What I’m learning about the second wave of insurance digital transformation in Indonesia

In the insurance industry, digital transformation has been a key focus since my arrival early in 2026. While my background is in software and enterprise systems, I've found that insurance requires a unique combination of specialised knowledge, coordination, and professional judgement within everyday operations.

Many insurance processes have evolved due to the nature of the business – dealing with financial protection, personal and medical information, risk assessment, contractual obligations, and long-term commitments. Accuracy, compliance, and accountability are paramount, so technology should enhance existing processes and knowledge, rather than replace them outright.

The first wave of digital transformation laid important groundwork, digitising customer applications, enabling electronic document submission, providing agents with advanced digital tools, and making policy information more accessible through online and mobile channels. This digital foundation has improved accessibility and created the infrastructure for future advancements.

I view the second wave as a logical progression, focusing on helping the organisation behind the services work with information more intelligently. It builds upon the first wave's success in creating digital channels, data, and interconnected enterprise systems. Digitising documents improves their movement, storage, and retrieval, but AI and document intelligence offer the next opportunity to make the information within those documents easier to understand and use.

For instance, an insurance application may contain identity documents, financial data, medical declarations, and supporting documents. While much of this information arrives digitally today, the next step is to assist professionals in understanding the combined context more efficiently. Technology can support document classification, optical character recognition (OCR), information extraction, completeness checking, and consolidation before the case reaches the professional responsible for assessment. The goal is not to eliminate professional judgement but to provide a better starting point for it.

AI implementation in insurance goes beyond the capabilities of the AI model itself. It requires understanding the underlying business processes, identifying authoritative information systems, applying relevant business rules, determining data access permissions, managing verification requirements, and ensuring transparent and auditable record-keeping.

Insurance organisations have established layers of controls, business rules, review mechanisms, and professional responsibilities due to the real-world consequences of their decisions. When AI becomes deeply integrated into these workflows, these structures become even more critical.

In essence, enterprise AI in insurance should be seen as a combination of AI capability, enterprise integration, business rules, governance, and human expertise. The AI model is just one component within a broader operating architecture. The greatest potential for AI lies not only in model implementation but in creating value across the entire operating system.