Let’s begin with the term ‘pre-mortem’. Unlike a post-mortem that analyses the reasons behind a particular outcome, a pre-mortem is a structured exercise done before you launch a major initiative. Everyone imagines the strategy has failed badly two years in the future and works backward to list the reasons. The team then turns those hypothetical failure causes into risk-mitigation actions or design changes while there’s still time.
Why is doing pre-mortems so hard?
Because it’s not just logic at play—it’s ego, politics, and fear. Leaders hesitate to run pre-mortems because they’re already emotionally invested in the strategy. Confirmation bias creeps in—we look for reasons it’ll work, not why it might fail. And then there’s the fear factor: calling out what could go wrong can feel like you’re betting against the team. So the session becomes a formality. Risks are raised, maybe even nodded at, but rarely owned or acted upon.
The tool to use: ChatGPT o3 model. Access via https://chatgpt.com/
Example:
A chief strategy officer at a telecom firm greenlights a bold expansion into the Asia-Pacific market using AI-driven cybersecurity. Before execution, she runs a pre-mortem with OpenAI’s o3 model using the following prompt: Assume that the following strategy, which was selected as the most promising option after red teaming and simulation, has failed spectacularly two years after implementation.
Strategy chosen: [[Insert final strategy description here]]
Your task is to conduct a pre-mortem analysis—working backward from failure to identify what could have gone wrong.
Critically evaluate and respond to the following:
1. What were the early warning signs we missed or ignored?
2. What flawed assumptions turned out to be false?
3. Which internal weaknesses—talent, systems, incentives, org structure—amplified the failure?
4. What external shocks (market, regulation, geopolitical, tech evolution) derailed the strategy?
5. Where did execution break down (timing, leadership, resourcing, dependencies)?
6. Which stakeholders (clients, partners, employees) resisted or disengaged, and why?
7. What feedback loops or course-correction mechanisms were missing or underused?
8. If you could go back, what 3 specific safeguards or contingency plans would you embed in the strategy before launch?
Be brutally honest. Your goal is not to defend the strategy but to make it failure-proof.
What makes ChatGPT o3 special?
1. Advanced reasoning capabilities: The o3 model excels in complex tasks requiring step-by-step logical reasoning.
2. Multimodal integration: o3 seamlessly combines text and visual data, allowing it to interpret and reason about images, charts, and graphics within its analytical processes.
3. Real-time tools access: The model incorporates live tools usage into its reasoning, enabling it to extend its capabilities at the inference time.
Also read
How to crack research papers at breakneck speed
How to identify if an image is generated by ChatGPT
Mastering complex research papers faster with NotebookLM’s Mind Maps
Eliminating repetitive phrases in ChatGPT responses
Mint’s ‘AI tool of the week’ is excerpted from Leslie D’Monte’s weekly TechTalk newsletter. Subscribe to Mint’s newsletters to get them directly in your email inbox.
Note: The tools and analysis featured in this section demonstrated clear value based on our internal testing. Our recommendations are entirely independent and not influenced by the tool creators.
Jaspreet Bindra is co-founder and CEO of AI&Beyond. Anuj Magazine is also a co-founder.
