Ethical Aspects of AI in customer realtionship management
meganathanrbm
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16 slides
Jul 21, 2024
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About This Presentation
Bridging the gap between design and development in web applications presents several
challenges. Translating intricate Figma designs into functional code requires meticulous
attention to detail, ensuring pixel-perfect implementation for a visually consistent user
experience. Additionally, identifyin...
Bridging the gap between design and development in web applications presents several
challenges. Translating intricate Figma designs into functional code requires meticulous
attention to detail, ensuring pixel-perfect implementation for a visually consistent user
experience. Additionally, identifying and rectifying UI bugs that may impact user interaction
and responsiveness is crucial. Furthermore, seamlessly integrating new functionalities into
existing projects necessitates careful consideration of code maintainability and performance
optimization.
Size: 66.12 KB
Language: en
Added: Jul 21, 2024
Slides: 16 pages
Slide Content
Ethical Aspects of AI in CRM Data Engineering and Management
INTRODUCTION
Introduction to Ethical AI in CRM AI has transformed the way businesses interact with customers, but it also brings ethical challenges that need addressing.
AI in CRM AI in CRM refers to the use of Artificial Intelligence technologies to enhance and streamline customer interactions, sales, and service. This involves the use of algorithms, data analysis, and automation to improve customer experiences and business efficiency.
Benefits of AI in CRM Enhanced Customer Insights Improved Customer Service Operational Efficiency Cost Saving
Ethical Considerations in Data Collection AI in CRM heavily relies on customer data for personalization and predictions. Ethical concerns arise regarding the collection, storage, and usage of customer data.
Bias AI algorithms can inherit biases from historical data, leading to unfair treatment of certain customer groups.
Fairness Ensuring fairness in AI models is crucial to prevent discrimination.
Transparency Transparency is essential to build trust with customers. They should understand how AI decisions are made.
Accountability Accountability means taking responsibility for AI-driven actions and rectifying errors.
Privacy and Security Protecting customer data is paramount. Unauthorized access or data breaches can lead to significant ethical and legal issues. AI-powered CRM systems must prioritize privacy and security.
Challenges Cost and Resources Resistance to Change Data Quality
Conclusion Ethical considerations are paramount in AI-driven CRM, shaping trust and accountability in customer interactions and business operations. Prioritizing ethics in AI integration not only fosters customer loyalty but also ensures compliance with regulations, providing a solid foundation for sustainable and responsible AI adoption.