This is Not AI: Unmasking the Pretenders in Martech and Customer Tech

Introduction

AI, off late, has become the magic word that promises to revolutionize everything (and gets budgets easily approved ??). From personalized recommendations to predictive analytics, AI seems to be the secret sauce for every marketing and customer engagement strategy. But wait! Not everything that claims to be AI is actually the real deal. While some platforms truly harness the power of AI, others are just using it as a fancy buzzword. Let’s have some fun unmasking these pretenders and discovering what’s really going on under the hood.

Features That May or May Not Be AI

Let's dive into the specifics and see which features are genuinely AI and which ones are just pretending—and how they might try to fool you.

1. Recommendations:

Potential AI: Real AI-powered recommendation systems use machine learning to analyze what you like and suggest things you’ll love. Think of it like that friend who knows your taste in movies and always has the best suggestions. Algorithms like collaborative filtering and neural networks learn from tons of data to make these spot-on recommendations.

Non-AI: Basic recommendation systems are like that friend who always suggests the same thing. They use fixed rules and static logic to suggest products or content, without learning from your preferences.

Falsely Presented as AI: Companies might market rule-based systems as AI by emphasizing their "personalization" and "automation" features, suggesting they can predict your preferences without actually learning from your behavior.

2. Predictions:

Potential AI: Predictive analytics powered by machine learning is like having a crystal ball that gets better the more you use it. These models forecast customer behaviors, like who’s going to churn or what sales will look like next month. They use techniques like regression analysis and decision trees, and they get smarter over time.

Non-AI: Simple statistical models are more like weather forecasts from last year—useful, but not improving. They apply fixed equations and don’t learn from new data.

Falsely Presented as AI: Vendors may tout basic statistical models as AI by highlighting their predictive capabilities without mentioning that these models do not improve or learn from new data over time.

3. Dashboarding:

Potential AI: AI-driven dashboards are like magic mirrors that show you real-time insights and adapt based on what you ask. They might even use natural language processing (NLP) so you can talk to them like a human.

Non-AI: Standard dashboards are like those old bulletin boards—useful but static. They show data using predefined templates and need manual updates.

Falsely Presented as AI: Companies might promote static dashboards as AI by focusing on their ability to integrate with multiple data sources and create detailed visualizations, glossing over the lack of adaptive learning features.

4. Customer Segmentation:

Potential AI: Advanced segmentation tools using machine learning are like a social butterfly who knows exactly which groups to introduce you to. They use algorithms like k-means clustering to find patterns in customer data.

Non-AI: Basic segmentation is more like assigning people to groups based on their T-shirt color. It uses fixed criteria and doesn’t adapt or learn new patterns.

Falsely Presented as AI: Marketers might claim their basic segmentation tools use AI by emphasizing their ability to segment customers automatically, without revealing that these segments don’t evolve or adapt based on new data.

How to Check the Truthfulness of AI Claims

Want to know if your Martech tool is truly AI? Here’s how to play detective and throw some jargon at those pretenders:

1. What type of machine learning algorithms are you using? - Look for mentions of specific algorithms like neural networks, decision trees, or reinforcement learning. If they dodge the question, be suspicious.
2. How does your system learn and improve over time? - Real AI should talk about data ingestion, model training, and continuous improvement. No mention of learning? No AI.
3. Can you explain the training process and the data sets used? - Genuine AI solutions will discuss their training process and the types of data they use. Vague answers? Probably not AI.
4. What performance metrics do you use to evaluate your AI models? - Look for metrics like accuracy, precision, recall, F1 score, or AUC-ROC curves. If they can’t name any, it’s a red flag.
5. How do you ensure the scalability of your AI models? - Real AI solutions will have a clear strategy for scaling their models to handle increasing amounts of data and user interactions. If they can’t explain this, be cautious.

Conclusion

In the world of Martech and Customer Tech, AI can be a game-changer, but only if it’s the real thing. By knowing what to look for and asking the right questions, you can separate the true AI tools from the pretenders. So next time someone pitches you their “AI-powered” solution, dig a little deeper and make sure it’s not just wearing a fancy hat.

I’m aware that both the tools and the industry professionals evaluating them will read this article. My hope is that it equips potential adopters with the right questions to distinguish between the pretenders and the true innovators in the AI space.

Stay savvy, stay curious, and don’t let the AI pretenders fool you!