Demystifying AI: Starting From Zero — What is AI Anyway?
Today, conversations about technology are still all over the spectrum. There are countless keynotes about AI from founders and various versions of C-suite leadership, to university scholars specializing in Data Science. For many, AI is here and has become a part of their business strategy, some know it is here but are skeptical, whereas others are still questioning its relevance or what it really is.
Artificial Intelligence is certainly a technology that is changing how large enterprise businesses function. It is also new enabling founders resulting in a surge of AI based start-up products with the hyper-growth being due to AI’s ability to assist or in some cases fully create digital products. While it does seem like AI is well on its way to be fully integrated everywhere, there are so many who are still trying to figure out what it is, and more importantly, if they need it or even need to pay attention to it.
If you are a leader of any functioning organization, you should be aware of what it is and how it is changing everything from the way we do business to how it impacts digital identity; however, whether you need to adopt it is something that needs a bit of a closer look.
With this series we will dig into these topics, with the goal of “leveling the playing field”, to make it accessible for everyone.
To get going we will start from some of the most frequently asked questions.
“Okay, I get that this is big. But what is it really? Is this just another tech trend, or something deeper? And what does it actually mean for my business?”
What AI Really Is (and Isn’t)
When people say “AI” today, they’re usually thinking about ChatGPT, Claude, Gemini, or similar tools. These are front-end applications powered by Large Language Models (LLMs) — systems that generate human-like text based on patterns in data.
They’re impressive, yes. But they are just one piece of the puzzle.
In reality, AI is much broader. It’s not a single tool or product — it’s an evolving field of computer science focused on building systems that can mimic or enhance human intelligence.
Think of it like this: AI isn’t just what you talk to — it’s also what’s reading, watching, listening, analyzing, deciding, learning, and in many cases, acting on its own.
Why AI Matters to Business
AI has the capability to enable businesses in ways that they may not have been able to accomplish previously:
- Flag unusual transactions before your accountants do
- Predict inventory issues before your suppliers cause delays
- Recommend upsells more accurately than your sales team
- Monitor customer sentiment in real time
- Or even identify brewing unrest in a crowd of thousands at a stadium
These aren’t hypotheticals. They are real-world applications of AI — each using different types of intelligence systems working behind the scenes.
When applied well, it can save time, reduce cost, improve decisions, and even protect your brand. Applied poorly, it creates noise, confusion, risk, and waste.
How AI Is Categorized: Three Practical Lenses
To make sense of AI, we can break it down in three ways:
1. Capability: What level of “intelligence” are we talking about?
- Narrow AI (ANI): This is what we use today. It does one task very well.
Examples: ChatGPT, Claude, quality inspection systems (e.g., in manufacturing), recommendation systems (e.g., in QSR) - General AI (AGI): Hypothetical for now — can learn and reason like a human.
Still in development, mostly theoretical. - Superintelligent AI (ASI): Think sci-fi level intelligence — beyond humans.
Hypothetical and nothing of concern right now — at least not to those who this article and overarching series is targeting.
Takeaway: Everything you’re using or considering today is Narrow AI. It’s powerful, but it’s not an artificial human.
2. Technology Type: What’s under the hood?
AI comes in different “flavors,” each with unique strengths:
- Symbolic AI (Rules & logic) — Systems which rely on logic, rules, and symbols to represent knowledge. The earliest versions go as far back as the 1950s and are still useful in industries like aerospace and health.
- Machine Learning (ML) — AI that learns from data, here are some of the most common forms…
- Supervised: Learns from labeled examples
- Unsupervised: Finds patterns on its own
- Reinforcement: Learns through trial and error
- Self-Supervised: Labels its own data
- Deep Learning: Complex neural networks (e.g., vision, speech)
- Neural networks: Computational models inspired by the human brain, made up of layered, interconnected nodes. They power many AI systems, especially in machine learning and deep learning.
- Agent-Based Systems — Systems composed of autonomous AI agents that perceive, reason, and act independently.
- Swarm Intelligence — Inspired by how birds, bees, and ants coordinate — consider this multiple autonomous AI agents or AI “nodes” functioning in this manner.
Takeaway: You don’t need to know how to build these — but it helps to know that AI comes in more than one form, and each has its place depending on the problem you’re solving.
3. Use Case: Where does AI show up in the real world?
This is the lens most useful to you as a leader.
- Natural Language Processing (NLP): Language understanding and generation.
(E.g., Chatbots, classification systems, language models) - Large Language Model (LLM): deep learning models (usually transformer-based) trained on massive text datasets to perform NLP tasks.
(E.g., ChatGPT, Codex, InstructorXL, SAP Joule, Harvey, BloombergGPT, S&P Kensho, Workday AI) - Computer Vision: Interpreting images and video.
(E.g., Quality inspection, anomaly detection, facial recognition) - Speech & Audio: Working with voice and sound.
(E.g., Voice assistants, real-time transcription, acoustic monitoring) - Robotics Physical automation of tasks.
(E.g., Automation, drones, self-driving vehicles) - Recommender Systems: Personalized content delivery, customer engagement & loyalty systems.
(E.g., Netflix, Amazon, loyalty programs in QSRs) - Decision Intelligence: Advanced forecasting and analytics.
(E.g., Forecasting, risk modeling, pricing strategies)
Example: Riot Prevention Use Case
Let’s say you want to monitor public safety at an event. People on the ground, and even with support from people monitoring via cameras, can only track so much. How can AI help?
- Use computer vision to detect anomalies or certain patterns in crowd behavior
- Use speech analysis to detect aggression
- Use decision intelligence to assess and alert
- Continue to learn and train (ML) models from human feedback (human-in-the-loop) to be more accurate
With AI, you would still have people on the ground, and humans monitoring remotely, but now they are empowered with an additional set of eyes and ears to be able to be proactive and respond to situations before they get out of control or even happen.
Takeaway: The most valuable AI conversations start with a problem — not with a tool.
So Why Now? What Changed?
AI didn’t arrive overnight. The foundations were laid over decades. But around 2022, something changed. Three big breakthroughs collided:
- Massive computing power became more affordable
- Unprecedented amounts of data became available
- Transformers (a new AI model architecture) unlocked new capabilities — especially in language
That’s what powered tools like ChatGPT and made AI suddenly feel “real.”
What You Should Do Next
You don’t need to chase every shiny object. Instead, focus on these three steps:
- Understand your environment. Where are decisions slow, errors frequent, or insights buried in data?
- Explore specific use cases. Don’t start with “AI.” Start with a business problem.
- Partner wisely. Work with people who understand both your business and the evolving AI landscape — avoid purists and seek out advisors that are pragmatic.
Final Thoughts
AI isn’t the future — it’s the “now”, and as a leader, your job isn’t to become an AI expert. It’s to become AI-literate — so you can ask the right questions, frame the right problems, and lead your organization with vision, not fear. Because, while the technology might be complex, the opportunity is clear: Those who understand and apply AI with purpose will shape the next generation of business.
In this first article in the series, we covered high level concepts related to AI, ensuring that concepts will be easier to understand as we dive deeper into AI, as well as helping you be ready to have these conversations with your customers, staff, and everyone that you interact with, even AI.
Do you have any questions about the information that was shared? What technical challenges are you seeing today that could use some strategic support?
If you liked this article, please follow me (James McGreggor) on LinkedIn and Medium. I will continue to dive deeper into AI and Web 3.0, exploring use cases in various industries.
For help architecting modern digital solutions please visit our company www.blueforgedigital.com.
Thanks for reading!
Author’s Note
This article was created through a process that leveraged generative AI to facilitate grammatical and organizational refinement to ensure clarity, correctness, and logical flow; all content and ideas were provided by the author, with the initial and final drafts being fully edited by the author.
