How Not to Be Fooled by AI (Consultants)

AI for Business Leaders, Managers & Consultants: Spot the Hype, Ask Better Questions, and Lead with Confidence

What you'll learn
- Understand the foundations of AI and machine learning—no math or coding required
- Cut through AI hype and spot what's real, what's risky, and what's just 'Chad the AI Consultant' showing off
- Challenge models, data, and metrics like a manager who knows where the weak spots hide
- Ask the right questions at every stage of an AI project, before it goes off the rails
- Apply what you’ve learned with a downloadable one-pager of claims, corrections, and smart questions to ask

Requirements
- No coding, math, or AI background required, this course starts from the ground up, then helps you see where things go wrong.
- Some experience working with data, digital products, or business decisions will help (but isn’t mandatory)
- Bring an open mind, a healthy dose of skepticism, and a willingness to challenge the hype

Description
AI is everywhere.

It’s reshaping decisions across industries.

But with bold claims and fancy buzzwords, it’s easy to feel lost or even be misled.This course helps you cut through the noise.You’ll learn how AI and machine learning really work, step by step, no coding, no equations, just practical insight.

Each section builds from the basics, giving you just enough of the underlying concepts before showing how they can be misunderstood, misused, or oversold.

What sounds smart isn’t always right.Whether you're new to the field or already working around AI, you'll find clear explanations that sharpen your understanding and strengthen your judgment.

We’ll do that by breaking down the kinds of things confident consultants love to say, like:“This model is 99.9% accurate.”or“Deep learning figures it out.”

Sometimes they’re right. Often, they’re not. This course helps you see the difference.

Here's what you'll learn, step by step:

1.Understanding AI and Model Flow

“AI is very different from machine learning.”

You'll start with the essentials: how machine learning models are built, how they differ from AI buzzwords, and what types of models exist (from supervised to generative). We'll walk through the full data-to-prediction process, including scores, probabilities, and decisions.

2.The Importance of Data Quality

“Garbage in, magic out - the model will figure it out.”

You'll explore common data pitfalls like sample bias and missing values, and why even sophisticated models break down when fed flawed inputs. We'll also question the idea that perfect data quality is all you need.

3.Model Evaluation and Metrics

“The AI works great, it is 99.9% accurate.”

We break down the misuse of accuracy and introduce better metrics. You'll learn how to evaluate models the way real decisions work, with nuance, thresholds, and trade-offs.

4.Responsible AI

“My data is anonymous and the AI model is fair.”

This section introduces fairness, privacy, and the pitfalls of black-box models. You'll explore tools like SHAP and counterfactuals, see why they’re both useful and limited, and learn how fairness can’t be boiled down to a single metric or technical fix.

5.Generative AI

“ChatGPT is just next word prediction.”

We demystify generative models: how they work, and why hallucinations, leakage, and societal bias matter. You'll learn what tools like ChatGPT can do well, and what theyonly appearto do well.

6.Why AI Projects Go Off the Rails

“Let’s see how we can use this fancy deep learning.”

We wrap up by diagnosing common project failures: misaligned goals, poor framing, unclear communication, and tech-driven enthusiasm without business grounding. You'll see how to improve cross-functional collaboration and keep AI work focused and effective.

By the end of the course, you'll understand how AI systems really work, not just in theory, but in practice, and you'll be ready to question claims, spot weak spots, and guide projects with confidence.

You’ll also get a one-pager summarizing all the key claims, corrections, business implications, and the critical questions to ask, ready to use in your next AI conversation.

Who this course is for:
- Business leaders, managers, and decision-makers who are (starting to) work with AI
- Consultants, policymakers, and analysts who need to evaluate or challenge AI results
- Product owners, AI translators and project leads trying to steer AI projects without getting lost in the math
- Anyone tired of AI buzzwords and eager to separate insight from hype
- No prior AI knowledge needed—this course starts from the ground up, then helps you see where things go wrong.
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