Why You Don’t Need Perfect Data to Launch AI 

In today’s insurance landscape, carriers are exploring artificial intelligence (AI) to boost efficiency, reduce costs, improve accuracy, and enhance the customer experience. But as with any major initiative, getting started often involves a range of organizational hurdles-from internal approvals to the decision of whether to build in-house or partner with a third-party vendor.  A common barrier? The belief that all data must be fully prepared and cleaned before AI implementation can begin.  

There are many tasks to be completed in preparation for an AI implementation. Those tasks include defining and obtaining approval for a business use case, assessing organizational readiness, infrastructure and integration planning, governance and compliance framework planning, change management plan development which includes communications & training, and preparation of the data. Wait, does the data need to be prepared and clean?

There are many articles on the web which talk about having clean data to have a successful AI implementation. Is that true or is it a myth? Perhaps it isn’t reality.  

Let’s dig into benefits of clean data, challenges that come with it, and how, ultimately, your data doesn’t need to be perfect for AI to work. 

The Myth (and Reality) of Clean Data for AI  

The conventional wisdom is that clean data is essential for AI success. But is this really true?   

There are benefits to having clean data, but first, let’s define clean data: It’s data that is accurate, consistent, complete, and properly formatted. It’s been scrubbed of errors, duplications, and irrelevant information to meet data quality standards across all relevant systems. 

Clean data offers real benefits: 

• Improved model accuracy: Consistent, well-organized data allows AI systems to detect patterns and make better predictions, which are essential to underwriting, claims processing, and policy servicing. This is especially important for insurance, which demands accuracy in risk assessment and pricing. 
• Operational efficiency: High-quality data reduces manual work and reprocessing. It’s a driver of automation and streamlined operations, which allows insurers to focus on more strategic tasks. 
• Regulatory compliance: Clean data ensures transparency and traceability in decision-making, helping insurers meet strict industry regulations. 
• Customer experience: Accurate data enables insurers to provide personalized service, faster claims handling, and seamless communication to build trust and customer retention. 

Think of training an AI model like sending a student to college. Just as college students learn best from textbooks that are accurate, well-organized, and free of errors, AI models also rely on high-quality, well-structured data to learn effectively. The better the data, the more capable the model will be in understanding complex patterns and producing reliable results – just as strong educational materials lead to better human learning outcomes. 

The Challenges of Clean Data   

However, despite its benefits, achieving clean data requires consistent effort-especially for insurers with legacy systems or siloed operations. Here are some key challenges: 

• Time- and resource-intensive: Cleansing and unifying data often requires cross-functional coordination, especially when dealing with inconsistent or incomplete historical records. 
• Ongoing maintenance: Clean data doesn’t stay clean without governance. It requires continuous monitoring and updates. 
• Technical barriers: Smaller carriers may lack the expertise and other resources to manage data transformation efforts, and internal resistance to change can slow momentum. 
• Risk of bias: Even clean data can be problematic if it reflects historical biases. Ethical and representative data is key to avoiding unfair or skewed AI outcomes. 

The good news is your data doesn’t need to be perfect for AI to work.  

AI Can Start with the Data You Have  

In a utopian world, insurance companies would have limitless access to clean, organized data-but in reality, that is not the case for most carriers. That’s when partnering with experienced insurance-specific AI platform vendors, like Roots, can come into the mix, as they take a different approach. 

Rather than requiring companies to clean their data, an insurance AI specialist can use sample documents such as past submissions or first notices of loss-to train the AI. These documents contain hundreds or even thousands of valuable data points, most of which aren’t captured in traditional systems of record. Carriers have limited quantities of such documents. A vendor could have millions. Also, a carrier might only extract about 50 data fields to quote and bind a policy, leaving immense quantities of rich, unstructured data locked in PDFs or ACORD forms, and stored in platforms like ImageRight, OnBase, or Alfresco. 

So, there’s no need to wait. AI can be trained directly on live samples to extract and learn from all available data – structured or unstructured.  

The Advantage of Comprehensive Data Capture  

By leveraging AI-powered document processing, insurers gain: 

• Reduced manual work and fewer errors: AI-driven extraction reduces administrative overhead and improves accuracy, which is especially critical in document-heavy sectors like insurance. 
• Faster workflows: Automating data capture accelerates decision-making. Real-time claims access enables quicker processing – in many cases, reducing processing time by more than 50%.  
• Stronger analytics: With complete data, AI models provide deeper insights for risk scoring, fraud detection, and pricing strategy. Turning unstructured data into insights strengthens underwriting and adjudication. With human bottlenecks eliminated, data is immediately usable in systems. 
• Regulatory readiness: AI tools ensure full data traceability – essential for audits and compliance. Unlike traditional systems that may overlook key fields or depend on manual review, AI platforms can capture and log all key fields. 
• Enhanced customer satisfaction: Faster service, fewer delays, and a more highly personalized experience are the result of smarter, more complete data handling. End-to-end automation frees teams to focus on customers – not paperwork. 

Don’t let the myth of "perfect data" hold you back. AI success doesn’t require a massive data-cleaning project – it just requires the right partner. By working with an AI company that leverages finely tuned models and offers services like data annotation and model training, your organization can accelerate implementation and improve accuracy without overburdening internal teams. With this approach, your organization can begin driving results – faster, smarter, and without unnecessary delays.  

Curious how insurers are putting Roots to work? Check out our case studies to see insurance-specific AI in action.