AI: The New QA Standard

Poor customer experiences (CXs) [may have] cost businesses worldwide as much as $3.7 trillion last year, even (Qualtrics): even though solutions to many of their costly CX challenges may already exist in their contact centers. They just need a way to make those solutions visible.

Right now, many contact centers, especially at medium-sized organizations, rely on legacy manual QA processes that analyze just 1% to 2% of the interactions the center handles. That’s not enough data to provide reliable insights at scale for CX improvements.

Meanwhile, the other 98% of their customer interaction data goes unused and fails to deliver value. This results in the contact center becoming a cost center: requiring storage, security, and risk management.

AI automation tools need to be integrated securely with other contact center tools and company systems...

AI-automated QA and speech analytics now offer companies a way to leverage 100% of their customer interaction data for insights that can enhance CX and drive customer retention. The same tools and data insights can also help to ensure compliance with increasing regulatory demands.

Together, these capabilities transform customer data from a cost center to a revenue center and increase the value the contact center delivers to the whole organization.

Extracting Insights at Scale

AI-powered QA systems can be configured to automatically ingest customer interactions across channels, including chat transcripts and voice call recordings.

The AI model analyzes each interaction in real time against key metrics. For example:

• Did the agent deploy the appropriate script, compliance language, tone, and empathy?
• Were any escalation triggers present during the interaction?
• What customer sentiment indicators were present during the interaction?

Rather than providing a QA snapshot, as manual spot checks do, this approach delivers comprehensive, continuous visibility into customer interactions.

Building Value with AI

Companies can use these insights to continuously improve CX, customer retention, and regulatory compliance.

Contact center insights for CX

Analyzing all interactions allows companies to identify CX-related issues across the customer journey, such as product and service quality complaints or agent performance challenges.

This allows the appropriate teams to address those challenges as they emerge. Within the contact center, managers can use the insights for objective, data-supported agent coaching aligned to specific CX improvement goals.

Improving retention with contact center insights

Fixing CX problems as trend lines emerge and training agents using real-world data can create a positive feedback loop that reduces the risk that customers will churn.

For example, properly trained agents with access to current data on known CX issues can resolve customers’ requests faster. Customers then spend less time waiting for resolutions and will feel better about their interactions with the company: which makes it less likely that they’ll switch to another company out of frustration.

Enhancing compliance at scale

When agents don’t follow their script for required compliance language, companies are at risk for regulatory penalties related to data privacy and consent. For companies in heavily regulated industries such as healthcare, banking/financial services, and insurance, the potential stakes for noncompliance are higher.

Beyond potential fines, contact center noncompliance can erode customer trust and damage the company’s reputation with consumers and the public. It’s far faster and more cost-effective to prevent these issues than to resolve them after the fact.

Preparing for Successful AI Implementation

Using AI for contact center improvements requires strategic planning that includes areas such as:

Data governance and compliance

Especially in highly regulated industries, organizations need to establish data privacy and security frameworks for their AI use cases to protect customers and comply with regulations.

Model training

AI tools must be taught how to function in their specific environments. That requires providing properly organized data related to their use cases, such as customer sentiment indicators, script adherence, and compliance language.

However, datasets can contain inherent bias that skews AI actions, so data points that indicate bias need to be removed before the set is sent to the AI model for training. This makes it easier to fine-tune the model’s actions without having to root out bias after the training stage.

Integration

AI automation tools need to be integrated securely with other contact center tools and company systems in order to get the most value from them.

Data science expertise

Companies that don’t have data science and analytics talent will need to bring on new people, engage in upskilling, work with third-party experts, or a combination of these options, to realize full value from their AI investments.

Change management

Any change in contact center operations can run into internal resistance. Strategic, ongoing change management that fosters a culture of innovation and rewards adoption can help employees embrace, rather than push back on, AI tools that can make their work easier and more rewarding.

Catch Up or Get On Board?

Many major enterprises have already begun to implement AI-backed QA and speech analytics programs, leaving medium-sized competitors to play catch up on moving away from small-scale manual QA.

I expect AI QA at scale to become table stakes for contact centers within three years. This will meet rising CX expectations to comply with stronger monitoring requirements, and to realize the retention gains and operational cost savings that AI can deliver.

For companies that aren’t yet using or investigating AI applications in the contact center, it’s time to begin.