Manual methods for quality assurance can no longer keep up with the volume and complexity of customer interactions.
Contact center leaders are well aware of this: our new report in partnership with Call Centre Helper, What Contact Centers Are Doing Right Now, shows that 44% of contact center professionals want to adopt auto or real-time QA.
Automated and AI-powered quality assurance can increase accuracy and efficiency while reducing costs and time. So why wouldn’t a contact center invest in this?
Despite the clear benefits of an AI contact center quality assurance program, widespread adoption is still lagging. Many contact centers hesitate to implement AI due to challenges such as budget, accuracy, security concerns, and a lack of internal expertise.
While these concerns are valid, you can overcome them with the right strategy.
In this article, we’ll explore the 6 biggest barriers to AI contact center QA and practical steps to solve each.
The 6 biggest AI-QA barriers + a fix for each
- Limited budget/ROI concerns: Start small. Try a pilot project and define your before/after KPIs (cost-to-score, QA coverage, CSAT, etc.) to prove the value.
- Lack of internal expertise: Build a cross-functional AI-QA squad. Lean on your vendor’s training and onboarding to ramp things up quicker.
- Integration with existing tools: Again, starting small is key. Begin with your minimum viable integrations (call recordings, metadata) and then use open APIs to expand over time.
- Distrust in AI accuracy/fairness: This is all about validation. Test AI-QA evaluations vs. manual scoring and keep humans in the loop for oversight and handling of disputes.
- Compliance and privacy uncertainty: Map all data flows involved. Make sure you enforce governance and access controls, and only choose vendors with recognized security certifications.
- Internal resistance from staff/stakeholders: Involve frontline teams in the workflow design process. Communicate transparently, and share early wins to build AI confidence.
#1. Limited budget and ROI concerns
49% of survey respondents said that budget was their top barrier to implementing AI in their QA program. (What Contact Centers Are Doing Right Now)
Budget limitations make it hard to justify investing in AI, especially when ROI is not always clear. This is not surprising, as contact centers don’t often have expansive budgets to invest in new tech.
The AI overwhelm is real, too. With the recent explosion of AI-powered tools, it can feel impossible to narrow down which one your contact center actually needs.
AI-powered tools, like a QA platform, are often not just a one-time fee, but rather structured as ongoing licensing or subscription fees. Training time for agents and QA teams can also add to the cost.
What’s the solution?
Block out the AI noise, and choose a vetted option
You don’t need to automate everything, nor should you. If you are to choose one AI-powered tool, let it be quality assurance. AI-QA analyzes 100% of customer interactions, rather than just a small sample, saving time and labor.
Run pilot programs to test performance and ROI
Start with a controlled pilot of one team or channel to get results without over-committing budget. A pilot lets you compare AI-QA performance against your existing manual processes, and capture data around time saved, accuracy improvements, and operational efficiency.
Before vs. after KPIs
Use baseline data from your manual QA process and compare it directly with results achieved through AI-QA, tracking metrics like cost-to-score, QA coverage, and scoring variance.
Connect AI-QA outcomes to metrics
By linking AI-QA results to measurable business impact like efficiency, CX, compliance, and retention, you build a clear, compelling narrative that AI-QA is not just a QA upgrade but a revenue and cost-optimization tool.
Example
When using a tool like AI auto scoring for QA evaluations, contact center managers see tangible ROI, including:
- 60%+ manual workload reduction
- 70%+ QA coverage improvement
- 90%+ cost-to-score reduction
AI contact center QA: Before and after comparison
|
Before (Manual QA) |
After (AI-QA) |
The Impact |
|
|
QA coverage |
Limited sampling (only 2 or 3% of interactions reviewed) |
Up to 100% of conversations scored automatically |
70%+ increase in QA coverage |
|
Cost-to-score |
High (labor-intensive evaluations, inconsistent throughput) |
Lower (automation reduces manual effort + improves efficiency) |
90%+ reduction in cost-to-score |
|
Workload |
Heavy workload for evaluators (scoring and admin) |
Less manual scoring means evaluators focus on calibration/coaching |
60%+ reduction in manual QA workload |
|
Time-to-score (per interaction) |
Hours, or even days depending on backlog |
Near real-time scoring and feedback loops |
<5 seconds average AI scoring time |
#2. Lack of internal expertise
39% of contact centers reported that a lack of internal expertise was their main barrier to implementing AI in their QA program. (What Are Contact Centers Doing Right Now)
Ideally, when implementing a new technology or tool into a contact center, someone on the team has some working knowledge of it. This, as demonstrated by our report, is clearly not always the case with incorporating AI into QA.
What’s the solution?
Leverage vendor onboarding, training, and support
If you’ve invested AI contact center QA, that shouldn’t be the last interaction you have with the company. Look to buy from companies that offer after-purchase support and help your team adjust to the new platform.
Build a cross-functional AI-QA team
This tool is connected to operations, QA, IT, training, compliance, and leadership teams, so a special AI team composed of members from each can ensure it is successfully implemented and maintained.
Upskill existing staff
Learning to master a new AI-powered tool is a valuable asset in today’s job market.
There will likely be many team members who are interested and eager to be trained on using AI contact center QA.
Example
According to McKinsey, demand for AI fluency has grown sevenfold in two years, making it the fastest-rising skill in U.S. job postings. Upskilling your staff with AI experience sets them up for long-term success and growth.
Download the contact center QA report now
#3. Integration with existing tools
33% of respondents reported that integration with existing tools was their biggest barrier to implementing AI in their QA program. (What Are Contact Centers Doing Right Now)
Contact centers already have existing tech stacks, and integrating a new tool shouldn’t make a mess of the systems in place. Tools that don’t integrate correctly can cause miscommunications, workflow disruptions, and data silos.
What’s the solution?
Start with minimum viable product integrations
Integrate only the essential systems first, the ones you must connect for AI-powered QA to work. Connecting the primary data sources, like call recordings and basic metadata, ensures the AI-QA system can start scoring and generating insights right away.
Use open APIs to ensure flexibility
Don’t make more work and choose a closed system. AI-QA with open APIs lets your CCaaS platform, CRM, and WFM systems exchange data seamlessly.
For example, an API can let an AI-QA platform pull call recordings from your CCaaS or push QA scores back into a dashboard your team already uses.
Example
Most contact centers already have a system of existing tools and platforms, including popular options such as Intercom, Genesys, Amazon Connect, Salesforce, Freshdesk, Zendesk, LiveChat, and more.
Whichever AI tool you choose, ensure it offers seamless integration with what you already have, and that the support team can assist if any issues arise.
#4. Concerns about AI accuracy and fairness
23% reported that the biggest barrier to implementing AI in their QA program was concerns about the fairness and accuracy of AI. (What Contact Centers Are Doing Right Now)
Some AI tools, such ChatGPT, have been known to make glaring errors when it comes to accuracy and bias. Naturally, this has led to skepticism around AI in certain contexts, like making decisions that impact agents.
What’s the solution?
Run side-by-side tests
Compare manual quality assurance scoring to AI scoring to demonstrate accuracy and consistency.
Ensure human safeguards
AI can do a lot, but it shouldn’t do everything. In the AI-QA process, humans are needed for calibration, compliance, and ultimately, decision-making.
Offer dispute and appeal mechanisms for AI scores
One of the benefits of AI contact center quality assurance is that it is consistent, accurate, and unbiased. But that doesn’t mean that there won’t be situations in which agents feel that the way a scorecard was graded was unfair.
Having a process to appeal scorecard ratings can support agents when they feel as if their actions were correct despite a low QA score.
Analyze variance across channels, regions, and teams
When you monitor how AI scores different groups, you can identify and correct patterns of bias or inconsistency early.
Retrain or fine-tune AI models as needed
AI won’t be perfect right away; AI scoring models improve when they’re exposed to more real data from your agents, channels, and customer interactions. With regular fine-tuning, AI scoring can be aligned with updated policies and scorecards.
Example
According to research from the University of Melbourne and KPMG, only 46% of respondents trusted the use of AI systems. Therefore, the accuracy of AI systems is crucial for getting the team on board, look for 90%+ AI scoring accuracy.
#5. Compliance and privacy risks
21% reported that the biggest barrier to implementing AI in their QA program was concerns regarding compliance and privacy risks. (What Contact Centers Are Doing Right Now)
AI systems often require access to sensitive customer data like call recordings, transcripts, and account details. Therefore, contact center AI compliance must be a priority; any AI tool used must meet strict regulatory requirements and industry-specific rules that define how data is stored, processed, and transmitted securely.
If an AI system isn’t fully compliant, it can expose the organization to legal risks, fines, and reputational damage.
What’s the solution?
Map data flows and assess privacy risks
Before implementing AI, contact centers should identify exactly what data the system will access, where it will travel, and how it will be stored. This mapping helps uncover potential privacy risks early and ensures all sensitive information is handled appropriately.
Set access controls and AI governance frameworks
Limiting who can access AI outputs and underlying data prevents misuse and strengthens compliance. A formal governance framework also defines how AI decisions are monitored, audited, and corrected to ensure ongoing fairness and safety.
Require vendors with certifications
Choosing AI vendors with rigorous security certifications ensures the AI platform meets industry-recognized standards for data protection. This reduces risk and gives internal stakeholders confidence that the technology is secure and compliant.
Example
Look for AI vendors that are ISO 27001:2022 certified and SOC 2 Type 2 compliant.
#6. Internal resistance
18% reported that resistance from staff and stakeholders was the biggest barrier to implementing AI in their QA program. (What Contact Centers Are Doing Right Now)
A common concern surrounding AI is that it will replace humans in their jobs. There may be worry that automation may devalue expertise and threaten job security. Even if AI is designed to make the jobs of humans easier and more efficient, contact center AI QA adoption may be slow.
What’s the solution?
Design AI QA workflows with frontline teams
This can give agents peace of mind that they’re not being replaced or losing control.
Acknowledge concerns and explain the “why”
Take team member worries seriously, and address them directly. Offer the opportunity to have an open discussion around the implementation of AI to dispel fears.
Emphasize human impact
AI shouldn’t be a replacement in quality assurance but rather an enhancement. It will provide more accurate coaching for managers and better development for agents.
Example
According to research from MIT, on average, the combination of humans and AI outperformed the baseline of humans acting on their own. This doesn’t mean humans should be replaced, but rather that AI can do a lot of the heavy lifting for repetitive, low-risk tasks.
Agents and leaders remain the decision-makers, and AI tools should support this “human-in-the-loop” model, letting humans and AI play to their strengths.
Want a deeper look at AI-QA blockers? Watch the webinar: What Contact Centers Are
A quick AI-QA rollout plan
- Start with a small, low-risk use case.
- Validate with data by measuring before-and-after KPIs.
- Involve humans in the loop early to review results and refine scorecards.
- Scale integrations and workflows gradually once performance is proven.
For a full, in-depth guide to implementing AI quality assurance in your contact center,
download our AI call center QA playbook.
Get ready to scale quality assurance with AI
Manual quality assurance struggles to keep pace in the modern-day contact center, so the need and demand for AI contact center QA are there.
Yet barriers around implementing AI quality assurance exist: Concerns around trust, accuracy, integration, compliance, and change management are all valid, especially in complex, highly regulated contact center environments.
However, when contact centers take a phased, human-in-the-loop approach and align AI initiatives with clear business goals, these challenges become manageable rather than prohibitive.
With the right technology, governance, and internal adoption, AI-powered QA can move from a perceived risk to a strategic advantage, enhancing consistency, improving coaching outcomes, and delivering measurable ROI at scale.
With AI Auto Scoring, advanced reporting, and integrated coaching, Scorebuddy enables teams to review up to 100% of interactions, accelerate feedback loops, and transform QA from a cost center into a strategic growth driver.
Download the report: What Contact Centers Are Doing Right Now.
