Abstract
Many organizations continue to manage artificial intelligence through use cases, applications, and vendor solutions. While this approach supports short-term implementation decisions, it becomes increasingly difficult to maintain as the AI landscape evolves. New solutions emerge weekly, capabilities converge, and previously distinct technologies become integrated into larger AI systems.
This article proposes an alternative perspective: shifting from an AI Use Case Inventory towards an AI Capability Model. Building on current developments in Generative AI, Reasoning AI, Agentic AI, and Physical AI, this model focuses on understanding what AI can fundamentally perceive, understand, reason about, and execute.
Based on this capability-centric view, a maturity framework and an AI Development Index (ADI) are introduced to systematically assess the technological maturity and business adoption of AI capabilities across industries, with particular relevance for insurance organizations.
The Problem with Traditional AI Classification
Historically, AI solutions have been categorized according to technologies.
Typical classifications include:
- Chatbots
- Voicebots
- OCR
- Computer Vision
- Machine Learning
- Generative AI
This approach was useful when technologies were largely isolated.
Today, however, a modern AI solution rarely consists of a single technology.
Consider a customer service voicebot.
A modern voicebot typically combines:
- Speech recognition
- Natural language understanding
- Retrieval systems
- Large Language Models
- Reasoning engines
- Workflow automation
- Speech synthesis
The question therefore becomes:
Is this solution a voicebot, an agent, a chatbot, a reasoning system, or a workflow engine?
The answer is all of the above.
Technology-based classification begins to lose explanatory power as AI systems become increasingly multimodal and interconnected.
A Different Perspective: AI as an Evolving Set of Cognitive Capabilities
A more durable approach is to classify AI according to the capabilities it possesses.
This perspective aligns closely with observations made by Jensen Huang (Youtube Link), who frequently describes AI as a system that learns structures rather than merely processing language. That shows as well, what AI can not do.
From this perspective, AI evolves similarly to human cognition.
The central question becomes:
What aspects of reality can an AI system understand?
This shift creates a more stable framework because capabilities evolve more slowly than products and technologies.
The First Dimension: Understanding
The first major capability layer concerns understanding.
Every meaningful AI system must first develop an understanding of a particular domain before it can reason or act within it.
Understanding Human Language
This is the capability most organizations associate with AI today.
The AI understands:
- Text
- Documents
- Policies
- Contracts
- Emails
- Conversations
Insurance examples include:
- Claims assistants
- Underwriting copilots
- Legal review systems
- Compliance assistants
This capability has already reached high maturity.
Most modern foundation models demonstrate strong performance in this domain.
Understanding Human Voice
The next capability extends language understanding into spoken interaction.
The AI understands:
- Spoken language
- Conversational context
- Accents
- Dialogue structure
Insurance examples include:
- First Notice of Loss (FNOL) voice assistants
- Broker support systems
- Customer service automation
The focus is no longer on speech recognition alone.
The capability involves understanding intent and context throughout an entire interaction.
Understanding Images
Computer Vision systems enable AI to interpret visual information.
Examples include:
- Vehicle damage recognition
- Property damage assessment
- Medical image analysis
- Fraud detection
Today, many image-based insurance applications focus on identifying visible objects and damage patterns.
For example:
- Broken windshield
- Dented bumper
- Water damage
- Roof deterioration
This represents an important but still relatively narrow level of understanding.
Understanding Physics
A significantly more advanced capability emerges when AI moves beyond identifying objects and begins understanding physical causality.
This is one of the most important future developments for insurance.
The AI understands:
- Forces
- Material behavior
- Impact dynamics
- Energy transfer
- Structural deformation
Consider a vehicle accident.
Current image recognition systems may identify:
- A damaged door
- A broken headlight
- Scratches on a vehicle
A physics-aware AI would additionally reason:
- From which direction did the collision occur?
- Is the observed deformation consistent with the reported accident description?
- What speed range likely caused the observed damage?
- Which secondary damages should be expected?
The AI is no longer simply recognizing damage.
It is reconstructing reality.
This represents a fundamentally higher level of intelligence.
For insurance claims management, this capability could become transformative.
Future systems may automatically assess:
- Plausibility of claims
- Fraud indicators
- Estimated impact forces
- Repair implications
The transition from “understanding images” to “understanding physics” represents a major maturity leap.
Understanding Human Behaviour
Another capability layer focuses on behavioral patterns.
The AI understands:
- Customer preferences
- Decision patterns
- Behavioral anomalies
- Fraud indicators
Applications include:
- Churn prediction
- Recommendation systems
- Fraud detection
- Customer journey optimization
Understanding Business Processes
AI increasingly learns how organizations operate.
The AI understands:
- Process flows
- Escalation paths
- Dependencies
- Operational bottlenecks
This capability enables process automation and process mining.
Understanding Financial Systems
This capability focuses on economic and financial relationships.
Examples include:
- Market dynamics
- Asset correlations
- Pricing mechanisms
- Risk structures
Applications include:
- Asset management
- Pricing optimization
- Reserving
- Capital modelling
Understanding Biology
One of the most advanced emerging capabilities.
The AI understands:
- Proteins
- Molecules
- Genes
- Cellular interactions
Although primarily relevant for healthcare today, long-term implications for health insurance and life insurance could be substantial.
As AI capabilities continue to mature, access to data alone will no longer be a sufficient competitive advantage. Organizations will increasingly need to make tacit knowledge accessible – the expertise, judgment, contextual understanding, and practical experience that have historically remained undocumented. Future AI systems will derive substantial value not only from understanding documents, images, voice, or physical reality, but also from learning and operationalizing the collective knowledge embedded within the organization itself. The ability to transform human expertise into organizational intelligence may become one of the most important differentiators of the next generation of AI-enabled enterprises.
In insurance, some of the most valuable knowledge often resides in experienced claims handlers, underwriters, sales people, actuaries, fraud specialists, and compliance experts. Their ability to recognize patterns, assess exceptions, interpret incomplete information, and apply contextual judgment is frequently not documented in systems or processes. Capturing and augmenting this expertise may represent the next major frontier beyond Generative AI and Agentic AI, enabling organizations to build AI systems that not only process information but also preserve and scale institutional knowledge.
Understand Data → Understand Physics → Understand Human Expertise (tacit knowledge) → Understand Reality
Beyond Understanding: The Capability Hierarchy
Understanding alone does not create business value.
Once AI understands a domain, additional capability layers emerge.
Generate
The AI creates new outputs.
Examples:
- Text
- Speech
- Images
- Video
- Code
Reason
The AI performs structured analysis.
Examples:
- Root cause analysis
- Risk assessments
- Recommendation generation
- Multi-step problem solving
Decide
The AI proposes decisions.
Examples:
- Risk recommendations
- Prioritization
- Next-best-action suggestions
Act
The AI executes tasks.
Examples:
- Updating systems
- Triggering workflows
- Creating claims records
- Scheduling actions
Collaborate
Multiple AI agents cooperate.
Examples:
- Claims handling ecosystems
- Multi-agent underwriting
- Autonomous process orchestration
Why a Maturity Model Becomes Necessary
As organizations adopt more AI capabilities, a new challenge emerges.
Not all capabilities are equally mature.
For example:
Text understanding is highly mature.
Physics understanding remains an emerging capability.
Yet both may appear under the broad label of “AI”.
Without a maturity model, executives cannot distinguish between:
- Proven capabilities
- Emerging capabilities
- Experimental capabilities
This leads to unrealistic expectations and poor investment decisions.
Capability Maturity Score (CMS)
The first component of the model measures technological maturity.
| Score | Maturity |
|---|---|
| 1 | Research |
| 2 | Experimental |
| 3 | Emerging Market |
| 4 | Commercially Available |
| 5 | Enterprise Ready |
| 6 | Commodity Capability |
Example:
| Capability | CMS |
|---|---|
| Text Understanding | 6 |
| Voice Understanding | 5 |
| Image Understanding | 5 |
| Physics Understanding | 2 |
| Agentic Execution | 3 |
| Multi-Agent Collaboration | 2 |
Business Adoption Score (BAS)
Technological maturity alone is insufficient.
Organizations must also understand adoption.
| Score | Adoption |
|---|---|
| 1 | No Adoption |
| 2 | Proof of Concept |
| 3 | Pilot |
| 4 | Productive Use |
| 5 | Enterprise Scale |
| 6 | Industry Standard |
Example:
| Capability | BAS |
|---|---|
| Text Understanding | 6 |
| Voice Understanding | 4 |
| Image Understanding | 4 |
| Physics Understanding | 1 |
| Agentic Execution | 2 |
The AI Development Index (ADI)
Combining both dimensions creates a more meaningful measure.
The AI Development Index is calculated as:
ADI = Capability Maturity Score × Business Adoption Score
This creates a dynamic measure that reflects both:
- Technical feasibility
- Real-world business utilization
Example:
| Capability | CMS | BAS | ADI |
|---|---|---|---|
| Text Understanding | 6 | 6 | 36 |
| Voice Understanding | 5 | 4 | 20 |
| Image Understanding | 5 | 4 | 20 |
| Physics Understanding | 2 | 1 | 2 |
| Agentic Execution | 3 | 2 | 6 |
Strategic Value of the AI Development Index
The AI Development Index enables leaders to answer questions that traditional AI inventories cannot.
For example:
- Which AI capabilities are already mature?
- Which capabilities should be piloted today?
- Which capabilities should be monitored?
- Which capabilities remain too immature for production deployment?
- Which future developments could fundamentally reshape insurance?
Most importantly, it shifts the conversation away from vendors and products.
Instead, it focuses attention on the underlying evolution of intelligence itself.
Conclusion
The future of AI governance, portfolio management, and strategic planning will likely move beyond cataloging use cases and technologies.
A capability-centric perspective provides a more stable framework for understanding how artificial intelligence evolves.
The proposed model follows a simple logic:
Understand → Generate → Reason → Decide → Act → Collaborate
In regards to “understanding”, the generated output will increase in perceived quality, if more understanding as capability growths (e.g., physics, tacit knowledge, reality).
Within this framework, the maturity of individual capabilities can be measured through the Capability Maturity Score and their business relevance through the Business Adoption Score.
Together, these dimensions form the AI Development Index, creating a structured and measurable view of the evolution of artificial intelligence.
For industries such as insurance, this approach provides not only a better understanding of today’s capabilities, but also a roadmap for identifying the next generation of AI systems that will emerge over the coming decade.
