2025 Recap:
When AI Hype Met Software Reality
29/12/2025
4 MIN READ /
2025 was not the year AI slowed down. It was the year reality caught up.
Across the market, ambition outpaced execution. AI initiatives multiplied, budgets were approved at a rapid pace, and expectations were sky-high. But by the end of the year, a more sober reality emerged: many AI projects failed to deliver meaningful business value, not because the models were weak, but because the foundations were.
At code4thought, 2025 was a defining year. We gained more clarity about where AI is actually creating value. Where it breaks. And where the real risks — and opportunities — are hiding.
This post reflects on what 2025 taught us about AI, software quality, and trust — lessons the market learned the hard way.
A market correction driven by quality, not regulation
One of the strongest signals we observed in 2025 was a shift in buyer mindset. While regulation and compliance remained relevant, they were no longer the primary driver of decision-making. Organizations began asking harder, more pragmatic questions:
- Why are so many AI initiatives stalling or being abandoned?
- Why does performance degrade once models reach production?
- Why do AI-assisted systems amplify existing weaknesses instead of fixing them?
The answer, repeatedly, came back to software quality.
AI did not introduce new problems. It exposed old ones. Poorly structured codebases, undocumented logic, weak testing practices, and unmanaged technical debt suddenly became training data for AI systems. The result was predictable: faster delivery of flawed outcomes.
This realization reshaped how conversations evolved. Compliance became a by-product of doing things right — not the reason to start.
Laying the groundwork: from tools to capabilities
Internally, 2025 was the year we built foundations rather than chasing scale.
Key milestones included:
- iQ4AI, our AI quality and risk assessment platform, matured significantly. Crucially, it aligned with newly upgraded international standards, reinforcing a core belief: standards provide a shared language of trust in a fragmented AI landscape.
- The launch and first successful delivery of a hard-coded credentials identification and removal service, addressing a long-standing but often ignored software security risk.
- The introduction of the AI Readiness & Adoption service, driven by a simple but uncomfortable truth: many organizations are not ready to build AI, and discovering that early is far cheaper than finding it late.
- Expansion of bias and fairness audit capabilities, including work aligned with New York City bias audit requirements, highlighting the growing global expectation for accountable AI, regardless of political or regulatory volatility.
None of these were “fast wins.” They were strategic bets on where AI conversations would inevitably land: risk, trust, and return on investment.
Broken Systems = Broken AI
By the end of 2025, one conclusion became unavoidable:
AI does not fail because it is immature. It fails because the systems, data, and engineering practices around it are.
The market is no longer asking whether AI should be adopted. It is asking under what conditions it can be trusted to deliver.
In our next post, we look ahead to 2026 — a year that will demand fewer experiments, stronger foundations, and far greater accountability from anyone serious about AI at scale.