Trump’s AI Order: What It Means for Insurance AI

A lot of insurance AI strategies are built on an assumption that turns out to be fragile: that state-by-state rules will keep expanding, so “multi-state compliance” is the permanent tax you pay for using AI at scale.

Trump’s December 18, 2025 executive order aims to flip that assumption by pressuring states not to regulate AI and nudging the country toward a single, lighter-touch federal framework. If you’re in underwriting, claims, fraud, or digital distribution, this isn’t political trivia. It’s a planning input for 2026 budgets, model governance, vendor contracts, and product roadmaps.

This post sits in our AI in Government & Public Sector series for a reason: public-sector choices on AI oversight don’t stay in government. They set the guardrails for private-sector use, especially in regulated industries like insurance where AI decisions can affect pricing, eligibility, and outcomes.

What the executive order actually tries to do

The executive order’s core move is straightforward: federal agencies are directed to identify “burdensome” state AI regulations and discourage them—including by tying federal funds to compliance and by challenging state laws in court.

The practical effect: less certainty, not more

Insurers might hear “federal framework” and think “one rulebook, finally.” I wouldn’t bank on that—at least not in the near term. An executive order can start processes and signal enforcement priorities, but it doesn’t automatically erase state laws. The likely result for the next 6–18 months is a familiar one: uncertainty.

Here’s the reality most teams will face:

• State laws already on the books don’t vanish overnight.
• Court challenges will take time.
• Federal agencies can change their posture quickly, especially with leadership changes.

If you’re running AI in underwriting or claims, the smartest stance is: act as if you’ll be audited tomorrow, but architect as if the regulatory map could shift next quarter.

What it doesn’t cover (and why insurers should care)

The order reportedly doesn’t seek to preempt certain categories, such as AI-related child safety protections and rules around how state governments procure and use AI.

That matters because insurance AI doesn’t live in a vacuum. Your customer engagement models touch households, minors (in some lines), and state-run systems (DMV data, public records, unemployment verification, workers’ comp ecosystems). Even if broad private-sector AI rules get chilled, sector-adjacent rules can still bite.

Why states regulated AI in the first place—and why insurance is in the blast radius

States didn’t start passing AI laws because they’re bored. They did it because AI systems are increasingly used for high-impact decisions—the kind that can alter a person’s financial life.

Insurance fits this category cleanly:

• Underwriting models influence eligibility, pricing, and coverage terms.
• Claims triage models influence speed, scrutiny, and sometimes settlement paths.
• Fraud models influence investigation intensity and potential false positives.

The consumer concern driving state policy is also simple: if a model makes a harmful decision, people want to know why—and regulators want a way to test whether the system discriminates.

A useful litmus test: if you wouldn’t be comfortable explaining a model’s behavior to a regulator, a plaintiff’s attorney, and a policyholder in the same week, your governance isn’t finished.

The “Big Tech vacuum” problem shows up as vendor risk

One critique of the executive order (from consumer and civil liberties groups) is that it gives large AI developers too much room to operate without oversight.

Insurers should translate that into operational terms: vendor dependency risk.

If your decisioning stack depends on:

• a foundation model you can’t inspect,
• training data you can’t audit,
• and model updates you don’t control,

…then a lighter regulatory environment doesn’t automatically reduce your exposure. It can increase it because the pressure to “ship faster” grows while the paper trail stays thin.

The insurance impact: underwriting, fraud, and customer engagement

This executive order is about federal-state power, but the consequences land in everyday insurance workflows.

Underwriting: the fight over “fairness” doesn’t go away

Even if state AI regulation slows, unfair discrimination claims won’t. For underwriting teams, the bigger risk is mixing up:

• regulatory permission (what’s allowed), with
• defensibility (what you can justify under scrutiny).

Practical examples of where insurers get burned:

• A model uses a proxy variable that correlates with protected classes.
• A third-party data source introduces hidden bias.
• A rating factor is “legal,” but the explanation is incoherent to consumers.

If your underwriting AI affects pricing or eligibility, your baseline should include:

1. Documented feature rationale (why each major factor belongs).
2. Disparate impact testing across relevant segments.
3. Stability monitoring (drift can create new bias over time).
4. A human escalation path for edge cases.

Fraud detection: higher accuracy isn’t the same as lower risk

Fraud models are often treated like “back office” AI, but they can create front-line consumer harm through false positives.

As AI-driven scams increase (deepfakes, synthetic identities, automated claim stuffing), insurers are right to invest in advanced detection. The trap is forgetting that fraud workflows are enforcement workflows.

If your fraud AI triggers:

• payment delays,
• extra documentation requirements,
• EUO/referrals,
• claim denials,

…you need governance that matches the impact.

A strong, defensible fraud AI program includes:

• Tiered risk scoring (don’t treat a low-score flag like a high-score flag).
• Reason codes that are stable and reviewable.
• Investigator feedback loops to reduce bias and improve precision.
• Controls against “automation bias” where adjusters over-trust the score.

Customer engagement: compliance isn’t just about decisions

Customer-facing AI (chatbots, email personalization, voice assistants) often escapes the strictest decisioning rules. But it’s where reputational damage happens fastest.

If the federal environment becomes lighter-touch, expect more experimentation—especially around:

• AI-assisted sales scripts
• automated policy changes
• claims status communications

Your differentiator won’t be “we use AI.” Everyone will. Your differentiator will be we use AI without making customers feel trapped in a black box.

A simple standard I’ve found effective: every automated customer interaction should have a clear off-ramp to a human and a clear record of what the system did.

Government & public sector angle: what happens next (and why it matters)

The executive order sets up a predictable chain reaction in the public sector: agency review, funding leverage, legal challenges, and state pushback.

Expect lawsuits—and plan for “dual-track” compliance

Multiple state officials have already signaled litigation. A bipartisan group of attorneys general previously urged Congress not to block state AI regulation for a decade.

For insurers, the operational takeaway is not “wait and see.” It’s run a dual-track plan:

• Track A: Continue meeting existing state requirements and NAIC-style model governance expectations.
• Track B: Prepare for a potential federal framework that standardizes reporting and overrides parts of state law.

Build your governance so you can map controls to either regime without rebuilding from scratch.

The myth: “Less regulation means faster adoption”

Most companies get this wrong. Adoption is usually slowed by three internal constraints, not by state law:

1. Data readiness (quality, lineage, consent).
2. Model risk management (testing, documentation, monitoring).
3. Change management (training, workflows, accountability).

A lighter regulatory environment might change external pressure, but it doesn’t eliminate those constraints. If anything, it increases the penalty for sloppy deployments when the first high-profile failure hits the news.

A practical 2026 checklist for insurance leaders using AI

If you want to generate leads from this moment (and avoid unpleasant surprises), focus on governance that’s concrete enough to survive policy swings.

1) Inventory “high-impact” AI systems now

List the models that influence eligibility, pricing, claims outcomes, or investigative actions.

• What decision do they affect?
• Who owns the model?
• What data sources feed it?
• What’s the fallback if it fails?

If you can’t answer those in one page, you’re already behind.

2) Add contract language that anticipates regulatory whiplash

If you buy AI capability from vendors, your contracts should cover:

• audit support and documentation access
• change logs for model updates
• incident notification timelines
• data usage limits and retention
• ability to run fairness and drift testing

This is where many insurers get stuck later—when the model works but nobody can prove why.

3) Treat explainability as a product feature, not a compliance task

Policyholders don’t ask for “model interpretability.” They ask why the premium rose, why a claim is delayed, or why they were declined.

Build explanation workflows that provide:

• a plain-language summary
• a small number of consistent drivers
• next steps for reconsideration

This reduces complaints, improves retention, and lowers regulatory friction regardless of which level of government is in charge.

4) Prepare for AI-specific market conduct scrutiny

Even if AI rules loosen, market conduct exams won’t ignore AI. Examiners will still ask:

• How do you test for unfair discrimination?
• How do you govern third-party data?
• How do you monitor drift?
• What human oversight exists?

If you can answer with artifacts (policies, logs, test results), you’re in control.

Where this leaves insurers heading into 2026

Trump’s executive order is a signal: federal power is being used to discourage state AI regulation, and the regulatory center of gravity could shift.

For insurers, the winning approach isn’t rooting for more rules or fewer rules. It’s building AI programs that are auditable, explainable, and resilient—so you can keep deploying underwriting, fraud detection, and customer engagement AI even when the legal map changes.

If you’re planning AI investments for 2026, ask your team one hard question: could we defend our most important AI decisions if the regulatory pendulum swings the other way—fast?