Introduction Link to heading

For most of the past thirty years, software quality was judged through a simple lens. If the system worked, the business could operate on it, and users were not constantly complaining, executives called it good. The internal workings of the system were largely invisible to board rooms. Architecture, maintainability, long-term cost. These were topics left to engineering teams, not senior decision-makers.

That mindset persisted because it was sufficient during a time when systems changed slowly, data volumes were limited, and software played a narrower role in everyday operations. Today, software defines how businesses operate, scale, and compete. Yet, many organizations still evaluate software using the same criteria as used in 1995.

But the industry has shifted. The expectations placed on software have shifted. The capabilities available to teams have shifted. Good software now has a much broader meaning, and ignoring that widening scope has consequences that no manager can afford to overlook.

This first article sets the foundation for the entire series. It explains why the definition of “good software” has diverged into two competing realities—and why the gap between them is now creating operational risk, financial waste, and strategic misalignment.

Everything that follows in the series builds on this point:

the definition has split, and management must finally acknowledge it.

The split definition of good software ∙ 03 December 2025

What “Good Software” Used To Mean Link to heading

Until recently, the working definition of good software inside most organizations looked something like this:

  • It runs without catastrophic failures
  • It satisfies the core business requirements
  • It supports current workflows
  • It doesn’t require endless firefighting
  • Users can complete their tasks
  • The system can be extended with reasonable effort

That definition made sense in an era where uptime was measured weekly, deployments were infrequent, and feature requests were negotiated in advance over a long period. In that environment, “working software” equaled “good software” because the thresholds were lower and the expectations narrower.

Tech managers, therefore, developed an intuition: if the system functions, the job is done. And the function was visible. Structural integrity was not. Today, that intuition is no longer reliable.

Why the Old Definition Fails in 2025 Link to heading

Modern systems operate under pressures and constraints that didn’t exist twenty years ago. Systems must now withstand:

  • real-time operational loads
  • continuous delivery
  • volatile usage patterns
  • global user bases
  • security threats evolving daily
  • deep integration across multiple SaaS and internal platforms
  • rapid regulatory changes

To handle these realities, systems must be structurally robust. That means:

  • architecture aligned with growth
  • maintainability built into the code
  • operational resilience under edge conditions
  • predictable lifecycle cost
  • clear boundaries between components
  • clean data flows
  • safeguards against cascading failures

These dimensions directly influence organizational agility and cost structure. But they are not visible through traditional management lenses.

A system that “works” today might still be a liability tomorrow. A system that merely runs is not the same as a system that scales. And a system that satisfies stakeholders now might still impose high hidden costs later.

This is why the old definition fails: the complexity of modern software environments has outpaced its leaders’ mental model.

The Business Case for Structural Quality Link to heading

Investing in software quality is often framed as an engineering preference. In reality, it is a financial decision with clear evidence behind it.

The Design Management Institute’s long-running analysis shows that companies with strong structural and design quality outperform the S&P 500 by 211 percent over a ten-year period.

Conversely, McKinsey estimates that 40 percent of all IT budgets are spent on technical debt - not innovation.

These numbers reflect two truths:

  1. Quality compounds positively in companies that prioritize it.
  2. Technical debt compounds negatively in companies that ignore it.

The difference is management attention. Organizations that tie software quality to business outcomes tend to achieve greater success. Organizations that treat software merely as feature output lose.

The AI Productivity Shift—And Its Side-Effects Link to heading

The recent wave of AI-assisted development widened the gap between perception and reality. Companies see teams shipping more code at a faster rate and believe it signals progress. But the data suggests otherwise.

According to the Google DORA 2024 report, AI-assisted development increased coding speed by 55 percent, while delivery stability dropped by 7.2 percent.

AI accelerates output, but it does not improve context or long-term judgment. It can generate solutions quickly, but it cannot understand:

  • architectural intent
  • ownership boundaries
  • maintainability trade-offs
  • future requirements
  • organizational constraints
  • security implications

The resulting systems are functional but fragile. They appear complete until they are placed under real operational pressure, at which point underlying weaknesses become expensive.

This divergence between visible progress and hidden instability is now the core management risk. Without new metrics and new thinking, leaders will mistake short-term speed for long-term health.

AI hasn’t changed what constitutes good software. It has changed how easily teams can create software that looks good but fails under scrutiny.

The Split in Definitions: Enterprise “Good” vs. Individual “Good Enough” Link to heading

A second force is reshaping the definition of quality: the rise of no-code and the shift toward individual creation. Platforms like Lovable demonstrate this trend clearly. According to published case studies, the platform grew from $0 to $30M ARR in 120 days and serves tens of thousands of creators.

This shift has made software creation widely accessible. Designers, operations teams, analysts, and even frontline staff can now build tools without engineering support. This is a genuine positive. It empowers experimentation and accelerates internal innovation.

But it creates a second definition of quality.

Enterprise-Grade “Good Software.” Link to heading

Enterprise systems must:

  • operate reliably under high load
  • be maintainable by multiple teams
  • integrate cleanly with other systems
  • withstand security and regulatory scrutiny
  • support long-term change without collapse
  • avoid compounding technical debt

This category requires standards, clarity, and discipline. The system is built to outlive its creators.

Individual “Good Enough Software.” Link to heading

Individual creators need tools that:

  • solve their problem today
  • are fast to build
  • can be rebuilt later if needed
  • do not require sophisticated architecture
  • have low maintenance expectations
  • connect to only a few systems

These systems are intentionally disposable. They are not designed for scale. Their value is speed and convenience.

The Collision Link to heading

Problems arise when the CTO fails to distinguish between these two categories.

  • When enterprise systems are built like prototypes, they become brittle.
  • When prototypes are allowed to become business-critical, they buckle under load. This misalignment is now common. And it is costly.

A Blind Spot: Intent vs. Outcome Link to heading

Most teams still rely on the outdated assumption that “if it works, it’s good.” The question they rarely ask is the one that matters most:

Does this software need to outlive the person who built it?

If the answer is yes, the system requires enterprise-grade quality, including reliability, maintainability, structural coherence, predictable cost, and clear architectural boundaries. These are not engineering ideals. They are prerequisites for sustainable operations.

If the answer is no, speed is the priority. A prototype built in a day is more valuable than a perfect solution built in a month. Disposable tools are strategic assets when used correctly.

A CTO’s blind spot is failing to classify systems upfront. When intent is unclear, teams adopt the path of least resistance. They produce working software, but don’t understand whether “working” will still be sufficient a year later.

Systems without clear intent default to short-term decisions. Those decisions become embedded. And the cost to unwind them grows with every release.

Don't fall for the blind spot of classifying systems upfront ∙ 03 December 2025

The Cost of Misclassification Link to heading

Several industry resources reflect the consequences of misaligned quality expectations:

  • 40 percent of IT budgets lost to technical debt (Gartner)
  • Significant stability drops with AI-accelerated development (DORA 2024)
  • Exponential growth in low-quality code, as summarized in the SEI Carnegie Mellon blog on quality engineering;
  • A growing market where “good enough” solutions expand but rarely scale

Additionally, Mary Shaw’s work at Carnegie Mellon explains why software engineering must apply systematic evaluation, not intuition, when judging quality:

These academic and industry findings point to the same conclusion: Systems without intentional quality boundaries impose unnecessary long-term cost.

Why Management Must Update Its Definition Link to heading

Modern product and engineering teams already understand the new definition of good software. They live with the consequences of poor foundations every day. Managers, however, often continue to judge quality from the outside: successful demos, delivered features, and minimal user complaints.

That perspective is no longer adequate.

Today’s systems degrade silently under the surface—through duplicated logic, fragile integrations, incomplete tests, unpredictable dependency chains, and layers of AI-generated code that lack architectural discipline.

Management must update its definition for four reasons:

  1. The external environment has changed - Systems must absorb more volatility and more dependencies than ever before.
  2. The internal environment has changed - Teams mix human-generated and AI-generated code. Without clear standards, variance explodes.
  3. The competitive environment has changed - Companies that invest in structural quality move faster and safer.
  4. The cost structure has changed - Technical debt behaves like compound interest. Delay multiplies cost.

Ignoring these shifts is not a neutral act. It is a strategic risk.

The First Move: Re-Establish Intent Link to heading

Many organizations could improve software quality dramatically by asking one simple question early in any project:

What is the intended lifespan and role of this system?

From that question, three categories emerge:

  • Systems that must outlive their creators: These require discipline, architecture, reviews, and standards.
  • Systems meant to be replaced: These require speed, experimentation, and minimal ceremony.
  • *Systems that should be bought, not built: If off-the-shelf solutions meet the requirements, they should replace fragile custom implementations.

By classifying upfront, leaders eliminate unintentional debt, wasted effort, and unclear ownership. Quality becomes intentional rather than accidental.

Summary Link to heading

The definition of “good software” has changed. It now contains two incompatible realities: enterprise-grade durability and individual “good enough” disposability. Both matter. Both have value. But they cannot be treated the same.

Most executive teams still operate under the old definition, and this gap is now one of the primary reasons organizations create fragile, expensive, and unsustainable systems.

In this series, we will examine how management behavior, team dynamics, AI-driven development, and organizational incentives shape software quality in 2025. But everything starts with the acknowledgment that “good” is no longer a single category.

Knowing which one you’re building is the first step toward making the right decisions.