How to Overcome AI Contact Centre QA Barriers

This blog summarizes the key points from a recent article by David McGeough at Scorebuddy, where he breaks down the most common barriers to AI in the contact centre and how organisations are starting to overcome them.

Traditional quality assurance methods are struggling to keep pace with the scale and complexity of modern customer interactions. It’s no surprise that 44% of contact centre professionals are now looking to adopt automated or real-time QA, according to our latest report with Call Centre Helper.

While the benefits of AI-powered QA are clear, adoption isn’t moving as quickly as expected. Concerns around cost, accuracy, security, and internal capability are still holding many teams back.

1. Budget Pressure and Proving ROI

49% of survey respondents said that budget was their top barrier to implementing AI in their QA program.

Limited budgets make it difficult to justify AI investment, especially when ROI isn’t immediately clear. Many contact centres also feel overwhelmed by the number of AI tools available, making it harder to decide where to invest.

On top of that, AI solutions often come with ongoing subscription costs, as well as training requirements for QA teams and agents.

How Teams Are Addressing It:

• Focus on a single, high-impact use case like QA rather than trying to automate everything at once
• Run small pilot programmes to test performance and demonstrate value
• Compare before-and-after KPIs such as QA coverage, cost-to-score, and efficiency
• Link AI outcomes to measurable business metrics like CX, compliance, and retention

2. Gaps in in-House AI Expertise

39% of contact centres reported that a lack of internal expertise was their main barrier.

Many teams don’t have prior experience implementing AI tools, which can slow adoption and create uncertainty around how to use the technology effectively.

How Teams Are Addressing it:

• Rely on vendor onboarding, training, and ongoing support
• Build a cross-functional AI-QA team across operations, IT, QA, and compliance
• Upskill existing employees to build internal AI capability over time

3. Challenges Integrating With Existing Systems

33% of respondents said integration with existing tools was their biggest issue.

Contact centres already rely on multiple platforms, and adding AI into the mix can create disruption if systems don’t connect properly. Poor integration can lead to silos, workflow issues, and data inconsistencies.

How Teams Are Addressing It:

• Start with minimum viable integrations such as call recordings and metadata
• Expand gradually using open APIs
• Ensure compatibility with existing tools like CRM, CCaaS, and WFM platforms

4. Concerns Around AI Accuracy and Fairness

23% highlighted trust in AI scoring as a key barrier.

Scepticism remains due to concerns about bias, inconsistency, or incorrect evaluations, particularly when AI is used to assess agent performance.

How Teams Are Addressing It:

• Run side-by-side comparisons between manual and AI scoring
• Keep humans involved for oversight and decision-making
• Introduce dispute processes for score reviews
• Continuously monitor and refine AI models to improve accuracy

5. Compliance and Data Privacy Risks

21% of respondents identified compliance and privacy as a major concern.

AI systems often process sensitive customer data, making regulatory compliance and secure data handling essential to avoid legal or reputational risk.

How Teams Are Addressing It:

• Map data flows to understand how information is accessed and stored
• Implement governance frameworks and strict access controls
• Choose vendors with recognised certifications such as ISO 27001 and SOC 2

6. Resistance From Employees and Stakeholders

18% reported internal resistance as their main challenge.

Concerns about job security and the role of automation can slow adoption, even when AI is intended to support rather than replace human roles.

How Teams Are Addressing It:

• Involve frontline teams in designing AI workflows
• Communicate openly about the purpose and benefits of AI
• Emphasise that AI enhances human decision-making rather than replacing it

A Phased Approach to AI-QA Adoption

Across all six barriers, a consistent theme emerges: successful teams take a gradual, structured approach.

• Start with a small, low-risk use case
• Validate impact using clear before-and-after metrics
• Keep humans involved in calibration and decision-making
• Scale usage once results are proven

This approach helps organisations move from hesitation to measurable impact, turning AI-QA from a perceived risk into a practical operational advantage.

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This post has been re-published by kind permission of ScorebuddyCX - view the original article.

Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Reviewed by: Robyn Coppell