Contact centers’ artificial intelligence (AI) adoption rates are rising. From 2024 to 2032, market research predicts that the global call center AI market will grow at a 19.8% compound annual growth rate (CAGR).
As AI systems touch more aspects of customer service, there has never been a greater need to establish Responsible AI frameworks to address ethical risks. Below is a guide to help you navigate technological advancement while championing the principles of Responsible AI.
Defining Responsible AI
Before diving in, let’s define Responsible AI. Responsible AI is a term used to establish clear principles and guidelines that ensure AI’s ethical development, deployment, and use. In implementing AI technologies, Responsible AI emphasizes fairness, transparency, accountability, bias mitigation, privacy, and regulatory compliance.
Creating a Responsible AI Framework
Successful AI implementation starts with embedding into the company culture. To do this, companies must create a Responsible AI framework that ingrains ethical best practices into their organizational mindset.
Developing a comprehensive framework is the first step towards providing team members with the resources and skills to use AI effectively and responsibly. On top of that, encourage managers to communicate openly with employees and hold them accountable for promoting Responsible AI policies, processes, and governance.
...companies must create a Responsible AI framework that ingrains ethical best practices into their organizational mindset.
Here are four principles to consider.
1. Privacy. AI systems should respect customer privacy and safeguard sensitive data. Call centers must navigate a complex landscape of privacy regulations to protect sensitive information and remain compliant.
U.S. federal and varying state laws dictate call recording and monitoring requirements, often necessitating consent from all parties to prevent legal breaches. The Telephone Consumer Protection Act (TCPA) also restricts call centers from using automated dialing systems, pre-recorded messages, or unsolicited text messaging without consent.
At the same time, other countries, such as Canada, the U.K., the European Union, Scandinavian nations, Australia, Japan, and New Zealand have their own regulations, particularly on data privacy, which are often more stringent than those in the U.S.
Compliance with these regulations is essential for preserving customer trust, securing data, and delivering exceptional service. Wherever customers may be.
2. Equity. AI solutions in call centers should be equitable and empowering for all users. Algorithms can inadvertently favor certain groups over others. Since AI models learn from historical data, promote equity by carefully curating diverse datasets that don’t perpetuate stereotypes and inequalities.
Start by establishing clear lines of responsibility and oversight for AI systems within departments.
3. Transparency. Transparency encompasses many aspects of AI in business. For starters, AI systems should be easy for customers and employees to understand by providing clear and transparent reasoning behind decisions. Companies must also communicate how AI systems store and use customer data.
When deploying AI features and programs in contact centers, open communication is vital for companies to maintain trust and enhance customer and employee experiences.
Start by outlining how service processes use AI, the nature of data utilization, and the measures in place to safeguard privacy. Businesses can alleviate concerns and build confidence among employees, customers, and stakeholders by demystifying AI operations. Transparent communication ensures all stakeholders understand its purpose, functionality, and benefits.
4. Accountability. While companies strive to create ethical AI systems, there must be procedures in case AI causes harm or operates against ethics.
Start by establishing clear lines of responsibility and oversight for AI systems within departments. Form a committee of individuals with diverse expertise, including technology, legal, ethics, business operations, and customer service. The committee’s perspectives will ensure the AI strategy adheres to ethical standards and complies with regulations.
Consult Legal Advisors
In addition to a governance committee, contact centers must have legal advisors available to help navigate the complexities of legal and regulatory compliance.
Laws such as the Payment Card Industry Data Security Standard (PCI DSS) protect sensitive customer information during transactions. PCI DSS sets stringent guidelines for handling payment card data, including prohibitions against the recording and storing of certain types of information by contact centers.
Since AI runs on historical data, by consulting with legal advisors, businesses can ensure the design and operation of systems are within relevant regulations. An advisor role can include policy development, strategic decision-making, and monitoring AI initiatives.
Set Clear Benchmarks
Companies have a personal stake in establishing benchmarks for AI for various reasons. Primarily, benchmarking sets best practices, providing a structured framework for developing, deploying, and continuously improving AI technologies. Goals may include customer satisfaction, operational efficiency, technological innovation, and compliance with regulatory standards.
Benchmarks should include quantitative and qualitative metrics that align with business goals and ethical standards. Here are benchmarks to consider.
- Quantitative Benchmarks. Companies can gauge AI’s influence on customer satisfaction levels by measuring customer satisfaction scores (CSAT) and Net Promoter Scores (NPS).
- 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.
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.
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.
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.
Agents should be aware of potential limitations and biases of AI and prepare to address them in customer interactions.
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.