AI has been a recurring topic in contact centre conversations, particularly in Quality Assurance.
But are we losing sight of what’s truly important? Quality Assurance isn’t just about ticking boxes and AI; it’s about setting standards, monitoring performance, providing feedback, and continuous improvement – all aimed at ensuring a consistently high level of customer service.
So here we’re not just talking about why AI’s exciting (although it is), we’re diving into the wider considerations you’ll need to be across in order to not get left behind.
3 Key Considerations
1. Going Beyond Spreadsheets and Manual Processes
The days of relying solely on spreadsheets and manual processes are numbered. The effort required to maintain these outdated methods is increasingly disproportionate to the insights gained.
With AI becoming an expectation in modern QA platforms, the question isn’t “Should we adopt AI?” but rather, “How can AI help us do our jobs better?”
2. Dispelling the Myths: AI as an Augmenter, Not Replacer
One of the most prevalent misconceptions is that AI will replace QA teams. Another is a lack of understanding about what AI does in the context of QA.
It’s not about replacing human expertise; it’s about augmenting it. As a QA manager, you should be asking, “What benefits does AI bring to my team and processes?”
3. Addressing the Unseen 99%
Traditional QA often operates on evaluation quotas, covering perhaps 5 conversations per agent per month.
This may sound sufficient, but it equates to less than 1% of total conversations, leaving us in the dark about the remaining 99%. You should be asking “Is this hit-or-miss approach truly effective?”.
How AI Can Help
Targeted Quality: Unlocking the Potential of Your QA Team
Without an army of QA analysts, achieving high coverage percentages is unrealistic, so it’s vital to target the conversations that truly matter.
What’s more valuable: a random conversation or one where customer vulnerability and agent underperformance have been potentially flagged by AI for assessment? This targeted approach truly unlocks the potential of your QA team.
Boosting Coverage: the Role of a QA Co-Pilot
By leveraging AI as a QA co-pilot, it’s possible to boost that 1% coverage to up to 10%. AI can score elements of the conversation, generating valuable insights and feedback, making the human component of QA even more effective.
The Future of QA: Automation Augments Human Expertise
AI is not going to replace us, but those who use AI will replace those who don’t. Automating as much of the QA process as possible only serves to make the work that the human component does much more valuable.
Remember though: before embracing QA technology, you’ll need to take a hard look at your current methods of evaluating.
If it’s overly manual and complex, the transition to automated QA may be challenging. While QA tech is powerful, it can’t replace the nuanced understanding of a human brain. The goal is to strike a balance between automation and human input.
AI has found its rightful place in QA, not as a replacement but as a catalyst for more effective strategies. It’s time to shift our focus from what AI can do, to what we can do with AI.This blog post has been re-published by kind permission of EvaluAgent – View the Original Article
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