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How to Avoid 6 Common Insurance AI Implementation Challenges

Written by Diane Brassard | November 12, 2025

When a broker submits an account or a policyholder files a claim, they expect a fast, accurate response. Instead, they often encounter time-consuming manual workflows, duplicative reviews, and inconsistent, error-prone processes. In a market where today's digital-first consumer expects nothing less than the customer experience delivered by Amazon, Apple, or even their bank, inefficiencies like these can define your brand.

AI can accelerate underwriting, streamline submissions, and automate claims indexing. But insurers must first understand it and optimize their internal operations and business for AI transformation. Without taking these critical steps, insurers risk turning AI into just another layer of complexity on top of already fragmented workflows.

Let’s explore potential setbacks insurers face when they rush to AI – and the benefits of establishing operational clarity before any large-scale technology adoption. 

 

 

Challenge 1: Setting Mission and Scope Parameters

For AI to deliver on its promise, insurers must start with deep operational self-awareness. Before implementing AI, insurers must define what success looks like and establish clear boundaries for deployment. Steps toward this goal can include:  

  • Mapping Processes End-to-End: Clearly document processes across all operations. Understand how processes vary across business units, geographies, and product lines.
  • Identifying Customer Impact Points: Learn brokers' and policyholders' pain points. Map the manual handoffs that are most error-prone.
  • Quantifying Pain and Value: Calculate how many hours are spent on repetitive intake and what percentage of submissions or claims require rework. Understand the root causes and cost of missed SLAs.
  • Aligning on Success Metrics: Have a consistent definition for goals and measurements – e.g., reduce cycle time 50%, provide brokers with quotes within 24 hours, ensure claim acknowledgment within two hours.

With clear, discrete goals and metrics, AI becomes a targeted enabler of better experiences rather than an abstract tool. 

 

 

Challenge 2: Building and Implementing an Inclusive "AI-First" Culture 

One of people’s biggest fears about AI is that it will eliminate their jobs. Underwriters, claims adjusters, and customer service experts may resist adoption if they see technology replacing their expertise rather than enabling it.

The key to breaking through cultural resistance is shifting the conversation: AI adoption should not start with technology but with the customer and broker experience. This conversation should begin with specific questions, like:

  • How fast do brokers expect endorsements to be processed?
  • What frustrates policyholders most about the claims process?
  • What turnaround times differentiate us from competitors?

This critical step should yield the right information to map backward into the processes and technologies required to deliver experiences that match customer expectations. AI initiatives are not just cost-reduction projects but also customer-experience improvements that tie directly to revenue generation and brand reputation enhancement.

Transform change resistance into partnerships by presenting AI as part of insurance’s technological improvement continuum. AI automation and intelligence eliminate repetitive work, freeing your teams to focus on decision-making, relationship-building, and service.  

 

 

Challenge 3: Modeling and Creating Consistent Workflows to Drive Transformation   

To deliver maximum benefits, AI requires consistency. However, carriers of all sizes are often plagued by inconsistency across processes.

To overcome this historical inertia, insurers must conduct thorough internal process discovery using journey maps, swimlane diagrams, and stakeholder interviews to document "as-is" workflows. This discovery phase should:

  • Clearly document processes across all operations – from submissions to quotes, from FNOL to claim payments
  • Highlight variations, exceptions, and points of manual intervention across business units, geographies, and product lines
  • Identify where delays, errors, or lack of transparency have the greatest impact on trust brokers’ and policyholders’ trust
  • Prioritize high-impact journeys for automation rather than attempting to transform everything at once

As the old saying goes, “madness is doing the same thing repeatedly and expecting different results.” Without standardized workflows, AI will simply automate further fragmentation within your business. By ensuring that transparent, consistent processes are in place, you create the conditions for AI to deliver predictable, reliable outcomes that build trust with brokers and policyholders. 

 

 

Challenge 4: Integrating Legacy Tech 

Many insurers still operate on legacy policy admin or claim systems and literally thousands of other digital tools. These platforms often don't communicate with each other, and processes are patched together with email, spreadsheets, or manual workarounds.

AI doesn’t magically unify this complexity. It must be built around it before delivering value. Successful AI integration requires a realistic assessment of technical constraints and a strategic approach to integration:

  • Understand how legacy systems interact, where data flows break down, and which integration points will enable AI to function effectively.
  • Focus on systems and processes where AI can deliver the greatest value to the customer – e.g., claims intake or new business submissions – rather than attempting wholesale replacement.
  • Partner with vendors who understand insurance and its specific rules and regulations to ensure AI solutions bridge existing infrastructure.
  • Establish clear baselines for current system performance (turnaround times, error rates, SLA compliance) to measure AI's incremental improvements.

Realistic expectations go a long way. AI can’t create tech nirvana from chaos, but effectively designed and integrated AI is proven to deliver faster quotes, quicker claims acknowledgment, and loyal brokers and agents. 

 

 

Challenge 5: Creating Robust, Resilient, Flexible Governance and Compliance Frameworks 

AI systems must be transparent, auditable, and explainable to satisfy our industry's complex and rigorous regulatory environment. If insurers can't demonstrate how AI makes decisions, they may be creating risk rather than reducing it.

Build in governance from the start by establishing clear audit trails, transparency protocols, and compliance frameworks before AI goes live. This ensures regulators, brokers, and policyholders can trust the outcomes.

Strong governance also requires training staff on how human oversight strengthens AI performance and maintains accountability for automated decisions. 

 

 

Challenge 6: Alignment with Vendors to Ensure Execution and Continuity 

Not all AI vendors come to the table with an equal understanding of insurance. Generic AI providers may lack knowledge of insurance’s complex and rapidly evolving privacy and security standards.  

AI vendors don't just provide technology – they act as partners and educators that can help insurers to:

  • Run process discovery workshops
  • Build business cases tied to customer outcomes and ROI
  • Develop governance frameworks to satisfy regulators
  • Train staff on how to collaborate with AI agents

This consultative approach is critical because it supports viewing every process in fine detail, even for immense global businesses. Vendors who bridge this gap set their clients up for long-term success and ensure continuity as AI initiatives scale across the organization. 

 

 

Laying the Foundation for Long-Term AI Value 

Insurers that succeed with AI won't necessarily have the biggest budgets or the newest systems. They will win through AI readiness by establishing well-defined processes, reliable data, strong governance, and engaged employees to create a relentless focus on the customer experience. Following this path puts you in a position to use AI not just as a cost-cutting tool, but as a foundation for delivering differentiated services that win business and build loyalty.

AI is one of the most powerful tools available to insurers today, but it is not a magic wand. Without transparent processes, aligned stakeholders, and a focus on customer and broker experience, AI risks automating inefficient processes, rather than eliminating them.

To meet rising customer expectations in the AI age, insurers must first look inward: mapping workflows, identifying pain points, and aligning stakeholders on what "good" looks like. AI is just the latest and most powerful of a long line of technological breakthroughs to affect our industry. As with past tech revolutions, the key to success is to start with clarity of mission and end with an actionable plan for implementing and scaling insurance AI in your business. 

 

Download How to Navigate Change in the AI-Powered Insurance Future to discover proven strategies for engaging, inspiring, and empowering your teams for future success.