Quality Assurance (QA)

What is Quality Assurance (QA)?

Quality assurance, or QA, in a contact center or operations context is the practice of evaluating customer interactions, calls, chats, and emails, against a defined scorecard to assess accuracy, compliance, professionalism, and resolution quality. Traditional manual QA relies on a small human review team listening to or reading a limited sample of interactions, because human review time is finite, and that sample has historically covered only a small fraction of total interaction volume. AI-enabled QA, sometimes called Auto QA, changes the coverage math entirely by automatically scoring every interaction against the same scorecard criteria a human reviewer would use. This extends evaluation from a small sample to the full volume of customer contacts without a corresponding increase in QA staff. QA data feeds directly into agent coaching, performance management, and process improvement, and its value depends on that connection. A QA program that scores interactions without translating findings into coaching and process change produces reporting but not improvement.

Why it matters

The gap between manual and automated QA coverage carries real operational consequences. Reviewing only a small percentage of interactions means an organization is effectively blind to whatever is happening in the vast majority of its customer contacts, including compliance risks, service quality problems, and coaching opportunities that never surface in the sampled data. In regulated industries specifically, healthcare, financial services, and insurance, a missed required disclosure or an incorrect regulated statement in an unreviewed call carries real financial and legal consequences that low-coverage manual sampling cannot reliably catch. Full-coverage automated QA converts quality monitoring from periodic spot-checking into continuous assurance, catching issues as they happen rather than during an infrequent audit cycle. That shift turns QA from a compliance reporting function into a real-time operational control. It also changes what leaders can act on, since a complete view of every interaction reveals patterns, recurring objections, product confusion, or process friction, that a thin sample would statistically miss, and it lets teams intervene before a small issue becomes a systemic one.

One enterprise contact center operator, Fiserv, moved from evaluating just 1% of calls manually to 96% coverage after deploying automated quality assurance, illustrating the scale of coverage gap AI-enabled QA can close (Verint, 2026).

How Quality Assurance (QA) works

• Scorecard definition: A defined set of criteria, compliance steps, required disclosures, tone, and resolution accuracy, establishes what good looks like for an interaction.
• Interaction scoring: Calls, chats, and emails are evaluated against the scorecard, either by a human reviewer on a sample basis or automatically at full coverage using AI.
• Dispute and calibration: Agents can dispute scores they believe are inaccurate, and periodic calibration sessions keep reviewers, human or AI, applying the scorecard consistently.
• Coaching integration: Scores and specific findings route directly into agent coaching workflows rather than existing only as a standalone report.
• Trend analysis: Aggregate QA data across many interactions surfaces systemic issues, a common compliance gap or a recurring product knowledge weakness, that individual scores alone would not reveal.

Common challenges and how to prevent them

The most common challenge in QA is coverage. Traditional manual QA typically reviews only 1% to 5% of interactions, which means the vast majority of customer contacts go completely unevaluated, and any issue occurring outside that small sample stays invisible to the organization. A second challenge is turning scores into improvement. QA data that stays in a report without feeding directly into coaching produces measurement without change, and agents come to see the process as punitive record-keeping rather than a tool that helps them improve. A third challenge is scorecard drift, where criteria written for one set of customer issues become outdated as products, policies, and customer needs change, without a regular review cycle to keep the scorecard relevant. Organizations that adopt AI-enabled QA to close the coverage gap, route findings directly into structured conversation intelligence and coaching workflows rather than standalone reporting, and review scorecards on a recurring cadence tend to convert QA from a compliance checkbox into a genuine driver of service quality improvement. Firstsource has shown the scale of that upside in practice: automation delivered a 12X increase in QA capacity for a top US bank while cutting processing time by 90%.