AGENTS SHOULD BE AWARE OF POTENTIAL LIMITATIONS AND BIASES OF AI AND PREPARE TO ADDRESS THEM IN CUSTOMER INTERACTIONS .
These indicators offer insight into customer sentiment towards the company . Additionally , the accuracy of AI-driven responses , the efficiency of AI in handling queries , and average resolution time are vital benchmarks .
These benchmarks can help identify AI ’ s performance , enabling the identification and mitigation of associated risks . Taking a proactive approach to risk management is crucial for AI initiatives ’ enduring success and sustainability .
• Qualitative Benchmarks . Qualitative benchmarks involve non-numeric criteria that assess the qualities , characteristics , impacts , and ethical considerations of AI systems . These benchmarks evaluate overlooked aspects of AI , such as user experience , ethical alignment , and social impact .
For example , evaluating AI systems for their adherence to ethical principles , such as fairness , justice , and non-discrimination , often requires qualitative analysis through case studies , honest reviews , and stakeholder consultations . These benchmarks could aim to assess the social impact of AI , including its effects on employee satisfaction and privacy .
While quantitative metrics might measure the accuracy of an AI system ' s explanations , qualitative benchmarks assess the comprehensibility and usefulness of these explanations to end-users .
BALANCE AI AND HUMAN AGENTS
Design AI to complement human agents that support and respect their role and expertise rather than replace human workers . Begin with a commitment to ethical AI design using the responsible AI framework above , prioritizing fairness , accountability , and transparency .
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Businesses should understand and ensure their AI strategies consider how AI and humans can work together . Like developing AI systems that provide real-time support and information to human agents during customer interactions . While AI handles routine inquiries and administrative tasks , agents can support more complex , sensitive , or nuanced customer issues requiring empathy and critical thinking .
AGENTS SHOULD BE AWARE OF POTENTIAL LIMITATIONS AND BIASES OF AI AND PREPARE TO ADDRESS THEM IN CUSTOMER INTERACTIONS .
PROVIDE ETHICAL AI TRAINING FOR EMPLOYEES
Training human agents on ethical considerations and biases in AI is imperative . Agents should be aware of potential limitations and biases of AI and prepare to address them in customer interactions . Training can also inform employees about legal and regulatory frameworks governing AI use .
Start by gradually training employees who are more likely to use AI systems to ensure a smooth transition . Then , move on to other departments to slowly integrate feedback into designing , developing , and deploying AI systems .
A steady approach makes integrating feedback easy for IT teams . It also shows employees how their feedback has improved AI-driven experiences for their company and customers , fostering a culture of responsibility and accountability .
Training also empowers employees to innovate responsibility . Employees are the face of the company , and they understand the nuances of everyday customer interactions . By providing employees with the tools and guidelines they need , they can identify opportunities to improve and use AI responsibly and ethically .
RESPONSIBLE AI
TEST , TEST , TEST
Regularly auditing AI systems for biases helps companies implement corrective measures to mitigate any identified issues .
Companies can also train AI on various datasets and involve diverse stakeholders in AI development and review .
Involving human agents in AI training allows for a practical , hands-on approach to enhancing performance . Agents can offer feedback on the AI ' s responses , drawing from their experience and understanding of nuanced customer interactions . Combining human empathy with AI improves AI ' s ability to understand and respond to real-world questions and expectations .
Another way to assess AI systems ’ efficiency is to go directly to customers . Gather customer feedback through support portals and surveys to identify areas for improvement and make informed decisions to drive customer satisfaction . Direct feedback helps evaluate the AI system ’ s accuracy , speed , and overall performance and determine whether it meets customer needs and expectations .
Building a Responsible AI framework and ethical practices is just the start of how contact centers can successfully implement AI to improve employee and customer experiences .
As AI use accelerates in contact centers and touches aspects of customer experience , companies should hold firm to the latest trends and evolving regulations . With a solid foundation of Responsible AI , companies can swiftly adjust to AI advancements and be ready to inspire their employees , customers , and stakeholders .
Rebecca Jones is President of Mosaicx , a leading provider of customer service AI and cloud-based technology solutions for enterprise companies and institutions . Rebecca joined the West Technology Group , owner of Mosaicx , in January 2021 , after a 25 + year career focused on growing businesses , people and client success .