Demystifying AI and ML: What They Are, How They Work, and Why They Matter
Artificial Intelligence (AI) and Machine Learning (ML) have become part of everyday vocabulary in business and technology, often used interchangeably or representing a mystical “black box” of sorts. To move past buzzwords and into real-world impact, it’s essential to understand what these terms mean, how they relate, and how to think about applying them in your own work.
Artificial Intelligence (AI) and Machine Learning (ML) have become part of everyday vocabulary in business and technology, often used interchangeably or representing a mystical “black box” of sorts. To move past buzzwords and into real-world impact, it’s essential to understand what these terms mean, how they relate, and how to think about applying them in your own work.
In this article, we’ll walk through:
What AI and ML are
How they differ (even though they’re related)
Where generative AI and large language models (LLMs) fit into the picture
How to reframe a business or technical problem as a machine learning problem
What AI/ML make possible in practice
What Is AI? What Is Machine Learning?
At its core, Artificial Intelligence refers to the broader field of creating systems that can simulate or replicate intelligence as humans have defined it. This includes capabilities like reasoning, planning, understanding language, recognizing patterns, and adapting to new inputs.
Machine Learning is a specific subset of AI. Rather than being programmed with explicit instructions for every scenario, ML systems learn from data. They find patterns, adjust to new information, and improve over time through feedback. Nearly all the AI you interact with today are powered by ML. You might hear terms like “Classic ML,” which typically refers to models built to solve specific problems or use cases.
Think of it this way:
AI is the goal: making machines that exhibit intelligent behavior
ML is one of the primary methods we use to reach that goal today
There are different types of AI recognized by tech firms:
Artificial narrow intelligence (weak AI): perform a narrowly defined task better than a human
Artificial general intelligence (strong AI; AGI): perform on-par with, or perhaps outperform, humans in nearly any intellectual task
Artificial super intelligence (ASI): outperform humans in almost every field
Artificial narrow intelligence is the furthest humanity has reached so far, but many big players have their sights set on AGI.
The Relationship Between AI and ML
While AI and ML are closely connected, the distinction between them matters:
AI is the umbrella term for technologies that attempt to capture intelligent behavior
ML is a technique used to build those technologies by training systems on data
ML powers everything from spam filters to recommendation engines, fraud detection, computer vision, and even self-driving cars. And when you hear someone say, “AI did this,” there’s a good chance it was a machine learning model behind the scenes.
Generative AI and Large Language Models (LLMs): Where They Fit
In recent years, generative AI has taken center stage. This is driven largely by the rise of Large Language Models (LLMs) in consumer-facing products and offerings, starting with the release of ChatGPT, which has seen the fastest-growing user base in history. These models generate human-like text, code, images, and more, based on the patterns they’ve learned from massive datasets.
So where do LLMs fit in?
LLMs are a type of machine learning model, specifically trained on large amounts of language data
They’re built using deep learning, a subfield of ML inspired by the human brain’s neural networks
Generative AI refers to systems that can create new content, rather than simply making predictions or classifications
This makes generative AI different in form, but not in principle. It still learns patterns from data, but its output is creative or compositional rather than purely analytic.
Reframing a Problem as a Machine Learning Problem
A critical step in applying ML is problem framing.
Not every problem is a good fit for ML. But many challenges that involve uncertainty, variation, or high-volume decision-making can be reframed as ML problems.
Here’s how to spot the opportunity:
Is there a decision or prediction you make repeatedly? If you’re using rules or gut instinct to make the same call repeatedly, ML may help.
Do you have historical data? ML needs examples to learn from, such as past decisions, outcomes, behaviors.
Can you define a clear output? ML requires a target variable: something you want to predict, classify, rank, or generate.
Examples of reframed problems:
“Which customer leads are most likely to convert?”
Reframed: Given the attributes of each customer lead, what is the probability this lead will make a purchase within the next x days?
ML Method: Classification w/ probability output
“How should we route incoming support tickets?”
Reframed: Given the content and metadata of an incoming support ticket, which support team or agent should it be assigned to?
ML Method: Classification
“What’s the best price to offer for this item?”
Reframed: Given the product features, inventory level, seasonality, competitor pricing, and historical sales, what price is most likely to maximize profit for the likelihood of a sale for this item in the next x days?
ML Method: Regression w/ downstream optimization
“What should this product description say?”
Reframed: Given information about the product (attributes, target audience, examples), how can we generate a compelling and accurate product description to optimize for SEO and the intended customer segment(s)?
ML Method: generative AI
Framing is where the value starts. The best-performing models often come from the clearest framing and not necessarily the most complex algorithms.
What AI and ML Make Possible
Adopting AI and ML isn’t just about automation: it’s about unlocking new capabilities. They enable organizations to:
Make data-driven decisions at scale
ML models can evaluate thousands of variables simultaneously to guide complex decisions. For example, a supply chain team might use ML to predict demand fluctuations based on weather, holidays, and regional trends. This enables smarter inventory planning and helps reduce waste. Likewise, a manufacturing company might predict capacity on a production line based on available resources.
2. Improve consistency and create auditable decision frameworks
Humans are inconsistent, especially when fatigued or under pressure. In a landmark 2011 study published in PNAS (Danziger et al., 2011), researchers examined over 1,100 judicial parole rulings. Judges were significantly more likely to grant favorable rulings (65%) early in the day, but those rates fell to near-zero just before breaks, only to rebound afterward. This phenomenon, known as decision fatigue, shows how even trained experts can be influenced by timing and energy levels. Regardless of role, humans make many decisions each day and not all of them are optimal.
While ML models work within their limits, they don’t get tired. When designed responsibly, they can help make decisions auditable, explainable, and reproducible.
However, this consistency is a double-edged sword. If the historical data used to train the model contains human biases (think discriminatory hiring patterns or inequitable lending decisions) the model will learn and perpetuate those patterns at scale. That’s why fairness auditing, data scrutiny, and domain expertise are essential parts of building responsible AI systems.
3. Augment human expertise
Rather than replace experts, AI systems are increasingly designed to extend human capacity. For example:
An analyst might use ML to surface anomalies in financial data
A product designer might use generative models to explore hundreds of visual variants
A developer might lean on AI-assisted coding tools to generate boilerplate or refactor legacy scripts
The result? Less time on repetitive tasks, more focus on judgment and creativity.
4. Adapt and evolve with changing environments
Unlike static rule-based systems, ML models can be retrained with new data to reflect changes in behavior, market conditions, or customer preferences. This makes them a powerful fit for environments where adaptability is key.
From Understanding to Application
AI and ML are not magic: they’re methods. Powerful ones.
The real opportunity lies in knowing when and how to use them. That begins with clarity: about your problem, your data, and your goals. Then comes thoughtful design, experimentation, and iteration. And finally, the ability to integrate these tools into systems, workflows, and decisions. The goal is to serve people, not replace them.
If you’re exploring how to bring AI or ML into your organization, whether to automate a process, uncover insights, or prototype a new product, Moser Consulting can help. From initial exploration to production-grade deployment, we bring clarity, rigor, and practical experience to every engagement.
If you’re interested in AI & ML systems, check out the book and links below:
Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud by Marco Tanquillin, Valliappa Lakshmanan & Firat Tekiner (O’Reilly Media, Inc, 2023)
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen (O’Reilly Media, Inc, 2022)
What is artificial intelligence? (Microsoft)
