For the past two years, the tech world has lived in a state of Artificial Intelligence (AI) euphoria. Phrases like “We’ve implemented AI” or “Our team runs on Copilot” were badges of innovation. However, according to analysis published in TNW (The Next Web), this “grace period” is coming to an end. As we approach 2026, CFOs and investors are preparing to demand hard economic figures rather than vague promises.
In his analytical piece, tech expert Alex Circei brutally exposes the new reality facing the IT industry. The question is no longer “How modern are we?” but rather a strict demand: “How has this investment actually changed business outcomes?”
The Great Illusion: “Local Speed” vs. “Systemic Results”
The trap many IT leaders fall into is the mismeasurement of “productivity.” AI vendors frequently push the statistic: “Our tool makes developers 55% faster.”
However, the Software Development Life Cycle (SDLC) consists of much more than just writing code. It involves planning, code review, testing, integration, and deployment.
- The Paradox: If AI accelerates code generation by 2x, but the processes for reviewing and integrating that code remain unchanged, the overall project timeline barely shifts. On the contrary, an influx of “raw” code can push the team into a severe bottleneck.
The hidden threat: inflation of technical debt
One of the most serious issues raised in the analysis is the matter of technical debt.
Statistics show that the average developer spends approximately 45% of their time not on creating new features, but on maintaining old systems and fixing bugs.
If a company focuses solely on increasing code volume using AI, it leads to system complexity. Consequently, instead of creating new value, the team becomes bogged down in “patching” the imperfect code generated rapidly by AI. This translates to a slowdown for the business.
The 2026 strategy: time to talk numbers
To succeed in budget defenses and strategic sessions, leaders must fundamentally shift their approach:
- Reinvest Saved Time into Quality: Time saved by AI should not be spent on writing more code, but on increasing system stability. Refactoring (cleaning code), increasing automated test coverage, and security are the areas where AI yields the highest returns.
- DORA Metrics Are Not Enough: Previously, “How fast do we deploy?” was the key question. Now, you must answer: “What percentage of the team’s capacity is going toward creating real customer value, versus how much is consumed by maintenance?”
- AI for the “Heavy Lifting”: Utilizing AI not just for new features, but for tasks developers dislike—such as updating legacy systems (migration) and documentation—provides the highest ROI.
The companies that win in 2026 will not be those with the most AI licenses. The winners will be those that use AI to liberate their teams from mundane daily tasks, redirecting their potential toward solving strategic problems. The time for storytelling is over. The era of numbers and real results is beginning.
Gulnoza Mikhailovna
