What True Data Readiness Looks Like in Insurance 

In the insurance industry, data readiness tends to show up in small, everyday moments. Reports pull different numbers depending on the source. A pricing update raises follow‑up questions. A leadership request that looks straightforward takes longer than expected because teams need time to reconcile the data before sharing an answer. 

With how much data is available, the ability to use it confidently can feel harder than it should. 

That experience is common across the industry. According to a white paper from Insurance Business America, 72% of insurers still rely on Excel or internally built tools to manage critical workflows, which adds friction to reporting, decision‑making, and change management. Over time, that shows up less as a tech problem and more as friction in everyday work. 

When people talk about data readiness, this is often what they mean—how easy it is for teams to get answers they trust and move forward. 

Why “AI readiness” keeps coming up (and what it’s really pointing to) 

Much of today’s discussion around data readiness is framed around AI. Insurers are exploring AI across underwriting, claims triage, fraud detection, and customer service—and leaders want to understand how insurance companies are using AI without introducing new risk. 

But many find themselves blocked before those efforts scale. 

An AM Best 2026 survey of more than 150 rated insurers and MGAs found that 45% cite data readiness as the biggest barrier to deploying AI, followed closely by security concerns (43%) and legacy system integration challenges (41%). Even among insurers that consider themselves “progressive,” 46% say they are not ready to implement AI today. 

AI is often where the conversation starts, but they also reveal something deeper. 

These same issues slow insurers down long before AI enters the picture. If teams don’t trust the data behind underwriting guidelines, claims prioritization, fraud alerts, or customer communications, they can’t automate decisions safely—or explain outcomes when regulators ask questions. While AI isn’t the cause of the problem, it often surfaces weaknesses insurers already deal with every day. 

The “readiness gap” is really three gaps 

When insurers talk about being “not ready,” the issue usually falls into three familiar categories: 

The decision gap. 
Teams struggle to answer core business questions quickly and consistently. The data exists, but pulling it together requires manual work and last‑minute reconciliation. 

The ownership gap. 
When reports conflict, it’s unclear who’s responsible for resolving the difference. Definitions vary across systems, and accountability is shared, or even absent. 

The proof gap. 
Even when teams agree on an answer, they struggle to explain how it was produced. Lineage, controls, and documentation are incomplete, making audits and reviews harder than they need to be. 

These gaps are seen and felt on a daily basis. In fact, a SAS insurance survey found that 41% of insurers say poor data quality is the biggest obstacle to robust decision-making, followed by lack of collaboration and unclear data ownership (36% each). 

Readiness Questions to Consider

Instead of thinking about maturity models or transformation roadmaps, insurers can assess readiness with a simpler test. Without extraordinary effort, can your organization do the following: 

• Traceability: Trace a key metric—from dashboard to source system and business definition. 
• Change safety: Make upstream system changes without silently breaking downstream reporting. 
• Ownership and escalation: Identify a clear owner and escalation path when numbers don’t match. 
• Quality with context: Measure quality in business terms that reflect how the data is used. 
• Secure access without delay: Give the right people access quickly while meeting security and compliance requirements. 

Most organizations agree these capabilities are essential. According to Harvard Business Review Analytic Services, 94% of organizations say connected data, processes, and applications are critical for AI success—yet only 27% say those elements are actually well connected. 

That gap explains why many AI efforts stall even after initial testing look promising. 

Treating data like a product + delivery discipline 

We’ve seen successful insurers shift how they think about data, beyond simply storing it. It’s when they start tackling how they organize work around data that progress really starts to take shape. 

They focus on core domains such as claims, policy, billing, customer, and fraud, and define what “good” data means for each. Ownership is explicit. Someone approves definitions, resolves conflicts, and prioritizes fixes when quality issues arise. 

They also establish steady routines. Definitions are reviewed regularly, quality issues are visible in business terms, and system changes are assessed for downstream impact before they cause disruption. 

This approach doesn’t promise transformation overnight, but it makes data reliable enough that teams can consistently use it with confidence. 

Where to begin when everything feels interconnected 

For many insurers, the hardest part is knowing where to start. Data issues span systems, teams, and workflows, making it tempting to try to fix everything at once. 

A more realistic path we’ve seen many insurers take starts with a single, impactful workflow: claims intake, underwriting appetite, fraud detection, or customer servicing. They identify the three to five decisions that workflow depends on and map only the data needed to support and explain those decisions.  

Once it’s stabilized and one repeatable pattern is in place—clear ownership, usable quality signals, traceability—it becomes easier to extend the same approach elsewhere. 

That matters because most insurers can’t pause operations to modernize everything at once. With most still relying on outdated technology for critical processes, progress depends on building momentum through small, durable wins. 

Readiness shows up as speed 

When an organization’s data is ready, everything else tends to fall into place. 

Questions get answered faster. Rework drops. Teams spend less time reconciling numbers and more time improving outcomes. Risk is easier to manage because decisions are explainable and defensible. 

So when leaders ask how insurance companies are using AI, the real answer often starts here: with data that teams trust, understand, and can put into action, even under pressure. 

At Insight Global, we support organizations as they strengthen data foundations, untangle ownership, and move forward in a way that fits how their teams actually operate. Connect with us today.