Fix the Why, Not the What
Every workaround is a decision to let the real problem survive.
A client's checkout breaks on mobile. The developer inspects the page, finds a CSS conflict, patches it with an override, and ships it in twenty minutes. The client is happy. Two weeks later the same checkout breaks again, this time on a different device. The developer patches again. A month later, a theme update triggers a cascade of layout failures across four pages. The root cause was never the CSS. It was a third-party app injecting unscoped styles into the storefront. Every patch addressed what happened. None addressed why.
This pattern is not limited to code. It shows up in operations, marketing, hiring, and every other system that accumulates decisions over time. A sales pipeline stalls, so you add another follow-up email. An employee underperforms, so you add another layer of reporting. A campaign underdelivers, so you increase the budget. Each response treats the symptom. The cause persists, and the interventions stack up until the system is more patch than structure.
The instinct to patch is understandable. Band-aids are fast. They feel productive. They resolve the immediate pressure. And in a client-facing business, speed matters. Nobody wants to tell a client "we need three days to investigate" when a visible problem is costing them revenue right now.
But the speed is an illusion. A twenty-minute patch that needs to be repeated four times across six months costs two hours of engineering time, plus the client's eroded confidence, plus the cognitive overhead of maintaining a system that everyone knows is fragile. A two-hour root cause fix that eliminates the problem permanently is cheaper by every measure except the one that matters least: how fast the first response looked.
The discipline is straightforward but uncomfortable. When something breaks, ask two questions before writing any code or changing any process. First: what broke? This is the symptom. It is what the client reports, what the error log shows, what the dashboard displays. Second: why did it break? This is the cause. It is upstream, often invisible, and frequently unrelated to where the symptom appeared.
The gap between "what" and "why" is where most fixes go wrong. A page loads slowly, so you optimise the images. But the images were fine. The slow load was caused by a JavaScript bundle that blocks rendering because a tracking pixel was installed incorrectly six months ago. Optimising the images made the page slightly faster but did not fix the problem. The next developer who touches the tracking configuration will trigger the same issue.
In our own projects, we enforce a simple rule: no band-aids, no workarounds. Each fix must address why it broke, not what happened when it broke. This sounds rigid, but it is actually liberating. It means every problem gets solved once. It means the codebase, the operations manual, the client's store, and the pipeline all get cleaner over time instead of accumulating scar tissue.
The practical application is a diagnostic step before any intervention. Before fixing, reproduce the failure. Before reproducing, read the logs. Before reading the logs, understand what the system was supposed to do. This sequence takes longer at the start and saves everything at the end.
Consider a real example from platform engineering. A trading bot's live results diverged wildly from its backtest. The obvious fix was to tune the strategy parameters. Tighter stops, different indicators, adjusted thresholds. But the actual cause was infrastructure: the execution loop ran every fifteen minutes instead of every minute, so stop-loss orders were triggering at prices far worse than intended. No amount of strategy tuning would have fixed a polling interval problem. The "what" was bad trade results. The "why" was an execution pipeline that could not keep up with market movement.
Another example from client delivery. A Shopify store's navigation kept breaking after theme updates. The quick fix each time was to manually rebuild the menu. The root cause was that the navigation had been configured via the REST API, which Shopify deprecated in favour of GraphQL. Every theme update reset the REST-configured menus. Switching to GraphQL for navigation management fixed it permanently. The "what" was broken menus. The "why" was a deprecated API that the theme update process did not respect.
The same principle applies to business systems, not just technical ones. If client projects consistently go over scope, the fix is not to add more buffer to estimates. The fix is to identify why scope expands. Is the briefing process capturing the wrong information? Is the client adding requirements after approval because the approval step is not clearly defined? Is the team building features the client did not ask for because the brief is ambiguous? Each of those causes demands a different intervention. Adding buffer addresses none of them.
The cost of workarounds is not just the time spent patching. It is the compounding fragility they introduce. Every workaround is a load-bearing assumption that the next person to touch the system will not understand. It will not be documented, because workarounds never are. It will not be obvious, because if it were obvious, the root cause would have been found the first time. It will break eventually, and when it does, the failure will be harder to diagnose because the diagnostic path is now obscured by layers of prior patches.
The organisations that build lasting, reliable systems, whether in software, client delivery, or operations, are the ones that insist on the uncomfortable investigation before the comfortable fix. They accept that the first response will be slower. They accept that the client will sometimes wait longer than they would like. And they accept that the short-term cost of doing it properly is always less than the long-term cost of doing it twice.
Fix the real problem: a business that depends on you.
Most experts patch around the same constraint for years, that nothing runs without them. The 12 Principals programme addresses the why, turning your methodology into systems that operate without you driving every interaction.
