Build vs Buy Is Dead: GenAI Insurance Playbook

A lot of insurers are still arguing about whether they should build AI in-house or buy an off-the-shelf insurtech solution. That debate made sense when “AI” mostly meant narrow models trained on your historical data, glued into a workflow with a pile of integrations.

Now that foundation models and multimodal generative AI are mainstream, the real decision has changed. The winners in 2026 won’t be the teams that built the fanciest model from scratch or signed the biggest software contract. They’ll be the teams that configure an LLM-first insurance workflow that fits their underwriting rules, claims appetite, risk pricing strategy, and customer experience—without waiting years.

This post is part of our AI in Insurance series, and it focuses on a practical question I keep hearing from underwriting, claims, and IT leaders: If build vs buy is outdated, what replaces it—and how do we choose well?

The new decision: configure, don’t “build or buy”

Answer first: In the GenAI era, the most useful frame is configure vs customize—because the foundation model is rarely your differentiator.

Traditionally, “build” meant hiring data scientists, collecting labeled data, training models, deploying, monitoring, and iterating. “Buy” meant accepting a vendor’s workflows and tuning around the edges.

With LLM-first systems, you’re often starting from a capable model and making it valuable through:

• Process design (where the AI sits inside underwriting, claims, or service)
• Guardrails (what it can’t do, what it must cite, when it must escalate)
• Knowledge integration (product docs, underwriting guidelines, policy wordings, claims playbooks)
• Evaluation (how you test accuracy, compliance, and customer impact)
• User experience (how a human actually uses it under time pressure)

That’s why the “build vs buy” dilemma is fading. You’re not choosing between “years of R&D” and “generic software.” You’re choosing how quickly and safely you can configure a specialized insurance copilot around your business.

Why this matters right now (December planning season)

Budgets and roadmaps for 2026 are being finalized. This is when teams lock in big platform decisions—data modernization, core system upgrades, new claims tooling.

The mistake I see: insurers commit to a multi-year “AI platform build” that’s too broad, or they buy point solutions that don’t fit the reality of underwriting and claims operations. A configuration-first approach lets you start smaller, prove ROI, and expand.

Buying off-the-shelf: fast value, real limits

Answer first: Buying works when you need speed and proven workflows, but it breaks down when you can’t adapt the solution to your underwriting and claims rules.

Off-the-shelf insurtech has real strengths:

Speed to market (the 8–12 week benchmark)

Many specialized vendors can get a first production workflow live in roughly 8–12 weeks, especially for customer service and agent enablement use cases. Compare that with the typical multi-year internal program once you factor in data access, security reviews, integration, and operational change.

If you’re under pressure to reduce call handling time, improve first-contact resolution, or standardize guidance to agents, buying can be the sensible move.

Cost clarity (and fewer hidden engineering costs)

When insurers underestimate “build,” they usually ignore:

• Security and compliance engineering
• MLOps/LLMOps operations
• Change management and training
• Continuous evaluation and drift monitoring

Buying converts some of those into a vendor cost. That’s often a good trade.

Embedded insurance domain knowledge

Specialized vendors can encode insurance-specific workflows—policy interpretation, claims triage, escalation logic, audit trails—better than generic enterprise AI tooling.

Where buying fails: If the product is rigid. In insurance, rigid systems create shadow processes, manual workarounds, and compliance risk.

Building from scratch: control is expensive (and slower than you think)

Answer first: Building makes sense only when you’re creating a core competitive capability that can’t be reasonably configured from a specialized LLM-first platform.

Insurers like building because it promises control: data, security, model choice, UX, roadmap. That’s valid—insurance is regulated, and reputational risk is real.

But here’s the reality: a “from scratch” GenAI program isn’t just model work.

The hidden workload: LLMOps, not just data science

Modern GenAI delivery requires operational muscle:

• Prompt/version management and approvals
• Retrieval pipelines and permissioning
• Output evaluation (accuracy, hallucination rates, bias checks)
• Monitoring by use case (claims vs underwriting behaves differently)
• Incident response and audit readiness

If you don’t have that maturity, “build” often becomes a long experiment that never crosses the adoption gap.

The build trap: beautiful model, unusable workflow

Insurance AI succeeds when it improves day-to-day decisions:

• Underwriting: faster risk assessment with consistent appetite enforcement
• Pricing: better risk segmentation without regulatory surprises
• Claims: shorter cycle times and cleaner documentation
• Service: fewer repeat calls and fewer escalations

I’ve found that internal builds frequently over-invest in the model and under-invest in UX and operational fit. Users abandon tools that don’t reduce their cognitive load.

Configuring specialized LLM-first solutions: the practical middle path

Answer first: Configuration-first GenAI works because it combines foundation model capability with insurance-specific control—without forcing you to become a model company.

This approach (highlighted in the source article) is the most aligned with how insurance organizations actually operate: lots of rules, lots of exceptions, lots of audit requirements.

Multimodal GenAI changes the “data problem”

Insurance data isn’t neatly structured. It’s voice calls, emails, PDFs, photos, forms, adjuster notes.

Multimodal models reduce friction by handling:

• Claim photos and damage descriptions
• Adjuster narratives and repair invoices
• Customer emails and chat transcripts
• Policy documents and endorsements

That matters directly for claims automation and customer engagement—two pillars of AI in insurance.

Foundation models shift differentiation to “insurance configuration”

Most insurers won’t train a foundational LLM. It’s not a good use of capital.

So where does differentiation come from?

• Your underwriting guidelines and risk appetite
• Your pricing and discount logic
• Your claims handling philosophy (settle fast vs investigate deeply)
• Your regulatory obligations by region
• Your brand tone and customer experience standards

In other words, configuration.

UX becomes the ROI engine

A sentence worth keeping: The best insurance AI is the one people actually use.

For operational efficiency, GenAI must sit inside the user’s flow:

• Underwriters need decision support inside their workbench, not a separate chatbot.
• Claims handlers need draft summaries, reserve suggestions, and next-best-actions while the file is open.
• Agents need real-time guidance during calls, not a report two days later.

When UX is right, you see measurable outcomes: reduced handle time, better documentation quality, fewer reopens, faster quote turnaround.

Where GenAI fits in underwriting, pricing, and claims (real examples)

Answer first: GenAI delivers the fastest impact when it reduces reading, searching, and summarizing—then ties outputs to rules.

Here are three configuration-friendly patterns insurers are using right now.

1) Underwriting copilot for appetite + document triage

• Extract key risk factors from submissions (PDFs, emails, loss runs)
• Map to underwriting guidelines
• Generate a structured risk summary with “what’s missing” flags
• Route to straight-through, referral, or decline paths

Practical guardrail: require citations to internal guidelines for any recommendation.

2) Claims automation for cycle time and leakage control

• Summarize FNOL narratives into standardized notes
• Draft customer updates in approved tone
• Detect inconsistencies (timeline conflicts, missing invoices)
• Suggest next actions based on claim type and severity

Operational win: better file quality with less manual typing—especially useful when winter weather events spike claim volume.

3) Risk pricing support without turning pricing into a black box

GenAI shouldn’t “set prices.” It should help pricing teams move faster:

• Explain rating changes in plain language for internal stakeholders
• Draft filings/supporting documentation templates
• Surface historical rationale for prior pricing decisions

Practical stance: keep pricing logic deterministic; use GenAI for explanation, documentation, and analysis.

A decision checklist insurers can use next week

Answer first: Choose based on time-to-value, configurability, and governance—not on whether the AI is “built” or “bought.”

Use these questions in vendor selection or internal architecture discussions:

1. Can business owners configure it without engineering bottlenecks? Look for workflow tools, approval flows, and safe configuration patterns.

2. Does it support insurance-grade governance? You want audit logs, role-based access, content controls, and evaluation reports.

3. How does it handle your knowledge sources? Policy wordings, endorsements, UW guidelines, claims playbooks—permissioned and versioned.

4. What’s the evaluation strategy? Ask for measurable tests: factuality, citation accuracy, escalation rates, and human override rates.

5. Where does it sit in the workflow? If users have to leave their core system, adoption will be slower.

6. What’s the integration story? Core systems, CRM, telephony, document management, and data warehouses.

What to do if you’re starting from zero

Answer first: Start with one high-volume workflow, one business owner, and a measurable baseline.

If you need a practical launch plan:

• Pick a use case with volume and pain (claims notes, underwriting triage, contact center guidance)
• Define 3 metrics (for example: average handle time, claim cycle time, referral rate)
• Pilot with a small group (10–30 users) for 4–6 weeks
• Expand only after you can show adoption and quality controls

This approach fits the broader AI in Insurance narrative: prove value in operations first, then scale into underwriting and risk pricing with stronger governance.

Where this goes next

The “build vs buy” argument isn’t the real work anymore. The real work is building an insurance organization that can operate GenAI safely: governance, evaluation, workflows, and adoption.

If you’re planning your 2026 AI roadmap, focus your next conversation on configuration: Which workflows do we want to standardize, accelerate, and audit—without losing human judgment? That answer will guide your platform choices far better than an ideological build-or-buy stance.