Challenge: What are the three biggest challenges for quality in the AI era?

1. Ad hoc business requirements 
2. AI Product testing
3. Matching the pace with tech evolving 

How to deal with Velocity?

How to deal with Quality?

How do LLMs work?

The three most pressing challenges for quality in the AI era are ensuring fairness by eliminating bias, maintaining transparency through explainability, and safeguarding compliance with ethical standards. Together, these issues shape trust, influence adoption, and determine the long-term sustainability of AI systems.

Three challenges I see

1. Inconsistent Outputs AI models can generate different results even when given the same input, making reliability and repeatability difficult.

2. Quality of Training Data The accuracy of AI predictions depends heavily on the data it was trained on. If the data is incomplete or incorrect, the model can produce flawed outcomes.

3. Limited Understanding of Business Context AI often struggles to fully grasp complex business logic, regulatory requirements, and nuanced user experience expectations, which can lead to solutions that miss critical real‑world constraints.

In the AI era, software testing is shifting from simply validating deterministic features to actively engineering trust in unpredictable systems. Based on current industry insights, here are the three biggest challenges:
1. Non-Determinism
Unlike traditional software, AI systems are probabilistic and complex. The same input can produce different outputs across runs, making traditional "expected vs. actual" assertion testing highly ineffective. Additionally, the "black box" nature of AI models means there is often a lack of transparency in how decisions are made, which makes root cause analysis and debugging incredibly complex. Quality teams are forced to shift toward intent-based validation and define acceptable response boundaries instead of looking for exact matches.
2. Data Integrity and Drift
AI models devour tremendous amounts of data, and their output is only as good as the information they ingest. Ensuring training data is clean, unbiased, and properly labeled is a massive hurdle. Even if a model is highly accurate at launch, real-world data distributions shift over time, causing the model's performance to degrade silently. This requires a complete paradigm shift from one-time release testing to continuous quality monitoring and evaluating datasets as first-class test artifacts.
3. Trust and Governance
As AI accelerates code and software creation, organizations face the friction of trusting outputs that may be "nearly right" but are not necessarily production-ready. AI often fails convincingly through believable hallucinations, which can create a false sense of security and amplify flakiness in unstable systems. The ultimate challenge is establishing clear accountability, mitigating security risks, and orchestrating risk-based validation at scale so enterprises can move at the speed of AI without compromising compliance.

Three biggest challenges for quality in the AI era (from my recent testing journey):

1. Non-deterministic behavior (Same input, different output)
In one of my recent projects involving AI-assisted workflows, I noticed that the same prompt was returning slightly different results across runs.
From a testing perspective, this breaks the traditional “expected vs actual” validation model.

Example: While validating AI-generated content, I couldn’t rely on exact match assertions. Instead, I had to shift towards contextual validation (relevance, correctness, safety).

Challenge: How do you define “pass/fail” when outputs are probabilistic?

2. Test data & environment complexity (especially with scale)
During a load test using JMeter, I created hundreds of entities (Facilities, Subjects, Metadata, etc.). When combined with AI-driven logic, the system behavior became harder to predict and validate.

Example: Missing pagination in listing screens went unnoticed initially because AI + large datasets masked visibility issues. Only after deeper manual exploration did the gap become obvious.

Challenge: AI + large-scale dynamic data can hide critical usability and performance issues.

3. Observability & debugging gaps
When something breaks in traditional systems, we trace logs and API responses. With AI, failures are often not binary — they are subtle degradations.

Example: During login flow testing in JMeter, failures were inconsistent. Some were due to system issues, others due to unexpected responses — but distinguishing between infra vs AI-driven anomalies was difficult.

Challenge: Lack of clear debugging signals — is it a bug, bad prompt, model limitation, or data issue?

My key takeaway:
In the AI era, quality is no longer just about finding bugs — it’s about understanding behavior, defining new validation strategies, and continuously learning the system.

• Data quality and grounding — AI is only as reliable as the data it learns from.

• Trust and hallucinations — ensuring AI outputs are accurate, explainable, and reliable.

• Evaluation at scale — testing non-deterministic AI systems requires new metrics and continuous monitoring.

1. Data Quality and Integrity : Low Quality 

1. Low quality data : It reduces accuracy and wrong predictions
2. Bias in training data : creates unfair or deviated outcomes
3. Inconsistent outputs : AI models can generate different outputs for a same input
4. Limited understanding of business context 

🎉 Challenge Winner Announcement!

Big thanks to everyone who jumped into the challenge—we loved seeing the energy!

A huge congratulations to ​@sachin_sync for taking the win! 🏆

Keep an eye on your inbox; we’ll be reaching out shortly to coordinate your prize delivery.

Congratulations ​@sachin_sync 🎉👏

Thank you so much! Really enjoyed the challenge and happy to be part of it 😊