The Economist poses that question this week at an important time as the vendors, technology and implementation partners that insurers choose need to be viable, collaborative, and deliver the optimal mix of skin in the game, operationalisation expertise and ability to significantly help insurers meet their strategic vision and goals.

That is a challenge that needs solutions that AI will not solve this decade- setting the vision and goals to deliver the changing needs of commercial and personal customers as key challenges upset the status quo of products that have changed little over the decades.

  • Extreme and unpredictable weather events across the globe
  • Cyber security threats
  • Electric and autonomous vehicles
  • New governments' impact on trade, tariffs, regulation, interest rates, war, and peace
  • Business interruption
  • Proliferated distribution leading  a drive to the bottom ie focus on price

AI will not solve these challenges- astute C-Suites,super-forecasters, and ideation practitioners need to plan for viable, competitive differentiation. 

Then they can choose the right partners and technologies to achieve these goals. To leverage AI requires a raft of essential decisions and investments eg

  • Training, resourcing, change management, and buy-in of all employees, stakeholders and partners
  • A modern MACH-architected technology ecosystem infrastructure 
  • Data fluidity and maturity bridging the current gaps, silos and incompatible technology stacks
  • Manage the combination of enterprise deployed tools and private tools- staff are using tools and generally being secretive about that usage

On the latter bullet point- “One study found that 78% of software engineers in America are using AI at least weekly, up from 40% in 2023, as are 75% of human-resources staff, up from 35%. And OpenAI says 75% of its revenue comes, tellingly, from consumers rather than from corporate subscriptions.”  The Economist

Back to the Economist's article- Who do you bet on?

“Investors have poured money into superstar firms like OpenAI. But in practice there is not much difference in performance and capabilities between the flagship models offered by OpenAI, Anthropic, and Google. And other firms including Meta, Mistral and xAI are close behind.”

The Economist

These companies are betting the future on large language models (LLM) but the costs and time to market are mind-boggling. Many say that it will be better to focus on small language models (SLM) tightly trained on just relevant and current data. It is vital you get the advice of companies that can operationalise all these options so that you make the right choice

I have discussed all these issues over the last few months and have published them below in a newsletter format.

GenAI passed Gartner's peak of expectations- CompositeAI is the key to value
‘By the end of 2024 ( and during 2025), value will be largely derived from projects based on familiar AI techniques, either stand-alone or in combination with GenAI, that have standardized processes to aid implementation. Rather than focusing... Read more 
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C-suite expectations may be too hasty (and underinformed) to deliver meaningful AI value in a responsible manner.
'CIOs are under increasing pressure to generate business value from generative AI, but Salesforce CIO Juan Perez is among those IT leaders who believe such impatience from the C-suite could doom many projects . “The explosion of AI has really put... Read more
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Prioritising outcome goals leveraging AI- productivity/cost-cutting, increasing revenue, transformative competitive advantage, or all of these?
I should emphasise that Generative AI is one of many tools that insurers can leverage to help achieve corporate goals. GenAI will not ensure an insurer will set the right vision, goals, and strategies, or allocate the optimal resources to be... Read more
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Forces Horizon avoids AI generating a ‘sea of sameness’ in job applications
Large Language Models (LLMs) have now trained on such large data sets that they've run out of data. One of the reasons OpenAI, Claude and other vendors now create synthetic data. By definition this creates a dumbed-down version of the data... Read more 
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Insurers Can Parlay Technology into a Competitive Edge- but too many don't!
"Making deft use of data—from information collected by flood sensors at a manufacturing plant to a driver’s photographs of a crumpled car panel—has become a prime source of competitive advantage for insurance carriers. Zettabytes of data, much of... Read more
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How technology in claims processing is changing consumer choices in insurance
“According to a recent survey by Insurity, a pioneer in cloud-based insurance software and analytics, a noteworthy 52% of consumers expressed a preference for insurers who invest in new technologies to enhance the claims experience following... Read more 
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Surveys highlight data maturity holding back AI deployment ambitions
Three recent surveys with excellent representative samples research different aspects of business and all come up with the alarming confession by insurers that their ambitions to leverage enterprise and third-party data to drive transformation and... Read more 
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I'll leave the answer to the article title to The Economist_

‘The AI race, then, will take many forms in 2025. Yet the point at which investors lose their nerve is often when new technologies quietly start gaining traction. Will the bubble burst, or will the technology start to deliver? The answer in 2025 may be: a bit of both’