The widespread and rapid adoption of generative artificial intelligence (AI) suggests that we are at a technological inflection point not seen since the advent of the personal computer or the dawn of the internet. Generative AI, popularized by products such as ChatGPT, could realize the long-touted promise of the technology to disrupt traditional paradigms across the breadth of human activity. Whether that will hold true remains to be seen. But at WTW, generative AI has already impacted our approach to innovation faster and more fundamentally than any other technological development. We believe it will play a critical role in widespread innovation.
Generative AI is the subset of artificial intelligence that focuses on creating new and original content. It uses machine learning algorithms to generate novel responses to user prompts such as images, music, text or even entire virtual worlds, based on patterns it has learned from vast datasets or large language models (LLMs). Gartner compares generative AI’s impact to that of the steam engine, electricity and the internet, adding, “The impact of generative AI will grow as people and enterprises discover more innovative applications for the technology in their daily work and life.”
And according to Reuters, generative AI has seen exponential adoption at an unprecedented rate, outpacing TikTok to reach 100 million active users faster than any platform in history.
At WTW, technology has always been an essential element of innovation. By leveraging AI, we are seeing how we can innovate faster, more efficiently and more effectively. We’re using AI to find ways to use technology creatively and prudently to enable our clients to meet their most vexing challenges. Even in these early versions of generative AI we are seeing how it can play a principal role in our innovation efforts.
AT WTW, we’re using generative AI throughout each phase of the innovation process: Meticulous market research, pilot testing and the integration of invaluable stakeholder feedback.
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Each innovation project starts with research. With the help of generative AI, we are now able to synthesize research at speed, identifying key trends and themes much more quickly.
Let’s say, for example, that we wanted to develop innovative solutions to help our clients manage wildfire risk. Since wildfire risk management encompasses themes such as insurance policy terms and conditions, and vegetation management, we might start by uploading all our white papers and articles our research teams find on those topics into our generative AI tool. While previously, we would have had to read and summarize all this information ourselves, now generative AI can create summaries for us, streamlining a long and demanding process that did not always generate the desired results. But with the generative AI “trained” on the topic of wildfire risk we can efficiently provide the necessary background information to inform our innovation process.
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When an idea hits a critical mass of supporting research, we look to prototype a solution.
Now that the LLMs behind generative AI have reached maturity, we can fashion technology interfaces with plain language. While building conversational experiences on messaging platforms may have an embedded irony, this approach nonetheless will lower the barrier for entry. In other words, language is the new code.
If we take our wildfire risk management use case example, older paradigms would invest much thought and effort into thinking about the user experience, typically trying to push the boundaries of browser/web technology, which are fairly well-saturated with creative approaches. Now this process can be significantly simplified. Rather than obsessing about the size, color of buttons and the proportionality of screen elements, solutions can be developed through conversation with AI.
Our wildfire innovation project can interact with its target users by providing them with functionality both capable of providing context and using prompts to request specific information. For example: “Generate a quote for a physical risk policy to cover wildfire risk,” or “Calculate the annual cost of a policy for all of my global facilities.”
Taking such a conversational approach enables us to concentrate business logic into a single “cognitive” system that can be enhanced by non-technical users providing feedback (training/fine-tuning) into a self-improving system. Most importantly, it can be built quickly by eliminating the need to concentrate on user interface.
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Once we have our concept in prototype, we’ll test it with key stakeholders, including clients, end users and our senior leadership team. In the past, processes such as client or stakeholder interviews and surveys and desk research could be both time-intensive and arduous. Accumulating and analyzing this data often demanded substantial resources.
Today with AI, this process requires a fraction of the time and effort as before. AI can scale the quantity of interviews and client input using methods such as virtual town halls or focus groups, broaden the reach of our research, process and analyze the amassed data and summarize key insights and connect themes.
As we venture into this realm of generative AI-powered innovation, let us tread with care. Let us use this transformative technology not only to shape our products and services but also to shape a better world. Together, let's harness the power of generative AI to craft a legacy that enriches lives and transcends boundaries. The moment is now, and the call to action resounds: Let's build, with sand as stone, the foundation of a brighter tomorrow — a future built on wisdom, compassion and a commitment to the greater good.
