How AI-First Testing Is Redefining QA Metrics

AI is changing how software is tested. The shift goes beyond buzzwords like “self-healing tests” or “auto-generation” toward real AI tools that promise speed and smarter insights. But most QA teams are still early in adoption – and the reason is clear: bad data. Messy test cases, poor traceability, and siloed coverage make it hard for AI to deliver value. An LLM can generate a test case, but if the input is vague, it’s just polished noise.

The bigger problem is measurement. Old metrics – pass/fail rates, defect counts, coverage – don’t reflect AI’s role in preventing bugs, adapting suites, or predicting risk. QA now spans the whole lifecycle, and AI augments testers by handling repetitive work, surfacing risks earlier, and optimizing coverage. Yet without new metrics, these gains stay invisible.

The key questions have changed: How much of your suite is AI-created? How accurate are its predictions? How many defects did it prevent before release? These are now central to measuring quality.

This article breaks down which traditional metrics still matter, where they fall short, and what new KPIs define QA in the AI era. If your dashboards don’t capture adaptability, prevention, and human-AI collaboration, they’re measuring a world that no longer exists.

Traditional QA Metrics: A Quick Recap

Quality Assurance has long relied on a set of standardized metrics to evaluate the health and effectiveness of testing processes. These metrics have served as useful indicators in traditional, waterfall-style and early Agile environments – but in the context of today’s fast-paced, AI-assisted software delivery pipelines, they often fall short.

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Let’s briefly review the most common QA metrics and explore their limitations in modern development workflows.

1. Test Case Coverage

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What it measures: The percentage of total requirements or code paths covered by test cases.

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Why it matters: It helps teams assess how much of the system is being verified through testing and is often used to gauge readiness for release.

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Limitations in modern environments:

• High coverage ≠ high confidence. Even 95% coverage may miss critical real-world usage scenarios, especially in complex microservices or AI-driven systems.
• False sense of completeness. Teams may "check the boxes" with superficial tests that don’t reflect true business logic or user behavior.
• Case in point: A team may have 90% test coverage for an e-commerce checkout flow – but still miss critical edge cases like third-party payment gateway failures under load.

2. Defect Density

What it measures: The number of defects identified per unit (e.g., per 1,000 lines of code or per feature module).

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Why it matters: Defect density can help pinpoint problematic areas in the codebase or determine whether a release meets quality thresholds.

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Limitations in modern environments:

• Reactive, not predictive. It reflects the outcome after testing rather than helping prevent future issues.
• Doesn’t reflect severity. 10 minor UI bugs may look worse on paper than 1 critical backend failure.
• Case in point: In fast CI/CD pipelines, defect density spikes may appear during periods of high feature velocity but may not accurately reflect declining product quality.

3. Test Execution Time

What it measures: The amount of time required to run a given suite of tests – usually regression or integration tests.

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Why it matters: Shorter test cycles support faster feedback loops, which is critical in Agile and DevOps environments.

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Limitations in modern environments:

• Optimization can become misleading. Cutting test time by skipping test depth or coverage can backfire if critical bugs go undetected.
• Unscalable with complexity. As systems become more distributed (e.g., across APIs, mobile platforms, or AI services), execution time naturally increases unless test strategies evolve.
• Case in point: A team that prioritizes test speed might run only smoke tests in CI, missing data integrity issues in downstream analytics pipelines.

4. Pass/Fail Rates

What it measures: The percentage of test cases that pass or fail during a test cycle.

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Why it matters: It provides a quick glance at whether the software is functioning as expected under test.

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Limitations in modern environments:

• Oversimplified view. A 100% pass rate might mean tests are too shallow, or worse — irrelevant.
• Flaky tests distort signal. Intermittent failures (due to environment instability or async UI behavior) can inflate failure rates and erode trust in test results.
• Case in point: A team may ignore failing tests labeled as “non-blocking” even though they consistently flag a regression that only occurs under specific edge conditions.

5. MTTD / MTTR (Mean Time to Detect / Repair)

What it measures: MTTD (Mean Time to Detect) tracks how long it takes from the moment a defect is introduced to the moment it’s discovered. MTTR (Mean Time to Repair) captures the average time needed to fix that defect once identified.

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Why it matters:

• Short MTTD means teams are catching issues quickly, often before customers even notice.
• Low MTTR reflects a responsive, efficient engineering process where issues are resolved with minimal disruption.
• Together, these metrics give leadership a pulse check on quality responsiveness: how fast a team can react to change and risk.

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This is why they’re a staple in DevOps and SRE dashboards. They serve as early warning signals for systemic inefficiencies. For instance, if your MTTD is consistently high, it may point to inadequate monitoring or blind spots in test coverage. If MTTR balloons, it may signal bottlenecks in triage, root-cause analysis, or deployment pipelines.

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Limitations in modern environments:

• Detection isn’t binary anymore.
  In classic software, a bug is either “found” or “not found.” In AI environments, failures often surface as degraded accuracy, bias, or unexpected edge behavior. For example, a recommendation system might not “break” but might suddenly drop its precision by 10% for a subset of users. MTTD doesn’t capture the fuzziness of defect discovery in AI systems.
• Repair isn’t a straightforward fix.
  Fixing an AI defect may not mean patching code – it could mean retraining a model with better data, tuning hyperparameters, or even re-architecting features. That process can take days or weeks and often introduces new risks. Unlike fixing a missing null check, “repair” is iterative, non-deterministic, and heavily data-dependent. MTTR becomes much harder to define.
• Automation changes the baseline.
  With AI-first testing tools, anomaly detection, and self-healing pipelines, the “time to detect” might shrink drastically because AI can flag risks in near real-time. But this can flood teams with false positives or low-severity issues, muddying the metric. A high volume of rapid detections doesn’t necessarily mean quality is improving – it might just mean the signal-to-noise ratio is dropping.
• Continuous learning loops blur timelines.
  In production ML, models retrain continuously. If the system “auto-corrects” itself before human detection, what does MTTD even mean? The metric assumes humans are the main detectors and repairers, but AI can increasingly act on its own.

The Bigger Picture

Traditional QA metrics – like defect density, pass/fail rates, and test execution times – still provide value, but they were designed for relatively static development lifecycles. In today’s high-velocity, AI-assisted development environments, these metrics can no longer capture the full reality of quality assurance.

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Modern software delivery is shaped by:

• Frequent releases. Weekly or even daily deployments make metrics like “defects per release” less meaningful, as releases become smaller but more numerous. For example, in continuous deployment pipelines, a single “release” might only contain a handful of code changes, yet metrics still need to reflect the cumulative quality trend rather than just isolated snapshots.
• Rapid requirement shifts. Agile backlogs are no longer static for even a sprint. AI-driven analytics or user feedback loops can reprioritize features mid-cycle, making static coverage percentages obsolete. Imagine a scenario where an AI analytics system detects a sudden surge in mobile traffic from a specific region – test focus shifts immediately to localization and performance on devices common in that market.
• Complex system dependencies. Microservices, third-party integrations, and distributed architectures mean that quality is no longer confined to a single application. A “pass” in one service might still mask latency spikes caused by dependency chains. For example, a payment service might pass all functional tests but still fail under real-world conditions when an upstream fraud detection API introduces delays.
• AI-augmented decision-making. AI now informs not just testing, but also prioritization. Predictive analytics can identify “hot spots” in code likely to produce defects, which can skew traditional coverage metrics. In one enterprise case, an AI model deprioritized low-risk modules to focus testing on newly refactored checkout logic – cutting test execution time by 40% but rendering the old “tests executed vs planned” metric irrelevant.

Shifting Toward Contextual, Real-Time Metrics

Instead of relying solely on lagging indicators like post-release defect counts, AI-first QA strategies emphasize:

• Risk-weighted coverage. Measuring not just how much of the code is tested, but whether the most business-critical and failure-prone paths are covered.
• System resilience metrics. Monitoring recovery times, error rates under load, and behavior under unexpected dependencies.
• AI model performance indicators. In AI-driven testing, the accuracy of anomaly detection, false positive/negative rates, and self-healing success rates become part of the QA dashboard.

Example: The “Invisible” Bug Prevention

In one retail mobile app, AI-powered predictive testing identified a high-risk code change affecting the checkout API. Traditional regression metrics would have treated this as “covered,” but AI flagged the scenario as likely to cause cart abandonment due to latency. The team executed targeted performance tests and resolved the issue before release – avoiding a potential 15% drop in conversion. Without modern, AI-informed metrics, this preemptive save wouldn’t have been visible in traditional QA reports.

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The bigger picture is not just about faster testing – it’s about shifting metrics to reflect real-time quality, business impact, and system resilience, ensuring QA remains relevant in a landscape where software and user expectations evolve by the day.

What Is AI-First Testing?

AI-first testing is a modern approach to quality assurance where artificial intelligence actively assists – or even takes the lead – in creating, executing, and maintaining test cases. Unlike traditional automation, which relies on predefined scripts and static rules, AI-first testing uses algorithms such as machine learning and natural language processing to make testing smarter, faster, and more adaptable.

In this model, AI is not just a tool you occasionally use – it becomes the foundation of your testing strategy, embedded in every stage of the software testing lifecycle.

Core Capabilities

1) Natural Language Test Creation

AI-powered platforms can turn plain English requirements into executable tests.

Example: A QA analyst types, “When a user logs in with valid credentials, they should be redirected to the dashboard.” The AI converts this into a runnable automated script for multiple devices and browsers without any coding.

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2) Self-Healing Tests

AI can detect when a UI element changes – like a button ID or page layout – and automatically update test scripts to prevent failures.

Example: A button label changes from “Pay Now” to “Proceed to Payment.” Traditional automation breaks; AI self-healing identifies the new locator and continues the run without intervention.

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3) Predictive Test Coverage

AI models analyze historical defect patterns, commit history, and code changes to identify high-risk areas in the application.

Example: After a major API update, the AI predicts a higher probability of payment failures in mobile checkout and automatically prioritizes related test cases.

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4) AI-Driven Triage and Analysis

AI can sift through massive test result logs, categorize failures, and suggest probable causes.

• Example: Instead of manually investigating hundreds of failed test cases, QA receives a report showing “85% of failures linked to API rate limit errors” with direct links to affected scripts.
• Case: A SaaS company reduced defect triage time by 70% when their AI system started clustering failures and mapping them to likely root causes.

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Benefits Over Traditional Testing

Speed. AI reduces the time required to write, execute, and maintain tests. What once took days can often be completed in hours, with faster feedback loops for developers.

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Adaptability. In agile and continuous delivery environments, requirements and codebases change rapidly. AI-first testing adapts in real-time to UI changes, shifting priorities, and evolving risk areas.

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Reduced Manual Overhead. By automating test maintenance, triage, and coverage analysis, QA teams can focus on exploratory testing, usability evaluations, and strategic quality planning.

Why AI-First Testing Needs New Metrics

AI-first testing changes not only what is measured, but also why it’s measured – because the nature of test creation, execution, and maintenance fundamentally shifts.

1. The “Who” Changes – Human vs. AI-Generated Tests

In traditional testing, every test case was manually written by a QA engineer, making “test case count” a reliable measure of effort. But in AI-first testing:

• AI generates tests automatically based on code changes, user behavior, or historical bug data.
• The volume of tests is no longer a direct reflection of QA team effort – it’s a reflection of how well AI is tuned and trained.

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Example:
A retail app’s AI system generated 200 regression tests overnight after detecting new code dependencies. The QA team manually wrote only 10 high-priority exploratory tests. In old metrics, it might appear that “only 5% of tests” were human-authored – yet in reality, 95% of coverage came from AI with far greater breadth than the human team could have achieved in the same time.

2. The “What” Changes – Dynamic vs. Static Coverage

Coverage in traditional QA is static – often defined as the percentage of code or requirements covered by tests at a specific point in time. AI-first testing introduces dynamic coverage:

• AI prioritizes tests based on changing risk areas, so coverage shifts from “everywhere equally” to “where it matters most right now.”
• Static coverage percentages might appear to drop, but actual risk-weighted coverage can increase.

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Example:
In a SaaS platform release cycle, code coverage fell from 80% to 68% when using AI-first prioritization. Traditional metrics flagged this as a regression. However, incident rates in production dropped by 30% because AI focused testing on modules that recent commits had made riskier, catching more real-world defects.

3. The “How” Changes – Self-Healing, Prediction, and Suggestions

AI-first testing doesn’t just run tests faster – it changes the mechanics of testing itself. Traditional QA metrics like pass/fail ratios, test execution time, or defect density measure the end results. But with AI, much of the value happens before failures ever surface. Let’s break down the three biggest shifts:

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Self-Healing:
In a traditional setup, a simple UI change – say, a button ID updated from submitBtn to confirmBtn – could cause dozens of automated tests to fail. Teams would spend hours triaging “false” failures, only to discover nothing was truly broken.

• With AI-based self-healing, the test dynamically recognizes the change, adapts, and continues without interruption.
• Traditional metrics don’t count this invisible save. From the dashboard’s perspective, “nothing failed.” Yet in reality, AI prevented a cascading failure storm.

Case in point: A financial services app saw test suite flakiness drop by 40% once AI began auto-correcting locator mismatches. Old metrics like “failures per run” showed little movement because those failures never surfaced – they were auto-resolved. A new metric like self-healing success rate was the only way to reveal the improvement.

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Prediction:

AI doesn’t just run tests – it prioritizes them. By analyzing defect history, code churn, or even developer commit patterns, AI predicts where defects are most likely and runs those tests first.

• This slashes the time-to-defect detection (TTDD). A bug that would have been caught at the 80% mark of a regression cycle might now be caught in the first 20%.
• The old pass/fail ratio looks identical (a failure is a failure), but the business value is dramatically different: teams know about risks earlier, and customer impact shrinks.

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Example: An e-commerce platform integrated AI-powered test prioritization during holiday load-prep. Defects in payment processing were consistently detected within the first two hours of a regression cycle, compared to the 8–10 hours it used to take. MTTR didn’t change much, but risk exposure dropped sharply – a nuance invisible in traditional dashboards.

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Test Suggestions:

AI also identifies what’s missing. By scanning production logs, analyzing customer journeys, and mapping them against existing automated tests, AI highlights coverage gaps and suggests new test cases.

• Traditional metrics assume test coverage is static: you define the suite, and then you measure it.
• AI makes coverage dynamic, continuously surfacing blind spots that teams may never have considered.

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Example: A healthcare SaaS provider discovered that password reset flows – frequently used by clinicians logging in from shared terminals – were barely covered in regression testing. AI flagged the gap based on log data, and adding those tests reduced incident tickets by 15% in the following quarter.

Emerging Metrics in AI-First QA Environments

In AI-first QA, numbers no longer capture the real dynamics of quality assurance. AI doesn’t just execute; it adapts, predicts, and prevents. That requires a new metric framework designed to show how much intelligence the system itself is contributing.

Here are the key emerging metrics reshaping how quality is measured:

AI Contribution Rate

How much of the test workload is AI creating, maintaining, or repairing? This metric shows the proportion of automation driven directly by AI.

• Example: A retail mobile app reports that 40% of regression test updates (mostly locator repairs and minor workflow changes) were auto-handled by AI over three release cycles. Without AI, these would have required dozens of engineer-hours. AI Contribution Rate translates those hidden labor savings into measurable impact.

Test Intelligence Accuracy

This measures how precise AI is when making predictions: whether it’s identifying flaky tests, ranking defect likelihood, or prioritizing risky areas of the codebase.

• Example: A telecom provider found that AI correctly predicted 75% of the most failure-prone modules in the last six releases. Tracking predictive accuracy over time shows whether AI insights are sharpening or drifting – essential for trust and adoption.

Test Suite Adaptability

In fast-moving environments, adaptability matters as much as coverage. This metric tracks how quickly and successfully AI updates the suite after UI or API changes.

• Example: A cloud services platform rolled out a breaking API change. Instead of weeks of manual updates, AI self-healed 85% of the impacted scripts within hours, allowing regression testing to proceed on schedule. Adaptability metrics make these silent accelerations visible.

Coverage Confidence Score

Coverage percentages alone don’t tell the whole story. This composite metric blends:

• Raw coverage numbers,
• Sensitivity to recent code changes, and
• AI-driven prioritization of high-risk areas.

Example: A fintech team reports only 78% raw coverage, but their Coverage Confidence Score rates at 92%, because AI prioritizes payment authorization, fraud checks, and compliance-critical workflows. The number reflects not just “how much” is covered, but “how smartly” it’s covered.

Defect Detection Lead Time

This measures how much earlier AI surfaces issues compared to traditional workflows. It shifts focus from if bugs are found to when.

• Example: An e-commerce site found that AI-augmented prioritization consistently uncovered checkout-related defects 6 hours earlier than the baseline regression cycle. The lead time meant fixes were applied before peak user traffic, avoiding costly production incidents.

False Positive/Negative Reduction Rate

AI’s role isn’t just finding defects – it’s reducing the noise from unstable or irrelevant tests. This metric tracks how much AI lowers wasted triage time.

• Example: A healthcare SaaS provider cut false positive test alerts by 50% after enabling AI flakiness detection. Teams spent less time chasing “phantom bugs” and more time on high-value defect resolution.

Automation ROI (AI-Augmented)

Classic automation ROI looked at time saved versus manual execution. In AI-first QA, the equation includes AI + human productivity gains: fewer broken tests, faster defect discovery, reduced triage, and prevention of release blockers.

• Example: A global logistics company calculated that AI-assisted QA reduced test maintenance costs by 30% annually, while also cutting defect escape rates by 20%. The new ROI metric validated the business case for scaling AI adoption.

Best Practices for Adopting New QA Metrics

Switching to AI-first QA metrics isn’t just a matter of adding a few new KPIs to your dashboard. It requires a mindset shift, cultural buy-in, and a structured rollout strategy. Here are the best practices to make the transition effective and sustainable:

1. Start with a Hybrid Approach
Don’t throw out traditional metrics overnight. Begin by running AI-driven metrics alongside familiar ones like defect density, pass/fail ratios, or MTTR. This hybrid phase builds confidence and helps teams understand what’s changing.

Example: A banking app team tracked both “failures per run” and “self-healing success rate” over two release cycles. The old metric looked flat, but the new one showed a 35% reduction in avoidable failures. By running both, they could explain why the old KPI wasn’t telling the full story.

2. Involve Testers Early
AI-driven metrics must align with the day-to-day workflows of testers. If QA engineers don’t understand or trust the numbers, adoption will stall. Involve them early in defining what to measure and how.

Example: A healthcare SaaS company piloting “defect prevention rate” involved testers in logging which AI-predicted risks were acted on. This not only made testers feel ownership but also surfaced gaps in AI’s predictions, helping refine the model.

3. Visualize Intelligently
Dashboards shouldn’t just display raw counts – they should highlight how AI is shaping outcomes. Visualization is key to communicating value to both QA teams and leadership.

Example: An e-commerce company built a QA dashboard that surfaced three new AI metrics at the top: “defects prevented,” “AI prediction accuracy,” and “false positive reduction.” Seeing those numbers move week-to-week made leadership more willing to invest further in AI-first tooling.

4. Iterate Continuously
AI metrics aren’t static. As the AI learns, as workflows change, and as the codebase evolves, the metrics themselves may need recalibration. What matters in year one may not be what matters in year three.

Example: A telecom provider initially focused heavily on “self-healing rate.” But once that stabilized above 90%, the more valuable metric became “coverage confidence score,” which measured whether AI was targeting the right areas of risk. Iteration ensured metrics stayed relevant.

5. Ensure Stakeholder Alignment
Leadership and non-technical stakeholders need to understand why these new metrics matter. Without alignment, AI-first QA risks being seen as “extra numbers” rather than real business impact.

Example: A logistics company held quarterly reviews where QA leads explained how “defect detection lead time” correlated with fewer customer-facing incidents during peak shipping seasons. By tying AI metrics to business outcomes, they secured executive buy-in and budget for further AI adoption.

Conclusion: 10 takeaways to steer your QA metrics into the AI-first era

#1. Quality is now about intelligence applied, not tests executed.
Pass/fail, density, and raw coverage still help, but they miss AI’s hidden work – preventing failures, prioritizing risk, and self-healing scripts. Treat “quality” as the combined output of humans + machine intelligence.

#2. Hybrid metrics win the transition.
Run new KPIs (e.g., Self-Healing Success Rate, AI Contribution Rate) alongside legacy ones for at least 2–3 release cycles. This side-by-side view explains why an unchanged pass rate can mask a 30 – 40% drop in avoidable failures.

#3. Risk-weighted beats raw coverage.
Measure whether high-impact, failure-prone flows are protected (e.g., payments, authentication), not just how many files are touched. A fintech team with 78% raw coverage but a 92% Coverage Confidence Score is better positioned than a flat 90% that ignores critical paths.

#4. Speed now means “earlier,” not only “faster.”
Track Defect Detection Lead Time – how many hours (or builds) earlier AI surfaces issues vs. your old workflow. An e-commerce org that finds checkout bugs 6 hours sooner avoids peak-traffic incidents without changing MTTR.

#5. Count the invisible wins.
Defects prevented, flake suppressed, and alerts deduplicated rarely show up in defect density. Add Defect Prevention Rate and False Positive/Negative Reduction to reveal time saved in triage and the incidents that never reached production.

#6. Adaptability is a first-class quality attribute.
Measure how quickly tests update after UI/API drift (e.g., % auto-healed within hours). A team that auto-repairs 85% of locator breaks keeps cadence intact – that is quality in CI/CD.

#7. Trust in AI requires auditing AI.
Track Test Intelligence Accuracy (e.g., precision/recall of risk predictions, flaky-test identification). When accuracy dips, recalibrate models and training data instead of blaming the pipeline.

#8. Data discipline is make-or-break.
Messy test artifacts produce noisy AI. Standardize test case structure, enforce traceability (req → test → defect), and label failures consistently; then watch AI metrics stabilize and become decision-worthy.

#9. Tie metrics to money.
Evolve to Automation ROI (AI-Augmented) that credits prevention, reduced maintenance, earlier detection, and fewer rollbacks. A logistics org showing 30% lower maintenance + 20% fewer escapes doesn’t argue for budget – it documents it.

#10. Make it a program, not a project.
Institutionalize cadence: quarterly metric reviews, dashboard refreshes, and stakeholder briefings. Involve testers early, teach leaders what’s changing, and iterate your KPIs as the AI (and your system) learns.

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