The traditional Waterfall method, a linear and sequential process for software development, was for a long time a cornerstone of project management. However, in the dynamic and ever-changing world of modern software development, this approach often proves inefficient and unsuitable for contemporary needs.

Its place is frequently taken by iterative approaches, such as Scrum or smaller Waterfall cycles, which offer more dynamic and effective alternatives for achieving success in IT projects.

See also: Multi-Agent Systems Reshape Human-Machine Collaboration

The Other Side of the Coin

The fundamental flaws of the Waterfall methodology stem from its tendency toward excessive upfront planning. Teams often overcompensate, trying to address every possible requirement early in the project, which leads to an overly expanded project scope. Fear of future budget constraints results in the inclusion of unnecessary and low-priority features, increasing the complexity of the project and hindering its execution. Consequently, projects often become inefficient and prone to failure.

Another critical problem of the Waterfall method is the delayed response to user needs and product delivery. Users and stakeholders typically cannot evaluate the product until the later stages of development. By this point, resolving issues becomes costly and time-consuming. This delayed feedback loop limits the ability to effectively align the product with user needs and expectations.

Moreover, the rigidity of the Waterfall method assumes that requirements remain constant, which is rarely true in today's dynamic business environments. This lack of flexibility makes it difficult to adapt to changing market conditions, customer preferences, or technological advancements, often leading to wasted resources and diminished project value.

The Iterative Approach as a Key Solution

The iterative approach offers an appealing alternative. By building systems step by step in smaller cycles or mini-Waterfall projects, this approach allows teams to focus on delivering the most critical features first. This strategy enables faster delivery, earlier user feedback, and quicker issue resolution. The iterative model better aligns development efforts with business goals and ensures the product remains relevant and valuable throughout its lifecycle.

A key principle underpinning the success of the iterative approach is the Pareto Principle, also known as the 80/20 rule. In IT projects, this principle suggests that 80% of a product's value often stems from just 20% of its features. By prioritizing high-impact features and deferring or eliminating low-value ones, teams can optimize resource usage and shorten development timelines. This approach avoids excessive costs and complexities associated with rarely used edge cases, ensuring a more efficient use of time and resources.

The iterative approach also accelerates time-to-value by enabling users to access functional elements of the system earlier than traditional methods. Early delivery increases user satisfaction and provides tangible evidence of progress to stakeholders. Continuous delivery builds trust and fosters collaboration between development teams and business stakeholders, creating a positive cycle of improvement and adaptation.

See also: The Need for Agility in Insurance

Factors Building Success

For iterative approaches to succeed, decisive leadership is crucial. Managers must effectively prioritize, having the courage to defer or discard unnecessary features when necessary. Strong collaboration between business and development teams ensures alignment of priorities and makes the iterative process deliver meaningful results.

Equally important is the support and understanding from executive leadership, which should approve budgets in phases. This staged approach to financing allows for flexibility, enabling teams to respond to emerging challenges and opportunities without being constrained by rigid upfront planning.

Adopting the iterative approach also requires a cultural shift within the organization. Teams must move away from the mindset of achieving perfection from the start and instead focus on experimentation and learning from early iterations. Promoting agility and flexibility empowers teams to innovate and adapt to changing circumstances, supporting a more resilient and effective development process.

In Summary

While historically significant, the Waterfall methodology is increasingly unsuitable for modern, complex projects. Its inefficiencies, lack of flexibility, and delayed responsiveness make it ill-suited for today's dynamic environments. Iterative approaches provide a powerful alternative, delivering flexibility, faster delivery, and cost efficiency. By adopting iterative strategies and fostering the right organizational mindset, companies can unlock greater value and achieve sustained success in their software development processes.

Despite a positive overall sentiment about 2025, business leaders must navigate significant challenges to profitability, employee productivity and organizational resilience.

This complexity arises from an ever-changing risk environment. Key risks include climate change, natural disasters, cyber threats like deepfakes, the growing adoption of AI, and geopolitical uncertainties. Organizations must adapt swiftly to remain competitive and resilient.

Building resilience through comprehensive risk management

Integrating risk management into corporate culture is critical for turning disruptions into opportunities in 2025. Businesses must plan for climate-related risks, as climate change has escalated the frequency and intensity of natural disasters, leading to average annual global losses of $151 billion.

Organizations should conduct a business impact analysis to understand risks and recovery strategies. Extending business interruption policies to 24 to 36 months to accommodate long-term recovery needs and updating replacement costs in property insurance policies annually are essential steps.

Moreover, creating a "people plan" is vital, as disasters affect employees as much as operations. Establishing robust communication systems for employee support and providing essential resources such as food and medical benefits during crises ensure both operational continuity and workforce stability. Organizations that prioritize resilience are better equipped to maintain operations and recover quickly, gaining a competitive edge.

See also: Cyber Incidents Top Global Business Risks in 2025

Leveraging data and analytics for strategic decision-making

One of the essential actions businesses need to take in 2025 is leveraging data for strategic decision-making. The shift from cautious experimentation to widespread adoption of analytics is accelerating. Businesses that harness advanced analytics gain crucial insights into risk exposures and can "see around the corner."

Leading organizations are using data-driven strategies to optimize insurance costs, enhance employee benefits and improve decision-making alignment across the organization. By embracing analytics, companies can improve resilience and employee engagement, positioning themselves as leaders in navigating emerging risks.

Addressing cyber and geopolitical risks

Addressing emerging cyber and geopolitical risks is also a significant focus in 2025. The digital and geopolitical landscape presents challenges that businesses must address to secure profitability. Cyber threats, including ransomware, deepfakes and social engineering, are growing in sophistication. Projected global costs from deepfake fraud alone are estimated to have reached $1 trillion in 2024.

However, not all companies currently hold cyber insurance policies. To enhance cybersecurity preparedness, businesses should evaluate exposure to cyber risks, consider data retention policies and employee training, and offer employees cyber protection tools to safeguard both personal and corporate information. Securing comprehensive cyber insurance coverage tailored to evolving threats is crucial.

Geopolitical risks, often perceived as distant issues, have direct impacts, such as supply chain disruptions and regulatory changes. To mitigate these risks, companies should invest in political risk insurance to manage potential losses from political violence or government actions and use trade credit insurance to protect against payment defaults by foreign entities. Collaborating with vetted insurance partners in target regions ensures compliance with local policies and strengthens international operations.

Enhancing employee productivity and well-being

Employee productivity remains a top priority for 2025, as businesses face rising costs for medical benefits, pharmacy drugs and compensation. Companies are increasingly aligning total rewards with productivity metrics, emphasizing efficiency and innovation.

To enhance workforce vitality, more businesses are adopting a human-centric approach, building workplaces that prioritize well-being, flexibility, social connection and continuous learning. Leveraging data analytics helps identify cost-saving opportunities, especially in healthcare and pharmaceutical expenses. Additionally, reducing financial stress by offering solutions such as personal insurance coverage and financial coaching supports employees' financial health and improves productivity.

See also: Rising Climate Risks Demand New Strategies

Treating insurance as a strategic asset

Insurance should be a strategic tool for mitigating complex risks, protecting reputation and safeguarding the bottom line in 2025 and beyond.

Companies must conduct annual reviews with their broker to ensure policy limits and replacement costs are up to date. Additionally, they should explore alternative risk transfer solutions, such as captives and parametric insurance, to address gaps in traditional coverage.

By guiding organizations in adopting advanced analytics to better understand risks and employee needs and offering tailored solutions for employee well-being and financial security, brokers enable companies to achieve growth, resilience and operational excellence.

Thriving amid uncertainty

Insurance in 2025 requires integrating data analytics with strategic risk management. Organizations can navigate challenges by treating insurance as a strategic asset while building resilience against emerging risks. Expert partnerships and employee wellness programs help companies adapt to uncertainty.

Accurate and well-governed data is critical for success in today's data-driven P&C insurance industry. But the sheer volume and complexity of data presents significant challenges to insurers, which must manage complex regulatory requirements while ensuring accuracy for underwriting and claims processing.

Conventional data governance methods often struggle to keep pace, leading to data quality issues, compliance risks, and missed opportunities for insights. Generative AI offers the opportunity to automate processes, improve data quality, and gain competitive advantage through well-informed decisions and streamlined operations.

At its core, Generative AI uses deep learning architectures to create models that learn complex statistical representations of the data they have been trained on and generate new data that conforms to the learned distributions. Unlike discriminative AI/ML models, which are designed for tasks like classification and prediction based on existing data, Generative AI models focus on generating new data. This capability enables a wide range of applications, from synthesizing text and images to generating synthetic data for testing and training other models and creating metadata or data quality rules for an analytical pipeline.

See also: How AI Can Maximize Unstructured Data

Here are some of the ways P&C insurance companies are leveraging Generative AI to address key data governance challenges and unlock opportunities:

• Improving data quality through automated metadata generation and data quality rule creation
• Streamlining data stewardship and metadata management processes
• Strengthening compliance with evolving regulatory requirements

Generative AI helps insurers automate the generation of metadata for business attributes, tailored to their specific terminology and standards. This not only standardizes existing metadata but also generates information for missing metadata, which is particularly helpful with legacy systems that may not have good metadata definitions in place. Effective implementation of AI models and integration with data quality and data observability platforms enables continuous monitoring and improvement of data quality. This approach helps generate comprehensive data quality rules, consistently identify critical data elements for operational reporting and apply company-specific language for business glossary definitions.

Data stewards play a vital role in data governance, ensuring quality, integrity, and security of the company's data assets. Generative AI significantly reduces the workload for the data stewards by enhancing metadata management and keeping data catalogs current and comprehensive. Strategic integration of AI into data governance workflows can deliver time savings ranging from 50% to 90% for data stewards on tasks such as generating business definitions, identifying critical business attributes, and creating data quality rules, helping data stewards focus on strategic, human-in-the-loop activities that maximize the value of their subject matter expertise.

See also: A Data Strategy for Successful AI Adoption

AI-driven data classification can significantly strengthen regulatory compliance and enhance the protection of sensitive information. By analyzing both sensitivity (e.g., PII, PHI) and contextual attributes (e.g., document type, ownership), AI models, including those leveraging computer vision and natural language processing, extend discovery and classification to documents, images, and other unstructured data. This contextual understanding enables far more effective classification than traditional methods based on text patterns.

Data protection and governance platforms now provide pre-trained models and the flexibility to incorporate custom-trained models to accomplish this. Language models can also automate the monitoring and analysis of regulatory changes published by state insurance departments, the NAIC, and other relevant authorities, assessing their impact on existing policies and processes.

The convergence of AI and data governance presents a significant opportunity for P&C insurance companies to transform their operations and gain competitive advantage. However, successful implementation requires deep expertise across all these domains.

Artificial Intelligence (AI) is disrupting the insurance space at a remarkable rate, building a new ecosystem based on accuracy, efficiency, and customer-centricity. It is changing the way we look at all the legacy insurance functions, like claims processing, personalized customer experience, and fraud detection.

As the adoption of AI expands, more tailored products can be delivered, claims can be responded to faster, and risks can be mitigated. Progress never comes without hurdles, though, and new challenges in the form of regulatory compliance and data security make it imperative for insurance enterprises to have a strategic approach while implementing AI. 

In this article, we will explore how AI can deliver impact across different touchpoints of the insurance value chain.

Disrupting Underwriting and Claims Processing

Underwriting is one of the highest-value activities across the policy lifecycle, performed by highly skilled and highly paid resources. Most insurance carriers follow a first in, first out (FIFO) system with no consideration for the criticality of a claim, which places undue pressure on underwriters and leads to a sub-optimal experience for customers. AI's intervention at this touchpoint of the insurance value chain can be transformative as it can enable automated screening of proposals and ensure that they are assigned to the right resources based on priority.

Generally, settling a claim has taken a few weeks or even months due to issues with potential fraud detection and the efficiency of verification processes. However, AI can accelerate this process by up to 40%, satisfying customers while reducing operational costs. AI also makes it possible to identify patterns in claims data along with easy verification of records, including visual evidence, mainly due to the advent of image recognition technology. Early warning and fraud detection using AI help in identifying certain patterns that can help define rules to better identify potential fraud based on data accumulated over time. Furthermore, the use of AI in claims digitalization can better enable straight-through processing, first notice of loss (FNOL), ease of registration and faster settlements.

See also: How AI Will Transform Insurance in 2025

Maximum Impact on Tailored Customer Experience

Traditional insurance players haven't had access to precise information like customer behavior, lifestyle factors, or past interactions. With AI, insurance companies can deeply analyze the vast amount of customer data available to them and develop highly personalized insurance products and experiences. Customer-oriented data is fed into an AI-enabled chatbot that provides automated recommendations of the best-fit insurance products. Such personalization of the insurance products can directly boost customer loyalty and satisfaction.

Insurance GPT: the New Norm

Insurance companies are looking to develop a custom-built AI GPT tool that is focused on the specific needs and workflows of their industries to significantly enhance customer experience, boost operational efficiencies, and ensure data engineering readiness.

Here are some of the areas where insurance GPT can have the biggest impact:

• Policy Distribution: Insurance agents or websites typically specialize in selling targeted policies. However, this specialization limits their ability to identify and capitalize on cross-selling and upselling opportunities. With the advent of insurance GPT, agents can go beyond their area of specialization and effectively position other complementary policies or offerings that may be of use to a customer. Agents can also look up queries in real time, handle objections effectively and better articulate the combined value proposition of different offerings.
• Service Management: Effective service management entails handling structured and unstructured data that might be found in various form such as web forms, Excel sheets and PDFs. AI can greatly simplify the process of converting unstructured data to structured data with a pre-built context to increase operational efficiencies.
• Integration: As in every industry, insurance companies are also mired in legacy investments in technology that may not be compatible with modern AI journeys. Hence, it is important for an insurance GPT to be compatible with third-party applications and ensure seamless integration by serving as an AI and user experience (UX) wrapper.
• Reporting: Data that is often found in silos within legacy systems across different form factors isn't very usable. An insurance GPT can facilitate greater visibility and transparency by leveraging AI for dashboards, management information systems (MIS) and reporting, thereby keeping businesses ready for the data and AI revolution.
• Customer Engagement: With the sheer amount of data that is generated in the insurance industry, account managers or agents find it difficult to have the necessary insights needed to ensure a great customer experience. Insurance GPT can streamline all customer conversations and provide a 360-degree view of a customer, which can help an insurance provider drive better customer engagement while improving policy renewal rates.

See also: How AI Is Changing Insurance

Conclusion

As the use of AI expands, it is bound to leave a huge footprint on the current regulatory environment, which includes data privacy laws and ethical usage standards for AI. Making sure that modern AI tools comply with rules and regulations around data security and consent is essential to gaining public confidence and building accountability and transparency.

In 2025 and beyond, AI is set to drive significant transformation across the insurance industry, delivering substantial benefits in areas like risk management, claims processing, fraud detection, and underwriting. Insurers must be ready to fully leverage AI's potential by making strategic investments in technology and developing a well-structured roadmap to tackle emerging challenges as the industry's AI adoption accelerates.

By making this technology shift responsibly, insurance companies can unlock immense value for customers, offering unprecedented agility and predictive capabilities. As insurers evolve to meet shifting customer expectations and regulatory requirements, AI emerges as both a transformative tool and a catalyst for a more resilient, customer-focused future.

U.S. insurers appear to remain generally optimistic about investment conditions for 2025 and expect to continue to take on more investment risk, according to a recent survey of 310 investment decision makers in the U.S. insurance industry commissioned by Conning.

The recently completed election season also seems to be weighing on insurers' minds as the domestic political environment was the top portfolio concern of 10 choices listed, even though the survey was conducted after the November 2024 elections. Portfolio yields and market volatility were tied as the second greatest concern. Inflation, which had ranked as the top concern in the previous three investment risk surveys, fell to seventh among respondents.

Insurers continue to express interest in private assets and expect their current exposures to grow. The bulk of respondents (71%) currently have between 5% and 20% in private assets. In two years, a majority (63%) expect to have between 10% and 25% in private assets.

The majority of respondents also said they expect to maintain or increase duration in 2025 as well as exposure to floating-rate assets, and almost all are confident they can meet the liquidity demands of their business.

See also: 20 Issues to Watch in 2025

Optimism, Less Inflation Concern, Comfort With Liquidity

Overall, the respondent group remained optimistic regarding the investment environment in 2025, although the optimism has slipped a few points since the last survey and pessimism has risen slightly.

For the third year in a row, the majority of respondents also said they expect to increase their investment risk in the year ahead, although their inclination has been easing over the past three surveys. Respondents with the largest firms were the least likely to increase risk and the most likely to decrease it. Those outsourcing asset management were also more likely to decrease investment risk than those managing assets internally.

A major change in the leading portfolio concerns during the next two to three years among respondents was the significant decline in inflation worries, which had been the top concern among respondents the past three surveys (and remained the primary worry for the smallest firms). Of a list of 10 portfolio concerns, the domestic political environment was first, followed by investment yields and market volatility (tied), geopolitical events and the impact of artificial intelligence/model risk.

The political environment category was added in the prior year's survey and proved to be a major worry again in this survey, although it was not a leading concern for the largest insurers, which identified yields and market volatility as their top threats. Monetary policy rated among the lowest in the list of concerns (except for insurers with between $5 billion and $10 billion in assets).

Liquidity was the lowest concern (it was slightly higher for property-casualty firms than life), a point to note given the increasing interest in adding less liquid private assets to portfolios. In separate questions, respondents voiced confidence in having the necessary portfolio liquidity to meet business needs. Nearly half (48%) said their portfolios had an appropriate amount of liquidity, and 30% said they had too much. Only 12% said they had too little. And the vast majority (92%) agreed or strongly agreed that their companies are well positioned to address liquidity needs for their operations.

Cost control and the need for expertise in analytical capabilities in risk management and asset allocation remained the top reasons for insurers to outsource asset management. The next greatest reason to consider outsourcing was accessing different investment strategies.

Patience With Asset Allocation, Seeking More Private Assets

Respondents also indicated they do not anticipate a rush into particular asset classes, another sign of restraint compared with the prior year's survey. For example, 63% of respondents to the 2023 survey expected to increase exposure to investment-grade fixed income, not surprising given the opportunities apparent from rising yields in late 2023. Six other categories saw at least 50% of respondents expect to increase exposures. In our most recent survey however, none of the 12 asset classes listed saw more than 47% of insurers expecting to add exposure; responses generally were higher in the "no change" or "decrease" options in comparison with the prior year results.

However, momentum appears to remain for moves into private assets.

The exposures to private assets from our most recent survey respondents almost matched the prior year's survey, with 71% currently holding between 5% and 20%. While respondents expect moderate growth in their exposures, there was some tempering at the higher end: 17% expect to have 25% or more in the asset class, down from the 25% who projected this level of exposure in the prior year's survey.

Private assets are not without their own risks, as insurers know, and tops among them for respondents was the impact on liquidity; 31% said they were "very concerned" about it, clearly an area of focus for insurers given their stated comfort with current portfolio liquidity overall. Concern about sourcing private assets, having data/analytics to support them, and management/board approval, were less of a concern.

Floating Rate Strategies and Duration

Responding to a separate question, the large majority of those surveyed said U.S. Federal Reserve policy affects their investment strategy "moderately" or "significantly," and Fed policy has a significant impact on floating-rate strategies. In the year ahead, 53% said they expect to increase exposure to floating-rate strategies, and a further 25% said their exposure will remain the same.

While insurers expect to increase exposure to shorter-duration floating-rate assets, overall duration is expected to increase, suggesting the duration barbell will be popular this year. A number of insurers in 2024 sought to extend duration in the portfolio, a strategy that appears popular for 2025: Nearly two thirds of insurers (64%) said they expect to increase duration this year, and only 14% expect to decrease.

Market conditions in 2024 seemed to suggest that portfolio turnover might be on the rise, and half of all respondents confirmed that they had more turnover in 2024 than the prior year; only 22% said it was lower, with 28% saying it was the same. Of those who had more turnover, the leading reason (62%) was a tactical decision given market opportunities. Of those who had lower turnover, their leading reason (53%) was a limited ability to reposition. In both groups, change in risk preference was the least-chosen reason.

See also: What Trump 2.0 Means for Insurance

Investment Managers' Role in Market Navigation

The survey finds that insurers remain a generally optimistic group but do see challenges in the year ahead. While the scourge of inflation appears to have eased, at least for the time being, and interest rates have declined, uncertainty remains over the future direction of rates and the potential for volatility, both in markets and the political environment. Insurers continue to broaden and diversify their portfolios and are expressing clear interest that they expect to add more private assets, and they will likely be careful to assure that they can maintain their confidence in meeting their companies' needs for liquidity.

The circumstances of each insurer's business goals, risk tolerance, and book of business help reinforce the view that insurers often require customized solutions to address their unique needs. They may be best served by investment managers with a deep understanding of insurance asset management to help them develop valued investment strategies and navigate any volatility that may arise.

Many employees today may find themselves in the beginning months of a new benefits period. Yet whether they re-enrolled in the same policies they had previously or elected new ones, they might not make full use of their non-medical workplace benefits throughout 2025 without fully understanding the coverage and services available to them.

To help change this trajectory and empower consumers to take advantage of their benefits, carriers today are investing in digital tools more than ever before to provide members with resources that meet their well-being needs. These solutions are focused on making learning about and accessing both insurance and non-insurance coverages as simple and intuitive as possible. This presents a unique opportunity for brokers to connect the dots for employers and their workforces. By advising their clients on how to make the most of digital tools and resources across benefit offerings, brokers can help support employee well-being in the year ahead.

See also: How Wearables Can Improve Worker Safety

Showcasing the richness of available support

To encourage engagement and offer comprehensive coverage, some carriers have begun to include wellness resources directly within traditional insurance products. If employees don't know about these benefits, they can't use them—and the implications can be significant.

Take the millions of full-time working Americans who have non-work-related caregiving responsibilities. A recent Guardian study found that these caregivers are two times more likely than non-caregivers to need to take a leave of absence from work. Employer-provided disability insurance may offer curated caregiver support solutions, including digital planning tools, a caregiving concierge, and a peer support network. These resources can help to minimize the need for an employee to take a leave of absence for caregiving duties. Given this approach remains innovative today, supporting awareness and access through digital self-service channels is critical.

Tobacco cessation is another example. While smoking rates are down overall, there is an alarming increase in teenage vaping rates, and almost a third of working Americans continue to use tobacco products, according to Guardian research. Yet two-thirds of workers say they would be very interested in using a tobacco cessation program through their dental insurance plan if it was complimentary.

Ultimately, embedded wellness offerings that leverage digital touchpoints to offer frictionless access to services can help make a difference when it comes to fostering employees' mental, physical, and financial well-being. As workers look to make the most of their benefits this year, brokers can help their employer clients educate their workforce about any wellness offerings included in their benefits package to ensure they use all available digital features and, in turn, tap the full scope of these resources.

Leveraging digital platforms to streamline information

Digital features are key to enhancing wellness offerings, but they're not the only way that tech-enabled solutions can encourage benefits usage and support well-being. There is also an opportunity to make important benefits information and related resources easier to access.

What does that look like in practice? Our research shows that more than 51% of employers would be interested in a single platform through which all wellness-related benefits could be made available. Further, employees who have access to a more digital benefits experience self-report better well-being than workers who don't.

In response, carriers are working to bring this digital experience to life through various digital hubs designed to help ease access to wellness benefits for members. These platforms typically include educational content on mental, physical, and financial well-being, as well as family and community support topics. Additional resources often include third-party fitness classes led by virtual instructors for people of all fitness levels, as well as wellness articles that are regularly refreshed and focus on the connection between mental and physical health.

For employers looking to offer a more comprehensive digital benefits experience for employees, brokers can use the opportunity to highlight the resources carriers are making available to streamline information and drive engagement.

See also: 'Bleisure' Travel Is on the Rise--and It's Complicated

Now is the time to get started

At a time when technology is integral to so many aspects of our everyday lives and carriers are embracing digital tools like never before, brokers can play a key role, supporting employers in helping their workforce to make the most of digital offerings.

When employees understand how to leverage the benefits available to them, including through embedded digital offerings or streamlined digital platforms, they're more likely to use them. It's a crucial way that brokers and employers can support employee well-being across mind, body, and wallet.

The climate in the Middle East has seen an increase in the frequency of both hailstorms and heavy rains.

Oman has been experiencing cyclones on an almost annual basis, with the latest being Cyclone Asna in August/September 2024. One of the most damaging to date was Cyclone Gonu, which hit Oman and the east coast of the UAE in 2007, resulting in 70 deaths in Oman and 10 in Fujairah, as well as significant damage to property (estimated $4 billion of damage in Oman).

Saudi Arabia is no stranger to extreme weather, with flash floods hitting the country on several occasions in the 1970s, 1990s and more recently in 2009 and 2011 when flooding hit Jeddah, causing average damages of around $3 billion per event.

The UAE has seen irregular extreme weather events over the last 70 years, mostly in the form of flash floods due to intense rains, and it is possible that future events will increase in magnitude as the climate changes.

Interestingly, none of the major natural catastrophe tools used by the insurance industry today show any sort of major (extreme) natural catastrophe risks in the Arab Gulf region (other than one-in-50-year return storm surges in coastal areas). The renewables industry, and solar farms in particular, have been severely affected by severe convective storms in other parts of the world, most notably in North America. Hail damage is a rare but not impossible phenomenon in the Middle East, and the UAE has recently seen hail damage to infrastructure and vehicles, even though the size of the hail has so far been modest. 

Will that change with changes to the climate? Although unlikely to have the same impact as on the North American renewable industry, some negative future effects are possible.

See also: Insurers Must Evolve to Survive Climate Crisis

Predictions

The confidence in the forecasts of cyclones and flash floods remains low due to their relative rarity and the scale of the event. While cyclones are large events that can be captured by weather forecast models, the direction of the cyclone and its impact is much harder to forecast. The intensity of a cyclone depends on a large number of factors, including the track the cyclone takes, which itself is influenced by several factors that are difficult to predict, e.g., sea surface temperatures and other weather patterns.

Flash floods are much harder to forecast, as the processes that lead to them are typically only a few kilometers in size, smaller than the resolution of most forecast models. Flash floods are typically caused by sub-daily rainfall extremes, caused by localized heating, resulting in an unstable atmosphere and leading to a convective storm (which can also cause hail). However, whether the rainfall is absorbed by the ground or overflows resulting in flash flooding depends on the preceding conditions – too wet a ground means it cannot absorb any more rain, too dry a ground reduces its ability to absorb rain.

Climate change projections for the Arabian Peninsula show an increased risk from extreme rainfall that most typically leads to flash flooding. A warmer atmosphere can hold more moisture, thus when the conditions that favor extreme rainfall, and thus flash flooding, occur there will be more moisture in the atmosphere, leading to more extreme rainfall.

Figure 1 shows changes in extreme rainfall by 2055 under the SSP2-4.5 warming scenario (the current most likely warming scenario) using data from climate risk models. It shows that there is a large variation within both Oman and Saudi Arabia to changes in the rainfall pattern. Southern Oman shows a large increase in rainfall, directly leading to an increased risk of flash flooding. While northern Oman shows a decrease in extreme rainfall, this does not necessarily result in a decreased risk of flash floods. A drier soil could result in less rain being absorbed by the ground, increasing the risk of flash floods when extreme rainfall does occur.

Saudi Arabia (at right) has a much larger region showing an increase in extreme rainfall, where the majority of Saudi Arabia sees an increase in extreme rainfall occurrence. This suggests that the risk of flash flooding across the country will increase.

Figure 2 also shows an increase in drought risk across Oman, Saudi Arabia and the UAE for the SSP2-4.5 scenario. This will lead to drier soil conditions, particularly in the summer, increasing the risk of flash flooding from extreme rainfall. The data is not able to capture the processes that lead to sub-daily rainfall events; however, it does show that the conditions favor an increase in flash flooding risk.

There are other perils that may become worse due to climate change and thus potentially start causing significant losses in the region, in particular hail. The impact of climate change on hail remains very uncertain, due to the scale at which the processes that lead to hail occur. However, most projections for hail are for an increase in size and occurrence. This could have a big impact on the renewable energy sector, specifically solar panels, which remain vulnerable to hail and haven't seen that kind of damage in this part of the world yet. We also need to consider the changing nature of perils; with cyclones holding more moisture, we could see a greater impact from the rainfall/flooding from a cyclone, and not the wind, as traditionally seen.

Impact of Mitigation Aids

Improved forecasts of cyclones can reduce the impact through mitigation measures, e.g., increasing the resiliency of buildings, boarding up windows, and removing potential debris. Examples of this can be seen most recently in Florida in preparation for Hurricanes Helene and Milton. While losses were still experienced, they were smaller due to the warning and preparation. Florida also requires that when greater than 25% of the roof is damaged, the whole roof is to be repaired to new building codes. This has resulted in an increased resilience of buildings to hurricane winds.

A similar approach can be taken to flash flooding, even if it is more difficult to forecast. Improved drainage, identifying potential blockages and flood resiliency schemes that absorb water for short periods can significantly reduce the impact from flash flooding. It is also possible to increase the resiliency of assets to flash flooding by identifying potential water ingress locations and adapting them to prevent the ingress of water.

See also: Severe Weather's Effects on Auto Claims

Conclusions

An analysis of Oman and Saudi Arabia shows localized increased risk of extreme rainfall and subsequent flash floods if the SSP2-4.5 scenario becomes reality. Analysis data can be used to reduce long-term risk to a project by applying mitigation measures for future scenarios at the design stage of the project.

Will we eventually see weather events having an impact on premiums given the rapid deployment of renewables in the region? Although unlikely in the near future, a single damaging event (even if isolated) could have a massive impact on the market. The recent flash floods in the UAE caused severe damage to the Noor 1 Concentrated Solar Plant, possibly reaching a staggering $500 million of insured damage.

Insurers have until recently been relatively happy ensuring that an asset is adequately protected for a one-in-100 or one-in-200-year flood. We are now starting to see instances where insurers move toward requesting the one-in-500-year flood to be the governing condition for flood protection. This trend is likely to continue and might also come with requirements for predictive models to be used for large market assets to determine present and future natural catastrophe risks. The key is how these risks are managed and resiliency is embedded.

The way consumers experience insurance is on the brink of change, as digital innovations revolutionize the user experience. Yet while many insurers are keen to embrace artificial intelligence (AI), few are using it to drive profound impact.

KPMG observed that the primary driver for digital adoption was to simplify existing processes. This tunnel-vision approach is all too common and misses the powerful potential of generative AI (Gen AI) to transform business models and the way modern insurers operate.

The insurance sector is ripe for reinvention, and the case for Gen AI adoption is compelling, so why does research by Boston Consulting Group indicate that just 10% of companies are applying Gen AI at scale?

Many will agree that the insurance industry is risk-averse, and this "digital reluctance" is likely to be a major stumbling block. Standing still in an ever-changing world is not an option.

Thankfully, a small, growing number of forward-thinking insurers are carrying the torch of innovation. They truly understand that Gen AI is key to transforming the entire value chain, redefining customer interactions, improving operational efficiencies and, ultimately, setting a new bar for industry standards in the digital age. Most importantly, they recognize that Gen AI cannot simply be an afterthought but requires a commitment to companywide integration and a desire to evolve continually.

See also: AI in Insurance: 2025 Predictions

Embedding Gen AI Across the Value Chain

When fully integrated into an organization, at the heart of corporate strategy, Gen AI can achieve new heights in insurance distribution, operations and customer excellence.

Gen AI can enhance agent recruitment and retention strategies, streamlining the hiring process by efficiently sifting through vast amounts of applicant data to identify top-quality candidates. These tools increase the number of high-caliber recruits and ensure a better fit for the organization's specific needs. Furthermore, Gen AI can identify high-potential agents early in their careers, facilitating their growth through targeted and personalized coaching, upskilling and reskilling and development programs, ultimately improving retention.

Productivity gains are plentiful, with Gen AI empowering agents through an optimized sales journey, providing real-time assistance by analyzing customer needs, identifying gaps and prompting agents with product recommendations tailored to customers' specific needs.

Through Gen AI, agents will receive leads enriched with customer insights and product recommendations that are best aligned to their preferences and needs. Draft emails and suggested marketing content will be ready to go at the push of a button, saving agents valuable time while ensuring engagement with customers remains personal and tailored. The nurturing of leads through to conversion will also be enhanced with automated and timely communications. Gen AI can also help manage agents' performance more intelligently by simulating agent and team performance and recommending action plans to achieve targets.

From an operational perspective, Gen AI can provide medical and financial document summarization for underwriters and claims assessors, resulting in scalable operations, increased staff productivity and unit cost reductions. Customer queries are addressed more effectively, with contextual responses based on previous customer conversations, knowledge bases and policy-specific information. Contact centers are better equipped to provide personalized responses based on customer history and product information, enabling staff to focus on resolving more complex cases. Moreover, Gen AI can inject highly sophisticated social media marketing capabilities into agency apps to create compelling ways for agents to attract and engage with an entirely new customer base.

See also: Blending AI With Human Interaction

Safeguarding the Use of Gen AI

Gen AI is more than just an operational add-on for insurers; it must be embedded into the organization's very culture to maximize operational efficiencies, enhance the user experience and build trust.

Transformational projects require innovative leadership, significant investment, companywide buy-in and rigorous safeguarding to ensure the use of Gen AI remains ethical, accountable and transparent. That's why it is essential to establish a robust framework to assess existing AI risks, including reliability, fraud, impersonation risks, privacy and data governance, and new-gen AI risks, such as the generation of false, misleading or biased information.

Embracing Gen AI is a substantial undertaking for the modern insurer, but with a robust action plan, sustained budget and dedicated project team driving it forward, companywide integration can be achieved in just a few short years.

Tesla's recent announcement that it is bringing underwriting in-house for its car insurance unit generated enthusiasm about the prospect of taking data-driven auto insurance to the next level. But I have a different take.

I agree that what Tesla is doing is the next logical step in having sensors in cars feed data about driver behavior directly to underwriters to improve their understanding of the risks. In time, that path could take us to a world where behavior is being monitored in real time and feedback provided to drivers in the form of higher or lower premiums. Encouraged to be careful, those drivers could make the roads safer for all of us. 

But I don't think Tesla is the right, well, vehicle for that next step, for a host of reasons.

I'll explain.  

Tesla CEO Elon Musk has frequently overpromised and underdelivered over the past decade, primarily on the prospect for fully autonomous driving but also on insurance. After he launched Tesla Insurance in 2019, he said it could soon account for 30% to 40% of Tesla's value. Based on Tesla's market cap today, that would imply a value of $330 billion to $440 billion. For comparison's sake, Allstate has a market value of about $50 billion. Anyone think Tesla Insurance, with a $42 million net underwriting loss for the first nine months of 2024, is worth six to nine times Allstate? 

Musk has said Tesla owners should get discounts on insurance because Tesla Autopilot is so much safer than human drivers, but analysts say he fudges the numbers on accidents, and a recent study found that Teslas actually had more accidents per vehicle than any other make in the U.S. last year, as they did in 2023. Starting with bad assumptions doesn't exactly lend itself to good underwriting. 

If your publicly stated starting position is that you should offer discounts, and your drivers are no safer or perhaps even less safe than those insured by others, you're in for a rough ride. Perhaps Musk has hired great people and perhaps they'll be very careful as they bring the underwriting in-house, but caution isn't part of Musk's personality or the company's. And we've seen how hard it can be to get the formula right even at seemingly prudent startups such as Root and Metromile. 

Musk has been known to power his way through problems before, including at Tesla, but he's CEO of five companies at the moment... and has a day job in Washington, DC that appears to be all-consuming. So he's unlikely to wade into Tesla Insurance any time soon.

Besides, Tesla has bigger problems these days than its insurance operation. Sales fell for the first time last year, even though the market for electric vehicles is growing rapidly and even though Tesla had posted sales gains of 38% and 40% in the two prior years. 

Tesla's stock has soared more than 40% since the U.S. presidential election on investor hopes that Musk's $278 million of contributions to the Trump campaign and vocal support via Musk's social media platform, X (formerly Twitter), will translate into success for the business. But the tenor of the Trump administration is, in fact, very much opposed to electric vehicles. Last week, Senate Republicans introduced bills that would not only repeal the $7,500 tax credit for electric vehicles that was Tesla's lifeblood in years past but would charge a $1,000 fee on every new EV sold.  

Musk may have other political problems, too. Suspicion is growing that the sales slump partly stems from Musk's hard-right turn politically, which has angered lots of people in what was considered to be Tesla's sweet spot: well-off consumers worried about climate change. An article in Wired says the potential trouble extends to Europe, where Musk has waded into politics in support of right-wing parties in England and Germany. Wired says Tesla sales dropped 13% in the European Union in 2024, and the decline may be accelerating. 

"Last month, Norway—where EVs overtook internal combustion vehicles in total market share in 2024—recorded a biting 38 percent slump. At the same time, Tesla sales in France fell by a thumping 64 percent. And it gets even worse: In Spain, Tesla sales plummeted by 75 percent.... Tesla registered only 1,277 new cars in Germany in January, a year-on-year drop of 60 percent.," Wired says.

I also think he's going to have a major problem come June, by which time he's promised to roll out a fleet of robotaxis. Based on the lofty stock price, I may be in the minority. But I've been following autonomous vehicles closely since starting on a book on driverless cars that Chunka Mui and I published a dozen years ago, and I can't believe the technology is ready. 

Waymo has been cautiously rolling out its robotaxis for years, based on technology that includes radar, lidar and cameras. It's only operating in geofenced areas that it's mapped in excruciating detail. Yet Musk says he'll be able to put out a fleet that uses only cameras and that can go anywhere. 

I just don't see it. 

Now, he's plenty good at distracting people with promises that he'll colonize Mars or whatever if we just give him some time. And he does have X/Twitter to broadcast his messages, as well as a supporter in the Oval Office. But I still believe he'll have to do major damage control. If history is any guide, he'll make some small gesture and promise that the robotaxi fleet is imminent -- he's been making such promises about Tesla Full Self-Driving since 2015.

I still believe in the basic vision behind what Tesla is trying to do with its insurance arm, including the move to bring underwriting in-house. Actual data on driving behavior is obviously far more helpful than something like a credit score when evaluating risk, and other recent attempts to supply insurers with more data face pushback. GM's OnStar has been banned from selling driver data to insurers for five years, and Arity has been sued for allegedly collecting data via cellphone apps without warning drivers that they were being monitored. 

Insurers such as Progressive will obviously still have access to driver data that they obtain directly from customers. But the hoped-for breakthrough in integration between car makers and insurers will have to wait, and I suspect Tesla won't be the first to get there. 

Cheers,

Paul

P.S. Here's how not to sell extended warranties on cars.

Insurance Thought Leadership

I'm interested in what you’ve written about using agentic AI in distribution. Could you explain the framework for how you're thinking about the use of AI agents in the agent and broker space?  

Abby Hosseini

AI has been part of the insurance industry for 25 to 30 years. We’ve been using different disciplines, such as natural language processing [NLP], deep learning, machine vision, and robotics, in the front office, back office, and middle office. A few years ago, we saw the rise of robotic process automation (RPA), and chatbots have been around for 20 to 25 years in the form of FAQs, NLP, and search engines. The latest trend involves generative AI, which produces content or knowledge, while agentic AI takes it a step further by doing something with that knowledge.

Agentic AI orchestrates processes or uses machine learning to achieve specific outcomes. There's a distinct difference between generative AI, which focuses on knowledge creation, and agentic AI, which can make processes happen. 

Companies are trying to understand the new technology, deciding which large language model to use, and considering security, governance, and risks. There's been a lot of focus on choosing among Google, OpenAI, Anthropic, and Microsoft, but less on actually implementing the technology. A significant challenge is that, without good data, there is no AI. Many process owners in insurance and banking struggle with poor data quality or lack confidence in the data used for decision-making.  

Insurance Thought Leadership

Why is the data not clean enough, and what can be done about it?

Abby Hosseini

Good question. That's a good segue into the distribution side of the house, because data is created by agents, mostly independent agents, as well as by customer service and claims adjusters. If you're lucky, you have good data entry tools and core systems with some governance or control around what to input into the system. However, poor data entry is causing data quality issues. A lack of governance around the definition of the data or the ability to understand what a field means is another issue. For example, if a field is called "premium earned," what's the exact definition of it? Because there's a lack of common definition and people have taken liberties with how they input data, you end up having challenges with creating a single view of the customer.

For instance, if I say "Jane Doe" versus "Doe, Jane," or spell the word "senior" versus typing "Sr.," the computer doesn't understand these differences and can't tell that the entries are the same. When machines operate on the data, they need some normalization or cleaning. 

In insurance, we expect agents to cross-sell, and for them to do that they need a good understanding of the household and knowledge of what products the person or household is using. A lot of that is missing.

If you have four or five different underwriting systems, as most large carriers do, there is no connection between them. For example, when I was at Mercury, if you were a homeowner customer, you were instantiated in the home system. If you were an auto customer, you were instantiated in the auto system. But there was no linkage or index that says this person here is the same as that person there.

There are a lot of these kinds of problems, but there's also the fact that there's a new age of abundance when it comes to the availability of data, particularly from the internet. Being able to wrangle that data, purchase it, bring it in, and integrate it with your own data has been a challenge.

Insurance Thought Leadership

What’s the solution to the data cleansing issues?

Abby Hosseini

As far as data entry is concerned, you need a robust user interface with good business rules for every field. For example, when the pandemic happened, the company I was at realized we didn't have email addresses for about 45% of our customers. We asked ourselves why and discovered that the email address was an optional field in the agent portal. Someone, 10 or 15 years ago, decided emails were just nice to have. Then the pandemic hits, and you need to go digital, send notices and information, and you realize you can't quite do that.

We had to go back to our agent portal and make it clear that this field is not optional. But even then, people still mistype things. You need to have email address validation in place to catch these mistakes. Software can handle these issues, but there also needs to be human commitment to ensure the data entered isn't just lazy typing or missing critical information.

In insurance, particularly in personal lines, there's still a debate about who owns the customer—the agent or the carrier. This can lead to issues where the agent might not want to share too much information.

Insurance Thought Leadership

You've talked about three specific areas where agentic AI could be applied. Let's start with the sales assistant. What does the current landscape look like, and how might it evolve with AI?  

Abby Hosseini

The concept of AI copilots is already coming to fruition in many respects. These assistants aid salespeople with next best actions, offering contextual products or help, while providing a lifeline for policy, procedure, or product knowledge that the salesperson might be missing.

In complex product sales, like insurance, it's impossible for a human to remember all the specific rules across different locations. For example, knowing the garaging rules in Wisconsin versus Arizona. AI solutions can provide this information contextually, in line with the sales process. Anything related to guidelines, policies, procedures, cross-selling, or next best actions can be facilitated through these Gen AI solutions.

The level of intrusiveness for these copilots is customizable. They can function as a sidekick that the salesperson consults when needed, or as a more supervisory presence overseeing the human's actions.

We're not even considering the direct-to-consumer model yet. In online quote-and-buy scenarios, customers often get confused about certain terms or need product education. They may have questions about what they're buying. In this context of self-service for complex sales, the AI copilot can play a crucial role in educating the customer and helping them navigate the buying process.

Insurance Thought Leadership

I have to say, I always find it funny when I hear the term “next best action.” The first time I heard that, I thought, well, why wouldn't you tell me the best action?  Why just the next-best? I quickly figured out that the term refers to the best action to take next, but I still chuckle in my head.

Abby Hosseini

I think of it as an "if-then-else" scenario. When a customer calls and presents situation X, Y, or Z, there's a fork in the road based on that dialogue -- you either go this way or that way.

While you're solving the initial problem, you might identify an opportunity. That becomes what we call an "aspect section." For example, if you're talking to a homeowner, maybe they don't have an umbrella policy or a car policy. Or if you hear a dog barking in the background, you might ask if they have pet insurance. These opportunities become what we call the next best action, primarily in the context of cost.

Insurance Thought Leadership

Turning to the second of your points about how agentic AI can be used in sales: Beyond acting as a copilot, what would a sales agent in AI look like?

Abby Hosseini

Well, there can be instances where the AI isn’t just providing knowledge or product information but is managing a process. For example, in underwriting, if you have an expensive novelty car or a Monet painting in your house, you can't just write the policy; you have to have some proof. In the old days, pre-pandemic, we would tell the Porsche or Lamborghini owner to drive over to the agent and show them the car. The agent would go out, take pictures, and send them to the carrier. Then the pandemic hit, and nobody could visit anyone else.

So, how do you support that sales process? It needs to be a self-service process where the customer can take pictures and upload them on their own. The customer could call the carrier, and the carrier sends a text message back that says, "Click here." The agent then takes over, guides the customer through taking the picture, uploads it, and brings it back into the underwriting world. This process, which could take days or be very cumbersome before, is now much faster with a good digital experience powered by some sort of orchestration or AI. That's an example of assisting the sales process.

Insurance Thought Leadership

Pardon me while I go take a picture of my Lamborghini and send it to you.

Abby Hosseini

The other thing I wanted to mention, which I'm pretty excited about, is the potential of interactive AI in customer education. When you buy a new product, like a Cadillac EV or a Samsung TV, you own it and bring it home, but you might not know exactly how to operate it or have questions about it.

The ability to use interactive AI to educate the customer about the product is exciting. It's not just one-way communication but interactive. Today, we might Google questions like, "Why doesn't my TV turn on?" or "How do I turn off the child lock in the car?" If you're lucky, you might find the answer after some searching. But imagine if this process was more intuitive, where you could talk to an avatar and ask, "Tell me more about the child safety lock," and then immediately follow up with, "How long does it take to charge my car?"

That thick manual we used to get in the glove compartment could become an avatar you can talk to for contextual help. This isn't just about the sales process but also about post-sales product loyalty and experience, which can be really helpful.

Insurance Thought Leadership

I can imagine that sort of thing certainly applying to insurance. Instead of having to go through your big thick policy, you could ask, "Am I covered for fire?" or "How am I covered for fire?" and get specific answers.

Abby Hosseini

Yes, there are already some vendors out there that do this. They have systems where you upload your deck page, and they will interpret it and come back to you, telling you what your coverage is and what your exposure is.

Insurance Thought Leadership

To get to your third point on agentic AI: How do you see AI fitting into customer service?

Abby Hosseini

In customer service, there's traditionally been a two-pronged approach. There are about 1,800 chatbot vendors in the market, and chatbots have been around since the early 2000s. So, there's nothing new in terms of providing customer-facing FAQs. The focus has been less on customer service representatives having an FAQ system and more on self-help options for customers, which have been hit or miss. Some younger people often prefer not to talk to anyone and want to engage with bots to get what they need. However, the nature of insurance is such that you can't really sell anything with a chatbot. Eventually, you have to talk to a licensed agent. Sometimes, you don't have the digital processes to provide everything because you have to go through the agent. For example, with some carriers, if I buy a car and want to add it to my policy, I have to call my agent, who then has to call the carrier. It can take a day or two to get that done. If I want to add a name to my policy or drop a driver, all of that takes a long time.

With AI or some chatbot capabilities, I can now have a guided experience that asks about the new driver and then processes the backend changes to add that information. More and more digital policy support can be done with bots to a certain degree. You don't want to disintermediate the agent, and there are complex scenarios where you can't quite get it done without one. But if you look at your call center and analyze how many calls you're getting and why, there's probably a chance that 20% of those calls could be automated and avoided with self-help.

However, you have to promote the capability, as well. For example, in my previous company, we implemented what we called EFNOL, or electronic first notice of loss, for clients. Once we rolled it out, we had roughly about 10% to 11% adoption. I asked the head of claims why adoption wasn't picking up, and he didn't know. So, I went on Google and searched, "How do I file a claim with company XYZ," and the system came back with a phone number. It didn't mention that you could do EFNOL.

There you go. We didn't nudge the customer. We were still using old methods to advise the customer on how to engage. You can't expect that, just because you rolled out some digital experience, it will get adopted. You have to force it, nudge people, promote it, and make it a better experience than waiting on the phone. Those are parts of the strategy for promoting digital solutions. It's not enough to just build these agents and put them out there.  

Insurance Thought Leadership

I love the theory of chatbots but often find myself yelling at them. Have you ever dealt with a chatbot you like?

Abby Hosseini

Quite a few. They help with the mundane things. Has my payment been received, or what's my bill? When is my policy going to be canceled?

But you have to give the customer a choice. I can start in chatbot, elevate to a click to call, or elevate to a screen-sharing experience or a video sharing experience. Sometimes you start in one channel and then you go to the other channel because things get more complex. But it gets very frustrating when the chatbot doesn't answer your question and doesn't offer you any other way to engage.

Insurance Thought Leadership

If you give me five options, none of them are what I want, and there's nothing that says "talk to an agent," I get very unhappy very quickly.

Do you have any final words of wisdom?

Abby Hosseini

When I was a CIO and CTO, we had 10,000 agencies and 40,000 agents. We provided some level of delegated management of the users and the organizational hierarchy of these large agencies. However, onboarding was a struggle. Compliance with NAIC and other licensing adherence was a struggle. Commission distribution, commission calculation, and analytics around agents' performance were always a struggle.

When you look at where good analytics and AI can come in, I think it's across the whole spectrum: onboarding, agent education, compliance, commission and incentive management, and then reporting and analytics from a productivity perspective. In every aspect, some form of machine learning, analytics, or AI can be applied to really modernize that space.

There's risk involved with not having properly licensed agents. There's always an appetite or a need for just-in-time appointments. There are all kinds of human errors with commissions, and there's third-party reporting to regulators that has to happen. When you look at the whole value chain of distribution management, it's antiquated at best because most carriers have legacy custom commission systems and clunky onboarding.

That got us thinking about how, with some of these newer technologies that have come to the market, we can automate and streamline that experience.  

Insurance Thought Leadership

OK, this is super. I really appreciate your taking the time.

About Abby Hosseini

As principal and chief digital officer, Abby Hosseini leads the strategic development and evolution of Exavalu industry solutions and practice areas. His strategic consultation and assistance to client CIOs/CTOs during digital and operational transformation ensures organizational growth for clients. He has over 33 years of experience as a senior technology executive, including 22 years as CIO/CTO, driving large and complex IT transformations for some major organizations like Mercury Insurance Group. At Mercury, Hosseini also managed the company’s core insurance platforms, including custom software and Guidewire InsuranceSuite and InsuranceNow (SaaS) platforms implementation over 14 years. Hosseini holds a bachelor’s degree in mathematics/CS from UCLA and completed his MBA from Pepperdine University.