Amidst the explosive growth of generative AI, effective leaders have begun to identify key … [+]
Amidst the explosive growth of generative AI, effective leaders have begun to identify key opportunities to use its tools to support and fuel innovation.
Gartner compares generative AI’s impact on technology 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.” According to Reuters, generative AI has been adopted at an unprecedented rate, outpacing TikTok, reaching 100 million active users faster than any platform in history.
Because generative AI is the subset of artificial intelligence that creates new and original content, early adopters believed it would quickly replace the need for human users to innovate and ideate. Such predictions proved premature as generative AI tools themselves often stop short of creating new ideas. The tools are, however, effective at using machine learning algorithms to generate novel responses to user prompts such as images, music, text or even entire virtual worlds, based on patterns learned from vast datasets or large language models (LLMs).
With thanks to WTW’s Katie Plemmons, Dan Stoeckel and Chervey Fan, effective leaders strategically build early generative AI tools into the innovation process through meticulous market research, pilot testing and the integration of essential stakeholder feedback:
1. Conducting Research – The most successful innovation projects generally start with research. With the help of generative AI, effective innovation leaders now are able to synthesize research, identifying key trends and themes much more quickly. For example, a team that wants to develop innovative solutions to manage wildfire risk might start with research. Since wildfire risk management encompasses many themes, such as insurance policy terms and conditions, and vegetation management, an effective team might start by uploading all the white papers and articles their research teams find on those topics into a generative AI tool. Now, instead of employees reading and summarizing all this information, generative AI tools can accurately summarize the findings, streamlining an often long and tedious process.
2. Building prototypes – When an idea hits a critical mass of supporting research, the next step generally is to develop a prototype solution. With generative AI tools containing LLMs, effective teams can fashion technology interfaces with plain language. Building conversational experiences on messaging platforms can lower barriers to entry and accelerate prototyping. Using the wildfire risk management solution example, previous innovation processes would require the investment of considerable thought and effort around the user experience. This typically involved pushing the boundaries of browser/web technology, which are already saturated with creative approaches and workarounds. Now generative AI simplifies this process. Rather than being consumed with the size, color of buttons and the proportionality of screen elements, solutions can be developed through AI prompts and iteration. The wildfire innovation project team can interact with generative AI tools by 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 teams to concentrate business logic into a single “cognitive” system enhanced by non-technical users providing feedback (training/fine-tuning) into a self-improving system. Equally important, solutions can be built more quickly since generative AI tools eliminate the need to concentrate on user interface.
3. Testing Prototypes – Once effective teams create a concept prototype, they test it with key stakeholders, including clients, end users, senior executives, and potential investors. In the past, client or stakeholder interviews, and surveys and desk research could be both time-intensive and arduous. Gathering and analyzing these data often required substantial resources. Today with generative AI, this process takes 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 research, process and analyze the amassed data and summarize key insights to connect themes.
Effective leaders venture into the realm of generative AI-powered innovation with an understanding of both the capabilities and limitations of specific tools. They tread with care, remaining open-minded and curious as they learn to harness the power of transformative technology to shape, test, and eventually market new products and services.