According to a recent analysis by IDC Financial Insights, AI is expected to generate a cumulative economic impact of $19.9 trillion by 2030, reflecting a compound annual growth rate of 3.5%. Notably, 50% of this impact will be concentrated in North America, while 25% will come from the EMEA region, with the remaining 25% from Asia-Pacific. This distribution largely favors areas that had robust foundational infrastructure at the beginning of the AI revolution.
A crucial takeaway from the IDC report is that AI's economic influence extends beyond direct investments in AI services and solutions. Its disruptive potential is significantly driven by ripple effects throughout the economy. AI affects various sectors along the supply chain, affecting both backward providers of AI solutions (like network infrastructure, hardware, and data storage companies) and forward buyers of AI technology (businesses that integrate AI into their operations to enhance performance).
Additionally, the report highlights "induced effects," where AI influences consumer households, resulting in higher salaries for AI professionals and the emergence of new roles such as AI ethicists, algorithm auditors, and prompt engineers. This rapid adoption of AI technologies is poised to have far-reaching economic consequences, reshaping industries, creating markets, and transforming the competitive landscape.
Since 2023, the insurance industry has entered the digital business era, with generative AI emerging as a key player. While insurers are making substantial investments in generative AI, success rates for deploying this technology vary across different regions. According to a 2024 survey by IDC, nearly all industry professionals anticipate that generative AI will significantly alter competitive dynamics within 18 months, which has heightened the emphasis on integrating this technology throughout the insurance value chain.
Despite this enthusiasm, challenges persist. In 2024, only 68% of the average 24 generative AI proofs of concept met their key performance indicators, and only two were fully integrated into production. This highlights the difficulties organizations face when moving from experimentation to full-scale deployment. Over the past 18 months, insurance CIOs have launched numerous business-led AI initiatives, but these efforts have often resulted in scattered, fragmented, and sometimes redundant applications—a phenomenon IDC refers to as the "GenAI scramble."
Consequently, many insurance carriers have fallen into a productivity trap, focusing on short-sighted value-generation opportunities rather than fostering collaboration or planning for scalability. This approach has limited their ability to create reusable data and models across departments, leading to execution failures.
Underwriters in commercial lines are investigating how generative AI can enhance data submissions for complex risk programs and streamline access to unstructured information. Similarly, claims adjusters are assessing how generative AI can aid in cognitively demanding tasks such as fraud detection and improve claims negotiation strategies to minimize leakage. Compliance experts are also curious about how vendors are using generative AI to alleviate the challenges of regulatory reporting and compliance.
See also: Cautionary Tales on AI
While these initiatives are noteworthy and offer valuable insights for technology leaders to better understand generative AI, they do not fully harness the transformative potential of this technology. To effectively leverage generative AI's capabilities and innovate business models within the industry, a more comprehensive integration and strategic approach are crucial.
Several key factors are preventing insurers from successfully moving AI projects from concept to production:
- High Costs Undermining ROI Goals: The top challenge is the inability to meet return on investment objectives. C-level executives face immense pressure to deliver ROI, and business leaders have little tolerance for generative AI project failures. Investments are scrutinized for tangible business impact. Contributing factors include weak strategies for monetization, superficial feasibility assessments, changing use case requirements during development, and ad hoc deployments that lead to poor infrastructure decisions.
- Shortage of Skilled AI Developers: Finding developers with the right AI expertise remains a challenge. Many organizations struggle to secure talent capable of executing AI projects effectively.
- Poor IT and Line-of-Business Coordination: AI projects are often viewed as IT responsibilities, with limited accountability from the business side. However, success requires strong collaboration between IT and business units. AI use cases frequently involve cross-departmental data, requiring multiple layers of validation to prevent issues like data toxicity or misalignment.
- Inadequate Infrastructure for Scalability: Organizations often struggle to move from experimental setups to scalable, AI-native infrastructure. Optimized and portable workloads are crucial, but many insurers face difficulties in making this shift. Inadequate architecture increases infrastructure costs, especially in areas like training, tuning, and inference.
Is generative AI just a passing trend? While challenges certainly exist, the preliminary data suggests that underestimating its potential would be a significant miscalculation.
Recent IDC surveys indicate that insurance organizations stand to gain considerable advantages from effectively implementing generative AI. Early adopters in the sector are already seeing marked improvements in operational efficiency, productivity, and profitability—especially those that have advanced their AI maturity and are better equipped to manage business risks. A clear link between digital revenue share and AI maturity underscores the necessity of enhancing digital capabilities to fully leverage these benefits.
To successfully pivot to AI by 2025 and drive meaningful business transformation, insurers should focus on three key areas:
- Develop a Comprehensive AI Strategy: Insurers must prioritize the early integration of generative AI technologies. Appointing an AI orchestrator can facilitate cross-functional collaboration, ensuring efforts are directed toward high-impact use cases. Enhancing customer experience through intuitive, AI-powered digital platforms is essential, along with reimagining business models to foster innovation and strengthen capital management.
- Establish a Unified AI Governance Framework: Maintaining data integrity and alignment with overarching AI strategies is crucial. Insurers should prepare their data for readiness by consolidating systems and standardizing processes to unlock efficiencies. Additionally, addressing talent shortages and regulatory challenges through responsible governance solutions is vital.
- Adhere to the "Buy, Reuse, Build" Principle: Technology investments should focus on cost-effectiveness and operational efficiency. Insurers should first purchase or reuse existing tools before building custom technologies. This approach ensures efficient deployment and optimizes AI-related investments. Leveraging AI for cloud cost optimization and governance through FinOps practices will enhance resource management, ensuring that cloud infrastructure operates efficiently and maximizes returns on IT investments.
See also: Who's Getting Results From AI, and Why?
Insurance decision-makers will need to develop a strategic plan for AI adoption, including how to overcome key obstacles. Those that do will be able to move beyond the current "GenAI scramble" and successfully navigate AI-driven business transformation.
The 2025 edition of IDC's Worldwide Insurance FutureScape is designed to help insurance decision-makers develop a strategic plan for AI adoption. It highlights the critical steps insurers must take over the next five years to move beyond the current "GenAI scramble" and successfully navigate AI-driven business transformation. To learn more about IDC's Worldwide Insurance FutureScape, please click here.
