Businesses are increasingly using strategic foresight methodologies like scenario planning to anticipate and address the escalating organizational uncertainties driven by climate change, global conflicts, and rapid technological advancements. However, traditional approaches to scenario planning have some inherent shortcomings. They include inadequacies in identifying the most relevant trends and external forces, based on varied levels of uncertainty; limits on how many scenarios they can consider in depth; and the lack of direction they provide for how to assess and prepare for multiple highly divergent scenarios simultaneously.
A promising alternative is emerging that can overcome these challenges. It entails enlisting the assistance of cutting-edge generative AI powered by large neural network models such as OpenAI’s GPT and Anthropic’s Claude. They can significantly enhance an organization’s capability to conduct robust contingency scenario planning faster and at a much lower cost than conventional processes. This approach is particularly beneficial for resource-constrained organizations like small and medium-sized enterprises (SMEs) that are operating in highly challenging environments, such as those prone to extreme weather events.
In addressing this topic, we have leveraged our diverse experiences studying and conducting experiments using generative AI for strategic planning in academic, commercial, and government contexts, and advising organizations on contingency strategies in multiple domains, including supply chains, defense, and pandemic response. Our conversations with chief experience officers (CXOs) at over 10 companies and recent feedback gathered from a large conference of chief procurement officers (CPOs) in the United States indicate that many organizations, including Fortune 500 firms, are actively exploring the use of generative AI for strategic foresight, including scenario planning.
How Generative AI Can Help
Here’s how generative AI can help address many of the core limitations of traditional scenario planning approaches in three stages: scenario creation, narrative exploration, and strategy generation.
Scenario Creation
Generative AI can rapidly process vast sets of internal business data, global news sources, political developments based on internal communications, social media and news, industry reports, think tank studies, and scientific literature to assist strategists in comprehensively framing the overall planning exercise. They can then highlight the most salient strategic concerns, game-changing trends, and key driving forces of uncertainty for planners to explore. The planning team can use these outputs to develop a comprehensive set of draft scenarios for consideration.
Once these draft scenarios have been created, the generative AI system can systematically combine scenario elements and refine parameters. By assessing the logical consistency of these combinations, less-plausible scenarios can be filtered out, leaving a more-manageable set of scenarios to take forward.
Narrative Exploration
The advantages of utilizing generative AI extend beyond scenario creation. It also facilitates the development and exploration of a comprehensive narrative for the scenario, making it more memorable and palatable to a broader audience within the organization, thereby aiding in obtaining buy-in from team members. This process of narrative development involves pinpointing baseline scenarios, enriching them with combined trends of varying importance, and identifying key challenges, concerns, and limitations.
The generation of a narrative is a critical step, where the generative AI system’s ability to produce rich, detailed, and balanced narratives becomes particularly handy under time constraints. It helps organizational decision-makers and influencers, beyond the strategic planners, understand the scenarios, as well as the nuances and trade-offs involved.
Strategy Generation
Empowered by rich details in the scenario narrative, the planning team can prompt the generative AI tool to propose strategies and actions tailored to address the challenges presented by specific scenarios. This is an interactive process in which planners can provide feedback on the strategies proposed at every step and seek clarifications or modifications.
Planners can also furnish the generative AI tool with criteria to assess the strengths and weaknesses of these proposals. These criteria may involve evaluations of how prospective ideas align with priorities such as resilience to withstand and recover from disruptions, efficiency in resource utilization, and the organization’s ability to garner positive attention while avoiding negative attention.
A Sample Simulation
As an illustration, we simulated a contingency planning exercise at a fictional U.S.-based company called ElectroInnovate, a global consumer electronics manufacturing firm with a worldwide supply chain.
Scenario Creation
In the scenario creation step, the process commenced with our description of ElectroInnovate to the generative AI tool we used: an OpenAI GPT. (You can try out the What If, What Now GPT for scenario planning.)
We tasked it with identifying the key resources, external forces affecting the company’s business, and external parties such as customers, investors, and competitors. Subsequently, we requested the system to rank these areas of concern in terms of their estimated significance based on two things: 1) data provided by Resilinc, a firm that one of us (Peter Guinto) works for that tracks global supply chain disruptions, and 2) generative AI’s vast general knowledge.
The generative AI tool listed risks related to human resources, technological advancements, and government actions. Following this, we prompted it to identify trends relevant to each of the above categories. It then generated hypothetical trends in each category such as employees’ skills gaps in using emerging technologies like AI, the increasing focus on green technology, and government initiatives related to data privacy and intellectual property legislation.
In response to our instruction, the tool also categorized the trends in terms of their probability and impact, using estimated probabilities of these factors occurring and the impact they would have. For example, the tool generated a trend titled “Advancements in Artificial Intelligence” and outlined the need to change the workforce’s skills, upgrade the technology infrastructure, and adjust HR policies in order to adopt AI. For example, it generated a baseline scenario for human resources that examined how AI would require the company to retrain existing staff and recruit specialists to acquire skills in AI, data analytics, and other technologies such as internet of things and blockchain, and revise HR policies to encourage ongoing learning.
Narrative Exploration
Next, as part of the simulation, we instructed the system to create a narrative scenario to add richness to the baseline scenario. The goal of this step was to flesh out the nuances of the scenario by examining different aspects. In our case, it provided a narrative titled “Smart Future: AI in Electronics Manufacturing.”
First, the narrative provided more details on what ElectroInnovate would need to do to integrate AI into its operations. For example, it said that the company would have to hire AI specialists who could oversee the integration of advanced AI into the company’s manufacturing processes.
Next, the narrative pointed out that the financial implications of the scenario for ElectroInnovate. It highlighted that significant initial investments would be required for such as data centers and processing hardware, models, training programs, and the hiring of skilled AI specialists. The narrative also raised the question regarding how, due to operational efficiencies and cost reductions expected from AI integration, financial resources could be best allocated for the greatest long-term benefit.
The narrative further highlighted that this scenario would necessitate ElectroInnovate to make substantial upgrades, such as improvements to hardware capabilities, strengthened network security, and modifications to existing software to make them compatible with AI systems. Moreover, robust data management systems would be needed to support AI’s data requirements. The narrative raised the question of how ElectroInnovate might prioritize these technological upgrades.
The narrative indicated that for ElectroInnovate’s supply chain, AI could help enhance inventory management, fine-tune demand forecasting, streamline logistics, and optimize production scheduling. The AI generated questions for the reader to consider such as the key steps ElectroInnovate needed to take to prepare its supply chain for AI integration.
Finally, the narrative described how the integration of AI could affect ElectroInnovate’s brand reputation. While successful implementation could position the company as a pioneer in the industry, mishandled AI integration could harm its reputation. It raised the question of how ElectroInnovate might communicate the benefits and implications of AI integration to its customers and the wider public transparently and positively.
Strategy Generation
In the next stage of the simulation, we asked the generative AI tool to generate appropriate strategies based on the scenario narrative it had created.
The first idea it proposed was a comprehensive training program to provide employees with skills in AI technology, data analysis, and machine learning.
The second idea was for ElectroInnovate to integrate AI across manufacturing processes and supply chain operations to optimize processes and create a technological edge over competitors.
The third was to build trust and support among stakeholders through transparent communications about ElectroInnovate’s AI strategy. To protect the company’s reputation as a pioneer in AI-enabled electronics manufacturing, they would convey the benefits of the AI strategy and proactively address stakeholders’ potential concerns.
The final idea it proposed was for the company to forge outsourcing partnerships. The system said that by working with leading external AI experts, the company could more quickly gain access to needed expertise, which would help it more rapidly build its in-house capabilities. They could also provide ElectroInnovate with cutting-edge thinking that would assist it in keeping pace with AI as it evolved.
Three Lessons for Strategic Planners
The first lesson for strategic planners who intend to use generative AI for scenario planning is that while these AI capabilities for integrating various data sources to identify trends and create scenarios are tremendous, they require timely and relevant data. Without such data, such exercises may be unhelpful, or, even worse, misinformative.
Second, by crafting detailed narratives, generative AI can play a crucial role in simplifying decision-making and achieving organizational buy-in for strategic decisions. Rich, interesting, and balanced scenario narratives can play a timely role in helping employees understand the scenarios as well as the advantages and limitations associated with them. However, the richness and appropriateness of such narratives require instructions and feedback from the strategic planners.
Third, generative AI can be used to generate and evaluate strategies. However, these strategies could be thin in detail and limited in scope. As such, these AI-generated strategies are best used as raw ideas and jump-off points for further thinking, detailing, and refinement rather than final strategies that should be adopted without reservation or further analysis. Instead, generative AI should be used to complement parallel human efforts at identifying, evaluating, and executing appropriate strategies promptly.
With the integration of comprehensive generative AI capabilities into their contingency planning processes, companies can rapidly and systematically explore a wide variety of scenarios and strategic options quickly and inexpensively. While human judgment remains essential in contingency planning, generative AI allows organizations to generate and consider far more scenarios and strategic factors than people, on their own, could. Generative AI promises to be an invaluable tool in making companies more resilient to whatever emerges from over the horizon.
Ren Ecosystem