The insurance industry is under increasing pressure to adopt artificial intelligence (AI) to improve efficiency, reduce operational costs, and deliver faster services to customers. However, new research shows that many insurers are struggling to fully benefit from AI because their internal data systems and operational processes are not yet ready.
A recent report titled Insurance Operations & Financial Transformation 2026, published by Autorek, a provider of AI-driven financial control and reconciliation solutions for the insurance sector, highlights how outdated infrastructure, fragmented data, and manual workflows are slowing down digital transformation. The report is based on a survey of 250 insurance managers across the United Kingdom and the United States, offering a detailed look at the operational challenges that are preventing companies from successfully implementing AI.
The findings reveal a clear pattern: insurers believe AI will shape the future of the industry, but most organizations still lack the data structure and operational efficiency required to make AI work at scale.
This article explores the report’s key insights, including the causes of operational inefficiencies, the barriers to AI adoption, the role of data governance, and why fixing the data foundation is essential before AI can deliver real value in insurance.
The Growing Importance of AI in the Insurance Industry
Artificial intelligence has become one of the most discussed technologies in the insurance sector. Companies expect AI to improve underwriting accuracy, speed up claims processing, automate reconciliation, detect fraud, and reduce operational costs.
According to the survey, 82% of insurance firms believe AI will dominate the industry in the coming years. This shows strong confidence in the technology’s potential to transform operations.
However, the same report reveals a major gap between expectations and reality.
Only 14% of companies have fully integrated AI into their operations, while 6% report no AI usage at all.
This gap suggests that the problem is not a lack of interest in AI, but rather the difficulty of implementing it in complex, data-heavy environments like insurance.
Operational Inefficiencies Are Slowing Down Progress
One of the biggest findings in the report is that many insurance companies are still dealing with structural inefficiencies that increase costs and reduce productivity.
Survey responses show several recurring problems:
- 14% of operational budgets are spent correcting manual errors
- 22% of managers say reconciliation complexity drives cost increases
- 22% link inefficiencies to governance and audit risks
- Nearly half of firms have settlement cycles longer than 60 days
These numbers highlight how much time and money insurers spend managing internal processes instead of focusing on innovation and customer service.
Manual reconciliation, disconnected systems, and slow settlement cycles create operational drag that makes it difficult to introduce advanced technologies like AI.
Transaction Volumes Are Rising, Increasing Pressure on Operations
The report also predicts that transaction volumes will increase by about 29% over the next two years.
This growth means insurance companies will have to process more policies, claims, payments, and financial records than ever before.
If existing systems remain unchanged, operational costs are expected to rise at the same pace as transaction volume. This is because many insurers still rely on:
- Manual data entry
- Multiple disconnected databases
- Legacy software systems
- Complex reconciliation workflows
As the number of transactions grows, these inefficiencies become more expensive and harder to manage.
Without automation and better data integration, scaling operations will become increasingly difficult.
Why Data Fragmentation Is the Biggest Barrier to AI
One of the most important conclusions in the report is that data fragmentation is the main obstacle to AI adoption in insurance.
Most insurance companies do not store their data in one centralized system. Instead, information is spread across multiple platforms, departments, and databases.
According to the survey:
- Firms manage an average of 17 different data sources
- Many companies say this complexity increased after mergers and acquisitions
- Fragmented data makes governance and auditing more difficult
AI systems need clean, structured, and consistent data to work effectively.
When data is scattered across different systems, AI cannot easily analyze it or produce reliable results.
This means that before AI can deliver value, insurers must first organize and standardize their data.
Legacy Systems Make Integration Difficult
Another major challenge identified in the report is the presence of legacy technology.
Many insurance companies still use older software platforms that were not designed to work with modern AI tools.
These systems often:
- Store data in incompatible formats
- Require manual updates
- Lack integration with newer applications
- Increase maintenance costs
Because of this, integrating AI into existing infrastructure becomes complicated and expensive.
Companies may need to redesign their data architecture before they can successfully deploy AI solutions.
Limited Internal Expertise Slows AI Implementation
In addition to technical issues, the report highlights a shortage of internal expertise as another barrier.
Many insurers do not have enough staff with experience in:
- Data science
- AI development
- Data governance
- Automation systems
Without the right skills, companies struggle to plan, implement, and manage AI projects.
As a result, some organizations invest in AI tools but fail to use them effectively.
This contributes to the gap between expectations and real-world results.
Reconciliation Processes: A Practical Starting Point for AI
The report suggests that insurers should begin their AI journey in areas where automation can deliver quick results.
One of the best candidates is financial reconciliation.
Reconciliation involves matching transactions, payments, and records across different systems.
It is typically:
- Rules-based
- Repetitive
- Data-heavy
- Time-consuming
Because of these characteristics, reconciliation is well suited for automation and AI.
By applying AI to reconciliation first, companies can:
- Reduce manual errors
- Lower operational costs
- Speed up settlement cycles
- Improve financial accuracy
This approach allows insurers to see measurable benefits before expanding AI to more complex areas.
Why Automation Alone Is Not Enough
The report also warns that automation will not solve problems if the underlying data structure is weak.
Placing AI or robotic process automation (RPA) on top of fragmented systems can actually increase costs instead of reducing them.
When data is inconsistent or incomplete, automation requires more maintenance and manual correction.
This is why the report emphasizes that data standardization must come before large-scale automation.
Companies need to:
- Clean their data
- Consolidate systems
- Define governance rules
- Create consistent data formats
Only after these steps can AI scale efficiently.
Cloud-Based AI May Offer a Solution
One recommendation in the report is the use of cloud-based AI platforms instead of fully in-house systems.
Cloud solutions can help insurers:
- Connect multiple data sources
- Process large volumes of information
- Reduce infrastructure costs
- Scale automation more easily
Because cloud platforms are designed for integration, they can simplify the process of managing complex data environments.
This makes them a practical option for companies that struggle with fragmented data systems.
Structural Problems Create Long-Term Costs
The report describes a clear link between structural issues and operational expenses.
When workflows are structured but data is unstructured, companies face constant delays.
For example:
- Reconciliation may follow clear rules,
- But the data needed for reconciliation comes from many different systems,
- Each system requires manual checks,
- This increases cycle time and cost.
These inefficiencies may seem small individually, but together they create significant financial impact.
Fixing these structural problems can improve both efficiency and scalability.
Firms That Fix Their Data First Will Gain Advantage
The report suggests that companies that solve their data problems early will outperform competitors.
When data is standardized and well governed, automation becomes easier, faster, and cheaper.
This leads to:
- Lower reconciliation costs
- Faster settlement cycles
- Better compliance
- Improved reporting accuracy
Over time, the performance gap between prepared and unprepared firms is expected to grow.
Organizations that delay data modernization may struggle to keep up as AI adoption increases across the industry.
AI Can Handle Complexity That Traditional Automation Cannot
Traditional automation tools like RPA work well for simple, repetitive tasks.
However, they often fail when processes involve multiple systems and inconsistent data.
AI has the potential to manage this complexity more effectively.
With AI, insurers can:
- Analyze large datasets
- Identify patterns
- Detect errors automatically
- Connect fragmented information
This makes AI especially useful for industries like insurance, where operations involve many transactions and data sources.
But again, AI can only work properly when the data foundation is strong.
Day-to-Day Operations Make Change Difficult
Another challenge highlighted in the report is the difficulty of making structural changes while running daily operations.
Insurance companies cannot stop processing policies and claims while they upgrade their systems.
This means transformation must happen gradually, which slows progress.
Legacy technology, operational workload, and budget limits all affect how quickly companies can modernize their data infrastructure.
As a result, many firms know what needs to change, but struggle to implement those changes.
Cost Reduction May Be the First Major Benefit of AI
The report notes that the full impact of AI on insurance performance is still uncertain.
However, one benefit is already clear: cost reduction.
By improving reconciliation, reducing manual work, and eliminating errors, AI can lower operational expenses.
Even if AI does not immediately transform every part of the business, reducing costs is a strong enough reason for companies to invest in data modernization.
Once the foundation is in place, additional benefits such as better analytics, faster decisions, and improved customer service can follow.
The Future of AI in Insurance Depends on Data Quality
The key message from the Insurance Operations & Financial Transformation 2026 report is simple:
AI will only succeed in insurance if companies first fix their data.
Without clean, connected, and well-governed data, even the most advanced AI tools cannot deliver reliable results.
To prepare for the future, insurers need to focus on:
- Data standardization
- System integration
- Governance frameworks
- Automation readiness
- Cloud-based infrastructure
Organizations that invest in these areas now will be better positioned to use AI effectively.
Conclusion
Artificial intelligence has the potential to reshape the insurance industry, but technology alone is not enough.
The biggest challenge facing insurers today is not AI itself, but the condition of their internal data systems.
The Autorek report shows that fragmented data, legacy platforms, manual processes, and limited expertise continue to slow progress.
With transaction volumes rising and operational costs increasing, the need for change is becoming urgent.
Companies that organize their data, modernize their infrastructure, and build strong governance frameworks will be able to use AI to reduce costs, improve efficiency, and scale their operations.
Those that do not may find themselves falling behind in an industry where data quality is quickly becoming the most important competitive advantage.