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11 Must-Know AI Terms for Product Professionals in 2024

Himani Tiwary
Published: 20 June, 2024

Hey, product fam! The digital product landscape is evolving at warp speed, and AI is not just leading the charge; it’s becoming the new norm. We must adapt and level up our skills as product professionals (think Product Managers, Business Analysts, Project Managers – you get the idea!). This article post is your one-stop shop for understanding the essential AI terminology you’ll encounter. We’re building your personal AI glossary of AI key terms because understanding AI is no longer a luxury; it’s a necessity in the digital product world.

Why AI Terminology Matters: Understanding the Jargon

AI Terminology

Image source: vecteezy

Before we dive into the specifics, let’s get on the same page about categorising these AI terms. Here’s the breakdown, presented in a way that’s easy to digest:

  • AI Core Terms: These are the fundamentals – the building blocks of AI knowledge.
  • AI Business Terms: These terms focus on the business side of AI, like ethics and adoption strategies.
  • AI Tools & User Terms: This is where we’ll explore specific tools and user interactions in the AI world. (we will cover it in our next blog post)

Imagine this: you’re invited to an AI team meeting where they’ll discuss your company’s AI strategy and upcoming projects. You want to avoid walking in there feeling lost, right? Just like learning a new skill (like swimming – and trust me, jumping straight into the deep end isn’t ideal!), we need to build a solid foundation in AI first.

That’s where these core AI terms come in. Consider this a 30,000-foot view to familiarise yourself with the AI world. Don’t worry; we’re not aiming to make you an expert yet – baby steps!

AI Core Terms: Crash Course for Product People

#1 What is Artificial Intelligence (AI)?

Artificial Intelligence: This is the big kahuna — an expansive field of computer science engineering focused on creating very intelligent machines that mimic human thinking, such as learning and problem-solving. Think of AI as your future teammate, helping to automate processes and enhance your digital products.

Who invented AI?

Fun fact: AI has existed since the 1950s. A computer science pioneer, Alan Turing, laid the groundwork with the Turing Test, a way to measure a machine’s intelligence.

How is AI used in digital products?

AI in Action: As a product manager, AI can take automated tasks and processes of your current product to the next level. Real-world AI use cases are:

  • AI chat Vs Automated chatbotsThink chatbots that evolve beyond FAQs to handle complex live chat conversations. 
  • Human KYC vs AI KYC (Know Your Customer): Imagine opening a bank account with online KYC, done by support centre staff, and having this authentication over a video call with an AI-looking human rather than an actual human.
  • Content research via blogs vs. Gemini: Gemini is my favourite content research tool. It is like having Google as your content research tool.

#2 What is Generative AI?

Generative AI (GenAI): This is fantastic. Generative AI can create new text, images, videos, or music using user prompts. These AI systems are trained on massive datasets, allowing them to identify patterns and generate new content. 

Think of it as an AI artist trained on massive datasets to generate new content based on user requests.

GenAI Tool Example: Have you ever heard of ChatGPT? That’s a popular generative AI tool.

#3 What is Machine Learning (ML)?

Machine Learning (ML): This is a subfield of AI where machines get smarter by analysing data without explicit instructions. Think of it as learning by doing. 

Popular ML techniques include:

  • Supervised Learning: Imagine training a model (computer code) to identify spam emails. This is supervised learning, where the data is labelled (spam or not spam) to help the model predict new, unseen data.
  • Unsupervised Learning: This is when the machine uncovers hidden patterns in unlabeled data, such as customer segmentation based on purchase history.

I know this might seem like a lot to take in, but don’t worry – you’re doing great! Here’s a more straightforward way to think about Machine Learning: imagine intelligent machines are students, and we humans are their teachers. We guide them on what to learn, how to understand it, and how to distinguish right from wrong. That’s the fundamental essence of Machine Learning

#4 What is Deep Learning?

Deep Learning: Consider Deep Learning a sophisticated subset of ML inspired by the human brain’s structure and function. These models use complex structures called artificial neural networks to process information-rich data such as images, text, and speech.

Think of it as a super brain for computers!

Natural Language Processing: Your Computer Best Friend Who Understands You

#5 What is Natural Language Processing (NLP)?

Natural Language Processing: Let’s talk about how AI understands us humans – natural language processing (NLP, for short). It’s a subfield of AI that makes computers whizze at deciphering our language, just like magic.

Think about it: you wouldn’t want your product stuck in a one-way conversation, right? NLP closes the gap between humans and machines, allowing AI to interpret, manipulate, and even generate human language. This unlocks a world of possibilities for product managers like us.

Real-world NLP in Action:

  • Have you ever wondered how your email filters know which emails to send to your spam folder? That’s NLP in action! It analyses the content of emails to identify patterns and categorise them accordingly.
  • Remember the struggle of sorting through endless emails? Gmail’s nifty feature that sorts emails into “Primary,” “Social,” and “Promotions” tabs? Yep, that’s NLP too! It analyses email content to understand the sender and purpose, making navigating your inbox easy.
  • Have you ever chatted with Siri or Alexa? Those virtual assistants use NLP to understand your voice commands, recognise speech patterns, and respond in a way that makes sense. Pretty cool, huh?

AI Tool Spotlight: ChatGPT – An NLP Powerhouse

ChatGPT is an excellent example of an NLP tool in action. This nifty AI can hold conversations, translate many languages, and write different kinds of creative content, such as lyrics, books, and blogs, all thanks to its NLP capabilities.

AI Algorithms: The Brains Behind the Machine

#6 What is an AI algorithm?

AI Algorithm: Have you ever wondered what makes your favourite recommendation engine tick? Or how does your spam filter know which emails to banish to the junk folder? The answer lies in the fascinating world of AI algorithms!

Think of an AI algorithm as a super-complex recipe for a computer. Just like a recipe tells you how to cook a delicious meal, an algorithm gives the computer instructions. But instead of mixing ingredients, these instructions tell the computer how to learn and solve problems.

Here’s what makes AI algorithms unique:

  • They’re like expert tutors for computers: Imagine a computer that gets smarter by the day. AI algorithms analyse data, identify patterns, and use those patterns to make predictions or decisions. The computer is constantly learning and improving based on the information it sees.
  • They power the magic of machine learning. Machine learning (ML) is a subcategory of AI in which computers learn without explicit programming. AI algorithms are the secret sauce behind ML, enabling computers to get better at a task the more data they see.
  • For example, an AI algorithm powering a recommendation engine might analyse your past purchases and browsing history. Based on these patterns, it can suggest new products you might be interested in. Pretty cool, right?

The complexity of AI algorithms can vary greatly. Some might be relatively simple, while others involve intricate layers of neural networks mimicking the human brain. But no matter how complex, they all share the same core function: guiding the computer’s learning process.

So, the next time you interact with AI-powered technology, remember the silent workhorse behind the scenes – the AI algorithm!

Large Language Models (LLMs): The Supercharged Storytellers of AI

#7 What is a Large Language Model (LLM)?

Large Language Model (LLMs): Have you ever used a tool that can write different kinds of creative text formats, from poems to code? That’s the magic of Large Language Models (LLMs).

Imagine a super-powered AI trained on a massive library of books, articles, and code. 

That’s essentially what an LLM is – a robust language model that can understand and generate human-like text.

Here’s what makes LLMs so unique:

  • They’re the Shakespeares of the AI world: LLMs can create different creative text formats based on your instructions. Need a product description with a touch of humour? An LLM can do that. Want to draft a catchy marketing email? No problem for an LLM.
  • They’re pushing the boundaries of AI: LLMs are a vital part of generative AI, a field focused on creating new content. Imagine a world where AI can write product descriptions, marketing copy, or even design elements – that’s the exciting potential of LLMs.

But how do LLMs work? They rely on a particular type of deep learning model called a transformer. Think of a transformer as a super-powered translator that can understand the nuances of human language. The transformer learns to identify patterns and relationships between words by analysing massive amounts of text data. This allows LLMs to not only understand what you’re saying but also generate new text that’s relevant and coherent.

LLMs are still under development, but they significantly impact various industries. For example, they’re being used to create chatbots that can have more normal human-like conversations with end users. They’re also being used to generate realistic product descriptions and even write different kinds of creative content.

The future of LLMs is bright. As these models evolve, we expect even more exciting applications for product managers and businesses.

The Awesome Power of Models: AI's Secret Weapon

By now, you’ve heard the term “model” thrown around a lot in the AI world. So, what exactly is an AI model?

Imagine you’re on a treasure hunt, sifting through a mountain of clues. An AI model is like your super-powered map, helping you identify patterns and zero in on the hidden treasure.

Here’s the lowdown on AI models:

  • They’re like pattern detectives: These models are computer programs trained on massive datasets. By analysing this data, they can uncover hidden patterns and relationships that would be invisible to the human eye.
  • Think of data as puzzle pieces: The more data you feed an AI model, the better it gets at solving puzzles. Each data point is like a tiny puzzle piece, and the model figures out how they all fit together.
  • They unlock the secrets in your data: These patterns hold the key to valuable insights and predictions. For example, an AI model might analyse customer purchase history to predict what products a customer might be interested in buying next.

There are two main types of AI models you’ll hear about:

  • Machine Learning (ML) models are like workhorses, trained on specific algorithms to tackle particular tasks. They learn and improve over time as they’re exposed to more data.
  • Deep Learning (DL) models: Think of these as the super-powered cousins of ML models. They use complex neural networks inspired by the human brain to process information. This allows them to handle even more intricate tasks and data types.

Behind the scenes, your data science team is the mastermind behind these models. They’re the ones who define, fine-tune, and implement these models, making sure the AI delivers the best results possible.

So, the next time you hear about AI models, remember – they’re the secret weapon that unlocks the power of data!


Demystifying AI: Business Buzzwords Explained

Let’s shift gears and explore the business side of AI. Although these might sound complex, understanding them is crucial for navigating the world of AI-powered products.

#8 What is Data Science?

Data Science: Think of data science as the ultimate detective work for your business. Imagine a vast treasure trove of data – customer purchases, website clicks, social media mentions. Data scientists are the sheriffs, unearthing hidden patterns and insights that can transform your business.

Here’s why data science is a game-changer for businesses:

  • Uncovers hidden gems in your data: Data scientists use a blend of math, statistics, and AI techniques to analyse massive datasets. They can identify trends, predict customer behaviour, and forecast future sales.
  • Answers your burning business questions: Have you ever wondered why customers abandon their carts or which marketing campaigns resonate the most? Data science can help answer these questions and provide data-driven solutions.
  • Empowers smarter decision-making: With data science insights, you can move beyond gut feelings and make strategic decisions backed by hard evidence. Data science processes enhance product management & development, targeted marketing campaigns, and a more competitive edge.

So, why do many organisations start their AI journey with a data science team?

  • Building a solid foundation: Data science lays the groundwork for successful AI projects. By understanding your data and its potential, you can identify the best use cases for AI implementation.
  • Transforming insights into action: Data science isn’t just about discovery; it’s about putting insights into action. Data scientists work closely with product managers and stakeholders to ensure findings translate into real-world business improvements.

In a nutshell, data science is the bridge between your data and actionable business strategies. By harnessing the power of data science, you can unlock AI’s true potential and drive innovation in your organisation.

Business Buzzwords Explained - AI Ethics

Let us now delve into a crucial aspect of AI – ethics. While AI offers exciting possibilities, ensuring its development and responsible usage are essential. This is where AI ethics come in.

#9 What is AI ethics?

AI ethics: Think of AI ethics as guiding principles. These principles are a compass for data scientists, researchers, and everyone involved in AI development. The goal of AI ethical practices is to create AI systems that benefit society and are implemented safely, securely, humanely, and environmentally friendly.

Why are AI ethics so important?

  • Mitigating Risks, Maximising Rewards: AI implementation can bring benefits and potential risks. AI ethics frameworks aim to identify these risks and ensure AI technology is developed responsibly, minimising the downsides and maximising the positive impact.
  • Building Trust and Transparency: For AI to be widely accepted, people must trust its development and use. AI ethics promote transparency, ensuring everyone understands how AI systems work and the data they use.
  • Avoiding Bias and Discrimination: AI systems can perpetuate biases in the data they’re trained on. AI ethics frameworks help identify and address potential biases to ensure AI operates pretty and inclusively.

The Global Landscape of AI Ethics:

  • A Shared Responsibility: Countries, organisations, and industry sectors are increasingly aware of the importance of AI ethics. Many stakeholders collaborate to create robust frameworks for responsible AI development and implementation.

The Future of AI Ethics:

AI ethics frameworks must adapt and address new challenges as AI continues to evolve. By working collaboratively, we can ensure AI continues to benefit society ethically and responsibly.

AI Adoption: Embracing the Power of AI in Your Business

#10 What is AI adoption?

AI Adoption: Imagine a world where your business can work smarter, not harder. That’s the promise of AI adoption – the strategic integration of AI-powered solutions to optimise operations, enhance decision-making, and unlock innovation.

Here’s why AI adoption matters:

  • Boosting Efficiency and Productivity: As companies move towards digital automation and streamline their processes, an AI-driven project can help with further automating repetitive tasks, which improves the overall process of the team leading them to be more efficient and increased productivity.
  • Data-Driven Decisions: AI can analyse humongous quantities of data, identify common patterns and trends humans would most likely might miss. These insights can inform better decision-making across the organisation.
  • Innovation Unleashed: AI opens doors to new possibilities, from developing chatbots that personalise customer experiences to creating predictive models that anticipate market trends.

There are two main ways to think about AI adoption:

  1. The Implementation Process refers to the strategic and systematic steps in integrating AI solutions into your business. It includes evaluating your needs, selecting the right AI tools, and ensuring successful implementation.

The End State: This refers to the ultimate goal of AI adoption – a company where AI is fully ingrained in its decision-making, strategy, technology, people, and processes. This level of integration allows AI to deliver its full potential for the business.

AI Services: Making AI Accessible for Everyone

#11 What are AI services?

AI Services: The world of AI can seem complex, but AI services are here to bridge the gap. These services offer pre-built machine learning models and other AI tools that make it easier for businesses of all sizes to leverage the power of AI.

Think of AI services as a pre-built AI toolkit. These tools can be customised to fit your specific needs, allowing you to apply AI to your applications and business operations without extensive in-house AI expertise.

Here are some examples of AI services you might encounter:

  • Generative AI: Allows you to create fresh and new content formats, like product descriptions or marketing copy, using AI.
  • AI-as-a-Service (AIaaS): This is a cloud-based solution that provides access to AI tools and resources on a pay-as-you-go basis.

A Final Note: The Evolution of AI Continues

The story of AI is one of continuous development. From the early days of Deep Blue, the chess-playing computer that challenged Garry Kasparov, to the sophisticated fraud prevention systems used in e-commerce, AI has come a long way. And the future? It’s filled with even more exciting possibilities!

By embracing AI adoption and leveraging AI services, you can ensure your business is well-positioned to thrive in the age of artificial intelligence.

Our AI exploration & upskills journey continues; stay tuned!

Join DSM Free Platform Today for continuous upskilling

At DSM, we’re excited to be at the cutting edge of AI implementation. We’re constantly learning and growing in this ever-evolving field, and we’ve gained valuable experience through our own AI projects. We’re passionate about sharing this knowledge and best practices with you.

This blog post is just the beginning of your AI journey! Explore DSM Free, a comprehensive platform designed to empower product professionals with the knowledge and tools to leverage AI in their digital products. Sign up for free today!

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To ensure we’re providing the most up-to-date information, we combine our practical experience with a strong foundation in research. We reference authoritative sources like IBM, Github, Tableau, and AWS Amazon to keep our training sessions informative and insightful.

Further Reading and Reference Articles:

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Himani Tiwary

Founder & CEO | Lead Trainer | Digital Skills Mastery

Himani is the CEO & Founder of DSM and is an expert at bridging the gap between strategy and execution of multimillion-pound digital products across multiple countries worldwide. With 15+ years of international experience across the UK, US, Ireland, Hong Kong and India, she is responsible for bringing tech, design and strategy together to create human-centric digital products that solve digital world problems.

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