AI In Quality Management: A Game Changer For Contact Centers

Artificial intelligence (AI) has permeated many aspects of our work and personal lives. From driving assistance to data analysis to chatbots, generative AI (GenAI) is finding its usefulness in a host of ways across all industries.

One of the most impactful uses is how AI is transforming customer experience (CX). Advances in technology allow contact centers to use data in much more manageable ways, redefine how to engage with customers, gain efficiencies, and leverage actionable insights to improve quality management (QM) and the customer journey.

When you unlock the benefits of AI in quality management, you’re able to move beyond basic process improvements to a direct, marked improvement in employee experience (EX) and ultimately CX. Defining the strategy upfront and understanding the considerations and challenges will help ensure successful implementation of automated AI in quality management.

How Quality Management Processes Have Shifted

It could be called the perfect storm for contact centers. Over the past few years, significant shifts have taken place:

• Traditional call centers have evolved from delivering service and support via one channel (voice) to providing omnichannel support.
• A plethora of data are now available. Data from omnichannel customer touchpoints — calls, social media, email, online chat, and more — provides companies with greater insights on customers. Contact centers on digital transformation journeys are leveraging data as a competitive differentiator.
• Remote and hybrid agents need cloud-based tools and advanced technologies to support customers around the globe.
• Customer support centers need to minimize labor and operating costs and are often focused on cost reduction strategies.
• Customer loyalty, needs, wants, and preferences have shifted. Complex support still often requires phone interaction, but customers more frequently prefer email and chat for simple, routine questions and support.

The Shift from Quality Assurance to Quality Management

Quality assurance (QA) in contact centers was a measurement and practice by analysts, managers, and call center leaders of how agents met (or didn’t meet) defined checkboxes, often for compliance and regulatory requirements, for handling customer interactions. But as customer preferences, support channels, and interactions flexed, so did the complexities of monitoring and analyzing the people, processes, and systems involved in customer interactions to ensure they are being handled by agents in a desired fashion. Customers have different expectations for service in each channel, and agents should act — and be measured — accordingly.

Customer experience is now focused on whether the customer left the interaction with a satisfying resolution and will remain loyal to the company — the effort today is more commonly known as quality management. For modern contact centers, QM means focusing instead on the success of the quality process and program for customers, agents, and the company as a whole.

The shift from QA to QM has been driven in recent years by the availability of new technologies like cloud deployment and GenAI. These tools and systems significantly help analysts and contact center leaders manage the complexities of their organizations’ CX strategies and customer satisfaction. 

The Role of AI in Quality Management

According to a June 2023 McKinsey report, about 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D. The report also states that using generative AI in customer care functions could improve productivity by 30-45 percent.

Integrating automation, artificial intelligence, and machine learning into QM processes holds tremendous promise for contact centers. The most prominent example is how AI can enable business leaders and organizations to review and analyze all customer interactions in near real time to quickly identify trends, sentiments, and future intentions.

Traditionally organizations invested in manual processes and quality teams to gather data about customer interactions, listening to and scoring a random small (less than 2 percent) sample of calls or sending feedback surveys, leading to insufficient actionable insights. For many years, this small sampling method was the norm for measuring customer satisfaction in call centers, but QA analysts and managers were actually more focused on doing compliance checks or monitoring agents than understanding customers. These processes had high costs but generated low value and non-strategic insights.

AI and Automation to the Rescue

Instead of analyzing a meager 2 percent of customer interactions, an AI-powered QM solution with predictive analytics capabilities can analyze the sentiment in every single interaction and provide insight to the ones that need review so contact center managers can spend more time with a proactive approach to fix quality issues rather than simply identifying them.

In addition, automated workflows and AI-assisted scoring can make quality analysts’ work more consistent and thorough, allowing them to better spot trends and potential quality issues and judge agents more fairly. They also allow analysts to quickly get full insight into every interaction for a big-picture view of quality that provides strategic insight to deliver better CX and make data-driven decisions. Leveraging an assisted model first allows key stakeholders, such as agents, analysts, and managers, to gain confidence in the technology before making massive changes to core processes.

On the agent side, using automation and AI-powered tools in your business can improve engagement and motivation among your agents by removing the repetitive tasks that bore or frustrate them. Without time-consuming tasks in the way, they’re free to work on helping customers resolve their issues better and faster. Agents who feel their work is meaningful and valued by your customers are more likely to be engaged and motivated in the contact center.

Advantages of Unlocking AI in Quality Management

Implemented properly as part of a company-wide strategy to deliver outstanding CX, AI-infused quality management provides many benefits for businesses.

1. Automated workloads – For analysts, automation and AI can take away some of the manual tasks that make a proper evaluation take so long. Automated workloads can queue up interactions for the analyst so they spend less time searching. You can also create different queues based on the type of interactions to target the areas where they need the most improvement in customer experience.
2. Faster feedback and informed decision making – AI can also help analysts in the scoring and feedback process to cut effort and time. The right solution can automatically assign coaching to help agents improve faster and arm them with preventive action. Agents can self-review the coaching and immediately put into practice tactics for continuous improvement to create better customer outcomes.
3. Optimized use of resources/operational efficiencies – If an agent isn’t meeting expectations, automation, and AI can help you compare them to their peers on a larger scale than analysts can do manually. If agents are consistently having issues with the same procedures resulting in operational inefficiencies, you can work to fix those processes and make them more effective for everyone. On the flip side, insights from AI can help you identify qualities of high-performing agents and leverage those best practices for coaching and training for other agents.
4. Reduced costs/cost savings – Not only do AI-driven tools reduce manual quality processes and give analysts time back, there are other ways that they can cut costs. When you use a solution that helps surface the topics customers need help with most, you can build your self-service tools based on data-driven insights to answer customers’ questions without involving an agent. You can also realize cost savings by better understanding what’s driving customer trust and expectations by categorizing topics. When you see the same topics come up over and over, you can work with other teams in your organization to fix the root issues and reduce interactions about it.
5. Better, predictive insights into customer experiences – Generative AI enables you to sift through data in record time, allowing you to utilize predictive analysis to identify trends, market shifts, and customer actions. In other words, you get to “hear” what your customers are saying and doing to make adjustments to QM processes and CX delivery.

Read the blog: 5 Ways AI and Automation Can Improve Your QM Processes

How to Implement AI in Quality Management

Here are four key steps to help you ease into AI in your quality processes.

1. Lead with a plan – Take stock of your current quality management processes and identify areas where AI could provide the most significant benefits to complement human intelligence. This will help you determine which AI-powered quality management tools to invest in.
2. Carefully evaluate the various AI-powered quality management systems, and choose the one that provides ease of integration and best aligns with your current and future business needs and objectives.
3. Train agents, analysts, and leadership – Don’t assume AI tools are magic and will automatically yield benefits. Ensure your team is well-versed in the use of AI-powered systems by providing comprehensive training and ongoing support. This will help them effectively leverage the technology, mitigate potential risks of failure, and contribute to the successful integration of AI into your quality management processes and workflows.
4. Track progress and optimize – Continuously monitor the performance of automated, AI-infused systems, assessing their impact on your quality management processes. Use insights gained to optimize the AI in quality management further to enhance their effectiveness.

Considerations and Challenges

It’s easy to see how modern contact centers can benefit from generative AI in quality management. However, before adopting AI in your business there are important best practices to consider before making that leap.

• Have processes in place to ensure the accuracy and reliability of the data used by AI algorithms. Robust data collection processes help address data biases and limitations, and ensure the availability of high-quality data for AI-driven insights.
• Ease into AI-infused quality management systems to overcome integration hurdles and invest in appropriate infrastructure and resources.
• Training employees on how to adapt to AI in QM processes helps overcome implementation hurdles and ultimately can improve the employee experience.

AI and automation are powerful, essential tools that can improve many areas of the contact center. Customers want you to use AI tools to serve them better. Using them to improve your quality management gives you a competitive edge by helping you find ways to create a better experience for your agents, analysts — and most importantly, your customer.

Ready to unlock the immense potential of AI in quality management for your contact center? Get started on your journey with our on-demand webinar “How to Infuse AI Into QM: What You Need to Know.”