An exploration of generative AI for scenario planning - applied to scenario planning

A light preliminary exploration of scenario planning applied to generative AI using generative AI

There is a lot of uncertainty as to how the current generative AI revolution will play out.

When there is a lot of uncertainty one of the best tools to help us imagine and explore different possibilities is scenario planning (a formal method developed at RAND and made famous by Shell during the oil crisis).

I wanted to see if generative AI could help with scenario planning so I did a light exploration. The results were encouraging and Ibbaka will do some more formal work on this. Here are the preliminary results.

Link to Perplexity thread

1. Critical uncertainties about the future of generative AI as generated by a generative AI

Prompt: "Scenario planning is a way to imagine different possible futures by combining critical uncertainties. A critical uncertainty is something where there is more than one possible, outcome and the outcomes have far reaching consequences. What are some of the critical uncertainties facing generative AI at this time?"

Accuracy and Reliability

One major uncertainty is how accurate and reliable generative AI systems will become. While they have shown impressive capabilities, issues like "hallucinations" (generating false or misleading content) remain a concern.?The ability to consistently produce factual and trustworthy outputs will be crucial for widespread adoption and integration into critical processes.

Regulatory Landscape

How governments and regulatory bodies will approach generative AI is still uncertain. The development of regulations and policies could significantly impact how these technologies are developed, deployed, and used across different sectors.

Market Structure and Competition

There's uncertainty about whether the generative AI market will remain concentrated among a few large players or if it will become more open and competitive. The balance between proprietary and open-source models could have far-reaching consequences for innovation and accessibility.

Ethical and Societal Impact

The long-term effects of generative AI on society, including potential impacts on employment, creativity, and human interaction, remain uncertain. How these technologies will influence social norms, education, and various industries is still an open question.

Data Privacy and Security

As generative AI systems require vast amounts of data for training, uncertainties persist around data privacy, potential misuse of personal information, and the security of AI systems against malicious attacks.

Intellectual Property Rights

The ongoing legal battles and debates surrounding the use of copyrighted material for AI training create uncertainty about the future of intellectual property rights in the age of generative AI.

Environmental Impact

The energy consumption and carbon footprint of training and operating large AI models raise questions about the long-term sustainability of generative AI technologies.

AI Safety and Control

As generative AI becomes more advanced, ensuring that these systems remain under human control and align with human values becomes increasingly uncertain and critica.

Integration and Adoption Rates

The speed and extent to which different sectors and organizations will integrate generative AI into their operations remain uncertain, with potential for significant disparities in adoption and resulting competitive advantages.

Technological Advancements

The pace of innovation in AI is rapid, and it's uncertain what new capabilities or paradigms might emerge that could fundamentally change the landscape of generative AI.

2. A set of scenarios

Note: I am not endorsing these scenarios, just reporting the results of an initial exploration.

Prompt: "Please take two of these uncertainties and generate a set of scenarios using the typical scenario planning approach where two critical uncertainties are combined to create four different possible future scenarios for generative AI."

Scenario 1: Controlled Innovation - The Cauitous Utopia

(Strict regulation + Slow technological progress)

• Heavy government oversight limits AI development and deployment
• Incremental improvements in AI capabilities
• Focus on safety and ethics in AI research

Scenario 2: AI Renaissance - The AI Golden Age

(Light-touch regulation + Rapid technological breakthroughs)

• Minimal restrictions on AI development and use
• Major leaps in AI capabilities and applications
• Widespread adoption across industries

Scenario 3: Cautious Evolution - The Controlled Renaissance

(Strict regulation + Rapid technological breakthroughs)

• Tight controls on AI deployment despite major advances
• Highly capable AI systems with limited approved use cases
• Ongoing debate between innovation and safety

Scenario 4: Stagnant AI - The Wild West of AI

(Light-touch regulation + Slow technological progress)

• Few restrictions but also limited progress in AI capabilities
• Disillusionment with AI's potential
• Focus shifts to other emerging technologies

1. Develop scenario narratives: For each scenario, we would flesh out detailed narratives describing the state of generative AI, its impacts on society and business, and key events that led to that future.
2. Identify implications: Analyze how each scenario would affect various stakeholders - AI companies, governments, businesses, and individuals.
3. Determine indicators: Identify early warning signs or indicators that might suggest which scenario is unfolding.
4. Formulate strategies: Develop flexible strategies that could work across multiple scenarios to help organizations prepare for different possible futures of generative AI.

3. Conclusions

This approach is worth pursuing. Now that I know it will work, sketched large, I am going to go a bit deeper.

Test different ways of generating the critical uncertainties.

Generate alternative scenarios and explore scenarios of scenarios (something I have long wanted to explore and that generative AI will allow me to do.

Formalize and document the approach.

A big caution though. A big part of the power of scenario planning is the conversations and community building that takes place as the critical uncertainties are uncovered, the scenarios are constructed, and the stories told around them. Generative AI is no substitute for this. One remedy is to use generative AI as a collaboration tool. The current environments do not do a good job with this, but I assume they will improve or an innovator will come up with an alternative.

It is important to go back to the roots as we do this kind of work. For me one of those roots is the Mont Fleur scenarios .