Three Big AI Buzzwords Insurers Need to Know in 2025

Not all AI trends are worth the hype, but these three? They’re going to make a real difference this year.

2025 marks a turning point for insurers. AI isn’t just a competitive advantage anymore–it's becoming essential for trust, agility, and smarter underwriting. Those who adapt will lead. Those who don’t will struggle to keep up.

These aren’t just buzzwords. Agentic AI, Explainable AI, and the latest wave of foundational models are redefining how insurers approach underwriting and risk selection. Here’s why they matter.

1. Agentic AI: From Assistance to Autonomy

What is Agentic AI?

Last year, AI copilots broke into the mainstream, providing augmented intelligence and decision support across industries, job functions, and personal applications. But now, we’re transitioning into the era of Agentic AI—where AI systems don’t just assist users but also plan, adapt, and execute tasks independently, making processes even more efficient. 

The big difference? Agentic AI can iterate, refine its outputs based on new data, and coordinate with other systems to drive better decisions and actions—without constant human input.

Why It’s Big in 2025

The recent launches of OpenAI’s Operator, Butterfly Effect’s Manus, and AgentForce from Salesforce (complete with a high-profile endorsement from Matthew McConaughey) have put agentic AI in the spotlight. These systems promise a future where AI takes on more responsibility in enterprise decision-making, allowing experts to focus on high-value judgment calls instead of busywork.

What This Means for Insurers

For underwriters, this raises an important question: how do we balance automation with control? Early predictions about AI in insurance historically swung between extremes—either minimal impact or full automation—but the reality lies somewhere in between.

At Kalepa, we believe AI’s role is to enhance the expertise and leverage of underwriters, not replace them. And a key way we increase their leverage is by employing AI Agents to take over the routine tasks that cause underwriting bottlenecks. 

Thanks to our agentic AI for Submission Intake, underwriters can avoid messy documents and emails while the AI iterates through to extract, normalize, and display all the key information. Our agentic AI for Portfolio Management reviews your entire book to understand if each submission moves you towards or away from reaching your desired portfolio, and it gives tailored guidance to the underwriter as a result.

Sometimes, this work is already delegated to a different person - offshore teams or UAs may be handling intake, while analysts might be building portfolio summaries in Excel. Kalepa’s AI Agents can handle these tasks in a fraction of the time with higher accuracy, freeing up those employees to focus on other challenges.

These AI Agents are operating without constant input and guidance on how to solve a problem, and they are helping underwriters make better decisions in less time. 

2. Explainable AI: Trust Through Transparency

What is Explainable AI?

Insurers can’t rely on black-box AI. If an underwriter doesn’t understand why a model made a certain recommendation, they can’t trust it—especially when an auditor or regulator comes knocking.

Explainable AI (XAI) is a field of research and a design principle focused on making AI decisions transparent and interpretable. Instead of just showing a risk score, XAI provides:

• Clear reasoning behind every prediction
• Plain-language explanations (not just a bunch of numbers)
• Direct pointers to data sources, so underwriters can validate insights

Why It’s Big in 2025

AI is facing more scrutiny than ever. With global regulations tightening around AI decision-making, insurers need systems that provide transparency, auditability, and compliance-ready explanations.

One of the biggest breakthroughs in XAI has been Retrieval-Augmented Generation (RAG) – a method that enhances AI models by gathering updated, relevant information from external sources instead of only relying on pre-trained knowledge. 

What This Means for Insurers

In underwriting, this means AI can pull directly from submission documents, underwriting guidelines, broker emails, and external risk data, ensuring decisions are grounded in verifiable sources.

Explainability can’t be an afterthought – it should be built into any AI application from day one. Each prediction or model should provide transparent, audit-ready reasoning, so underwriters can trust (and confidently act on) the insights they receive.

3. The Rise and Fall of New Foundational Models

What is a foundational model?

The foundational models are large AI models (including large language models, LLMs) that are trained on vast datasets so they can apply to many different problems. While Open AI ushered in the buzz of foundational models a few years back, many other players have been in the news in 2025.

DeepSeek made front page news at the beginning of this year, as a brand new foundational model developed by a Chinese AI startup that was outperforming Open AI’s GPT-4 in certain benchmarks—at a fraction of the cost. 

It’s part of a larger trend: foundational AI models continue to improve and drop in cost, and insurers need to be flexible and move fast to take advantage. 

Why It’s Big in 2025

The “DeepSeek Moment” proves that focusing on one particular LLM or use case is a losing strategy. Costs continue to plummet while performance improves, meaning the application of foundational models will be the real differentiator in 2025.

After only a few weeks, DeepSeek was out of the news cycle, as attention turned to new models like:

• Grok 3 (from xAI)
• Claude 3.7 Sonnet (Anthropic’s latest advancement)
• Qwen 2.5-Max (from Alibaba)
• Gemini 2.5 (from Google)

What This Means for Insurers

Success isn’t about fixating on the best model–it’s about ensuring AI remains effective, adaptable, and secure as the landscape shifts. It’s more important to flexibly capture the benefits from a variety of models to make a real impact on the business.

That means focusing on:

• Solving real underwriting challenges – AI should drive top and bottom line impact by enhanceing risk selection, decision-making, and efficiency. 
• Adapting to the best AI for the job – With each model having unique tradeoffs, insurers need to make sure they’re leveraging the best tool for each job.
• Security & compliance at every layer – AI needs to be set up to protect sensitive data, ensure auditability, and meet regulatory standards, so insurers can deploy AI with confidence.

New waves of AI models will continue to release, but what really matters is having the right architecture and applications to create real business impact.

Final Thoughts: AI in Insurance Is About Execution, Not Just Hype

The AI buzzwords of 2025—Agentic AI, Explainable AI, and the evolution of foundational models—aren’t just trends. They represent the next phase of AI-driven transformation in insurance.

✔ Agentic AI will push AI beyond assistance into proactive automation.✔ Explainable AI will ensure trust, compliance, and auditability.✔ The foundational model race will push insurers to adopt flexible, application-driven AI.

At Kalepa, we remain focused on turning AI advancements into real underwriting impact—helping insurers quote faster, bind more, and grow profitably.

Want to discuss how AI can transform your underwriting operations? Let’s talk.

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