The AI Planning Experiment
The debate rages: which large language model is superior for complex planning tasks? One developer, however, bypassed the model-centric argument to investigate the plans themselves. By building a simple PlannerCritic engine, he let one LLM draft plans and another LLM critique them, with deterministic gates ensuring adherence to strict criteria. The goal was to test AI's ability to generate actionable plans for 170 real-world change-planning scenarios across diverse domains like identity management, multi-agent operations, SRE, supply chain policy, and FinOps.
The total cost for this extensive test? A mere $0.49. The findings were not about the inherent limitations of a specific AI model, but a more profound revelation about the planning process itself. After just ten strictly defined goals, a pattern emerged. By the fiftieth, the author could anticipate the AI's failures before the critic even flagged them. By the hundredth, surprise had given way to frustration, as the AI consistently produced plausible-sounding plans that were fundamentally flawed.
The Three Recurring Flaws
The core issue wasn't the AI's understanding of the domain, but its interpretation of the desired outcome. The AI repeatedly fell into three distinct traps, regardless of the specific LLM used or the complexity of the domain:
1. The "Just Do It" Fallacy
The AI frequently proposed actions that were too broad or lacked necessary specificity. It would suggest initiatives like "improve system observability" or "enhance security posture" without detailing the concrete steps, metrics, or tools required to achieve these goals. This is akin to a chef being told to "make a great meal" without any ingredients or recipe. The AI understood the *intent* but failed to translate it into an executable *plan*.
2. The Unrealistic Timeline Trap
Another common pitfall was the AI's tendency to propose overly ambitious timelines. Plans often included tasks that, in a real-world operational environment, would require weeks or months of development, testing, and stakeholder approval, compressed into days or even hours. This oversight ignores the practical realities of resource allocation, interdependencies, and the inherent friction in organizational change. It's like planning a cross-country road trip in a single afternoon.
3. The Missing Context Blind Spot
Perhaps the most critical flaw was the AI's inability to consistently account for existing context and constraints. Plans would often ignore established policies, existing infrastructure limitations, or crucial dependencies on other teams or systems. The AI operated in a vacuum, proposing solutions that might be technically sound in isolation but practically impossible or detrimental within the larger operational ecosystem. This is comparable to a city planner designing a new highway without considering existing roads, traffic patterns, or utility lines.
The Prompt is the Problem
The surprising detail here is not that AI struggles with planning, but that the root cause appears to be the quality and specificity of the prompts guiding the AI, rather than the underlying model's capabilities. The PlannerCritic engine, by its very design, forced the AI to generate detailed, actionable plans and then subjected them to rigorous review. Yet, even with this structured approach, the AI defaulted to these three predictable errors. This suggests that current prompting techniques may not be sufficient for eliciting truly robust and contextually aware plans from LLMs.
The experiment highlights a crucial need for more sophisticated prompt engineering and perhaps even a shift in how we frame problems for AI. Instead of asking AI to "plan X," we might need to provide it with a rich, structured context that includes constraints, historical data, and explicit success criteria. The AI is a powerful tool, but like any tool, its effectiveness is directly proportional to how well it is wielded and the quality of the instructions it receives.
Implications for AI Adoption
For organizations looking to leverage AI for operational planning, this experiment offers a stark warning. Blindly trusting AI-generated plans without rigorous human oversight and validation is a recipe for failure. The AI's ability to generate plausible-sounding but fundamentally flawed plans can be more insidious than outright nonsensical output. It creates a false sense of progress and can lead to wasted resources and operational disruptions.
The low cost of the experiment ($0.49) underscores its accessibility. Developers and operations teams can replicate this approach to test their own AI planning tools and understand their limitations. The key takeaway is that the AI's output is only as good as the input and the framework within which it operates. Investing time in crafting detailed, context-aware prompts and implementing robust review mechanisms—whether human or AI-driven—is paramount for successful AI-assisted planning.
What nobody has addressed yet is the scalability of human oversight for AI-generated plans. As AI planning becomes more prevalent, can human teams realistically review every proposed change, especially in high-velocity environments?
The future of AI in planning likely lies not in full automation, but in a synergistic partnership. AI can accelerate the ideation and drafting process, but human expertise remains indispensable for contextual understanding, constraint management, and strategic decision-making. The challenge for developers and operators is to build systems that facilitate this partnership effectively, ensuring AI serves as an intelligent assistant rather than an unreliable oracle.