Every single one of you has seen those social media posts featuring logo clusters, a la "The Top 10 AI Tools for Every Single Need," where a forced AI tool is scraped up for even the most trivial task. Although one of AI’s primary value propositions is supposed to be saving time, all theoretical gains are washed away because people spend their days juggling a dozen different AI tools, or trying to shove AI into processes where it simply isn't needed. In my own career, I’ve sat in AI adoption working groups where the main question on the table was, "What can we do with AI, guys?" instead of focusing on what would actually be logical and efficient. Because a systematic process mapping isn’t done at the start, corporate budgets suffer increasingly deep wounds from countless software subscriptions, instead of these services being consolidated. On a macro scale, LLM output quality is directly dependent on input quality; meanwhile, data is being rammed into AI models at a mind-boggling scale, effectively degrading their overall quality.
Commandment I: Build workflows around specific operational bottlenecks, not external tool availability. Introduce AI only where it serves a concrete process requirement.
The Second Deadly Sin: Pride
We can still debate whether machine intelligence qualifies as a second industrial revolution, but AI is 100% a revolution for pseudo-experts. It allows them to generate absolute BS on a scale unprecedented in human history, and everyone online runs into it daily. Suddenly, with a single prompt, anyone can become a logistics expert, indulge in vibecoding, analyze performance campaign results, and generate high-level business plans—completely regardless of whether their education or experience allows them to critically evaluate the AI-generated output. If I had a nickel for every time over the past year a client or partner sent me an answer generated with a "debunk this" style prompt, I’d have a solid dollar by now, but that’s beside the point. Pseudo-experts destroy the credibility of actual specialists, give misguided middle managers ammunition to validate their own assumptions, and ultimately drive foolish business decisions based on incomplete or distorted data. For those who want to dig deeper into this technical reality, I highly recommend reading "Towards Understanding Sycophancy in Language Models" (Sharma et al., 2023).
Commandment II: Let software remain an accelerator rather than a surrogate for expertise. Tasks that cannot be critically audited without AI must not be executed with AI.
The Third Deadly Sin: Lust
Reading self-proclaimed AI experts swoon over any product with "AI" slapped onto its name is like watching a hormonal teenager in the early 2000s flip to the swimsuit section of a retail catalog. And just like those teenagers, these experts frequently make completely irrational decisions. One of the most glaring examples is the Allbirds story, where a shoe company’s stock value skyrocketed by 580% in a single day simply because they announced they were pivoting to lease server infrastructure for AI solutions. Another example: I was using Grammarly long before AI was even part of the cultural vernacular. Now that it’s been successfully wrapped in shiny AI candy paper, the brand is suddenly sexy again, and countless tech bros and influencers regularly include it in their "Top 10 AI Productivity Tools" lists. Sticking an AI badge onto a brand guarantees neither quality, innovative functionality, nor ROI.
Commandment III: Prioritise functional utility over marketing nomenclature. Evaluate SaaS architecture purely on technical capabilities and verifiable ROI.
The Fourth Deadly Sin: Sloth
Honestly, while I had to rack my brains to stick to the framework for the previous sins, this point writes itself. Among many evangelists, AI has achieved the logically impossible: making laziness a synonym for productivity. Let’s be completely fair—in the hands of a professional, AI objectively skyrockets productivity. High-end developers who have systematically fed Claude samples of their own code can compress 7 months of work into two weeks. I’ve personally wrapped up 2 months of manual data collection and processing for a digital strategy in just 2 weeks using AI. BUT—there is a massive difference between doing 20 hours of work in 5 hours (where 4 of those hours are honestly spent verifying sources, proofing copy, and identifying objective flaws) versus just exporting a single Gemini Deep Research report and forwarding it along without even reading it. And let's not even start on the half-baked infographics and social posts that literally still contain leftover prompt artifacts.
Commandment IV: Reallocate saved manual execution time into rigorous quality control, source verification, and critical analysis. An accelerated timeline does not exempt output from editorial oversight.
The Fifth Deadly Sin: Envy
As we’ve already established, companies love to brag about their AI integrations a little too much. Throwing all rational thinking out the window, both SaaS developers and enterprise users hear about a competitor's latest move and immediately rush headlong to copy it. More and more frequently, we hear stories of a C-level executive deciding to close the gap between themselves and a competitor within a month using an AI-fueled patch job, driving the
company straight into inevitable tech debt and other systemic issues born from a rush job. On the SaaS side of things, it’s well worth looking at the Klarna AI case study to see how the industry reacted when they rushed an OpenAI-backed chatbot rollout across multiple markets, shaking up the customer service landscape only to face long-term operational rebalancing and quietly reopening hiring for human support roles later on to fix the nuance gap.
Commandment V: Optimize for internal infrastructure stability and core customer requirements rather than competitor narratives. Reactive feature clones yield technical debt, not market share.
The Sixth Deadly Sin: Wrath
Now let’s talk about the logic that usually hides behind headlines like: "Company X Lays Off 300 People, Plans to Replace Them with AI." At its core, AI is every corporate chairman’s wet dream—earn more while paying less. Upper-middle management hopefuls understand the power of this talking point perfectly, marching into the boardroom with unprecedented Yearover-Year growth plans, drastic cost-cutting measures, and promises of endless profit. Talk is cheap, but when it’s time to transition from slides to execution, these same characters aren't exactly known for their patience when they hit justified pushback from department heads or technical specialists. What follows are layoffs, political ousters, and the promotion of loyal evangelists to top positions, complete with strict AI implementation mandates. When the promised results fail to materialize on the horizon, the blame is invariably pinned on the employees who remain.
Commandment VI: Treat technology as an augmentation of existing talent rather than an organizational replacement. Competent teams leverage infrastructure to scale; eliminating the team renders the infrastructure obsolete.
The Seventh Deadly Sin: Greed
In the context of AI, this sin goes hand-in-hand with the previous one. Both AI SaaS creators and stakeholders believe so deeply in the promised profits and unprecedented efficiency of AI that they are willing to sacrifice employees, reputations, and technical integrity to get it - choosing to build their businesses on shifting sands. AI is not the first hype cycle we’ve had to endure over the past decade. We can easily recall the NFT evangelists claiming that while a diamond can be destroyed, an NFT lives forever. Or look at another industry - video games - where over the past ten years, countless titles had unrequested, unnecessary multiplayer functionality bolted on simply because every publisher wanted to build the next Fortnite. Meanwhile, overhyped Live Services like Sony’s Concord ended up in the trash bin just two weeks after going live, despite millions in investment.
Commandment VII: Do not allow speculative margins to distort core business principles. Technological shifts change operational execution, but they never bypass the fundamental laws of profitability and value creation.
What to Take Away From This Sermon
AI, like any other tool, is not inherently bad. I will never call a utility useless or inefficient when I use it every single day myself - and used it to proofread the text of this very article. In my own love-hate relationship with the tech, I've moved past the initial infatuation into a stable partnership: I accept its flaws and limitations. The fundamental problem with AI isn't the technology itself. The problem lies with the wolves in sheep’s clothing who trade long-term corporate health for clicks, likes, or short-term valuation bumps, compromising responsible and sustainable business practices in the process. We practice what we preach at SerpCtrl - and we keep these seven commandments at the core of our daily operations.
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