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May 2
AI is now a strategic focus for insurers, promising to improve efficiency, decision-making and customer experience across the value chain. Over 90% of insurers globally are using or planning to use AI. However, while the vision is clear, many insurers struggle to turn AI investment into meaningful business outcomes.
The difference between hype and results often comes down to one thing: speed to value. In an industry defined by margin pressure, regulatory complexity and shifting customer expectations, insurers can no longer afford to wait years to see the return on their AI investments. The focus on speed to value is about translating AI capabilities into measurable improvements faster.
Multiple forces drive the pressure to deliver tangible value from AI quicker. While adoption is rising in some areas, economic uncertainty and rising competition are putting added pressure on insurers to reduce operational costs. Research shows that 60% of an insurer’s performance is driven by how well it operates.
At the same time, insurtechs and tech giants are reshaping the competitive baseline and setting new standards for speed, innovation and responsiveness. Policyholders are also raising expectations and requiring faster, more personalized, digital-first services at every touchpoint. Finally, industry regulatory demands require that insurers find ways to quickly adapt and take advantage of AI driven compliance efficiencies.
With so much at stake, the pressure to deliver results from AI investments is getting stronger. Many leaders are asking how they balance the need for quick wins with the promise of long-term value.
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High-impact, low-complexity use cases
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Deploy in iterative phases
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Make data a priority, not an afterthought
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Set clear, measurable KPIs
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Build collaborative, cross-functional teams
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Invest in workforce enablement
The first challenge that often slows down AI implementation in insurance is overly broad ambitions. Many businesses rush into large-scale transformations from day one without first demonstrating value in focused areas. This rush leads to delays and unmet expectations.
To overcome this challenge, insurers must begin with practical, clearly defined opportunities that deliver immediate, measurable value. Focusing on areas like document processing, claims triage and customer service allows insurers to leverage automation and AI-driven decision support with minimal disruption.
This approach builds momentum and demonstrates quick wins, creating a strong foundation for future, broader AI initiatives. For example, a South Africa-based insurer began by automating their outstanding claims reserving, transforming how they monitored and managed reserves. This success laid the foundation for further scalable, robust and tailored solutions.
Many AI initiatives struggle to gain momentum due to the complexity and scale of large, one-time deployments. As a result, there is an increased risk, slower implementation and missed opportunities for optimization.
Ultimately, AI delivery should be iterative. Insurers should adopt agile methodologies that allow their teams to test, learn and scale with reduced risk. More minor releases also create faster feedback loops, enabling real-time optimization of solutions. Similarly, cloud-native tools can further speed development and avoid high infrastructure costs. By starting small and scaling quickly, insurers can maximize their AI investments and see tangible results sooner.
Data readiness is foundational to AI success. Poor-quality, siloed or inaccessible data undermines the effectiveness of AI models, making it difficult to realize the technology’s full potential. Without proper data, businesses struggle to make decisions that accelerate their progress.
Data management processes sometimes cause delays and impedes AI solutions! Implementing improvements in data quality, accessibility and governance early can accelerate AI modeling and deployment and create standalone value in operational reporting and insights. By implementing advanced data solutions, insurers like BBiscuit have enhanced their decision-making, scaled more efficiently and gained a competitive edge.
Speed to value depends on being able to measure it. Deploying AI projects without having clarity around objectives or defining success metrics can make it challenging to measure their impact and justify continued investment.
As more insurers invest in AI, they must begin by building a business case that aligns with broader strategic goals. This business case allows them to identify KPIs, including processing time reductions, cost savings, CSAT improvements or increased straight-through processing. Qualitative gains, such as employee time saved or customer responsiveness, should also be captured.
Successful AI initiatives move faster and deliver more value when built collaboratively. Speed doesn’t just come from better tools or more innovative models; it comes from alignment. Strong AI programs don’t live in IT or data science. They bring together business leaders, end users and other key stakeholders from the start.
Bringing together diverse stakeholders from the onset ensures buy-in and helps ensure solutions are grounded in real business needs and aligned with customer expectations. This cross-functional approach sharpens focus on the correct problems and reduces development cycles. It also helps surface relevant use cases faster and ensures the end solution delivers practical impact, not just technical sophistication.
AI success is not just about the technology but also about the people. Studies show that companies that invest in workforce reskilling and change management are 1.6 times more likely to achieve successful AI outcomes. Yet, many insurance teams still feel uncertain or left behind when AI is introduced. When employees don’t understand how AI works and, more critically, how it supports their roles, adoption stalls and skepticism grows.
That’s why workforce enablement must be embedded from the start to serve as a catalyst for acceleration. Training programs should focus not just on the functionality of AI tools but on how they enhance, rather than replace, human expertise. Frontline staff are often best positioned to identify high-impact use cases, so involving them early builds trust and unlocks insights that might otherwise be missed.
Equally important is showcasing progress. Highlighting early AI wins, such as improved claims turnaround time or reduced manual processing, builds internal momentum, secures continued executive buy-in and reinforces the business case for scaling.
When insurers take a focused, execution-driven approach to AI, measurable results can be achieved in a few short months rather than years. More and more companies across the industry are realizing these outcomes by prioritizing value over experimentation.
For example, insurers report a 30-50% reduction in claims processing time by automating routine tasks and accelerating decision-making. Similarly, underwriting accuracy improves through AI-assisted risk modeling, helping teams better evaluate policies while reducing manual effort.
Fraud detection has also improved significantly, with machine learning and behavioral analytics enabling faster, more accurate identification of suspicious activity. AI is also helping insurers stay ahead of evolving regulatory demands. By automating reporting and enhancing data accuracy, insurers can respond more quickly to compliance requirements and reduce the risk of costly penalties.
On the customer front, faster, more personalized service drives notable increases in satisfaction and retention. Internally, AI is easing the burden of repetitive work, allowing employees to focus on more complex, higher-value tasks and ultimately improving productivity and engagement.
AI is transforming the telecom customer experience by enabling more personalized, proactive, efficient and seamless customer experiences. By leveraging the power of AI, data analysis and automation, telecom companies can enhance customer satisfaction, build stronger relationships and gain a competitive edge in the market. The applications of AI in this domain continue to evolve, promising even more innovative ways to improve the customer journey in the future.
The most successful insurers won’t be those with the largest AI budgets or the most complex labs. They will be the ones who quickly and consistently turn investments into measurable impact.
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