How generative AI will reshape insurance

The Financial Conduct Authority and the Advertising Standards Authority must ensure there are flexible frameworks in place that can keep pace with the rapid advances in AI technology. Generative AI can produce misleading or even fictitious adverts, potentially ushering in an era of deepfakes that trick consumers or leave them doubting what’s real. However, the AI bubble could eventually burst, bringing chaos and disruption to Wall Street, a situation many CEOs might welcome, as generative AI has shown to be better at doing their job than they are.

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When honing its AI approach for the year, the company aligned its plans with its broader 2025 growth goals, dubbed the Total Home Strategy. Lowe’s identified areas where leaders thought AI could help, anchored to shopping, selling and working experiences. From there, the company has pursued its top-tier use cases with the highest value. Autodesk, which makes software used by architects, designers and engineers, slashed 9% of its workforce, or 1,350 positions, this year. The San Francisco company cited geopolitical and macroeconomic factors along with its efforts to invest more heavily in AI as reasons for the cuts, according to a regulatory filing.

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With banks now able to access diverse customer data sets and harness the creative power of AI, the potential for personalised adverts and customised financial products is immense. But at the same time, the balance between relevance and privacy becomes increasingly delicate. IBM says insurance companies should use generative AI to build “more tailored” products that are flexible and linked to realistic risk data. They must also address the trust issue, ensuring that their AI behaves ethically and giving local (human) experts the ability to connect the algorithm to the insurance value chain is paramount. “We’re seeing a huge pace of change,” says Nadine Hanser, Director, Deloitte UK.

For two decades, Voices has been the go-to marketplace for brands, agencies, and creatives to discover and hire voice talent. In addition to its flagship assistant-style use cases, Lowe’s is leveraging AI across its business at different stages of maturity, from marketing to search capabilities on its website. The company also sees AI as a lever in its enterprisewide productivity push, which it hopes will generate around $1 billion in annual cost savings.

For more than two decades, the world’s biggest brands, like Shopify, Microsoft, and Cisco, have trusted Voices to ‘find their voice’. The Voices talent base is composed of many millions of actors across the globe, who trust Voices to support their interests and help them find meaningful work. AI can help develop, test and write code, provide financial advice and sift through legal documents. The bureau, though, still projects that employment of software developers, financial advisors, aerospace engineers and lawyers will grow faster than the average for all occupations from 2023 to 2033. Companies will still need software developers to build AI tools for businesses or maintain AI systems.

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In fact, some 75% of executives across all industries cite this as an obstacle to progress. Because gen AI uses natural language for prompts and instructions, it democratizes access to insights that were previously only available to data scientists and specialists. It will also be put to work within technology departments too, writing code and scripts, and helping to support integrations. Generative AI can help make the unpredictable predictable or, at a minimum, help companies understand the evolution of cost so they can better price their products.

• This will involve creating clear guidelines for the development, use and oversight of generative AI systems, balancing innovation with consumer protection.
• But the increased demand for voice AI is challenged by the scarcity of ethical, properly licensed voice data to train voice models.
• Two key factors she uses to evaluate such chatbots are the accuracy of information they provide and how consistently they answer the same questions even if the question is asked in different ways.
• Lagging indicators are the expected financial outcomes, such as revenue growth or conversion uplift.

According to one recent estimate, generative AI will need to produce US$600 billion in annual revenue to justify current investments – and this figure is likely to grow to US$1 trillion in the coming years. This widely used model describes a recurring process in which the initial success of a technology leads to inflated public expectations that eventually fail to be realised. After the early “peak of inflated expectations” comes a “trough of disillusionment”, followed by a “slope of enlightenment” which eventually reaches a “plateau of productivity”. Many projects using the technology are being cancelled, such as an attempt by McDonald’s to automate drive-through ordering which went viral on TikTok after producing comical failures.

Although generative AI fuels the potential for automation to eliminate jobs, AI can also enhance technical, creative, legal and business roles, the report said. There will be a lot of “noise and volatility” in hiring data, Madgavkar said, but what will separate the “winners and losers” is how people rethink their work flows and jobs themselves. Scott White, the product lead of Anthropic’s Claude AI product, described how he, despite not being a professional programmer, is now building production-ready software features himself. “This wasn’t possible six months ago,” he explained, highlighting the power of tools like Claude Code. Similarly, OpenAI’s head of product for its API platform, Olivier Godement, detailed how customers like Stripe and Box are using its Agents SDK to build out multi-agent systems.