AI is everywhere. The latest McKinsey survey on the state of AI found that 78% of organizations were using AI in at least one business function, up from 72% in 2024 and 55% a year earlier. And the numbers continue to climb.
But not all of it is necessary in the way we may think. And not all of it works as expected.
We live in a moment where generative AI steals the spotlight. Its ability to create at speed is compelling. But speed is not the only metric that matters.
What matters is relevance. What matters is fit. What matters is outcomes.
It’s not just about plugging in AI. We should ask better questions: What problem are we solving? What kind of “intelligence” does it need? What’s the most effective way to deliver results, not just deliverables?
What happens when we get it wrong
We have seen it before: AI deployed as a proof of concept, but never scaled. Generative tools used for workflows that demand precision; projects that start with “let’s add AI” instead of “what is the smartest way to solve this?”
And let’s embrace it: everyone wants to be part of the hype. We are facing an AI-FOMO that is rushing solutions and missing the actual opportunities.
Intelligence is not in the model. It’s in the match. The match between problem and method. Constraint and capability.
The result of poor alignment? Bloated infrastructure. Teams in the dark. Trust eroded.
The intelligence spectrum.
Artificial intelligence is not just one thing. It is a toolbox of different minds, each with its own way of learning, reasoning and/or creating. Knowing which one to use is what separates hype from value.
Let’s look at four of the most common tools in this box, each applied to solutions we use every day.
Symbolic AI.
-
What is it? Structured, logic-based systems.
-
Think: If-this-then-that, legal workflows, compliance logic. It’s applied in many conventional programs that approve or reject processes based on predefined rules, for example: insurance policies, healthcare procedures or administrative records.
-
Uses: Symbolic AI is great for traceability, auditability and rules that do not change overnight.
Machine learning (ML).
-
What is it? Pattern recognition from data.
-
Think: fraud detection, recommendations, demand forecasting. An example of a product that provides machine learning is Google Cloud Vertex AI.
-
Uses: Machine learning is great when applied to historical data to reveal insights that future behavior can follow.
Generative AI (GenAI).
-
What is it? A subfield of Machine Learning focused on content and creation at scale.
-
Think: Summaries, variations, drafts, creative augmentation. A well-known product offering GenAI is Midjourney.
-
Uses: GenAI is great when time-to-first-draft matters, or when inspiration needs a nudge.
Large Language Models (LLMs).
-
What is it? A specific application of GenAI to understand and generate human language
-
Think: Assistants, copilots, semantic search. Well-known products offering using LLMs are ChatGPT and Claude
-
Uses: LLMs are great when context matters and language is your interface.
Some cases need logic and traceability. Others are predictions from patterns. Sometimes we need speed and creativity.
These are only examples of the approaches most widely discussed and applied. There are others that solve different types of problems or complement these methods, such as Reinforcement Learning (decision-making through trial, error, and reward), Neuro Symbolic AI (combining statistical learning with explicit logic for explainability), Evolutionary or Genetic Algorithms (optimization inspired by biological evolution), and others. Each of these approaches has its strengths, and they can often be combined to match the shape of a problem. Ultimately, these approaches can be used to build intelligent agents designed to pursue specific goals with increasing autonomy.
Regardless of the model, there is something more important than what the system is: it’s how well it is set up to reason, to respond with relevance, and to produce value in context.
And that’s where many implementations fall short.
It’s not just about choosing the right kind of intelligence; it’s about optimizing how that intelligence infers.
Taken from: https://www.hugeinc.com/perspectives/ai-that-works/