The Harsh Reality of AI:

Let’s get straight to the point: a whopping 80% of AI projects fail.

The core issue isn’t the technology itself but the sheer misunderstanding of its application.

Companies often jump on the AI bandwagon without asking the most crucial question: Does this actually address a real problem for our users?

Instead, they chase the AI trend, leading to costly and ineffective ventures.

📊The Unvarnished Truth: Why Most AI Projects Crash and Burn

AI projects are failing at an alarming rate, and it’s time we address.

Why?

The reality is that most AI models, including 90% of Agentic AI models, never move beyond the demo phase. They’re stuck in a proof-of-concept cycle because they don’t deliver real-world results.

The underlying issue is often the data quality or the wrong application of AI for the problem at hand.

The excitement about AI often blinds businesses to the practical questions they should be asking:

- Does This Solve a Real Problem? Many AI projects flop because they address non-existent issues or fail to deliver real-world value.

- Is Your Data Up to Snuff? Poor data quality is a common culprit for AI’s shortcomings. If your data isn’t solid, your AI will likely be stuck in perpetual demo mode.

🧠 AI Education: Why It’s Critical for Business

One of the biggest downside in the AI hype cycle is the lack of understanding. When businesses don’t grasp the basics of AI, they’re ripe for exploitation by vendors touting “AI-powered” solutions.

Remember:

- AI Enhances, Not Replaces: AI should amplify human capabilities, not replace them. Understanding where human input ends and AI begins is crucial.

- Knowledge is Power: Without a grasp of AI fundamentals, you might end up investing in tools that don’t meet your needs.

:::Ready to make AI work for you? Schedule a Code Therapy session today and let’s dive into practical AI strategies that align with your business needs.

💼Marketing vs. AI Awareness Levels

In marketing, different levels of audience awareness determine how your customers will respond to your messaging.

For example:

-Are they completely unaware of your product?

-Are they problem-aware but unsure of the solution?

-Or are they solution-aware and ready to buy?

Understanding where your audience falls on this awareness spectrum is crucial to creating the right message that resonates with them.

GenAI tools operates on a similar spectrum, which is where AI Awareness Levels come into play.

Just like you need to know where your audience is in the buying cycle.

You also need to understand where an AI tool sits on the scale of human vs. machine involvement.

Enter the M2M (Marketer to Machine) Scale, a framework introduced by Paul Roetzer and Mike Kaput that helps marketers assess the level of independence an AI tool offers.

The scale ranges from fully human-driven systems to fully autonomous AI, and understanding where your tools land on this scale is key to making informed decisions.

Without that knowledge, you’re setting yourself up to fall for exaggerated vendor claims and poorly executed projects.

💻The M2M (Marketer to Machine) Scale Breakdown

Let’s dive into the five levels of AI independence on the M2M scale:

- Level 0: All human-driven—No AI is involved, and all actions rely on manual input from humans.

- Level 1: Mostly human-driven—Some intelligent automation exists, but human input and oversight are still required.

- Level 2: Balanced human-machine collaboration—AI manages some tasks, but it still needs input and guidance from humans.

- Level 3: Mostly machine-driven—The AI system can operate independently in some cases, but humans still intervene when needed.

- Level 4: Fully machine-driven—The AI system operates autonomously, often surpassing human abilities. Marketers only define outcomes while the AI executes tasks.

In the context of marketing, most AI tools today fall within Levels 1 or 2, meaning that while they offer some automation, human oversight and input are still essential. Companies that don’t understand where a tool sits on this scale risk buying into hype and ending up with tools that don’t meet their needs.

🧐How to Outsmart the Hype: Practical Questions to Ask

Before diving headfirst into an AI project, it’s essential to step back and ask some tough, practical questions:

1. Can this problem be solved without AI?

AI isn’t always the best solution. Sometimes a problem can be solved with traditional technology or even manual processes more effectively.

2. Will AI provide a substantial improvement, or is it just a shiny new toy?

Investing in AI for AI’s sake is a recipe for failure. Make sure AI is genuinely the best solution to improve your processes and add value.

3. Is the cost of AI worth the potential return on growth?

AI implementation can be expensive. It’s essential to calculate whether the potential gains from using AI justify the costs involved.

Two Sides of the Same Coin: AI and Marketing Awareness

The comparison between AI awareness levels and marketing awareness levels highlights one simple truth: successful AI implementation is about more than just technology.

It’s about understanding where the tool fits in the broader context of your business, knowing what it can and can’t do, and ensuring you have the right data to back it up.

In both AI and marketing, awareness is everything.

Just like you need to know where your audience stands in their buyer’s journey, you also need to understand where an AI tool falls on the M2M scale.

The key is understanding how to navigate the hype and make informed, strategic decisions that genuinely benefit your business.

📚Reference

The Root Cause of Failure for AI Projects: https://www.rand.org/pubs/research_reports/RRA2680-1.html

Intro to M2M Scale https://www.marketingaiinstitute.com/blog/introducing-the-marketer-to-machine-scale-for-rating-marketing-ai-technology

👨‍💻Ready to see how these insights can transform your business?

Schedule a Code Therapy session where we dive into your unique challenges and provide tailored insights and solutions just for you.