c-suite

The new economics of quality engineering:
When every minute costs $9,000

insight
November 07, 2025
9 min read

Author

Thomas Steirer

Thomas Steirer is Chief Technology Officer (CTO) at Nagarro. His focus is on developing scalable and sustainable solutions that are primarily designed to deliver valuable information.

Executive summary

In the digital core of Fortune 2000 enterprises, milliseconds determine market share, and trust can disappear in a single incident. That’s why reactive checks in quality engineering (QE) need to be replaced with a system that anticipates risk, accelerates delivery, and protects the brand. When it is elevated to a strategic system of trust, quality becomes an operating system that enables speed without undermining customer confidence.

Quality as a discipline has two horizons: stabilizing what is important in the present, second, doing that while continuously improving quality engineering (QE) in the long term. Businesses leading in quality, avoid defects, build resilience, accelerate change, and gain the confidence to move faster than their competitors. So, the leaders who really understand this, create the conditions for confident growth in an unpredictable world.

But building such a system is not something leaders can achieve by mandate alone. Internal teams are often consumed by delivery, and sustaining improvement requires external experience, perspective, and models with a proven track-record to make change stick.

The pressure cooker leaders are in

(and why testing alone won’t save you)

Digital businesses are spread across microservices, multi-cloud setups, mobile channels, and extensive data pipelines. With each added layer, there is an increase in complexity, and the blind spots where risks can hide. Even the best teams can falter when fixed deadlines collide with limited visibility.

The risks are no longer hypothetical.

The economics of failure are stark: downtime in large organizations can exceed € 7,500 per minute, not including reputational damage (source: Gartner). The average cost of a data breach reached $4.88 million in 2024, rising 10% year-on-year (source: securityweek). Organizations that use AI and automation in security have reduced those costs by around $2.2 million (source: IBM).

Exclusion also undermines trust. Despite years of progress, 95.9% of top homepages still have accessibility errors, averaging 56.8 errors per page (source: andycrater). Sustainability is another factor: data centers already consume 415 TWh of electricity (1.5% of global demand), a figure that could more than double by 2030 as AI use scales.

And leaders can’t shoulder this shift on their own. Continuous improvement is notoriously hard to sustain internally, tests calcify, automation grows brittle, and alignment across teams becomes a struggle. External guidance can normalize disruption, bring proven practices, and broaden the scope of quality to include user experience, accessibility, AI, and sustainability.

The dual horizon model

When incidents with fixed deadlines start accumulating, teams enter triage mode. While firefighting might keep things moving temporarily, it does not build a resilience or provide a lasting solution. What businesses need is to stabilize quickly, while investing in a long-term strength that builds continuous improvement.

Horizon 1: The fast lane

Stability is not an end state; it is a renewable capability. Here, every fix feeds a living system: feedback loops, automated audits, governance rituals, bug bashes, and continuous refactoring that keep test resources alive rather than fossilizing them. This is where quality evolves from a brittle protective measure to a self-healing fabric for the organization.

Horizon 2: Sustainable quality

Data backs this up. DORA’s research indicates that performance in software delivery is a predictor of business performance. High-performing organizations achieve both throughput and stability - driven by a culture of user-centricity and strong documentation. In practice, quality is not a trade-off, but a force multiplier.

Related insight: The fourth dimension of sustainability: Why software strategy is now a climate decision.

Why leaders cannot do it alone

Continuous improvement sounds simple in principle, but in practice, it is one of the most difficult things for companies to sustain. Test processes calcify, automation suites become brittle, and working methods become ingrained habits that are difficult to change. The problem is exacerbated in large businesses: multiple teams, tools, and standards lead to fragmentation, making it difficult to ensure consistency or transparency across all areas.

Sustaining improvement is often more challenging than initiating it. McKinsey notes that many transformations lose momentum because organizations slip back into old ways of working. One of the recommended safeguards is to “take an independent perspective” to guard against regress—bringing in external voices who can challenge assumptions, provide benchmarks, and normalize change. An outside perspective not only helps leaders avoid reversion, it also ensures that improvements become embedded rather than episodic.

This is where external consulting adds strategic value. For internal teams, any change feels disruptive, a “special event” that competes with delivery. For experienced consultants, change is a routine occurrence. They bring the experience of dozens of similar restructurings and can help normalize the upheaval by providing tested models, benchmarks, and governance mechanisms.

Consultants also broaden the discussion. Instead of just focusing on error rates or automation coverage, they are pushing leaders to look at quality through a new lens: user experience, accessibility, the quality of AI, and quality through AI, as well as sustainability in and through IT. This helps organizations establish not just a solution, but a system of fluidic intelligence, a renewable quality model that evolves over time, driven by feedback loops, governance rituals, and measurable outcomes.

Leaders can set the vision but sustaining it requires structures and perspectives that few organizations can build alone. Advisory is not outsourcing responsibility; it’s about sharing responsibility and embedding improvement as a living capability.

Stabilizing at scale: Lessons from an auto retailer’s quality challenge

As a major industry milestone approached, the organization faced over 50 production incidents in rapid succession. Executive escalations increased, and the fixed year-end shutdown meant slowing down was not an option. The only viable path was to rapidly raise quality standards and establish a system that would endure.

A Fast Lane program was launched to rapidly stabilize the business. Cross-continental champions and challengers conducted deep diagnostics, producing a prioritized backlog. Alignment artifacts, team topology, program timeline, consolidated backlog, and a quality-gate blueprint provided leaders and delivery teams with a single view of reality and a shared basis for decision-making.

The results were measurable within weeks. Critical user journeys were safeguarded through automation, the release cadence could regain predictability, and the identified disputes led to objective thresholds. The shift was clearly from reactive problem-solving to disciplined, governed improvement. Horizon 2, Sustainable Quality, ensured these gains were not temporary by embedding governance, audits, and renewal practices into the operating model.

The external perspective proved decisive here. Advisors who navigated similar high-stakes environments helped frame the right interventions, accelerate alignment, and establish structures that could be sustained without exhausting teams. Leadership provided intent, but shared ownership turned it into lasting capability.

Broadening the perspective of quality

Accessibility, by design

With 1.3 billion people (16% of the world) living with disabilities, accessibility is not a niche feature but mainstream quality. Yet persistent WCAG failures show how often it is sidelined, costing both users and brand trust.

AI in—and as—quality.

AI creates a double responsibility: assuring AI systems themselves for fairness, robustness, and privacy, while also using AI to improve quality through smarter test selection, defect clustering, and triage. The economics are compelling AI-driven controls are cutting millions from breach costs.

Sustainability through performance.

Data centers already consume ~415 TWh of electricity, with demand expected to more than double by 2030. Performance engineering is now carbon engineering. Every efficient code path and right-sized infrastructure decision has climate consequences.

Advisors help bring these overlooked dimensions into scope, ensuring quality is not only about stability but also inclusion, intelligence, and sustainability.

What leaders should actually do next?

The way forward is not through another checklist, but through a change in the way leaders think about quality.

The way forward is not through another checklist, but through a change in the way leaders think about quality.Start with what is most important: the customer flow that must not fail. Define the value at stake - financial, reputational, and operational. This leads to your quality economy, the foundation for investment and management focus.

Based on this, create a small number of common artifacts that will keep the company on track:

Team topology: who decides, who escalates, and who is responsible for resilience.
Timeline: where the immediate rush ends and the long-term rhythm of renewal begins.
Consolidated backlog: what should be stopped, what should be started, and what should be standardized.
Gate Blueprint: the non-negotiables- what will be measured, where, and why.

Then governed by two clocks:

A clock for stabilization in weeks.
A rhythm clock that brings improvements quarter by quarter.

When managers can see the topology, schedule, backlog, and gates, discussions become more focused. The focus shifts from reacting to issues or chasing speed to building trust into the way the organization operates, a shift that delivers real strategic value.

Sustaining improvement: Tailored quality playbooks

Most organizations recognize the need to rethink quality but struggle to sustain it. Continuous improvement, especially in testing, is where momentum fades. That’s why leaders increasingly look to advisors, partners who bring calm, experience, and repeatable models to make improvement a long-term reality.

1.

Quality Operating Model Playbooks that clearly outline how leadership, teams, and governance are interconnected.

2.

Fast lane accelerators provide stability within weeks without compromising delivery speed.

3.

Executive dashboards that present progress in the language of results, not activities.

4.

AI quality patterns that bring intelligence to testing, triage, and quality assurance at scale.

Together, these factors make transformation sustainable and measurable, a system that leaders can trust, and teams can work in successfully.

Sustaining improvement: Playbooks that endure

Most organizations recognize the need to rethink quality but struggle to sustain it. Momentum often fades after the first wave of fixes; continuous improvement in testing is where programs stall. Sustained progress typically stems from a clear operating model, disciplined execution, and, where beneficial, independent advisors who bring repeatable patterns.

A sustainable quality program should include:

Operating model and capability transfer: Make quality governable with clear decision rights, shared standards, and a champions network to ensure improvements outlast projects.
Fast lane (6–10 weeks): De-risk revenue now by automating top journeys, strengthening CI/CD gates, and standardizing rollback without slowing delivery.
Outcome dashboards: Turn engineering into board-level metrics, CoPQ, change failure rate, MTTR, SLOs, revenue at risk, and automation coverage, with leading and lagging indicators.
AI-enabled patterns: Put GenAI to work, natural language to tests, risk-based selection, self-healing suites, and intelligent triage to reduce noise and maintenance.

The bottom line: 7,500 euros per minute

Quality is no longer synonymous with testing. It's the system of trust that protects growth- and at € 7,500 a minute, downtime makes this a boardroom issue, not a technical detail. Add in the rising costs of security breaches, the risks of inaccessible products and the energy demand of AI, and the message is clear: testing alone cannot protect the business.

When quality is managed as a strategic, controlled discipline, it creates real benefits:

Resilience: fewer incidents, faster recovery, greater confidence in change.
Velocity with safety: predictable, automated, secure releases at scale.
Inclusion by default: built-in accessibility, growing markets and trust.
Security at scale: AI-driven controls that reduce breach costs and downtime.
Sustainability through performance: efficient systems that save energy and carbon as they scale.

But the reality is: no business can do this alone. Internal teams tasked with delivery struggle to maintain continuous improvement- tests calcify, automation becomes brittle, alignment between teams breaks down. Maintaining quality as infrastructure requires an external perspective, proven models and shared accountability.

Leaders set the vision, but lasting advantage comes from building it with partners. In a volatile world, quality is not an overhead, but an infrastructure for growth. And with the right partnerships, trust becomes the most enduring competitive advantage.

A QOM is a cross-team blueprint for how quality works at scale. It defines decision rights, gated controls, and shared metrics such as lead time, deployment frequency, change failure rate, and time to restore anchored by the rituals that keep them alive. Research from DORA shows these capabilities strongly correlate with organizational performance.

Because quality that ignores the user optimizes the wrong thing. DORA data shows teams that build with the user in mind achieve 40% higher organizational performance. Putting customer journeys at the center ensures that investments in quality translate directly into relevance, adoption, and trust.

They are no longer optional they are core dimensions of modern quality. Nearly 96% of top homepages still have WCAG accessibility failures, which limits inclusion and exposes organizations to risk. On the sustainability front, as AI and data workloads grow, performance engineering doubles as carbon engineering: efficient code paths, right sized infrastructure, and data minimization reduce both cost and carbon.

The economics are compelling. The average data breach costs $4.88M, while AI assisted controls reduce that impact by millions. Downtime in large enterprises costs up to $9,000 per minute, excluding the cost of reputational damage. Reliability, inclusion, and sustainability aren’t side benefits they are central to growth and competitiveness.

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The new economics of quality engineering:
When every minute costs $9,000

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Executive summary