The AI Illusion: What's Really Happening Behind the Curtain
In this blog
What AI actually is (and isn't)
Artificial intelligence isn't intelligence in the human sense. It's software designed to perform tasks. Tools like Copilot, ChatGPT, and Claude rely on LLMs (Large Language Models). They are trained using enormous data sets and high-performance GPUs in data centers.
AGI (Artificial General Intelligence) refers to human-like intelligence. What we have now is narrow AI, meaning tools that can only perform very specific tasks requiring human control. Despite popular belief, experts still don't expect general intelligence for decades. In a 2023 forecast compiled by Epoch AI, which analyzed expert predictions on AGI, the median estimated arrival of AGI ranges from 2040 to 2060. That suggests we are still decades away from general, human-like intelligence.
The training process is often just a simple "guess the next word." For example: "I am hungry, so I want to…" A human would likely finish the sentence with "eat," "have lunch," or "get food." But since AI doesn't know what hunger is, it just looks at millions of similar sentences and picks what usually comes next, like "eat" or "grab a snack."
So it might complete "I am hungry, so I want to eat." Not because it understands hunger or desire, but just because that's the most common next word in similar sentences. This leads to fluent responses but no true knowledge or intelligence.
As shown in the chart below, the majority of enterprise AI use cases focus on tasks like prompt chaining and summarization, not autonomous thinking. Only 2 percent of use cases involve true decision-making, which reinforces that most AI today is just advanced automation.
What's happening behind the scenes
- Prompt chaining means breaking down big requests into a series of smaller, rule-based tasks
- Summarization is just identifying commonly emphasized points, not a true understanding
- Copilots are just front-end wrappers for prompt chaining across your data
Next: What the future holds
AI will continue to show up in tools that we already use, such as email, dev environments and meeting apps. These tools won't think for you. They'll just automate simple tasks based on predictable structures and ranking by weight.
Agent-based systems will automate more workflows but still follow strict if-this-then-that logic. Governance and oversight will matter more than ever to ensure accuracy and security.
WWT's take
Don't chase AI just to keep up. Focus on AI's business value by starting with simple use cases for what we know AI can do. Summarizing reports, improving internal search, and reducing low-value manual tasks are good starts for a business case but require keeping humans in the loop.
Organizations should build governance models for testing, approvals, sandboxes, and audits of the use of AI.
The data shows that the adoption rate is accelerating across industries, especially in tech and finance, where AI is used to streamline repetitive processes. This is not about sentience or intelligence. It's about speed, automation, and efficiency.
POV: Is AI just a trend?
AI is powerful but not magical or sentient. It's a smart tool made of code, math, and infrastructure, not a conscious mind. Success depends on disciplined adoption, not panic or hype.
Generative AI tools like Copilot show measurable productivity boosts, but only when used for narrow tasks like code generation or content summarization. These tools work well as assistance but not as replacements for human reasoning or expertise.
New technology often feels magical before we understand it. When users first saw "Hello, World" on early computers, they thought the machine was alive. The same was true with early printers that responded to commands and were viewed as intelligent. This trend is repeating with the predictive text engines behind today's AI.