In a recent LinkedIn post, John Cutler discusses the potential pitfalls of integrating Artificial Intelligence into existing business processes, cautioning that AI can amplify rather than solve fundamental organizational issues.
Cutler highlights the danger of using AI to automate outdated or ineffective methods of software development and business planning. He expresses concern that AI tools, when applied without critical re-evaluation of underlying strategies, could lead to the generation of superficially polished but ultimately flawed outputs.
“If you are using AI to reinforce broken mental models of how great software products get built, write the same old PRDs that wither on first contact with reality, generate box-checking business cases designed to shut out critique, decompose work into tidy cascades nobody believes in, and rinse all the value out of your customer feedback, you will supercharge whatever was already broken at your company with a deluge of polished markdown.”
The Peril of Automating Broken Models
As John Cutler argues, the allure of AI lies in its ability to accelerate tasks, but this speed can be detrimental if the processes being accelerated are themselves flawed. He points out that AI can generate impressive-looking documents, such as Product Requirement Documents (PRDs) or business cases, but these will still fail if the foundational thinking behind them is unsound. According to Cutler, a PRD that is destined to fail upon real-world testing will simply become a more efficiently produced failure when generated with AI.
AI as an Amplifier, Not a Fixer
John Cutler’s central thesis is that AI acts as an amplifier. If a company’s existing mental models for building software are broken, or if its methods for gathering customer feedback are inefficient, AI will not inherently fix these problems. Instead, as Cutler warns, it will supercharge these existing weaknesses. This can manifest in various ways, from creating business cases that are designed to avoid scrutiny rather than invite constructive criticism, to breaking down work into cascading steps that lack credibility within the organization.
Customer Feedback and Decomposed Workflows
Cutler specifically calls out the impact on customer feedback and work decomposition. He suggests that AI might inadvertently strip the value from genuine customer insights if the feedback mechanisms themselves are not robust. Similarly, the trend of decomposing complex work into smaller, manageable tasks can become even less believable when presented through AI-generated cascades. In Cutler’s view, the output might look more sophisticated (e.g., “polished markdown”), but the underlying issues remain unaddressed, leading to a more efficient execution of bad strategy.
Ultimately, John Cutler’s message serves as a critical reminder for businesses considering AI adoption: technology should augment and improve sound practices, not serve as a shortcut to automate and disguise fundamentally flawed ones. He urges a focus on re-evaluating and fixing core processes before leveraging AI to scale them.
📝 About This Content
This article is based on insights shared by John Cutler on LinkedIn.
📅 Originally posted on April 22, 2026 | View original post on LinkedIn →