• Much of the growth of data centers is being driven by AI. About 20% of data centers' energy is currently due to AI, but that should grow to 40% by 2030, the report said.

   

• Why enterprise AI projects stall and how to open the value floodgates

• resumes from hopeful

• Mission-critical Applications Are Exposed
• Mission-critical RPO and RTO Policies: Help Needed!
• Many Incidents Are Ignored
• In the last 12 months, approximately how many of the following security incidents has your organization experienced due to the following threats? (N=600)
• Response Effectiveness Perceived As Very High Across Respondents
• Many Technologies At Play For Response...Adding Complexity?
• Bye Bye Data...
• Approximately what percentage of your organization's data was recovered after the cyber-attack? (N=399
• There Is NO Quick Recovery for Most...
• How long would your organization take to fully recover from a ransomware or cyber incident event and resume operations? (N=600)
• Air Gapping Is Becoming A Standard

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• many of the most promising companies in insurtech 2. Source: Gartner, Compare AI Software Spending in the Insurance Industry, 2023-2027, 27 March 2024. 3. Source: Free Partners LLP, “The Re-Generation Report.” 4. Source: Accenture press release, “Poor Claims Experiences Could Put Up to $170B of Global Insurance Premiums at Risk by 2027, According to New Accenture Research.” 5. Source: Gartner, Top 10 Technology Trends for P&C Insurance CIOs in 2024, 18 March 2024, Sham Gill, James Ingham, Kimberly Harris-Ferrante. 6. Source: Deloitte, “The AI Imperative in Insurance,” March 20, 2024. 7. Source: SAS Institute, “Your journey to a GenAI future: An insurer’s strategic path to success.” 8. Source: Deloitte, “The AI Imperative in Insurance,” March 20, 2024. are AI-native platforms built to address insurance-specific challenges. For example, there are exciting platforms that leverage predictive AI to streamline claims processing by automatically flagging high-risk claims and fast-tracking simpler ones. One cybersecurity company leverages AI to pinpoint security vulnerabilities, model cyber breach scenarios, and quantify the overall cyber risk on a portfolio- and individual-policy basis.
• “Global AI software spending in the insurance market is forecast to increase 17.4% in 2024 to $9.5 billion and reach $15.9 billion by 2027, with a five-year CAGR of 18.2%.” 2
• With this necessary infrastructure emerging, insurance companies across the value chain can turn to AI to help alleviate industry challenges. Among them is a massive talent drain coming to the industry with waves of retirements expected in the coming years. The resulting vacancies will not be easily filled, as less than a third of young people surveyed recently in the U.S. and U.K. said insurance is an appealing career choice
• 94% of insurance CIOs saying that adoption of AI will
• be critical to success over the next five years

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• Embedded insurance offers a frictionless, convenient user experience by integrating coverage at the point of sale. This aligns with the consumer expectation for a seamless digital experience where insurance is embedded within the digital purchase journey whether buying a car, booking travel, or shopping online. Open APIs and cloud-based platforms enable seamless integration of insurance into third-party ecosystems. Millennials and Gen Z consumers prefer on-demand, subscription-based, and value-embedded services, which is slated to bolster the embedded insurance market growth through 2032. Collaborations between insurance companies and digital platforms like e-commerce sites, travel portals, and fintech apps have accelerated the demand for embedded insurance.
• Artificial intelligence is transforming embedded insurance by enabling hyper-personalized, real-time underwriting, pricing, and claims processing. It analyzes user behavior, transaction data, and contextual factors to offer relevant insurance products at the point of need, seamlessly integrated into digital platforms

• 1. A move beyond generative AI pilot programs     

   

       

   

   

  <p>But any investments in banking automation will need to be balanced with governance. AI autonomy must be guarded with human oversight to meet explainability and governance standards. If they proceed responsibly, though, early adopters will gain a competitive edge as they scale their AI implementations to unlock new levels of productivity.</p>\r "}}" id="rich-text-930178f562" class="cmp-text"> 

  Generative AI will move from experimental pilots to widespread adoption in banking, fundamentally transforming banking operations and risk management. Financial institutions will focus on integrating AI into risk management processes for things like fraud detection and regulatory compliance and monitoring. By improving data accuracy, AI will make fraud management more effective and reduce false positives in compliance monitoring.

• 1. A move beyond generative AI pilot programs     

   

       

   

   

  <p>But any investments in banking automation will need to be balanced with governance. AI autonomy must be guarded with human oversight to meet explainability and governance standards. If they proceed responsibly, though, early adopters will gain a competitive edge as they scale their AI implementations to unlock new levels of productivity.</p>\r "}}" id="rich-text-930178f562" class="cmp-text"> 

  Generative AI will move from experimental pilots to widespread adoption in banking, fundamentally transforming banking operations and risk management. Financial institutions will focus on integrating AI into risk management processes for things like fraud detection and regulatory compliance and monitoring. By improving data accuracy, AI will make fraud management more effective and reduce false positives in compliance monitoring

• 2. Increased regulatory pressure      

   

       

   

   

  <p>Banking regulations like the<a href=\"https://appian.com/learn/resources/resource-center/whitepapers/2024/matching-the-pace-of-regulatory-changes\" target=\"_blank\"> Digital Operations Resilience Act (DORA)</a> in the EU demand robust risk management across the supply chain. Banks with existing expertise in model risk management should establish AI-specific governance processes that meet compliance and reporting requirements.</p>\r "}}" id="rich-text-778e57a4ca" class="cmp-text"> 

  Regulatory requirements will focus on ensuring ethical and responsible use of AI—particularly as it relates to sensitive customer financial data. Banking industry compliance teams need strong AI governance to mitigate risks like cyber threats and maintain their competitive advantage. 

• 6. Growth in open banking brings cybersecurity concerns     

   

       

   

   

  <p>With more players joining the field of complex financial process chains, banks must address security concerns. To succeed, collaboration between banks and fintechs will be necessary. They will have to develop robust security measures and advanced security tools to protect against fraud and evolving cyber threats.</p>\r "}}" id="rich-text-0bb94985bf" class="cmp-text"> 

  Open banking application programming interfaces (APIs) are getting more popular, fostering innovation as fintechs use customer-consented bank data to offer new financial products. This drives progress and has a positive impact on competition, but it also creates complex ecosystems that increase cybersecurity risks.

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