top of page
  • Writer's pictureCatherine Richards

The Top 30+ AI/GenAI Terms Demystified

This glossary decodes the jargon and puts terms and concepts into a marketing context.

The Top 30+ AI/GenAI Terms for Marketers

AI (Artificial Intelligence): AI refers to smart technology that can analyze data, make decisions, and automate tasks. In marketing, AI helps optimize processes, personalize content, and enhance customer experiences.

GenAI (Generative AI): GenAI is a specialized form of AI that goes beyond analysis. It actively creates new content, such as text, images, or even music, based on patterns it has learned. For marketers, this means innovative content creation and customization.

The key difference between AI and GenAI is that AI optimizes and automates tasks leveraging data patterns. Generative AI uniquely creates new content and media, customizable for personalization. While AI drives efficiencies, generative models power creativity.

AI Agents: AI agents are software that can interact with their environment to achieve specific goals. An example would be an AI travel agent that can interact through websites or apps using its programming and access to relevant data such as flight schedules and hotel availability.

AI Ethics and Bias: Proactively addressing algorithmic bias, fairness, transparency, and accountability in marketing AI to build trust.

Algorithm Bias: Systematic and repeatable errors in AI systems that lead to unfair, unethical, or discriminatory outcomes for certain user groups.

The key difference between AI ethics and bias and algorithm bias is that AI ethics represents the upfront efforts to create equitable AI. Algorithmic bias refers to finding problems of unfairness needing improvement after a system is already built.

Artificial General Intelligence (AGI): AGI, a term expressing concern from researchers, envisions highly autonomous AI systems surpassing humans in various economically valuable tasks, posing ethical considerations and implications.

Artificial Superintelligence (ASI): A hypothetical futuristic form of AI theorized to greatly exceed human-level general intelligence, detached from practical realities in marketing.

In essence, AGI = AI matching or topping human aptitude, and ASI = AI astronomically beyond all human cognition.

Attribution AI: Applies machine learning to model touchpoint influence on conversions with enhanced accuracy over rules-based analytics, optimizing marketing budget allocations.

Chatbots: AI-powered systems that interact conversationally with users, enhancing engagement, qualifying leads, and collecting valuable analytics in marketing.

Conversational AI: The use of chatbots and virtual assistants for natural language processing, delivering personalized conversational experiences in marketing.

The key difference between chatbots and conversational AI is that chatbots are one conversational AI use case in the overall field of conversational AI.

Content Creation Automation: Using GenAI to automatically generate content for marketing purposes, producing on-brand materials tailored to different segments.

Customer Segmentation: Leveraging data, analytics, and AI technologies to systematically divide customers into distinct groups based on common attributes, behaviors, and needs. This enables finely tuned predictive segments that power highly personalized engagement.

Data Mining: Applying AI and Machine Learning techniques to extract patterns and insights from datasets, informing marketing decisions.

The key difference between data mining and machine learning is that data mining applies algorithms to extract insights from data, while machine learning goes further by dynamically improving its analytic model over time through continuous learning from new information.

Demand Forecasting: Leveraging predictive analytics and demand sensing inputs to determine upcoming marketing resource needs and inventory dynamics.

Explainable AI (XAI): Making AI models' reasoning understandable to humans, fostering trust and oversight. The goal of XAI is to create transparent "assistants" rather than black-box "oracles".

While true XAI transparency is still emerging, the increasing availability is a positive step. This allows marketers to gain some understanding of how AI is influencing their campaigns and decisions.

Experiential AI: Using AI in interactive technologies like AR and VR to evoke consumer engagement in immersive digital experiences.

Fidelity: How precisely AI models capture the nuances of human behavior and judgment. When assessing marketing AI tools, higher fidelity indicates greater precision emulating the nuanced richness of real consumer language, needs, and expression.

Hallucination: Hallucination refers to AI systems generating fabricated predictions or content that seem believable but do not accurately reflect reality. This can lead to misguided strategic decisions or an inaccurate understanding of customer behavior.

To spot and deal with hallucinations in marketing, use diverse, unbiased data to train AI tools, always double-check your data sources, and verify insights regularly to make sure your decisions are based on accurate information.

Hyper-Personalization: Using AI for highly customized, individualized messaging and experiences based on customer attributes and behaviors.

The key difference between personalization and hyper-personalization is that personalization tailors to groups sharing common attributes, while hyper-personalization uses AI to enable customization at an individual level.

To clarify the technology dynamics: AI analyzes data to power optimization and personalization for broader segments. Meanwhile, generative AI produces wholly customized creative content for hyper-personalized experiences tailored to individual customer signals and contexts.

Inclusive Design: Mitigating issues of bias in AI systems by intentionally involving diverse voices in the development process and representing a wide range of human conditions across training data.

Inference: Inference means an AI can gain insights on new data it hasn’t seen before. It recognizes similar patterns from the examples it trained on earlier. The more training examples, the smarter AI gets at making accurate predictions and recommendations.

Knowledge Base: Knowledge bases collect information into shared repositories and are found in GenAI tools like Jasper. Examples: product expertise, customer traits, behaviors, needs, and common objections.

AI uses a knowledge base to optimize. Generative AI uses them to create new things by expanding on what's already known.

Lifetime Value Models: Machine Learning (ML) algorithms predicting future monetary customer worth for targeting resources to the highest potential relationships.

Large Language Model (LLM): AI systems trained to generate high-quality, human-like text content at scale, enabling personalized content.

Here's a reference list of real-world LLMs

OpenAI's GPT-3 and Bard: These powerful models are known for their ability to generate diverse creative text formats like poems, code, scripts, musical pieces, emails, and letters.

Google AI's PaLM: This massive model excels in code generation, translation, and factual language tasks.

Google AI's Meena: This model specializes in conversational AI, focusing on engaging in open-ended, informative, and interesting dialogue.

Microsoft's Jurassic-1 Jumbo: This LLM boasts impressive performance in question-answering and factual language tasks.

Anthropic's AI Megatron-Turing NLG: This model stands out for its ability to generate long, coherent, and factual text formats.

Facebook AI's Blender: This LLM is known for its creativity and humor, capable of generating different writing styles and tones.

Here's a grouped list characterized by unique strengths

Textual creativity and diversity (GPT-3, Bard)

Coding and factual language (PaLM)

Conversational ability (Meena)

Question-answering and information accuracy (Jurassic-1 Jumbo, Megatron-Turing NLG)

Style variation and adaptive tone (Blender)

Machine Learning (ML): Algorithms learning from data, empowering adaptive strategies in marketing.

Machine learning powers a true feedback loop - as customers interact, algorithms get smarter, and strategies and experiences adapt in real time tailored to their evolving needs. This empowers marketers to create truly responsive and personalized engagement.

Natural Language Generation (NLG): Integrated into marketing automation, transforming analytics data into written insights for diverse audiences.

The key differences among LLMs, ML, and NLG lie in their core functions. Large language models (LLMs) focus on generating original text content from scratch, machine learning (ML) extracts patterns from data to optimize decisions, and natural language generation (NLG) tailors analytics data into written narrations, providing distinct capabilities in the realm of content creation and decision optimization.

Personalization: Using analytics to tailor content and offers to broader segments based on some shared attributes like interests and past purchases to boost marketing relevance.

Predictive Analytics: Using data and AI algorithms to forecast future outcomes and trends, optimizing resource allocation and marketing initiatives.

Prompt Engineering: Crafting specific instructions for AI systems to generate desired content, and optimizing customization in marketing.

Quantum Machine Learning (QML): A theoretical area with no current practical advantage over existing AI, requiring a pragmatic focus on proven analytics techniques.

Responsible AI: Ensuring ethical development and use of GenAI in marketing, avoiding bias, and promoting transparency.

Sentiment Analysis: Using AI to determine subjective opinions in textual data, monitoring and analyzing customer feedback in marketing.

Style Guide: Documented content standards and branding guidelines dynamically integrated by GenAI tools like Jasper to ensure that new content aligns with standards established by the brand.

Style Transfer: Applying artistic styles to images using AI algorithms, infusing brand aesthetics into visuals in marketing.

Style Tuning: Control parameters provided by generative systems allowing tailoring of content vocal tone, formality level, length, complexity, and more.

Text-to-Image Generation: Converting written descriptions into visually compelling images using AI for marketing.

Use Case: Use cases detail specific, real-world applications of GenAI capabilities addressing common marketing challenges and frictions.

Use cases can be generally categorized into prediction, language, and vision. Prediction is about forecasting future outcomes. Language relates to generating or comprehending text. Vision involves analyzing visual content.

Vision Recognition: Object detection, image categorization, and facial analysis for enhanced digital experiences and analytics.


About me

As a B2B enterprise marketer, I'm on a quest to define our unique value proposition in the age of AI. I'm the writer and editor of The Strategist Blog, where I explore AI's power to transform content creation and empower fellow marketers to shape the future of their work. That future is human-first, AI-powered. Let's go!


More posts in this series

Making GenAI Work for Work:

The ROI of REAL Connection

Find Your Use Case

Create a Marketing AI Council

Nobody is Coming to Save You


bottom of page