What Sapiens’ New Leadership Signals for AI Insurance

Private equity doesn’t buy insurance software for nostalgia. It buys it because carriers are finally paying for outcomes: faster underwriting decisions, fewer claims touchpoints, better retention, and lower operating cost. That’s why Sapiens’ acquisition by Advent—announced in August and closed on December 17, 2025—matters well beyond org charts.

Sapiens is now privately held and no longer traded on NASDAQ or the Tel Aviv Stock Exchange. Its long-time CEO Roni Al-Dor steps down on December 31 after 20 years, and Advent Operating Partner Mike Ettling becomes Executive Chairman and interim CEO. There are also new executives across finance, revenue, people operations, and two new Chief Customer Officer roles split by Life & Pensions vs. P&C/Reinsurance.

This post is part of our AI in Insurance series, and I’m going to take a clear stance: the leadership reset isn’t just governance—it’s a signal that Sapiens wants to operationalize AI at scale and sell it as repeatable SaaS value, not bespoke projects. If you’re an insurer evaluating platforms in 2026 budgeting season, this is the kind of change that can alter product roadmaps, implementation models, and what “good” looks like in claims automation and underwriting.

The real signal: private ownership pushes speed and focus

Answer first: Sapiens going private under Advent likely increases execution speed on product and go-to-market decisions—especially around AI features that can be packaged, priced, and delivered as SaaS.

Public company dynamics often reward predictability. Private equity ownership typically rewards focus, margin expansion, and measurable growth—and that combination usually leads to clearer prioritization: fewer “nice-to-have” initiatives and more investment into what scales.

For insurance technology, “what scales” increasingly looks like:

• AI-assisted underwriting that reduces cycle time and improves risk selection consistency
• Claims automation that lowers loss adjustment expense through straight-through processing
• Fraud detection and triage that stops leakage early, not after payment
• Customer engagement tooling that actually improves retention, not just NPS dashboards

Sapiens’ statement about “creating AI empowerment in the insurance sector,” becoming “more customer-centric,” and “moving towards becoming a SaaS company” is the important part. The ownership model makes that more plausible because it aligns the company’s internal incentives with repeatable delivery and faster iteration.

Why this matters to insurers planning 2026 transformations

Most carriers aren’t lacking AI ideas—they’re lacking AI production capacity.

I’ve seen too many “pilot-to-nowhere” programs: the model works in a sandbox, then dies on integration, governance, data access, or legal review. When a core platform vendor says it’s going AI-driven and SaaS-oriented, the practical question becomes: Will they deliver AI where the work happens? That means inside policy admin, billing, claims, and underwriting workflows—not in a disconnected side tool.

Leadership changes that map to an AI execution plan

Answer first: The new appointments line up with the operational muscles required to scale AI in insurance—financial discipline, go-to-market clarity, culture/skills change, and tighter customer feedback loops.

Here’s what changed after the close (effective December 17, 2025):

• Mike Ettling becomes Executive Chairman and interim CEO (while a permanent CEO is found)
• Paul Wheeler named CFO
• Ernesto Marinelli joins as Chief People Success Officer
• James Hannay appointed Chief Revenue Officer
• New customer engagement roles:
  • Tal Sharon, Chief Customer Officer (Global Life & Pensions and IPELS)
  • Sveta Hardak-Nissan, Chief Customer Officer (Global P&C and Reinsurance)

These aren’t cosmetic roles. They’re the pieces you put in place when you want to turn a complex enterprise software business into something more standardized and scalable.

Interim CEO + Executive Chairman: product strategy gets teeth

Ettling’s background across enterprise software firms (including SAP SuccessFactors and Unit4) is telling. Insurance platforms have historically been sold as “big implementations.” SaaS enterprise leaders tend to push:

• clearer packaging
• faster release cadence
• stronger customer success accountability
• measurable adoption metrics (usage, automation rate, time-to-value)

If that mindset carries into Sapiens’ roadmap, insurers should expect more pressure toward standard configuration over customization—and more “AI in the flow of work” instead of AI as an add-on.

A CFO appointment that likely supports SaaS transition economics

SaaS transitions are financially tricky: revenue recognition changes, services mix shifts, and there’s often a period where cash flow management becomes central. A CFO who has seen software transitions and PE environments can be a strong signal that the company is preparing for:

• more subscription-based pricing
• better unit economics tracking by product line
• tighter control of delivery costs (implementation and support)

From an insurer’s perspective, the benefit is potential clarity on total cost of ownership—if the vendor also invests in migration tooling and standard integrations.

Chief People Success Officer: AI isn’t a feature, it’s a skill shift

AI roadmaps fail when teams can’t adopt them. Period.

A people leader with enterprise scale experience suggests Sapiens is treating workforce enablement as part of execution: hiring, upskilling, and reshaping incentives so product, engineering, and services teams can ship and support AI features responsibly.

For carriers, this matters because vendor maturity impacts your risk: model drift support, explainability tooling, incident response, and change management all require a different bench than traditional configuration-heavy platform work.

CRO + two CCOs: tighter feedback loops and industry-specific focus

Insurance buyers don’t want “AI for everything.” They want AI that improves a few core metrics:

• underwriting cycle time
• claims touch time
• fraud hit rate
• customer retention
• expense ratio

Splitting customer leadership by Life & Pensions vs. P&C/Reinsurance is a practical move because the AI use cases and constraints differ:

• Life & Pensions: underwriting evidence, medical data workflows, long-duration servicing, policy changes
• P&C: FNOL automation, image/document intake, catastrophe surge handling, litigation routing

If Sapiens executes well, these CCO roles can translate customer pain into roadmap priorities faster—and keep AI capabilities aligned to regulated, auditable insurance processes.

Where AI value shows up first: underwriting, claims, and service

Answer first: The fastest, most defensible AI wins in insurance are operational—reducing cycle time and touchpoints—rather than flashy consumer chat experiences.

AI in insurance can be overhyped when the conversation stays at the “virtual assistant” level. The money is in workflows. Here are three areas where platform vendors like Sapiens can drive real improvements when AI is embedded directly into core systems.

AI underwriting: consistency beats heroics

Underwriting organizations still rely on expert judgment, which is valuable—but inconsistent processes create:

• variable pricing outcomes
• longer time-to-bind
• higher referral rates

Practical AI underwriting support looks like:

1. Risk triage: classify submissions into “straight-through,” “needs review,” “decline” based on appetite and features
2. Data extraction: pull key attributes from broker submissions, loss runs, medical evidence, and financial statements
3. Explainable recommendations: provide reason codes that underwriters can audit and override

If Sapiens’ “AI-driven, customer-centric future” is real, expect stronger productization around these steps—especially extraction + triage, because they’re the most repeatable across carriers.

Claims automation: touchless is the target, not the slogan

Claims departments feel AI pressure most intensely because policyholders judge carriers by claim experience. The highest ROI pattern is simple:

• automate the low-severity, high-volume claims
• route complex claims faster to the right expertise

That requires AI models that can read documents and images, classify coverage, and recommend next-best actions. It also requires strong orchestration: who approves, when to request more info, when to pay, when to refer to SIU.

If Sapiens is moving toward SaaS, insurers should push for standard claim automation playbooks (configurable, not custom-coded) that include:

• FNOL intake and summarization
• coverage/limits checks
• reserve suggestions with guardrails
• litigation propensity indicators

Customer engagement: prevention and retention, not “chat for chat”

Customer-centric AI in insurance shouldn’t stop at answering policy questions. The more valuable pattern is predict and prevent:

• detect churn risk and trigger save offers
• identify billing friction early (failed payments, confusing invoices)
• recommend policy updates (life events, coverage gaps)

The creation of two CCO roles suggests Sapiens wants tighter ownership of outcomes across the customer lifecycle. If they pair that with measurable adoption metrics, insurers may finally get vendor accountability beyond “we delivered the project.”

What insurers should ask Sapiens (and any vendor) right now

Answer first: If you’re buying AI-enabled insurance platforms in 2026, your vendor questions should center on governance, integration, and measurable automation—not model demos.

Use the leadership transition as a forcing function. Ask direct questions that reveal whether “AI-driven” means real operational change.

10 vendor questions that separate substance from marketing

1. Where does AI run? In-platform, or via separate tools that require custom integration?
2. What’s the default data model? What data is required to get value in 90 days?
3. How do you handle explainability? Do users get reason codes and audit trails?
4. What’s your AI governance model? Who signs off on model changes, and how is drift monitored?
5. What’s the human override pattern? Can underwriters/adjusters override and feed learning loops?
6. How do you support regulatory and compliance needs? Particularly for adverse decisions and claims disputes.
7. What’s included in SaaS vs. paid services? Be specific about configuration, integrations, and migrations.
8. What are your standard KPIs? Touchless claim rate, cycle time, referral rate, leakage reduction, retention uplift.
9. How do you handle catastrophe surge? Can the platform scale intake and triage without breaking workflows?
10. What’s your roadmap for industry-specific AI? Life underwriting evidence vs. P&C image handling aren’t the same.

If a vendor can’t answer these crisply, you’re looking at experimentation—not production-grade AI in insurance operations.

What to watch next: the 90-day tells after a leadership reset

Answer first: Over the next 90 days, watch for signals of product packaging, SaaS migration support, and customer outcome reporting—those are hard to fake.

Press releases are easy. Execution shows up in artifacts. If Sapiens is serious about an AI-forward SaaS direction under Advent, you’ll likely see:

• Clearer bundles: named packages for underwriting automation, claims automation, and customer engagement
• Migration accelerators: tooling, reference architectures, and fixed-scope implementation offers
• Customer outcome dashboards: metrics that quantify automation and time-to-value, not just system uptime
• Faster release cadence: more frequent updates with visible AI capability enhancements

And if you’re a carrier, you should mirror that discipline internally. Define success as measurable operational change: fewer touches, faster decisions, better loss outcomes.

“AI in insurance works when it’s boring: it removes steps, reduces rework, and makes decisions more consistent.”

Where this fits in the broader AI in Insurance story

This Sapiens leadership transition is one more sign that the industry’s AI conversation is shifting from “What can models do?” to “Who can operationalize them responsibly inside core systems?” That’s the real race in 2026.

If you’re evaluating platforms or planning modernization, treat vendor leadership and ownership changes as part of your due diligence. They affect product priorities, support models, and how quickly AI capabilities become standard features instead of custom projects.

If you’re mapping your 2026 roadmap, what’s the one workflow—underwriting, claims, or servicing—where you’d be willing to commit to an AI outcome metric (not a pilot) within the next 120 days?