, enabling continuous platform improvement.
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4. 🌱 Create an Embedded Insurance Ecosystem Trial
Objective: Develop a self-sustaining system by embedding insurance offers within broker workflows. Start thinking of your quoting systems as being headless with deep embedding available to any one of your partners across the critical value streams of insurance.
Whether that’s in a major Design shop or in an Auto dealer, it should be simpler to add headless insurance to the barring stream of a customer without annoying them and make it easy to use for the person involved in the sale… all the software used on computers should be unique to the customer journey. This is the future of software , that is shaped to the needs of the buyer and the seller and not the manufacturer.
How to Start:
Collaborate with key broker partners to pilot embedded insurance tools that suggest coverage options dynamically during interactions with commercial customers.
Track how brokers and customers respond to these suggestions, and measure conversion rates.
Expected Outcome:
This experiment validates whether embedded insurance can increase broker efficiency and customer satisfaction without disrupting existing workflows.
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5. 📉 Use Predictive Analytics to Forecast Claims and Renewal Trends
Objective: Apply applied physics models to predict future system behavior and optimize processes. Each of the predictive analytic models are implemented as agents with notification and alarm layers in order to start adjusting to specific needs across the chance of data that may have significant shifts in the recent past. Is more likely to be using business statistics here and then long form historical analysis due the nature of risk.
How to Start:
Train predictive models using historical claims data and external factors (e.g., weather, economic conditions).
Deploy the models to forecast claim volume and renewal probabilities for specific products or customers.
Integrate these forecasts into platform dashboards to help brokers proactively manage customer relationships.
Expected Outcome:
This experiment allows carriers to align pricing strategies and broker support efforts with predicted trends, enhancing both efficiency and profitability.
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These experiments provide practical starting points for a CTO to integrate design thinking, autopoiesis, and applied physics into their organization. Each initiative fosters continuous learning, user engagement, and system resilience, aligning technology efforts with evolving market needs.
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Rob Tyrie is a consultant and advisor specializing in the insurance in banking industry and supporting software companies engage with large scale insurance carriers, distribution channels and other partnerships. He’s been thinking about self-healing and resilient systems and insurance for over 25 years. As applied technology in each wave of computing from mainframe and now to generative AI.
You can drill down on more information about his practice on http://www.ironstoneadvisory.com
