Does it seem like everyone around you is casually tossing around terms like “generative AI,” “large language models,” or “deep learning”? Feeling a little lost on the details? We’ve created a primer on everything you need to know to understand the newest, most impactful technology that’s come along in decades. Let’s dive into the world of generative AI.
We’ve put together a list of the most essential terms that will help everyone in your company — no matter their technical background – understand the power of generative AI. Each term is defined based on how it impacts both your customers and your team.”
And to highlight the real-world applications of generative AI, we put it to work for this article. Our experts weighed in on the key terms, and we let a generative AI tool lay the groundwork for this glossary. Each definition needed a human touch to get it ready for publication, but it saved loads of time.
Generative AI Terms by Topic
AI CORE TERMS
AI TRAINING & LEARNING
Artificial intelligence (AI)
AI is the broad concept of having machines think and act like humans. Generative AI is a specific type of AI (more on that below).
- What it means for customers: AI can help your customers by predicting what they’re likely to want next, based on what they’ve done in the past. It gives them more relevant communications and product recommendations, and can remind them of important upcoming tasks (example: It’s time to reorder). It makes everything about their experience with your organisation more helpful, personalised, efficient, and friction-free.
- What it means for teams: AI helps your teams work smarter and faster by automating routine tasks. This saves employees time, offers customers faster service, and provides more personalised interactions, all of which improves customer retention to drive the business.
Artificial neural network (ANN)
An ANN is a computer program that mimics the way human brains process information. Our brains have billions of neurons connected together, and an ANN (also referred to as a “neural network”) has lots of tiny processing units working together. It’s like a team all working to solve the same problem. Every team member does their part, then passes their results on. At the end, you get the answer you need. With humans and computers, it’s all about the power of teamwork.
- What it means for customers: Customers benefit in all sorts of ways when ANNs are solving problems and making accurate predictions – like highly personalised recommendations that result in a more tailored, intuitive, and ultimately more satisfying customer experience. Neural networks are excellent at recognising patterns, which makes them a key tool in detecting unusual behaviour that may indicate fraud. This helps protect customers’ personal information and financial transactions.
- What it means for teams: Teams benefit, too. They can forecast customer churn, which prompts proactive ways to improve customer retention. ANNs can also help in customer segmentation, allowing for more targeted and effective marketing efforts. In a CRM system, neural networks could be used to predict customer behaviour, understand customer feedback, or personalise product recommendations.
Think of augmented intelligence as a melding of people and computers to get the best of both worlds. Computers are great at handling lots of data and doing complex calculations quickly. Humans are great at understanding context, finding connections between things even with incomplete data, and making decisions on instinct. Augmented intelligence combines these two skill sets. It’s not about computers replacing people or doing all the work for us. It’s more like hiring a really smart, well-organised assistant.
- What it means for customers: Augmented intelligence lets a computer crunch the numbers, but then humans can decide what actions to take based on that information. This leads to better service, marketing, and product recommendations for your customers.
- What it means for teams: Augmented intelligence can help you make better and more strategic decisions. For example, a CRM system could analyse customer data and suggest the best time for sales or marketing teams to reach out to a prospect, or recommend products a customer might be interested in.
Customer Relationship Management (CRM) with generative AI
CRM is a technology that keeps customer records in one place to serve as the single source of truth for every department, which helps companies manage current and potential customer relationships. Generative AI can make CRM even more powerful — think personalised emails pre-written for sales teams, ecommerce product descriptions written based on images alone, marketing campaign landing pages, contextual customer service ticket replies, and more.
- What it means for customers: A CRM gives customers a consistent experience across all channels of engagement, from marketing to sales to customer service and more. While customers don’t see a CRM, they feel the connection during every interaction with a brand.
- What it means for teams: A CRM helps companies stay connected to customers, streamline processes, and improve profitability. It lets your teams store customer and prospect contact information, identify sales opportunities, record service issues, and manage marketing campaigns, all in one central location. For example, it makes information about every customer interaction available to anyone who might need it. Generative AI amplifies CRM by making it faster and easier to connect to customers at scale – think marketing lead-gen campaigns automatically translated to reach your top markets across the globe, or recommended customer service responses that help agents solve problems quickly and identify opportunities for future sales.
Deep learning is an advanced form of AI that helps computers become really good at recognising complex patterns in data. It mimics the way our brain works by using what’s called layered neural networks, where each layer is a pattern (like features of an animal) that then lets you make predictions based on the patterns you’ve learned before (ex: identifying new animals based on recognised features). It’s really useful for things like image recognition, speech processing, and natural-language understanding.
- What it means for customers: Deep learning-powered CRMs create opportunities for proactive engagement. They can enhance security, make customer service more efficient, and personalise experiences. For example, if you have a tradition of buying new fan gear before each football season, deep learning connected to a CRM could show you ads or marketing emails with your favourite team gear a month before the season starts so you’ll be ready on game day.
- What it means for teams: In a CRM system, deep learning can be used to predict customer behaviour, understand customer feedback, and personalise product recommendations. For example, if there’s a boom in sales among a particular customer segment, a deep learning-powered CRM could recognise the pattern and recommend increasing marketing spend to reach more of that audience pool.
Discriminator (in a GAN)
In a Generative Adversarial Network (GAN), the discriminator is like a detective. When it’s shown pictures (or other data), it has to guess which are real and which are fake. The “real” pictures are from a dataset, while the “fake” ones are created by the other part of the GAN, called the generator. The discriminator’s job is to get better at telling real from fake, while the generator tries to get better at creating fakes. This is the software version of continuously building a better mousetrap.
- What it means for customers: Discriminators in GANs are an important part of fraud detection, so their use leads to a more secure customer experience.
- What it means for teams: Discriminators in GANs help your team evaluate the quality of synthetic data or content. They aid in fraud detection and personalised marketing.
What does an ethical AI maturity model look like?
Your customers expect you to use AI responsibly. You need to implement an ethical AI practice to develop and operationalise principles like transparency, fairness, responsibility, accountability, and reliability. Here’s how.
Ethical AI maturity model
An Ethical AI maturity model is a framework that helps organisations assess and enhance their ethical practices in using AI technologies. It maps out the ways organisations can evaluate their current ethical AI practices, then progress toward more responsible and trustworthy AI usage. It covers issues related to transparency, fairness, data privacy, accountability, and bias in predictions.
- What it means for customers: Having an ethical AI model in place, and being open about how you use AI, helps build trust and assures your customers that you are using their data in responsible ways.
- What it means for teams: Regularly evaluating your AI practices and staying transparent about how you use AI can help you stay aligned to your company’s ethical considerations and societal values.
Explainable AI (XAI)
Remember being asked to show your work in maths class? That’s what we’re asking AI to do. Explainable AI (XAI) should provide insight into what influenced the AI’s results, which will help users to interpret (and trust!) its outputs. This kind of transparency is important when dealing with sensitive systems like healthcare or finance, where explanations are required to ensure fairness, accountability, and in some cases, regulatory compliance.
- What it means for customers: If an AI system can explain its decisions in a way that customers understand, it increases reliability and credibility. It also increases user trust, particularly in sensitive areas like healthcare or finance.
- What it means for teams: XAI can help employees understand why a model made a certain prediction. Not only does this increase their trust in the system, it also supports better decision-making and can help refine the system.
Generative AI is the field of artificial intelligence that focuses on creating new content based on existing data. For a CRM system, generative AI can be used to create a range of helpful things, from writing personalised marketing content, to generating synthetic data to test new features or strategies.
- What it means for customers: Better and more targeted marketing content, which helps them get exactly the information they need and no more.
- What it means for teams: Faster builds for marketing campaigns and sales motions, plus the ability to test out multiple strategies across synthetic data sets and optimise them before anything goes live.
Generative adversarial network (GAN)
One of two deep learning models, GANs are made up of two neural networks: a generator and a discriminator. The two networks compete with each other, with the generator creating an output based on some input, and the discriminator trying to determine if the output is real or fake. The generator then fine-tunes its output based on the discriminator’s feedback, and the cycle continues until it stumps the discriminator.
- What it means for customers: They allow for highly customised marketing that uses personalised images or text – like custom promotional imagery for every customer.
- What it means for teams: They can help your development team generate synthetic data when there is a lack of customer data. Especially useful when privacy concerns arise around using real customer data.
Generative pre-trained transformer (GPT)
GPT is a neural network family that is trained to generate content. GPT models are pre-trained on a large amount of text data, which lets them generate clear and relevant text based on user prompts or queries.
- What it means for customers: Customers have more personalised interactions with your company that focus on their specific needs.
- What it means for teams: GPT could be used to automate the creation of customer-facing content, or to analyse customer feedback and extract insights.
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A generator is an AI-based software tool that creates new content from a request or input. It will learn from any supplied training data, then create new information that mimics those patterns and characteristics. ChatGPT by OpenAI is a well-known example of a text-based generator.
- What it means for customers: Using generators, it’s possible to train AI chatbots that learn from real customer interactions, and continuously create better and more helpful content.
- What it means for teams: Generators can be used to create realistic datasets for testing or training purposes. This can help your team find any bugs in a system before it goes live, and let new hires get up to speed in your system without impacting real data.
A hallucination happens when generative AI analyses the content we give it, but comes to an erroneous conclusion and produces new content that doesn’t correspond to reality. An example would be an AI model that’s been trained on thousands of photos of animals. When asked to generate a new image of an “animal,” it might combine the head of a giraffe with the trunk of an elephant. While they can be interesting, hallucinations are undesirable outcomes and indicate a problem in the generative model’s outputs.
- What it means for customers: When companies monitor for and address this issue in their software, the customer experience is better and more reliable.
- What it means for teams: Quality assurance will still be an important part of an AI team. Monitoring for and addressing hallucinations helps ensure the accuracy and reliability of AI systems.
Large language model (LLM)
An LLM is a type of artificial intelligence that has been trained on a lot of text data. It’s like a really smart conversation partner that can create human-sounding text based on a given prompt. Some LLMs can answer questions, write essays, create poetry, and even generate code.
- What it means for customers: personalised chatbots that offer human-sounding interactions, allowing customers quick and easy solutions to common problems in ways that still feel authentic.
- What it means for teams: Teams can automate the creation of customer-facing content, analyse customer feedback, and answer customer inquiries.
Machine learning is how computers can learn new things without being programmed to do them. For example, when teaching a child to identify animals, you show them pictures and provide feedback. As they see more examples and receive feedback, they learn to classify animals based on unique characteristics. Similarly, machine learning models learn from labelled data to make accurate predictions and decisions. They generalise and apply their knowledge to new examples, just as humans do.
- What it means for customers: When a company better understands what customers value and want, it leads to enhancements in current products or services, or even the development of new ones that better meet customer needs.
- What it means for teams: Machine learning can be used to predict customer behaviour, personalise marketing content, or automate routine tasks.
Machine learning bias
We’ve all heard the phrase “garbage in, garbage out,” right? Machine learning bias is just a turbocharged AI version of that. When computers are fed biassed information, they make biassed decisions. This can be the result of a deliberate decision by the humans feeding the computer data, by accidentally incorporating biassed data, or when the algorithm makes wrong assumptions during the learning process, leading to biassed results.
Example: If a loan approval model is trained on historical data that shows a trend of approving loans for certain demographics (like gender or race), it may learn and perpetuate those biases. This isn’t because of a prejudice in the system, but a bias in the training data. It will have huge implications for the accuracy and effectiveness of the system, and help build equality and trust among customers.
- What it means for customers: Working with companies that actively engage in overcoming bias leads to more equitable experiences, and builds trust.
- What it means for teams: It’s important to check for and address bias to ensure that all customers are treated fairly and accurately. Understanding machine learning bias and knowing your organisation’s controls for it helps your team have confidence in your processes.
This is a program that’s been trained to recognise patterns in data. You could have a model that predicts the weather, translates languages, identifies pictures of cats, etc. Just like a model aeroplane is a smaller, simpler version of a real aeroplane, an AI model is a mathematical version of a real-world process.
- What it means for customers: The model can help customers get much more accurate product recommendations.
- What it means for teams: This can help teams to predict customer behaviour, and segment customers into groups.
Natural language processing (NLP)
NLP is a field of artificial intelligence that focuses on how computers can understand, interpret, and generate human language. It’s the technology behind things like voice-activated virtual assistants, language translation apps, and chatbots.
- What it means for customers: NLP allows customers to interact with systems using normal human language rather than complex commands. Voice-activated assistants are prime examples of this. This makes technology more accessible and easier to use, improving user experiences.
- What it means for teams: NLP can be used to analyse customer feedback, power chatbots, or automate the creation of customer-facing content.
You don’t need an engineering degree for this one. Prompt engineering means figuring out how to ask a question to get exactly the answer you need. It’s carefully crafting or choosing the input (prompt) that you give to a machine learning model to get the best possible output.
- What it means for customers: When your generative AI tool gets a strong prompt, it’s able to deliver a strong output. The stronger, more relevant the prompt, the better the end user experience.
- What it means for teams: Can be used to ask a large language model to generate a personalised email to a customer, or to analyse customer feedback and extract key insights.
Sentiment analysis involves determining the emotional tone behind words to gain an understanding of the attitudes, opinions, and emotions of a speaker or writer. It is commonly used in CRM to understand customer feedback or social media conversation about a brand or product.
- What it means for customers: Customers can offer feedback through new channels, leading to more informed decisions from the companies they interact with.
- What it means for teams: Sentiment analysis can be used to understand how customers feel about a product or brand, based on their feedback or social media posts, which can inform many aspects of brand or product reputation and management.
Supervised learning is when a model learns from examples. It’s like a teacher-student scenario: the teacher provides the student (the model) with questions and the correct answers. The student studies these, and over time, learns to answer similar questions on their own. It’s really helpful to train systems that will recognise images, translate languages, or predict likely outcomes. (Check out unsupervised learning below).
- What it means for customers: Increased efficiency and systems that learn to understand their needs via past interactions.
- What it means for teams: Can be used to predict customer behaviour or segment customers into groups, based on past data.
Transformers are a type of deep learning model, and are especially useful for processing language. They’re really good at understanding the context of words in a sentence because they create their outputs based on sequential data (like an ongoing conversation), not just individual data points (like a sentence without context). The name “transformer” comes from the way they can transform input data (like a sentence) into output data (like a translation of the sentence).
- What it means for customers: Businesses can enhance the customer service experience with personalised AI chatbots. These can analyse past behaviour and provide personalised product recommendations. They also generate automated (but human-feeling) responses, supporting a more engaging form of communication with customers.
- What it means for teams: Transformers help your team generate customer-facing content, and power chatbots that can handle basic customer interactions. Transformers can also perform sophisticated sentiment analysis on customer feedback, helping you respond to customer needs.
Unsupervised learning is letting AI find hidden patterns in your data without any guidance. This is all about allowing the computer to explore and discover interesting things on its own. Imagine you have a big bag of mixed-up puzzle pieces, but you don’t have the picture on the box to refer to, so you don’t know what you’re making. Unsupervised learning is like figuring out how the pieces fit together, looking for similarities or groups without knowing what the final picture will be.
- What it means for customers: When we uncover hidden patterns or segments in customer data, it enables us to deliver completely personalised experiences. Customers will get the most relevant offers and recommendations, enhancing customer satisfaction.
- What it means for teams: Teams get valuable insights and a new understanding of complex data. It enables teams to discover new patterns, trends, or anomalies that may have been overlooked, leading to better decision-making and strategic planning. This enhances productivity and drives innovation within the organisation.
In machine learning, validation is a step used to check how well a model is doing during or after the training process. The model is tested on a subset of data (the validation set) that it hasn’t seen during training, to ensure it’s actually learning and not just memorising answers. It’s like a pop quiz for AI in the middle of the semester.
- What it means for customers: Better-trained models create more usable programs, improving the overall user experience.
- What it means for teams: Can be used to ensure that a model predicting customer behaviour or segmenting customers will work as intended.
Zone of proximal development (ZPD)
The Zone of Proximal Development (ZPD) is an education concept. For example, each year students progress their maths skills from adding and subtracting, to multiplication and division, and even up to complex algebra and calculus equations. The key to advancing is progressively learning those skills. In machine learning, ZPD is when models are trained on progressively more difficult tasks, so they will improve their ability to learn.
- What it means for customers: When your generative AI is trained properly, it’s more likely to produce accurate results.
- What it means for teams: Can be applied to employee training so an employee could learn to perform more complex tasks or make better use of the CRM’s features.
Take the next step with generative AI
Generative AI has the power to help all of your teams connect more closely with your customers, unlock creativity, and increase productivity. From a business perspective, there’s almost no part of your organisation that AI can’t make more efficient. Sales, service, marketing, and commerce applications are all able to use the power of generative AI to deliver better, more tailored solutions to your customers, and to do so quickly.
By letting AI assist us with the more routine tasks of helping our customers thrive, we’ll be able to free our human teams to do what they do best — come up with new ideas and new ways to collaborate, all while building those unique connections that only humans can.
Now that you’re up to speed on Generative AI for CRM, see it in action.