Generative AI and the Future of Foresight
How can AI enhance - and perhaps disrupt - the landscape of foresight work in the years and decades ahead?
FUTURE PROOF – BLOG BY FUTURES PLATFORM
Generative AI has ignited a mixture of excitement and fear within the foresight community. At Futures Platform, we’ve been running internal tests over the last months to understand how AI tools can enhance our foresight work, as well as to explore the implications of widespread AI adoption for the future of our field.
While it’s clear that AI can’t replace human expertise in foresight analysis, we found that it can be useful across multiple stages of the foresight process. Below, we delve into our findings and examine how AI has the potential to enhance - and perhaps disrupt - the landscape of foresight work in the years and decades ahead.
AI AS A RESEARCH ASSISTANT: SUPERCHARGING INFORMATION DISCOVERY
As a discipline, foresight requires staying ahead of the curve and continuously scanning the horizon for emerging change signals. Given the breadth of industries, geographies, and the rapid pace of transformation today, this task is inherently time-consuming and resource-intensive.
AI serves as an exceptional research assistant to expedite the horizon scanning process, as it can sift through vast volumes of data much faster than a team of multiple humans possibly can. At Futures Platform, for instance, we have long utilised AI in horizon scanning, where AI bots constantly roam news outlets and research journals to spot early indicators of change based on our predefined criteria.
Generative AI can also serve as a powerful springboard for research by generating insightful summaries on any topic, which can be tailored to your research question or existing knowledge. With AI augmenting the background research process, more time is freed up for humans to focus on higher-order strategic thinking. Ultimately, it is the human touch that will add the essential dimension of interpretation and contextual understanding to the research generated by AI.
Subscribe to our weekly newsletter
TREND ANALYSIS: IDENTIFYING CHANGE DRIVERS AND PROJECTING TRAJECTORIES
Trends are characterised by their objective nature. They’re often supported by statistical data and typically follow a recognisable, stable development path. Megatrends, in particular, exhibit long-lasting stability.
Hence, generative AI tools can swiftly aid in identifying the primary drivers of change behind trends, evaluating their future implications, and uncovering associated opportunities and threats.
Given the objectivity of trends and megatrends, we’ve found that AI can project their future trajectories with considerable accuracy. Yet, AI's analysis of trends and change drivers is typically standard and may not yield novel insights for those well-versed in the subject. It’s useful as a starting point, but results must always be validated and refined by human analysts.
Megatrends - What is a megatrend and why do they matter? | Source: PwC UK
Furthermore, in today's volatile, uncertain, complex, and ambiguous (VUCA) world, AI may fall short when it comes to analysing intricate interconnections between cross-industry drivers or envisioning future possibilities amidst unprecedented change. AI relies primarily on historical data, limiting its ability to navigate uncharted territory. Humans, on the other hand, have the capacity and intuition to imagine the unknown and push the boundaries of knowledge.
Foresight professionals also frequently work with emerging signs of change that have yet to gain mainstream attention. In such cases, we have observed limitations in the usefulness of AI, as generative AI models are often not trained on these novel phenomena. While newer models like ChatGPT-4 can access the internet and third-party datasets, their lack of comprehensive knowledge on the topic can still lead to suboptimal outcomes.
“AI relies primarily on historical data, limiting its ability to navigate uncharted territory.”
GENERATING SCENARIO NARRATIVES AND TIMELINE PATHS
Scenarios are captivating narratives that explore alternative future paths, aiming to expand our imagination and uncover a range of possible futures. Once you have the core ideas of your scenario outlined in a rough draft, generative AI can transform it into an engaging storyline that vividly portrays a possible future state.
In our internal tests, we have also found AI particularly useful in generating timelines for scenarios. Once a scenario narrative is developed, AI can assist in structuring the narrative into a series of key events that would need to take place for the scenario to unfold.
However, a critical perspective is crucial when utilising AI for this purpose. AI simply plots the scenario events within prescribed years, which can potentially result in illogical chains of events. Similar to previous cases, AI serves as an initial step, providing inputs for humans to refine and critically evaluate.
While you can also task AI with generating scenarios independently, the key in scenario work is the explorative learning process among those participating in scenario-building. And this dynamic can’t be achieved by pressing a button to get an AI-generated scenario report.
THE IMPORTANCE OF FORESIGHT METHODOLOGY WILL GROW
While generative AI can analyse trends and generate scenarios, a current challenge lies in the lack of transparency surrounding AI algorithms and their decision-making process. This creates a "black box" problem where it becomes difficult to ascertain how AI reaches its conclusions.
As an inherently collaborative discipline, foresight places equal emphasis on the process of systematically exploring alternative futures as it does on the final outcomes. As such, methodology will play a pivotal role in shaping the outcomes and ensuring the successful integration of AI into foresight practice.
The key lies in harmonising the power of AI with established foresight methodologies. By aligning our usage of AI with rigorous foresight practices, we can ensure that AI complements human expertise, enhancing the depth and breadth of foresight analysis.
“By aligning our usage of AI with rigorous foresight practices, we can ensure that AI complements human expertise, enhancing the depth and breadth of foresight analysis. ”
THE ROLE OF A FORESIGHT PROFESSIONAL IN A GENERATIVE AI FUTURE
AI excels in processing and analysing data, but it lacks the contextual understanding and intuition that human foresight professionals bring to the table. In the coming years, the role of a foresight expert may evolve to validate, contextualise, and refine AI-generated insights. The focus may shift from generic findings to more personalised content, where humans offer nuanced insights and recommendations to inform strategies, building upon the initial research provided by AI.
However, if the technology is not used correctly, there’s a risk that generative AI will yield less insightful foresight analysis, perpetuating the recycling of generic statements. Then it will be even harder to uncover meaningful insights amidst the noise. The hard work of critical thinking still rests with humans, even in the era of generative AI. Let’s hope they do it.
Explore further insights on the future of AI and 1000+ other trends with Futures Platform’s digital foresight solution
