" THE INTRODUCTION OF AI IN CUSTOMER SERVICE HAS BEGUN TO EASE THE TENSION BETWEEN QUALITY AND PRODUCTIVITY , BUT CHALLENGES REMAIN ."
FEATURE
AI-driven knowledge management is not just an operational improvement : it ’ s a strategic advantage that redefines how businesses meet and exceed customer expectations .
IS THERE A CONTINUED CONFLICT BETWEEN QUALITY AND PERFORMANCE / PRODUCTIVITY , EVEN WITH THE NEW AI-POWERED TOOLS , AS EXPRESSED IN “ I ’ D LIKE TO HELP YOU MORE , BUT I NEED TO FINISH THIS CALL .” AND IF SO , HOW DO CONTACT CENTERS SQUARE THIS ISSUE ?
DH : There has always been pressure on a customer service agent to get the job done as quickly as possible . But in the last decade , the pressure has eased ; contact centers seem to focus much less on handle times and more on customer satisfaction .
AI has promise in helping agents resolve problems more quickly , but it hasn ’ t fulfilled that “ magic box ” challenge yet . Contact centers should continue to focus on providing the highest quality interaction , regardless of whether AI is involved .
MP : Yes , the tension between quality and productivity remains a challenge for contact centers , even with the introduction of AI-powered tools .
While these tools can enhance efficiency by automating repetitive tasks and providing real-time insights , it ’ s essential to ensure they support , rather than overshadow , the human aspect of customer interactions .
To address this conflict , organizations should adopt a balanced performance measurement approach that values both quality and efficiency .
Implementing AI-driven tools that focus on context and sentiment analysis can help agents better understand customer needs while still meeting productivity goals .
By fostering a culture that emphasizes meaningful connections and providing proper training on these tools , contact centers can successfully enhance both service quality and operational productivity .
DS : CX automation provides contact centers with the ability to achieve the " holy grail " of improving quality , performance , and customer and agent experience , all while reducing costs .
Digital tools , such as intelligent virtual assistants , are becoming increasingly sophisticated for improved adoption and successful channel shift , lifting the burden of routine tasks from agents . This creates more space for agents to focus on more complex interactions that require human nuance .
AI-empowered agent copilots , such as real-time coaching bots , help streamline complex calls , reducing the time it takes to resolve issues by delivering real-time knowledge , guidance , and assistance at the right moments .
I like to think of bots as a virtual team of assistants behind every contact center agent , providing support in real time .
" THE INTRODUCTION OF AI IN CUSTOMER SERVICE HAS BEGUN TO EASE THE TENSION BETWEEN QUALITY AND PRODUCTIVITY , BUT CHALLENGES REMAIN ."
-- ELIZABETH TOBEY
Imagine each agent has a team of 10 bots helping them with tasks like starting applications , creating cases , or detecting customer sentiment .
For example , if the sentiment starts to trend negatively , the bot can alert the agent and offer tips on de-escalating the situation . Agents often recognize when a customer is angry , but by the time they do , it ’ s usually too late to prevent an escalation , which can extend call length .
AI-infused capabilities help solve this by detecting signals of frustration early , allowing agents to course-correct and resolve the issue more efficiently , leading to better experiences for both customers and agents .
CX automation , particularly through AI , is helping contact centers strike a balance between performance and productivity by streamlining routine tasks .
With AI handling administrative duties such as note-taking , drafting responses , and processing transactions , agents can focus more on delivering high-quality , personalized service without getting bogged down in time-consuming tasks .
This allows agents to prioritize customer interactions while AI takes care of the back-end work . As a result , agents can maintain a high standard of service , even while managing multiple priorities simultaneously .
ET : The introduction of AI in customer service has begun to ease the tension between quality and productivity , but challenges remain .
Many organizations still deploy AI in silos - such as bots operating on separate platforms from human agents - resulting in clunky handovers , customer frustration , and inefficiencies . Disconnected knowledge bases , workflows , and teams exacerbate these issues , driving agent turnover and inconsistency .
Success lies in seamless collaboration between AI and human agents , combining their strengths to deliver exceptional service .
By leveraging platform data and historical interactions , businesses can rapidly create precise AI agents and equip employees with AI copilots for real-time , role-specific support . This synergy enhances productivity , engagement , and learning , turning every employee into a customer service champion .
Brendan Read is Editor of Contact Center Pipeline . He has been covering and working in customer service and sales and for contact center companies for most of his career . Brendan has edited and written for leading industry publications and has been an industry analyst . He also has authored and co-authored books on contact center design , customer support , and working from home . Brendan can be reached at brendan @ contactcenterpipeline . com .
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