2026 Insurance Industry Predictions: AI Edition 

4 areas of focus for successful AI tech implementation

I’ll give a hundred dollars to the first person to send me a 2026 insurance industry predictions article that doesn’t mention AI. Odds are, I’ll be keeping my money. It’s clear to anyone who hasn’t been living under a rock that 2025 was the year of AI insurance solution piloting, and 2026 will be the year many organizations begin actually executing these technologies at scale. 

The problem? The vast majority (seriously, it’s over 95 percent) of corporate AI initiatives deliver zero measurable return.

https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

So, rather than rather than write a 2026 predictions article that states the obvious, we’re taking things a step further and predicting how insurance organizations can approach integrating AI into their tech ecosystem in 2026 for greater return on investment and long-term success. 

1. Start by digitizing core functions

In the beginning, everything was paper. As digital solutions began to emerge, some business leaders jumped on the bandwagon immediately, upgrading from spreadsheets and paper documents to modern technology. Others opted for slower adoption—making small upgrades like switching from fax to email, but not yet ready to embrace a full “digital-first” infrastructure. Then, the pandemic hit. 2020 was a pivotal year for the insurance industry as it forced business leaders to adapt quickly to meet consumer and employee demand for online services and collaboration. 

Fast forward to present day and, while you may struggle to find an insurance organization that still relies on mainframes, in-person meetings, and paper files to conduct their business, varying levels of digitalization persist across the industry. The rapid push for modernization means many insurers are now working off fragmented systems and dealing with messy data sprawl as a result of having pieced legacy tech together with more modern solutions. 

These bandaid solutions may work fine when it comes to business as usual, but when it comes to layering in emerging AI tools, the cracks could start to show. Like any complex technological integration, AI deployment demands multiple, simultaneous adjustments to a business’s resources, culture, and decision making. If your insurance organization is carrying a lot of technical debt, ceaning things up and getting your foundation to a more modern level is going to be a crucial precursor to taking advantage of shiny new tools. 

2. Clean up on aisle data

Data quality has always mattered when it comes to insurance distribution, and now AI is raising the stakes. Where tradtional workflows may have been able to tolerate a certain level of data messiness—with human analysts manually adjusting errors or inconsistencies—poor data quality can quickly snowball into critical failure for AI applications. Duplicated records or incorrect entries may not feel like much, but when we’re talking about a tool that relies on that data to identify patterns, make predictions, and otherwise inform overall business strategy, the impacts of bad data can be significant. Consider just a few of the consequences insurance leaders could run into when training an AI model with messy data:

• Delays and mistakes in claims processing 
• Non-compliance with regulations and legal requirements 
• Erroneous fraud detection
• Poor pricing models due to incorrect risk assessments
• Unintended discriminatory practices
• Reputational, financial, and legal damages due to the above

Bottom line: AI is only as good as the data behind it. If your distribution network and customer data isn’t up to date, accurate, and error-free, you’re still going to struggle with manual intervention, business slow downs, and increased NIGO rates post AI software implementation. Only now you’re also stuck paying a pretty penny for a solution you can’t gain much real value from. 

3. Don’t neglect your human talent

With so much focus on where large language models (LLMs) can plug into every aspect of a business, it’s safe to assume the human talent is feeling a little nervous about where they stand these days. We know there’s still a place for traditional insurance agents at the table, but what about the staff handling the back-end roles that will soon be handled in some degree by an automated tool? Is there a growth path for these employees? Will they be trained on how to implement AI into their current workflows for efficiency gains? 

Technology change necessitates cultural change. Handing employees a new system or software and expecting them to be successful with it on day one while neglecting to offer any formal training or or explanation as to how it will help staff solve real business problems is just unrealistic. In one McKinsey study, researchers found that 48 percent of US employees feel that receiving formal generative AI training from their organization would increase their daily use of those tools. Yet, nearly half of those surveyed marked their current level of support as “moderate or less.” There’s a gap between the level of AI support employees are looking for and the level they’re currently receiving. Minimizing that gap can get insurance organizations one step closer to successful AI software implementation. 

4. Consider new risks and plan accordingly

As with any new technology, AI introduces new risks and vulnerabilities, particularly when it comes to data security, transparency, and consumer fairness and equity. For example, AI has the potential to speed up claims processing, bringing efficiency to a historically slow and universally despised process. However, its algorithmic nature can also perpetuate biases and lead to unintentional discrimination. If insurers rush AI solutions into deployment before putting the appropriate guardrails in place, they may face:

• Regulatory fines
• Reputational damage
• Data breaches
• Eroding customer trust

It’s easy to focus only on the potential rewards of implementing a new technology, but long-term success isn’t possible if you ignore the potential risks. To promote the ethical and appropriate use of AI in the insurance sector, the National Association of Insurance Commissioners (NAIC) issued an AI Governance Framework which nearly half of all U.S. states have already adopted. Following guidelines like the NAIC’s framework, adopting best practices, and offering continuing education and AI-specific cybersecurity training can help insurance leaders deploy a successful AI strategy without compromising security and consumer trust. 

AI: A valuable business driver or an expensive risk? 

The answer depends on how you approach it. Ignore the risks, fail to get employees onboard, or attempt to layer it on top of fragmented systems and messy data and you might end up in the same boat as the majority of corporations struggling to gain any real value from AI. But get your hands around these four areas before committing to a purchase and you’ve got a much higher chance of success. 

If you’re ready to start dipping your toes into the world of AI for your insurance business, AgentSync can help. Our flagship solution helps agencies and carriers modernize core distribution channel management functions like licencing, onboarding, and compliance by automating the less glamorous back-office workflows and ensuring your distribution network data is always accurate and available. Get ahead of the curve—talk to an AgentSync expert today.