Despite growing investments in artificial intelligence and advanced analytics, insurers across Europe, the Middle East and Africa (EMEA) are finding it difficult to modernise their pricing systems, according to a recent industry insight by Earnix, a pricing and decisioning platform provider.
The company argues that while insurers have made visible progress in adopting new technologies, many transformation efforts are failing to deliver meaningful business impact due to a persistent gap between analytical insight and real-world execution.
Across the region, insurers have intensified efforts to improve pricing precision and responsiveness, particularly as market conditions become more volatile and regulatory expectations tighten. However, Earnix notes that these ambitions are often undermined by operational challenges that prevent pricing innovations from reaching production environments efficiently.
According to the firm, the problem is not a lack of data, talent, or investment. Instead, insurers are struggling to translate sophisticated modelling and analytics into actionable pricing decisions within live systems.
“On the surface, many insurers appear to be advancing,” the company said, pointing to increased adoption of artificial intelligence, new pricing tools, and innovation pilots across the industry. “But little changes where it matters in production,” it added.
The report highlights that, in many cases, pricing updates still take weeks to implement, with deployment processes heavily dependent on IT teams. Governance structures also remain fragmented, while artificial intelligence models are rarely embedded into live pricing decisions.
This, Earnix explains, has created a growing disconnect between analytical capabilities and commercial outcomes.
Execution, not insight, identified as core challenge
Earnix’s analysis reframes a widely held assumption in the insurance industry—that pricing transformation is primarily a data or modelling challenge. Instead, the company identifies execution as the central issue.
It notes that pricing processes in many insurance organisations remain fragmented, with different stages of the workflow handled across separate systems and teams. Modelling may take place in one environment, testing in another, while deployment is often carried out through legacy rating engines. Governance, meanwhile, is frequently managed through manual processes.
Each of these steps introduces inefficiencies, including manual handovers, duplication of work, and delays, which ultimately reduce the value of even the most advanced analytics.
“Insight is created but not realised,” the company stated.
Four structural barriers
Earnix further identifies four structural barriers that continue to slow down pricing transformation across the industry.
The first is a fragmented technology landscape. Many insurers still rely on a combination of spreadsheets, legacy systems, and standalone tools, making it difficult to maintain consistency and transparency across the pricing process. This fragmentation often results in manual processes, version control issues, and limited oversight.
Secondly, the firm points to heavy reliance on IT teams as a major constraint. Even minor pricing adjustments frequently require system recoding and formal release cycles, creating delays between decision-making and execution. This not only slows time-to-market but also limits insurers’ ability to respond quickly to changing market conditions.
Governance gaps represent a third challenge. As pricing models become more complex, regulatory scrutiny has increased. However, many insurers continue to depend on manual documentation and disconnected audit trails, which can heighten risk and reduce confidence in deploying more advanced pricing strategies.
The fourth barrier relates to the limited operational use of artificial intelligence. While many insurers are experimenting with AI models, Earnix observes that few have successfully integrated these models into live pricing environments. Challenges around deployment readiness, lack of clear implementation pathways, and low trust among business users often prevent AI from moving beyond the experimental stage.
The cost of the ‘execution gap’
These structural issues, according to Earnix, have created what it describes as an “execution gap” within insurance organisations.
This gap emerges when insights developed by actuarial or data science teams fail to translate into real-world pricing actions due to operational bottlenecks. As a result, models can become outdated before they are deployed, and opportunities to respond to market changes are frequently missed.
In some cases, teams resort to manual workarounds to bypass system limitations, further undermining the effectiveness of transformation initiatives.
The cumulative effect is a weakening of return on investment, as significant spending on pricing transformation does not translate into proportional business outcomes.
Rethinking the pricing lifecycle
To address these challenges, Earnix suggests that insurers need to move beyond a narrow focus on modelling and instead rethink the entire pricing lifecycle.
This includes integrating key functions such as modelling, simulation, deployment, and governance into more unified workflows, with the aim of reducing handoffs and improving efficiency. Faster deployment capabilities, the company argues, would allow pricing teams to implement changes more quickly and respond more effectively to market dynamics.
It also highlights the importance of embedding governance within pricing systems to ensure transparency and compliance, while maintaining the flexibility needed for innovation.
In addition, Earnix stresses that artificial intelligence can only deliver value when it is fully integrated into live decision-making processes, rather than being treated as a standalone or experimental capability.
Implications for the wider industry
While the analysis focuses on insurers in EMEA, the issues identified reflect broader challenges facing the global insurance industry, particularly as firms accelerate digital transformation efforts.
For insurers in emerging markets, including those in Africa, the findings underscore the importance of aligning technological investments with operational readiness. As many firms in these markets begin to adopt advanced analytics and AI-driven tools, the ability to execute pricing decisions efficiently will likely play a critical role in determining the success of these initiatives.
Earnix concludes that the future of pricing transformation will depend less on the sophistication of models and more on the ability of insurers to operationalise those models effectively.
“Transformation is not about building better models. It is about making those models work at speed, at scale, and under control,” the company noted.
Nigeria’s insurance regulator is intensifying efforts to expand access and deepen adoption of insurance products, placing collaboration, innovation, and structural reforms at the centre of its strategy to reposition the industry.
