Fix the Process, Then Add AI

AI is not a strategy or a magic wand; it simply accelerates existing processes. Its real advantage—handling unstructured data—demands that companies first structure and formalize the underlying workflows. Design clear triggers, transformations, and outputs, then apply AI to scale speed under human governance.

Key Points

• There is no standalone “AI strategy”; effective AI use is a subset of Business Process Optimization.
• AI doesn’t make organizations smarter—it accelerates whatever process exists, good or bad.
• AI’s unique value is handling unstructured data, which reveals and requires structuring previously ad hoc processes.
• You can’t automate what you haven’t designed: define triggers, transformations, and structured outputs before applying AI.
• Human governance still sets definitions and guardrails (e.g., what counts as risk); AI delivers speed, not wisdom.

Sentiment

The overall sentiment of the Hacker News discussion is predominantly in agreement with the article's core thesis: process optimization is paramount before applying AI. Many users provided strong anecdotal support for this viewpoint, often expressing frustration with the challenges of poor processes and the misguided application of technology. While the core message resonated positively, there was a noticeable undercurrent of critique regarding the article's perceived narrow scope of AI and its writing style, suggesting that while the foundational principle holds, the full spectrum of AI's transformative potential and the practicalities of process enforcement warrant further nuanced discussion.

In Agreement

• AI accelerates existing processes; if processes are bad, AI will only speed up the generation of bad outcomes ('automating stupidity = faster stupidity').
• Effective AI implementation necessitates prior business process optimization (BPO); there is no 'AI strategy,' only BPO.
• Documenting current processes, even if imperfect, is a crucial first step for clarifying what the process 'is' and gaining consensus among stakeholders.
• Many 'tech debt' issues are fundamentally 'org debt' or 'social problems' that cannot be solved purely with technical solutions.
• AI's distinct strength lies in handling unstructured data, but processes relying on such data are often themselves unstructured and poorly defined.
• The correct sequence for optimization is design, delete, simplify, accelerate, and then automate, with AI best applied in the later stages.
• Well-defined processes are essential for achieving consistent, quality work from average performers and for protecting against lazy or chaotic behaviors.
• The value of writing things down is often underrated, as it forces clarity and can reveal opportunities for improvement.
• Long-standing experience in general process automation confirms that ill-defined or nonsensical processes cannot be effectively automated.

Opposed

• The article's definition of 'AI' is too narrow, often conflating it solely with LLMs and overlooking other AI forms (e.g., in medical diagnostics) that can fundamentally transform or eliminate steps in a business process beyond mere acceleration.
• AI strategies extend beyond internal BPO to include improving customer funnels or launching new products, which the article largely overlooks.
• The writing style of the article is perceived as 'LinkedIn speak' or 'LLM-generated,' which some commenters found annoying or indicative of spam.
• Overly rigid processes can stifle high-performing individuals ('rockstars') and negatively impact organizational culture; 'people over process' is sometimes a valid approach.
• The assertion that 'intelligence (e.g., knowing what a \"risk\" means) still requires human governance' is challenged, as LLMs, having internalized vast amounts of data, might become more reliable than humans in certain judgment calls.
• The act of 'legibilizing everything illegible' might detrimentally affect organizational culture or be impractical.
• Sometimes blocking a process is a necessary tactic to gain leverage and force the resolution of long-standing systemic issues.
• LLMs are speeding up everything, including the speed of learning, implying that companies with bad processes might learn and evolve to good processes more quickly with AI's aid.