The challenge with using AI is having the right prompts to ensure you receive the best information available, how would you ensure as PM you don't try to influence the information when you don't like
Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
Excellent question.
One practice I use is to deliberately ask for disconfirming evidence rather than additional supporting evidence.
If AI provides a recommendation that aligns with my preferred view, I will often ask: • What assumptions could make this conclusion wrong? • What evidence points in the opposite direction? • What are the strongest counterarguments? • Under what conditions would an alternative option be preferable?
This helps reduce the temptation to keep refining prompts until I obtain the answer I want.
In my view, the challenge is not simply prompt engineering. It is maintaining intellectual discipline throughout the decision process.
AI can improve access to information, but it cannot prevent us from seeking validation for our existing beliefs. That responsibility remains with the project manager.
Perhaps the real test is not whether AI provides the right answer, but whether we are willing to seriously consider answers that challenge our preferred conclusions. Saving Changes...
To ensure you don’t bias or manipulate AI outputs when you dislike the initial results, apply a structured “Objective Prompter” approach:
Audit for Emotional Language: Check your follow-up prompts to ensure you aren’t using leading or frustrated language (e.g., instead of asking "Why is this wrong? Show me why it won't work," use "Provide an objective counter-argument or friction points for this scenario").
Enforce Role-Based Guardrails: Explicitly assign the AI a neutral, critical persona within your prompt. Use instructions like: "Act as an objective, risk-averse PMO auditor. Critique this approach without bias."
Mandate Multi-Perspective Outputs: Never ask for a single answer. Structure your prompt to demand alternative viewpoints by default: "Provide three distinct approaches to this problem: one optimistic, one conservative, and one disruptive."
Verify with Raw Ground Truth: Don’t let the AI guess or validate your biases; feed it unvarnished project data, industry standards (like PMI or IEEE guidelines), or hard metrics, and ask it to analyze the gaps objectively.
