AI is making us fail faster — and that's expensive
AI tools have fundamentally accelerated code writing, content generation, and task automation over the past two years. Developers ship faster. Marketing teams generate content in minutes. Managers build automations without IT involvement.
But there's a paradox that experienced technology leaders have noticed: AI sped up execution — and simultaneously raised the cost of planning mistakes.
When code is written manually over three weeks, a wrong assumption means three weeks of wasted effort. When AI generates the same volume of code in three days — the wrong assumption means three days, but the system is three-weeks-worth of complexity. And it still needs to be rethought from scratch.
How this plays out in practice
The classic scenario: a manager wants to automate order processing. Gives the AI system a vague request: "automate our order process." AI generates a working system — in three days, not three weeks. The system works. But it solves the wrong problem.
The issue wasn't the speed — it was that the team didn't spend enough time understanding what the actual process was and where the real bottleneck lived. AI accelerated the construction of the wrong solution.
Psychologist Daniel Kahneman described two thinking systems: System 1 (fast, intuitive, pattern-matching) and System 2 (slow, analytical, real decision-making). AI is a perfect accelerator for System 1. But System 2 — planning, asking the right questions, genuine understanding — AI cannot replace human judgment.
As noted in an analysis by Ksenia Moseenkova on theengineeringmanager.com: "AI hasn't made slow phases less important — it's made them more important. Wrong requirements now propagate faster through AI-generated code." (source)
The Baltic business perspective
Businesses across the Baltic region are increasingly considering AI automation projects — and this is encouraging. But a common mistake: reaching for AI solutions before understanding exactly what problem is being solved.
A beauty salon automates reservations — but doesn't realize that their actual losses come from last-minute cancellations, not the booking process. A restaurant deploys a chatbot — without analyzing which types of questions actually overload staff. The result: a technically working system that doesn't solve the business problem.
The "illusion of speed" — when AI builds thousands of lines of code in three days solving the wrong problem — is the most expensive mistake you can make with AI.
What to do differently: a practical checklist
Spend at least 2 hours clearly defining the problem. Write it concretely: "we lose X customers because Y takes too long."
Ask: if this process became 10x faster, would that actually solve our problem?
Define success criteria upfront: how will you know the AI project worked?
Start each week with 30 minutes on "are we moving in the right direction?" — not "are we moving fast?"
Build a minimal viable version and test with real users before expanding.
Use AI for planning too: ask it to identify risks and potential failures in your plan.
Track not just technical metrics (is the system running?) but business metrics (is the problem solved?) for the first month.
Document what's different from expectations — this is the most valuable input for your next project.
Conclusion
AI automation is a powerful tool — but not everything that runs fast runs correctly. The faster a solution is generated, the more important it is to understand precisely what needs to be solved.
Webedge.dev conducts requirements analysis before every AI project — not for bureaucratic reasons, but because it's economical. One week of proper analysis can save a month of rewrites. Contact us to discuss what AI automation would look like for your business.
FAQ
Minimum: 2 working days for genuine requirements discussion. More complex projects: up to two weeks. This is an investment, not a delay.
Yes — ask AI to generate a "pre-mortem" analysis: "What are the 5 most common ways this type of project fails?" This surfaces risks earlier.
The system works technically but employees don't use it. Or it solves a different problem than expected. Or metrics haven't improved after a month of use.
Use the "time-box" method: set a specific planning deadline ("by Friday we have defined requirements"), after which execution begins. This prevents endless planning loops.