We’ve all seen it: every other company now claims to be “AI-powered,” “AI-driven,” or to have a “cutting-edge AI engine.” The term “Artificial Intelligence” has become less of a technological description and more of a marketing buzzword—a way to charge more and sound smarter.
But for the sophisticated buyer—especially those of you looking for real innovation—it’s time to look past the veneer. At Interlink Commerce, we believe in honest technology, which is why we want to equip you with the knowledge to spot what we call “AI Washing.”
What Is AI Washing? (And Why Should You Care?)
AI Washing is the practice of deceptively labeling a product or service as using Artificial Intelligence when, in reality, it employs far simpler, often pre-programmed, logic.
Why does this matter to your business?
- You Overpay for the Ordinary: You end up paying an “AI premium” for technology that has been standard practice for years.
- You Misallocate Resources: You dedicate time and training to integrate a tool with the expectation of learning and adaptation, but receive a rigid system instead.
- You Miss Out on Real Value: By choosing a product based on buzz, you overlook solutions that offer genuine machine learning capabilities that actually deliver a competitive edge.
The Three Most Common Disguises of “Fake AI”
Many products labeled as AI are nothing more than clever applications of much older, simpler technologies. Here are the three most common “nuances” that are often passed off as advanced AI:
1. The Decision Tree/Rule-Based System
- What they call it: “Our AI provides intelligent recommendations.” or “The system uses deep learning to categorize data.”
- What it often is: A simple If/Then/Else logic flow, also known as a Decision Tree.
- Example: A support chatbot that asks, “Did you mean ‘Password Reset’ or ‘Billing Inquiry’?” If you type ‘Billing,’ it follows a pre-set path for billing issues. If you type something it doesn’t recognize, it defaults to a human agent. This is a script, not intelligence. Real AI would understand the intent of your question, even if you phrased it uniquely.
2. Basic Statistical Modeling
- What they call it: “Our AI predicts future market trends.” or “Our predictive engine optimizes your inventory.”
- What it often is: Standard Regression Analysis or a Moving Average calculation.
- Example: An inventory tool that uses your sales data from the last six months to forecast how much stock you need next month. This is a statistical projection, which we’ve done in spreadsheets for decades. Real AI would dynamically incorporate a wider variety of external data—weather patterns, competitor stockouts, social media sentiment—and adjust its prediction in real-time.
3. Sophisticated Filtering and Sorting
- What they call it: “Our AI personalizes the user experience.” or “Our content engine curates the perfect feed.”
- What it often is: Simple Algorithmic Filtering based on user-defined tags or explicit user actions (like a Collaborative Filter).
- Example: A product recommendation engine that suggests Product B because you bought Product A, and 80% of other users who bought A also bought B. This is simple correlation filtering. Real AI uses techniques like Natural Language Processing (NLP) and reinforcement learning to understand why you chose A and B and then introduces genuinely novel and unexpected suggestions based on inferred needs.
How to Unmask the Imposter
When evaluating a vendor that claims to use AI, ask these three critical questions. A company with genuine AI will be excited to answer them.
- “Where is the Learning Happening?”
- Fake AI Answer: “Our models were trained on millions of data points before you got it.” (This means it’s a fixed product.)
- Real AI Indicator: The system should improve and evolve after it’s deployed in your environment, becoming more accurate the more you use it. Ask them to describe a specific instance of the system failing, and then autonomously improving itself.
- “What Is the Role of the Human in the Loop?”
- Fake AI Answer: “The human simply approves what the AI suggests.” (This often means the human is doing the heavy lifting.)
- Real AI Indicator: The AI should handle edge cases—complex, unique situations—without constant human intervention. The human’s job should be to steer and refine, not to constantly validate basic decisions.
- “Can You Show Me the Data Set That Was Not Used?”
- Fake AI Answer: They will only talk about the specific, clean data they used.
- Real AI Indicator: Real Machine Learning can ingest and make sense of unstructured, messy data that wasn’t designed for it—emails, open-text feedback, images, etc. If the system only works with a small, perfectly organized data set, it’s likely running a simple pre-set program.
The Interlink Commerce Promise
We are the owners of Interlink Commerce, and we’re committed to delivering genuine, adaptive technology that respects your budget and your intelligence. We don’t use “AI” as a smokescreen. When we say a feature is intelligent, we mean it leverages genuine Machine Learning to deliver adaptive, demonstrable, and measurable improvement to your bottom line.
Don’t let the buzzword marketing fool you. Demand clarity, ask the hard questions, and choose partners who are transparent about the nuance of their technology.
What has been your experience with “AI-powered” products? Share your thoughts in the comments below!
