A

AI Agents: Advanced, next-generation systems that use AI to engage in natural, contextual conversations, learn over time, and adapt to user needs.

AI Assistants: These support human agents by automating repetitive tasks, suggesting real-time responses, and enhancing service quality.

AI Copilot: AI assistants that enhance human decision-making, providing real-time insights and support.

AI Governance: Policies and processes to ensure ethical, secure, and effective AI deployment, addressing risks like bias and data privacy.

AI Training Data: Datasets used to train AI models, where high-quality data ensures accuracy and unbiased performance.

AIOps (AI for IT Operations): The application of AI to automate and enhance IT operations, ensuring smooth customer-facing system performance and reducing downtime.

Agentic AI: AI designed to exhibit autonomous reasoning, goal-directed behavior, and a sense of agency, mimicking human interactions through natural language processing.

Algorithm: A set of rules or instructions designed to solve a problem or perform a task.

Artificial Intelligence (AI): The simulation of human intelligence by machines, enabling them to perform tasks such as learning, reasoning, and decision-making.

Automation Anxiety: Concerns about the impact of AI on human jobs and relationships within CX.

Autonomous Agents: AI systems capable of operating independently, performing tasks without constant human oversight.

B

Bias in AI: The risk of AI systems reflecting biases present in training data, leading to unfair or inaccurate outcomes.

Big Data: Large, complex datasets that require AI for analysis and actionable insights.

Build vs. Buy: Approaches to AI adoption:

• Build: Custom development tailored to specific needs.
• Buy: Off-the-shelf solutions for faster deployment.
• Buy-to-Build: Hybrid option combining purchased solutions with customizations.

C

Chatbots: Legacy systems that follow predefined scripts or workflows, with limited flexibility and handling capabilities.

Continuous Improvement Cycle: An iterative process for refining AI systems based on performance data and feedback, ensuring they stay aligned with customer needs.

Conversational AI: AI systems capable of advanced, dynamic conversations using natural language processing and machine learning, providing a more human-like experience than chatbots.

Conversational Designer: A specialist responsible for designing AI-driven conversations, ensuring interactions feel natural, effective, and user-friendly.

Customer Effort Score (CES): A metric to evaluate how easy it is for customers to resolve issues, often improved by AI-driven automation.

CX Automation: The use of AI and machine learning to streamline customer interactions and optimize experiences across touchpoints.

D

Data Privacy and Security: Practices ensuring customer data handled by AI systems is protected against breaches, unauthorized access, or misuse.

Deep Learning: A subset of machine learning that uses multi-layered neural networks for complex data analysis.

Digital Twin: A virtual representation of a physical product, process, or system, used in CX to model customer journeys or simulate interactions.

E

Edge Cases: Rare or unusual scenarios that AI systems may not handle effectively, requiring special attention for robust implementation.

Emerging Concepts: New and evolving ideas in AI technology that are reshaping customer experience.

Ethical AI: The design and deployment of AI systems prioritizing fairness, transparency, and user rights.

Explainability (or Interpretability): The ability of an AI system to provide clear, understandable explanations for its decisions, crucial for trust and accountability.

F

First Contact Resolution (FCR): A metric measuring the resolution of customer issues on the first interaction, often improved by AI systems.

G

Generative AI: AI systems capable of creating new content (text, images, videos) based on patterns learned from large datasets, used for content generation and personalization.

H

Hyper-Personalization: Advanced personalization using AI and big data for highly tailored customer experiences.

I

Intent Recognition: AI’s ability to identify the purpose behind customer queries, enabling smarter routing and contextual responses.

K

Knowledge Management Systems: Digital systems that store and organize information (FAQs, policies) to train AI and improve response accuracy.

L

Large Language Models (LLMs): AI models trained on massive datasets to understand and generate human-like language, powering tools like chatbots and content generators.

M

Machine Learning (ML): A subset of AI that uses algorithms and statistical models to enable systems to improve their performance on tasks through experience.

Metrics and Impact: Key indicators for measuring AI’s impact in CX, such as cost savings, CSAT, and operational efficiency.

N

Net Promoter Score (NPS): A measure of customer loyalty and satisfaction, often influenced by AI-driven CX enhancements.

Neural Network: A computing system inspired by the human brain, consisting of layers of nodes (or “neurons”) that process information.

O

Omnichannel Integration: AI’s ability to operate seamlessly across various interaction channels, ensuring consistent and connected customer experiences.

P

Predictive Analytics: AI techniques analyzing historical data to forecast future trends, needs, or behaviors.

Proactive Support: AI-driven capabilities that anticipate customer needs or problems, such as predictive maintenance alerts or tailored recommendations.

R

Real-Time Analytics: The processing and analysis of data as it is generated, enabling immediate insights and actions.

Recommendation Engine: AI systems suggesting products, services, or content based on user behavior and preferences.

S

Self-Service Automation: AI tools empowering customers to resolve issues independently, such as through chatbots or interactive knowledge bases.

Stakeholder Alignment: Ensuring all relevant parties—CX leaders, IT teams, and compliance officers—are aligned on AI goals and responsibilities.

T

Tracking Success Metrics: Key indicators like cost savings, customer satisfaction (CSAT), and operational efficiency to measure AI’s impact.

Training Data: The dataset used to teach AI models to recognize patterns and make predictions.

V

Voice of the Customer (VoC) Analytics: AI-driven analysis of customer feedback from multiple channels to uncover insights and improve CX strategies.

Voice Recognition: AI’s ability to process, understand, and transcribe spoken language, often a key component of voice-based CX solutions.