A recent announcement shows how quality systems are changing. The company Sama has introduced a new tool called “Smart Review” that shifts the traditional model of checking every item to a smarter model of real-time quality control in data labelling projects. The company claims review rates can fall to as low as 10% while still achieving 95%+ quality scores.

Let’s see how this change from old-style checking to real-time quality assurance AI is affecting industries.

What was the old model?

In many processes, whether labelling data for AI systems or running production lines in factories, the typical method has been spot-checking. That means after or during the work, someone inspects a sample of all the completed pieces (maybe 5% or 10% of items), and if those samples pass, the whole batch is accepted. This method carries risk: if problems lie in parts you didn’t check, errors slip through.

In manufacturing process automation, for example, you might have machines doing assembly, and then inspectors randomly pick units to check quality. The assumption being: if the sample is good, the rest are ok.

But as data-driven AI systems become more critical, the demand rises for faster feedback loops and higher trust in data quality. This is where the shift happens.

Enter real-time quality assurance AI and Smart Review QA

With the tool Smart Review from Sama, the concept is shifting from “review many” to “review smartly and in real time”. Here are some common observations:

• Instead of reviewing 100% of tasks, the process reviews smaller parts of tasks (as low as 10%) but uses analytics and automation to find where review is most needed.
• It combines human-in-the-loop (HITL) and automation so the system flags which tasks may need review.
• The aim is to maintain the high-quality target (95%+ quality scores) while reducing cost and time.

What this means in practice: As data labelling (which is key for AI) grows bigger and faster, using real-time quality assurance AI lets companies monitor and assure quality while operations run. Rather than waiting until a full batch is done and then checking, the system monitors as progress happens and addresses issues immediately.

Why the shift matters for QA paradigms and quality control

When you move to real-time assurance, you gain several benefits:

• Faster feedback: Errors are found sooner, meaning less waste and fewer reworks.
• Better resource allocation: Rather than reviewing everything equally, you focus review efforts on tasks with higher risk.
• Scale-friendly: As volumes grow (for example, in AI in quality control,data labelling, or manufacturing), reviewing every item becomes impractical. Real-time QA scales better.
• Higher trust: When quality assurance is active and dynamic, you build more confidence in outcomes (whether model training data or manufacturing outputs).

For manufacturing process automation, imagine sensors and AI continuously checking product metrics and flagging anomalies as they happen rather than waiting for batch inspection. This mirrors the real-time quality assurance AI approach.

How Smart Review specifically shifts the paradigm

For the tool from Sama, the shift is described as moving from reviewing every task to reviewing the right tasks. Key elements:

• Configurable Smart Review: Projects can set their review thresholds and criteria.
• Automated Smart Review: The system monitors performance data and dynamically adjusts review levels week by week. If quality stays high, automation drives more; if issues arise, human review steps up.
• The result: review rates reduce, quality remains high, and costs drop. The review levels are going down to 10% while still hitting 95%+ quality.

This is a concrete example of Smart Review QA in action. The idea of AI in quality control and especially “real-time quality assurance AI” is now moving from theory to real deployment.

What this means for organisations and individuals

If you work in an organisation where quality assurance matters, whether in data-labelling for AI, in  manufacturing, or in services, understanding this shift matters:

• Organisations will need to invest in systems that support real-time monitoring and smart review rather than just bigger teams for spot-checking.
• Processes will evolve; instead of bigger inspection teams, you’ll see more analytics, dashboards, anomaly detection, and human-in-the-loop workflows.
• For individuals (QA engineers, data-ops people, manufacturing QA), skills will shift you’ll benefit from knowing how to use analytic tools, understand human-in-loop workflows, and set meaningful thresholds.
• For those in AI training data, this reflects how AI in quality control is now treated seriously, not just as a cost centre but as a driver of trust.

Challenges and what to watch

Even with promise, there are things to keep in mind:

• The initial setup for real-time quality assurance AI may need investment in analytics, data pipelines, and monitoring tools.
• Human-in-the-loop remains important. Well, automation can help, but certain tasks still need human judgement (especially edge cases).
• As the review lowers (say to 10%), you must trust that the monitoring and flagging mechanisms are robust and calibrated, or else you risk missing errors. For example, only 12% of respondents in one survey said their data was high enough quality for AI use.
• Changing culture: some teams used to “inspect everything” and shifting to “inspect smart” takes mindset and process changes.

The broader impact for manufacturing and automation

In manufacturing process automation, the same principles apply as in AI data-labelling. Real-time quality assurance AI means:

• Sensors and machines capturing data continuously.
• Automation systems flag issues immediately when tolerances drift.
• QA is moving away from end-line inspection and towards continuous assurance.
• Organisations shifting QA paradigms: from reactive inspection to proactive control.

This shift will help companies maintain high quality while speeding up production, reducing waste and cost, and preparing for higher volumes and variability.

Bringing it all together

Real-time quality assurance AI and tools like Smart Review QA mark a change in how organisations think about quality. Rather than relying on spot-checks after the fact, they monitor as they go, use analytics to focus human effort, reduce wasted time, and scale better.

As the adoption of AI in quality control grows, organisations that shift QA paradigms now will likely gain advantages in speed, trust, and cost-efficiency.

Final word

For organisations and individuals keen to stay ahead in this shift, there’s a clear path: build capabilities around real-time quality assurance AI, adopt smart review workflows, and move QA from a back-end inspection activity to a live assurance layer.

If you’re looking to deepen your skills or your team’s skills in this area, consider pursuing the AI Quality Assurance Certification from AI CERTs. This will prepare you with the frameworks, tools and mindset needed for the new QA era.

Download the Program Guide

The future of QA is real-time, smart, and continuously assured. Take the step now!

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