When I talk about AI, I see a mix of emotions in people’s eyes—curiosity, excitement, and sometimes a hint of uncertainty. You understand how important AI is, but you might wonder where to start or how to keep up with this fast-paced technology. That’s exactly why I’m writing this guide—to show you that learning about AI is not only achievable but also an opportunity to grow, explore, and innovate.
This is do-able
Change is coming, and it’s going to be big. AI is set to transform industries, redefine roles, and open up new possibilities. But here’s the good news: You don’t need to be an expert from day one. What you need is the willingness to explore, learn, and see AI as a tool that you can master over time.
The future is yours to shape. Yes, AI is complex, but with the right approach, you can navigate this change and use it to your advantage. This isn’t about being overwhelmed—it’s about being prepared, informed, and ready to take on new challenges.
Play with the tools
AI tools can boost your productivity significantly. Think of them as an extension of your team, allowing you to delegate tasks and focus on what truly requires your attention. Start experimenting to understand their capabilities and limitations. But remember, these tools need guardrails. Always double-check their work, and be cautious about sharing sensitive information.
By embracing these tools, you’ll not only increase your productivity but also gain a deeper understanding of how they can best serve you and your organization.
Learn a few key concepts to start
As with any technology, there are lots of words and concepts to get familiar with, below you’ll find the ones I recommend you learn about first. A great introduction to all of these is Andrew Ng’s "AI for Everyone" on Coursera.
Machine Learning (ML)
Machine Learning powers things like image recognition, product recommendations, spam prevention and helping with medical diagnosis. ML is the development of algorithms that enable computers to learn from data. ML is great at learning simple concepts when there’s a lot of data available. However, it struggles with more complex concepts, especially when dealing with small data sets or new types of data.
ML transforms inputs into outputs, optimizing processes, but it doesn’t create new ideas. It’s essential to measure or baseline what you’re trying to improve before applying ML, so you know where to start. There are three main types: supervised learning (learning with labeled data), unsupervised learning (finding patterns in data without labels), and reinforcement learning (learning through trial and error).
Resource: Machine learning, explained (MIT Sloan)
Data Science
Data Science focuses on using data to answer questions and solve complex problems like predicting market trends, analyzing transportation routes and understanding customer needs. The process involves several steps: collecting and preparing data, exploring the data to find patterns, using statistical models to make predictions, and visualizing the results to communicate findings. The goal is to gain insights that can inform decisions and tackle difficult challenges.
Resource: Common Applications of Data Science (With Examples)(Indeed)
Large Language Models (LLMs)
While machine learning and data science have been around for a while, it’s the emergence of LLMs like ChatGPT, Claude, Gemini, and Llama that have truly captured the public’s imagination. If you want to make an AI expert groan, just refer to everything as a "GPT."
LLMs work by predicting the next word in a sequence, enabling them to generate coherent text. They can perform a wide range of tasks, from writing code and emails to answering complex questions and even generating images. However, despite their power, LLMs are still under development and can sometimes produce incorrect or misleading information.
Resource: A jargon-free explanation of how AI large language models work (ars Technica)
Neural Networks
Neural Networks are inspired by the human brain, mimicking how our brains learn and process information. In these networks, interconnected nodes (similar to brain cells) process and transmit data. This architecture is fundamental to many advanced AI applications, allowing for complex data processing and learning.
Resource: Neural Networks 101: An explainer (WeAreBrain)
Data
For AI systems to work effectively, they need large amounts of data, but not just any data—quality matters. Data should be accurate, relevant, and consistent. There are two main types of data: structured (organized and easy to analyze) and unstructured (more complex, requiring advanced processing techniques). AI companies are skilled at strategic data acquisition and use unified data warehouses to manage their data. However, before data can be used, it often needs to be cleaned and preprocessed to handle missing values, errors, and formatting issues.
Resource: How to Define and Execute Your Data and AI Strategy (HDSR)
Compute
Compute is the computational power needed to run AI models, similar to the engine of a car. This requires specialized equipment, like GPUs (Graphics Processing Units), and a robust infrastructure, which can be energy-intensive. As AI continues to evolve, efforts are being made to make this process more efficient over time.
Learn how to use AI responsibly
Bias
AI systems can inadvertently perpetuate or amplify biases present in the data they’re trained on, leading to unfair or discriminatory outcomes. Actively identifying and mitigating bias throughout the AI development process is crucial to ensure fairness and equity.
Privacy
AI often processes large amounts of data, raising privacy concerns. It’s essential to handle this data responsibly, respecting user privacy and adhering to relevant regulations to prevent misuse or breaches.
Intellectual Property
The use of AI raises complex questions about intellectual property rights, especially in content generation and copyright. Understanding the legal and ethical implications is crucial to navigating these challenges responsibly.
Explore How AI Will Affect Your Industry or Role
AI’s impact is rapidly evolving, and what’s current today might be outdated tomorrow. However, we’re already seeing some promising applications across various industries:
Healthcare: Medical imaging, disease diagnosis, advancing drug research and development.
Finance: Fraud detection, risk assessment, algorithmic trading
Education: Personalized learning experiences and adaptive feedback to students
Everyday Life: Virtual assistants, personalized content recommendations
How to Collaborate with Colleagues in AI
From my discussions with AI and ML experts, it’s clear they seek the same things most people want in the workplace:
Clear Objectives: They need clear goals, support, and understanding from leadership, particularly regarding their specific needs and challenges.
Contextual Understanding: Experts value input from those who deeply understand the subject, industry, customer, and business context.
Well-Researched Use Cases: They want well-researched and validated use cases that outline the current state, expected inputs and outputs, and performance metrics tied directly to customer value.
Commitment to Continuous Improvement: They need assurance that the project will continue to be refined and backtested post-launch; abandoning it early does little good.
Data Strategy: Robust data strategy —determining what to collect, what to buy, how to store and curate it—so that data drives any AI/ML effort.
Shifting Mindsets: Transitioning from a product mindset to an ML mindset involves embracing uncertainty, longer time horizons, and focusing on measurable performance metrics.
It’s easy to overemphasize technical skills when a new innovation emerges, but building a great team, product, or organization requires a mix of skills. Let’s ensure we balance technical AI skills with the other crucial elements needed for success.
Get Started
The goal isn’t to become an AI expert overnight but to start building a practical understanding that will grow over time. You might feel silly, awkward, or behind at first, and that’s okay—it will get better. Continue exploring AI at your own pace, using the concepts and practices outlined in this guide as a foundation for further learning.
Image: Something my oldest daughter and I had fun making