Buy, Build, or Partner? Navigating AI Strategy in Life and P&C Insurance

AI in insurance is no longer a futuristic concept. It is becoming foundational across Life and P&C underwriting, claims, and operations. But once you accept that AI is necessary, a more strategic question comes into focus: How do you get there?

For most insurers, it comes down to three options: buy, build, or partner. Each has its pros and cons, and the right answer depends on your goals, resources, and risk appetite. In this blog, we’ll break down the key considerations to help you navigate that decision, with insights for both Life and P&C carriers.

Understanding Your Options

Each path offers different advantages and challenges, and understanding these tradeoffs is key to choosing the right AI strategy.

1. Build: Internal Development

The pace of change in artificial intelligence is unlike anything we’ve seen before in tech. What feels cutting-edge today may be outdated in months, or even weeks. For companies trying to build AI solutions internally, this speed introduces both risk and opportunity. 

You could spend months or years developing a solution only to discover that the technology has shifted, customer expectations have changed, or a competitor has already deployed a more advanced, cost-effective model. At the same time, companies that are agile and adaptable will be better positioned to get ahead and stay competitive.

Companies that are agile and adaptable will be better positioned to get ahead and stay competitive.

Building your own AI platform internally offers control, customization, and potential ownership of valuable intellectual property. You can tailor it precisely to your business logic, train it on proprietary data, and integrate it tightly with internal systems. 

But this approach is often underestimated in terms of cost, time, and risk. A full build requires not just engineering talent, but dedicated teams for data science, data infrastructure, compliance, and maintenance. There’s ongoing investment in data cleaning, integration with legacy systems, model retraining, and regulatory updates. It’s rarely just a one-time expense.

Research suggests that a full internal build can take three to ten years to generate impact.

Research suggests that a full internal build can take three to ten years to generate impact. Compare that to one to two years with a buy or partner strategy. That’s a long gap to deliver value, especially in a competitive market. Technical challenges, shifting business priorities, and lack of internal alignment can also easily derail internal projects. Many never make it past pilot. When that happens, the resources are lost, and the original problem remains unsolved.

Building can work, but it requires strong leadership, deep pockets, and a long-term commitment. Even then, the total cost of ownership is often underestimated.

One of the biggest mistakes when building in this environment is over-investing in the model itself. Foundation models like GPT-4o, Claude 3, and Gemini are improving rapidly. Infrastructure, including cost per token, inference speed, and access to open-source alternatives, is shifting just as quickly. New approaches like retrieval-augmented generation (RAG), agent-based systems, and dynamic toolchains are redefining what’s possible, and what’s expected. Customer expectations continue to rise just as fast. What was innovative last year is now baseline.

Furthermore, cutting-edge tech alone isn’t enough. To succeed, AI must be built around real workflows, not just models. In industries like insurance, law, and healthcare, solving domain-specific problems—like accelerating underwriting or summarizing legal records—is more valuable than having the flashiest model. What matters is usability: clear outputs, strong UX, integration with existing tools, and easy adoption. 

To succeed, AI must be built around real workflows, not just models.

Staying model-agnostic is key. Committing to one model can backfire as newer, better options emerge. A modular architecture allows you to swap models without rebuilding your system. Tools like LangChain or custom orchestration frameworks support this flexibility.

The real competitive edge lies in proprietary data and feedback loops. While anyone can use foundation models, your structured, domain-specific data and user interactions can generate insights that general models can’t replicate, creating lasting value and defensibility.

Companies also need to stay adaptable. Today’s best architecture could be tomorrow’s bottleneck. The winners won’t be those with the most advanced models, but those who can move fast, stay modular, and solve real problems as the landscape evolves.

The winners won’t be those with the most advanced models, but those who can move fast, stay modular, and solve real problems as the landscape evolves.

2. Buy: Off-the-Shelf Solutions

Buying is the fastest path to implementation. Vendors offer tested, scalable tools that are often backed by proven performance and industry benchmarks. For insurers who need to move quickly or focus on immediate operational goals, this can be a smart choice.

For insurers who need to move quickly or focus on immediate operational goals, buying an off-the-shelf solution can be a smart choice.

The tradeoff is flexibility. An off-the-shelf solution may not fit your processes perfectly, and customization options are often limited. There’s also the risk of becoming too reliant on one vendor. If the system is deeply integrated into your processes or stores data in proprietary formats, switching to another provider later can be difficult and expensive. 

This is known as vendor lock-in, and it can limit your ability to adapt or negotiate over time. Data security and compliance should also be part of the evaluation, especially in regulated sectors like insurance.

It’s crucial to ask questions, such as:

• Has the platform undergone independent testing for bias?
• How accurate is the AI when applied to our real-world data and workflows?
• What kind of reliability, support, and model transparency can we expect over time?

It’s also important to assess onboarding and long-term support. A common complaint with off-the-shelf tools is that after purchase, teams are left on their own, without the training, resources, or guidance needed to fully adopt and use the platform effectively.

Still, for many carriers, buying offers a clear path to ROI without the burden of internal build-out.

3. Partner: Strategic Collaboration

Partnering with a vendor allows insurers to gain the benefits of customization and shared development, without taking on the full burden of building internally. A strong partnership means you can influence the roadmap, get integration support, and ensure the solution aligns with your workflows and business goals.

A strong partnership means you can influence the roadmap, get integration support, and ensure the solution aligns with your workflows and business goals.

This approach often combines speed with flexibility. You benefit from the vendor’s expertise and existing infrastructure, while also shaping a solution that meets your specific needs. Many partnerships also include ongoing product updates, regulatory support, and transparent collaboration.

However, success depends on choosing the right partner. Not all vendors are equal, and “AI” is used loosely across the industry. It takes careful vetting to find a partner with domain expertise, proven technology, and a track record of success.

It takes careful vetting to find a partner with domain expertise, proven technology, and a track record of success.

Another consideration is vendor stability. New or smaller startups can carry more risk. If they run out of funding or shut down unexpectedly, you may be left without a supported product. Betting on the wrong partner can cost time, money, and momentum.

Done well, a strategic partnership offers insurers a middle ground to AI implementation and development: a flexible, aligned solution without having to build it all from scratch.

What to Consider When Choosing Your Path

Start with the problem you’re solving. Is it about efficiency? Reducing risk? Speed? Scale? The more nuanced and complex the use case, the more important it is to work with a solution that’s built for your specific needs.

Ask yourself the following questions:

• Do we have the capability and budget to build and maintain this internally?
• How complex is the problem we’re solving, and how much customization do we need?
• What does success look like, from pilot to full deployment?
• How will we govern the AI, test for bias, and meet compliance standards?
• Can this solution scale across lines of business?

Life vs. P&C: Different Needs, Different Approaches

While the core question applies to all insurers, Life and P&C insurance carriers often arrive at different answers because their challenges are not the same.

In Life Insurance, the focus is on deep analysis of long-duration data, structured medical evidence, and ensuring compliance in every step of the process. Underwriting is complex and data-rich, and the AI needs to surface structured insights that are accurate, explainable, and auditable.

In P&C, success depends on speed and the ability to handle variation. Every claim is different, not just in terms of circumstances, but in how each individual recovers, responds to treatment, or experiences loss. Two claims may appear similar on paper but require entirely different approaches based on medical progression, causation, or external factors. 

There’s also a high volume of unstructured data, tight timelines, and increasing pressure from plaintiff attorneys to respond to numerous demand packages. Claims professionals must weigh multiple variables in real time. 

Therefore, an effective solution must go beyond surfacing information, it needs to interpret the data in context, connect relevant details, and support timely, defensible decisions, supported by human judgement.

Choosing the Right AI Partner

With so many vendors on the market claiming to offer AI for insurance, it’s important to look beyond the label. Ask the hard questions:

• Do they have true industry expertise, or are they offering general-purpose tools?
• Is their methodology transparent? Can they explain their models and how they handle bias?
• Are they aligned with your data security, compliance, and operational needs?
• Do they offer support, integration help, and ongoing collaboration?

A good AI vendor doesn’t just hand over a product. They help you integrate it, prove its value, and evolve it with your business.

How DigitalOwl Delivers on Every Front

DigitalOwl isn’t just a technology company. Our AI is trained by an in-house team of life insurance, P&C insurance, and medical experts to ensure the insights we deliver align with what underwriters and claims professionals actually need to make confident decisions. The information we provide is never guesswork, it’s constantly reviewed, refined, and updated to ensure it remains highly relevant and case-specific.

Our AI holds up to rigorous scrutiny. In recent independent third-party testing, our platform demonstrated 98.5% accuracy and showed no evidence of bias across a wide range of scenarios. 

We’re also built for the realities of insurance. DigitalOwl is fully compliant with HIPAA, GDPR, and SOC 2 Type II standards, and our platform integrates seamlessly into existing underwriting and claims workflows.

Finally, we know great technology only matters if people can use it. That’s why we’ve invested heavily in our customer support team, helping clients get up and running quickly and see ROI from day one. 

Aligning Your AI Strategy with Real-World Impact

Your AI strategy is not just about choosing technology, it’s about aligning with your business goals, capabilities, and risk tolerance. There’s no one-size-fits-all answer. But choosing the wrong path can have long-term consequences.

Build if you have the capacity, time, and internal alignment to do it right. Buy if you need fast results and operational lift. Partner if you want the benefit of deep collaboration and tailored solutions without going it alone.

Whatever path you choose, make sure the AI solution is secure, transparent, trusted by your teams, and meets the regulatory standards of your industry.

Download our AI Governance toolkit for an evaluation checklist of AI vendors and to learn how DigitalOwl meets the highest standards of regulatory and security compliance.