What to cut (and keep) when you upgrade your QA strategy with AI

BMW Group has developed the AI solution called SORDI.ai. It optimizes industrial planning processes and supply chains with GenAI: scans assets, creates their 3D “digital twins” that simulate real-life manufacturing, optimizes distribution efficiency, and reduces the development time.

Yet these advanced capabilities need similarly modern QA practices to resolve the flaky location tests and bloated regression pipelines. The root issue is often not the AI tooling, but the unchanged, legacy QA practices.

AI is powerful, but it cannot fix what you continue choosing to preserve. Many teams leap into AI without legacy QA cleanup, ending up with “smarter” tools that amplify redundant workflows. They now have a super-mega advanced tool… and bigger test suites, slower feedback, and continued manual churn.

Read further to get the decision-based playbook for the savvy QA leader: what to cut when adopting AI, what to keep, and how to implement an efficient and effective test automation with AI.

Why your QA strategy needs a reset before AI can succeed

The SORDI.ai case from the intro highlights both sides of this monad, the potential and the limits of AI in QA. Artificial intelligence accelerates test design and execution, it allows analyzing behaviors and generating intelligent test flows. 

Yet, the novelty forced BMW to recreate the quality assurance strategy almost from scratch, as many legacy QA processes were struggling with duplicated tests and brittle UI scripts.

The real cost of legacy practices

An average SMB’s tech team spends 20-40% of sprint time maintaining scripts, running redundant checks, and fixing locators. 

Under a typical SMB we assume a 50-person dev team shipping bi-weekly. So, this means 200-300 hours per month for manual reviews and duplicated testing effort. That’s engineering capacity that never moves product quality forward.

AI can’t optimize what’s broken

Automating a 5,000-test regression suite with 30% duplication has nothing to do with efficiency, it just accelerates waste. Let’s assume a fintech that introduced AI-based test generation. Without fixing the core, they can find themselves running “intelligent” scripts against workflows already covered five different ways. Yes, execution is really faster, but — the same blind spots and noise.

The opportunity to rethink testing from first principles

The real unlock comes not when teams just use modern QA practices, but when they reset QA around quality outcomes: faster risk detection, better coverage where it matters, and fewer false positives. AI then really removes redundant effort, adapts to change, and surfaces insights humans would miss.

What to cut when upgrading your QA with AI

Michelangelo once said that he doesn’t build anything, he just cuts the superfluous material. Similarly, AI is not about creating anything extra, but rather about trimming what doesn’t add value. 

The real challenge is not execution speed, it is clarity of signal. Before rolling out the AI tool comprehensively, run a two-week “test audit sprint” where engineers flag duplications, flaky areas, and manual chokepoints.

Now, what exactly to cut.

Heavy script maintenance

Why it no longer works: You have to update test scripts after every change in your code. Imagine you forgot to consider just one small change, but UI scripts ruthlessly break.

What AI delivers: Self-healing tests. That means the tool updates locators and adapts to UI changes automatically, and teams can focus on critical workflows.

Overlapping test cases

Why it no longer works: We are all tired of working noise, innit? Endless notifications, purposeless meetings, distractions… In terms of quality assurance workflow, this noise slows runs and misleads coverage stats.

What AI delivers: It clusters test executions, highlights overlaps, and recommends consolidation. Try a smart trick: Run NLP-based analysis on test descriptions. This unfolds semantic duplication that humans usually miss.

Manual regression cycles

Why it no longer works: When you run the full regression suite (and you typically do it every release), you basically delay deployments and exhaust testers. Paradoxically, less is really more. Not every release needs the entire cycle run.

What AI delivers: ML algorithm analyzes code changes, and the tool runs only tests that examine the impacted parts of the app. This way, you cut testing time but not testing confidence.

Reliance on static test data

Why it no longer works: Hardcoded datasets fail to represent real-world variability and hide defects.

What AI delivers: AI can generate synthetic but realistic data at scale, simulating edge cases, user behaviors, and dynamic environments that manual datasets never cover. Yet, it’s better to use external sources and tools.

Vanity metrics

Why it no longer works: Counting test cases executed or “% automated” inflates activity but says nothing about value.

What AI delivers: Impact-driven metrics are way better: defect detection rate, risk coverage, test confidence scores. They align QA with business outcomes.

What to keep (and evolve) in your AI-led QA strategy

The State of AI in Business 2025 report revealed that 80% of enterprises investigated AI tools or even started AI pilots, but only 5% got stable production implementation.  

The gap shows that AI can’t fully replace proven QA fundamentals. In practice, core and modern QA practices remain essential — they just need new workflows to align with next-gen pipelines.

So, what to keep and what to add?

Test planning and prioritization

AI optimizes coverage, but proper prioritization depends on product context and business risk. Modern testing tools can also prioritize suites, but they still need oversight. 

For example, customer-facing features in regulated industries require human-led prioritization to balance technical depth with compliance obligations. 

We expect AI to become a force multiplier once technology learn to take dynamic requirements, rules, and laws into account. If it’s already able to surface probable risk areas, with time, it’ll weigh those risks like a true leader.

Exploratory and usability testing

Generative AI has a serious game in generating edge cases, but it lacks cognitive empathy and user-centricity. 

Exploratory sessions uncover usability gaps, accessibility issues, and design flaws that scripted or AI-generated tests miss. Embedding structured exploratory sprints ensures AI-generated coverage doesn’t overshadow human judgment.

QA/developer collaboration

QA process optimization with modern technologies won’t make a difference if your feedback loops are misaligned. 

The tool will flag anomalies, but without explanations or human validation, it won’t make any sense. Devs still need to understand root causes and contextualize risk. 

The most obvious solution is to implement joint teams, devs + QA engineers (at least).

Ownership and accountability

Did you notice that with rapid tech evolution,  we diluted responsibility? Take the mentioned artificial intelligence — the governance isn’t defined across the entire world. And this is probably the most influential technology in history. So, what can we say about the less important yet still powerful ones?

We see the same in smaller scale at SMBs and enterprises. Who owns the AI QA strategy? And false positives? Who decides when to override AI-driven defect prioritization? 

Set clear accountability frameworks to ensure company-wide decisions are coherent, audited, and not left to chance.

Compliance controls

Test data is one of the most sensitive assets in QA. AI tools must operate within secure boundaries, GDPR or HIPAA requirements, and log all data transformations. Without this, AI efficiency will unintentionally introduce compliance risk.

A framework for reviewing your QA strategy before scaling AI

Don’t second a common mistake — don’t rush to scale AI across your product. First off, revise strategic things: AI should be integrated into new, redesigned processes, align AI outcomes with business objectives, change old QA metrics for real quality indicators that include AI deliverabilities. 

How to audit QA strategy

Your specific features matter. But the five steps below are commonly accepted practices that actually work. Follow and tailor as you gain experience. 

1. Remove tests with no business or user impact: Thousands of cases with minimal incremental coverage shouldn’t slow down your processes. AI-driven analytics can measure coverage overlap and help execute a legacy QA cleanup.
2. Replace brittle UI tests with AI-assisted flows: UI tests that break with every front-end update waste cycles. AI-based self-healing frameworks adapt to UI changes dynamically, reducing maintenance overhead.
3. Use manual testing where you still need humans: Creativity, context understanding, complex workflows, or compliance-critical edge cases — leverage human expertise on regression checks AI can cover.
4. Choose comprehensive AI tools: Choose software with continuous learning capabilities, which can understand your product better over time. This includes feedback from devs, testers, and users, self-healing capabilities, and end-to-end analytics.
5. Continuously monitor and refine your approach: Test the waters, define what works and what doesn’t, define KPIs, and iterate quarterly. Treat this intelligent test automation as an evolving ecosystem because it is.

How OwlityAI helps teams modernize QA without starting from scratch

Test automation with AI doesn’t mean a rip-and-replace of the existing systems. In practice, the fastest wins come from augmenting what already works. We built OwlityAI to accelerate modernization while respecting your current QA ecosystem.

How OwlityAI helps you

No sweet talk, we are not interested in unmet expectations. We keep the principle: under promise and overdeliver. Below are just a few features that will solve common QA strategy problems.

Scans existing workflows and auto-generates optimized tests

Your team no longer needs to rewrite old suites. OwlityAI identifies redundancies, removes obsolete checks, and produces streamlined test sets aligned with actual user flows.

Really automates 

Beyond linear scripting, OwlityAI uses adaptive models that self-heal when there are UI or API changes. This prevents brittle test failures and cuts maintenance time.

Integrates into your current CI/CD

Jenkins, GitHub Actions, or GitLab pipelines — any team using these systems can deploy AI-driven automation in days without re-architecting delivery workflows.

Assesses risks and prioritizes test coverage

The tool analyzes defect history, code churn, and usage analytics. Then, flags the highest-risk areas and ensures QA leaders allocate automation effort where it actually matters.

Creates continuous feedback loops

OwlityAI consolidates results into actionable dashboards and integrates with Jira/Slack, so developers, testers, and product owners share a single, real-time quality picture.

Bottom line

Don’t expect end-to-end test automation with AI to be a silver bullet. But it still helps remove roadblocks and inefficiencies in your current QA processes. 

The modern testing cuts waste, evolves QA practices, and actually delivers, but balancing legacy QA cleanup with intelligent augmentation is a must.

Keep what works, automate what doesn’t, and guide AI with human judgment. If you are ready to change the way you test, request a demo.