Most companies are building faster software but also more bugs

A software factory must be a cohesive production platform

Every company wants to move faster with AI. That makes sense. Large language models have dramatically reduced the effort required to write software, and teams are responding by adding coding assistants, review bots, and automation into their existing workflows. The problem is that speed alone does not create a software factory.

A software factory is not defined by how many AI tools a company uses. It is defined by how work moves through the organization. Every stage, planning, code generation, testing, security reviews, deployment, monitoring, and maintenance, needs to operate as one connected system. If these steps are disconnected, each tool optimizes only a small part of the process while creating new points of friction elsewhere.

This is where many organizations make an expensive mistake. They invest in individual AI products without changing the underlying operating model. Teams end up with separate assistants for coding, documentation, testing, and pull request reviews, but these systems rarely share context or enforce consistent standards. The result is fragmented workflows, duplicated effort, and software that becomes increasingly difficult to govern.

An effective platform creates a common source of truth. Every decision, code change, test result, and deployment should be traceable across the development lifecycle. Engineers spend less time searching for information and more time improving products. Leaders gain visibility into software quality, delivery performance, and operational risk instead of relying on isolated reports from different tools.

For executives, this is an organizational decision as much as a technology decision. AI should not be viewed as another software purchase. It should become part of the company’s production infrastructure. The companies that build integrated platforms will scale AI more effectively because governance, quality, and automation are designed into the system from the beginning rather than added later.

Luca Rossi, author of “The Era of the Software Factory,” argues that AI is changing the entire software production system, not simply making developers write code faster. That distinction matters. Organizations that recognize this shift will design their operating models around AI instead of treating it as an incremental productivity tool.

AI has shifted the development bottleneck from code generation to software quality

For decades, software development was constrained by one simple reality: writing code required specialized skills and significant time. AI has changed that equation. Today, generating functional code is faster and more accessible than ever before.

This changes the real question facing executives. The challenge is no longer whether an engineering team can build something. The challenge is deciding whether it should be built in the first place and whether it will create lasting business value.

When software becomes easier to produce, organizations naturally produce more of it. That creates a different kind of pressure. More applications, features, and services mean more systems to secure, monitor, update, and maintain. Every new piece of software introduces long-term operational responsibilities that continue long after development ends.

Technical debt becomes a strategic issue under these conditions. AI can generate code quickly, but it cannot automatically ensure that the architecture remains consistent, documentation stays current, or design decisions align with business priorities. Without strong governance, organizations risk accumulating software that works today but becomes increasingly difficult to maintain over time.

This is why leadership discipline becomes more important as AI capabilities improve. Product strategy, architecture reviews, engineering standards, and portfolio management become key business functions rather than technical formalities. Faster software production increases the importance of deciding what deserves investment and what should be declined.

Executives should also rethink how engineering productivity is measured. Traditional metrics such as lines of code, development speed, or the number of completed features reveal only part of the picture. More meaningful indicators include production stability, customer outcomes, system reliability, security performance, and the ongoing cost of maintaining software. These measures better reflect whether AI is creating durable business value instead of simply increasing output.

AI has removed one of the largest historical constraints in software development. That is a significant opportunity. But as one constraint disappears, another becomes more important. Organizations that succeed will not be the ones that generate the most code. They will be the ones that consistently make better decisions about what they build and ensure that those systems remain reliable over time.

Rapid AI-generated code can increase defects and operational complexity

AI has significantly increased the amount of software a single engineer can produce. That is a real improvement. However, increasing output also increases the amount of software that must be reviewed, tested, secured, maintained, and understood over time. If those capabilities do not improve at the same pace, organizations simply move the bottleneck downstream.

The data already shows this pattern. According to Faros AI, developer task throughput increased by 33.7%, while pull request merge rates rose by 16.2%. At the same time, the incidents-to-pull-request ratio increased by 242.7%, and bugs per developer increased by 54%. These numbers suggest that higher development speed does not automatically produce better software. It can also increase operational risk if quality controls remain unchanged.

Google’s DORA research reaches a similar conclusion. Greater AI adoption was associated with reduced delivery stability. This does not mean AI creates poor software. It means organizations need stronger engineering practices to convert higher development speed into reliable production systems.

AI accelerates both progress and inconsistency. Without clear engineering standards, software quality gradually becomes less predictable. Teams spend more time investigating failures, resolving unexpected interactions, and maintaining systems instead of delivering new business capabilities.

Leadership should therefore treat software quality as a measurable business outcome rather than a technical concern. Defect rates, production incidents, deployment stability, recovery times, and maintenance costs deserve the same level of executive attention as delivery speed. These metrics provide a more complete picture of whether AI investments are creating sustainable value.

The organizations that gain the greatest advantage from AI will not necessarily be those producing the highest volume of code. They will be those that can consistently produce reliable software while keeping operational complexity under control.

A software factory needs traceability, standardization, safety, and built-in quality across the entire development process

Building an AI-enabled software factory requires more than automation. It requires a disciplined operating model that makes every stage of software development visible, repeatable, and governed. Without these capabilities, organizations struggle to understand how software was produced, why problems occurred, and how they can prevent similar issues in the future.

One essential capability is traceability. Every software change should be connected to the requirements, AI interactions, testing results, approvals, and deployment history that produced it. When an issue appears in production, teams should be able to identify exactly where the process failed instead of reconstructing events manually. This level of visibility shortens investigation times, improves accountability, and supports regulatory and security requirements.

Teams should be able to reproduce previous AI workflows under the same conditions to validate outcomes or correct failures. State-machine-based workflows are generally better suited than simple looping processes because they preserve execution state and make it easier to inspect each stage of the workflow. For organizations deploying AI at scale, this improves reliability and operational control.

Standardization is equally important. Every enterprise has multiple development teams, established coding practices, and different technology stacks. If AI generates software without common standards, inconsistencies accumulate quickly. Shared templates, coding conventions, architecture guidelines, and governance policies allow AI systems to produce software that remains consistent across projects and business units.

Safety must also be built into the development process from the beginning. AI can generate software rapidly, but it can also introduce security vulnerabilities, compliance issues, or implementation errors if appropriate controls are missing. Automated testing, static code analysis, policy enforcement, and security validation should operate continuously throughout development rather than being delayed until the end of the release cycle.

Software specifications should be structured clearly, AI systems should receive standardized guidance, and automated validation should detect issues as early as possible. Preventing defects early reduces remediation costs, minimizes production disruptions, and improves delivery confidence.

For business leaders, these capabilities should be viewed as strategic infrastructure rather than engineering preferences. As AI becomes responsible for producing larger portions of enterprise software, governance, transparency, and quality become competitive advantages. Organizations that establish these capabilities early will be able to scale AI adoption with greater confidence while maintaining reliability, compliance, and operational resilience.

Main point 5: sustainable productivity is measured by the quality and durability of software

The conversation around AI often focuses on speed. Teams celebrate faster code generation, shorter development cycles, and higher output per engineer. These improvements are valuable, but they are only meaningful if they lead to software that performs reliably in production and continues to deliver value over time.

From a business perspective, software exists to solve problems, improve operations, support customers, and create new revenue opportunities. The number of features released or the volume of code written has little value if systems become unstable, expensive to maintain, or difficult to evolve. AI makes software creation easier, but it does not remove the responsibility to build systems that remain dependable long after deployment.

This changes how executives should think about productivity. Traditional engineering metrics, such as lines of code or development velocity, were already incomplete before AI. With AI generating substantial amounts of code automatically, these measurements become even less useful. Organizations need metrics that reflect business outcomes rather than activity.

A stronger set of indicators includes production reliability, customer satisfaction, deployment success rates, incident frequency, recovery time, software maintainability, security performance, and the long-term cost of operating applications. These measures show whether faster software development is creating durable business value or simply increasing operational workload.

Clear requirements reduce ambiguity before development starts, while standardized templates help AI systems generate more consistent outputs. Automated testing, static code analysis, and continuous validation should operate throughout the development lifecycle rather than being concentrated at the final review stage. Detecting problems early reduces correction costs and prevents defects from reaching production.

This approach also changes the role of engineering leadership. Success is no longer defined by maximizing development throughput alone. Leaders need to build organizations where speed and quality improve together. That requires investment in engineering standards, governance, platform capabilities, and continuous measurement of software health.

The broader opportunity is significant. AI allows organizations to create software at a pace that was previously difficult to achieve. Companies that combine this capability with disciplined engineering practices will deliver products more consistently, respond faster to market changes, and scale their software operations without allowing complexity to grow unchecked.

The central message is straightforward. Competitive advantage will not come from generating the most code. It will come from consistently delivering software that is reliable, secure, maintainable, and aligned with business objectives. As AI continues to improve, organizations that optimize for durable outcomes rather than short-term output will be better positioned for sustained growth.

Key takeaways for leaders

• Build a software platform: AI delivers the most value when it operates within an integrated platform that connects planning, development, testing, deployment, and governance. Leaders should invest in end-to-end workflows instead of isolated AI tools.
• Shift leadership attention from coding speed to product decisions: As AI removes many barriers to writing code, the competitive advantage comes from deciding what should be built and ensuring it creates long-term business value. Prioritize governance, architecture, and product strategy alongside engineering productivity.
• Measure AI success by reliability: Faster development can also increase defects and operational complexity. Track production stability, incident rates, and software quality alongside delivery speed to ensure AI improves business outcomes rather than simply increasing code volume.
• Build governance into every stage of software delivery: Standardization, traceability, automated testing, and security controls should be embedded throughout the development lifecycle. These capabilities make AI-generated software more reliable, easier to audit, and simpler to maintain at scale.
• Define productivity by durable business outcomes: The most successful organizations will not be those that generate the most code, but those that consistently deliver secure, maintainable, and production-ready software. Focus investment on engineering practices that reduce downstream defects and support long-term growth.