The End of the “Build or Buy” Dilemma in Insurance
The rise of generative AI and foundation large language models (LLMs) has presented insurance providers with a crucial decision.
Take, for example, a photo analysis solution for automating motor claims assessment or a personalization engine for enhancing the policyholder experience. Previously, insurers had two options:
- Buy off-the-shelf solutions – leveraging existing products.
- Build from scratch – developing proprietary solutions internally.
Each approach comes with its own set of benefits and challenges, making the decision a strategic one. However, with the emergence of generative AI and pre-trained, multimodal AI models, this dilemma is becoming obsolete. A new path is emerging: configuring specialized LLM-first solutions. Let’s explore this shift.
A. Buying Off-the-Shelf Solutions
Insurance companies are increasingly looking externally for solutions they once built in-house. The rise of lightweight APIs, cloud services, and connected point solutions has made this shift more attractive.
Key Benefits:
- Speed to Market – One of the main advantages of buying pre-built solutions is the ability to launch quickly. Using existing modules and frameworks accelerates development, allowing insurers to respond swiftly to market demands. Partnering with an insurtech provider can reduce deployment time significantly. For example, Zelros enables insurers to go live within 8–12 weeks, compared to the 3–4 years typically required for an in-house solution.
- Cost Efficiency – Leveraging existing solutions reduces development costs. With plug-and-play components, insurers avoid unnecessary reinvention, making this an economical option. Many insurtech firms have secured hundreds of millions in funding to build platforms—far beyond what a single insurer could allocate for a similar project.
- Industry Expertise – Many insurtech solutions come with embedded industry-specific knowledge. These solutions are designed to comply with insurance regulations while offering the flexibility to customize features without compromising compliance. Their expertise combines technical prowess with deep domain knowledge, ensuring high regulatory standards are met.
B. Building Solutions from Scratch
While developing a solution in-house provides full control over its design, it does not eliminate the need for external collaboration. Both approaches share common resources, frameworks, and methodologies—but internal development fosters closer teamwork, albeit with cultural and organizational constraints.
Key Benefits:
- Unmatched Customization – Building from scratch allows insurers to design a solution tailored to their exact needs, ensuring it aligns seamlessly with their unique business processes.
- Innovation at its Core – For companies seeking to push the boundaries of insurtech, internal development fosters innovation, enabling them to integrate cutting-edge technologies and develop proprietary solutions for market leadership.
- Complete Control – Companies that develop their own technology stack retain full control over security, compliance, and adaptability. This flexibility ensures they can evolve with future technological advancements without external limitations.
- Long-Term Investment – While the initial investment may be higher, building in-house creates a long-term strategic asset that aligns perfectly with a company’s vision and goals.
C. Configuring Specialized LLM-First Solutions
The rise of generative AI and large language models (LLMs) is changing how insurers approach AI applications. Rather than choosing between “buying” or “building,” companies can now configure AI-driven solutions tailored to their needs.
Key Advantages of LLM-First Solutions:
- Adaptability & Scalability – Configurable LLM-driven solutions offer flexibility while ensuring scalability. Insurers can customize AI-driven workflows to match their specific needs while keeping room for growth. Unlike static off-the-shelf solutions, these models evolve with the business.
- Multimodal Capabilities & Team Dynamics – LLMs can process diverse data types (text, voice, images, structured data), breaking down traditional silos in insurance operations. This shift also redefines data team roles, increasing the importance of LLMOps while challenging the traditional role of data scientists.
- Foundation Model Advantage – Unlike traditional AI models that require extensive training on proprietary datasets, foundation LLMs are “pre-trained” and adaptable. This democratizes AI adoption, making it accessible without extensive data science expertise.
- Configuration Over Creation – Instead of building AI models from scratch or buying rigid pre-built solutions, insurers can now configure AI-driven workflows using strategic prompt design. This shift allows business teams to shape AI-driven outcomes without needing deep technical knowledge.
- Value Redistribution in Tech Applications – The focus in AI-driven applications is shifting towards user experience (UX). With generative AI, UX becomes the primary differentiator, making design and seamless integration into existing systems crucial.
- The Rise of Specialized B2B SaaS Applications – A new wave of B2B SaaS applications is emerging, centered around LLMs. These solutions prioritize domain-specific configurations, often using low-code/no-code platforms, allowing insurers to implement AI without extensive technical resources.
Conclusion: The End of the “Build or Buy” Debate
With the rise of multimodal LLMs and generative AI, the traditional “Build vs. Buy” debate is fading.
Insurers are unlikely to develop their own LLM models due to the significant investment required. At the same time, they won’t adopt generic AI software that lacks flexibility. Instead, they will partner with specialized AI-first technology providers to configure AI-driven applications that align with their business objectives.
This approach allows insurers to increase agility while focusing on areas where they can gain a competitive edge—enhancing employee efficiency and improving policyholder experiences.