AI-Washing: The New Greenwashing in Software

​The AI Hype Is Real - But So Is the Confusion

Artificial intelligence is everywhere… or at least, that's what the marketing says. From "AI-powered" dashboards to "intelligent" assistants, it seems like every software product has suddenly become sentient.

But when you dig beneath the surface, many of these so-called AI features are just automation in disguise. Automation is powerful and impactful, so that's not a criticism. But calling everything "AI" muddies the waters.

We call this AI-washing, and it's becoming the new greenwashing, the marketing trend starting in the 2000s where companies started exaggerating their environmental friendliness to appeal to eco-conscious consumers. Products were labeled "green" or "eco-friendly" without meaningful changes to how they were made or used.

Why AI-Washing Hurts

AI-washing happens when companies label something as "AI" to make it sound more advanced than it really is. Even when unintentional resulting from misunderstanding or overzealous marketing, the impact is real:

• Clients chase complexity they don't need: Companies may worry that their current tooling isn't "AI-driven" and fear they're falling behind. This FOMO can lead to rushed decisions, vendor churn, and replacing perfectly good systems with more expensive, less maintainable ones.
• Budgets balloon trying to implement tech that doesn't fit: AI often requires data pipelines, model training, monitoring, and specialized infrastructure. If the problem doesn't warrant that investment, it's wasted effort.
• Trust erodes when "AI" doesn't deliver: When expectations are set by marketing rather than reality, disappointment is inevitable; and it reflects poorly on vendors and internal teams alike.

Sometimes all you need is good old-fashioned automation. A client recently came to us asking for an "AI-powered scheduling assistant." After a few conversations, it became clear they didn't need AI at all. They needed a rules-based system that could assign jobs based on technician availability and skillset. No machine learning required.

Automation and AI Overlap

Here's where the confusion often starts: automation and AI aren't opposites, and they're not interchangeable. They exist on a spectrum and often work together.

• Automation handles predictable, repeatable tasks. It's built on logic, rules, and workflows.
  
• AI handles uncertainty, pattern recognition, and probabilistic decision-making.

Sometimes, automation uses AI as an ingredient. For example, a document upload process might use an AI model to extract text from images or classify content, but the rest of the workflow is pure automation.

That doesn't make the whole system "AI-powered." But it also doesn't mean AI isn't involved in a very helpful way.

Not All AI Is Complex - And That's Okay

You don’t need a self-learning model running 24/7 to benefit from AI. Sometimes, a simple occasional use of AI (like using it to summarize a paragraph) is all you need.

That's still AI. It's just lightweight and task-specific.

The key is understanding what role AI plays in your system, and whether it's solving a problem that actually requires it.

When Complex AI Is Worth It

While some AI use cases are lightweight and task-specific, others truly benefit from deeper investment and deliver transformative results when done right.

For example:

• Predictive maintenance systems in manufacturing use historical sensor data to forecast equipment failures before they happen. These models require training, tuning, and ongoing monitoring, but they can save millions in downtime.
• Fraud detection in financial services often relies on anomaly detection algorithms that evolve as new fraud patterns emerge. These systems need constant refinement and access to large, diverse datasets.
• Natural language processing for customer support can go far beyond simple chatbots. With the right training, AI can understand intent, sentiment, and context which can reduce support costs while improving the customer experience.

These aren't plug-and-play solutions. They may require:

• Clean, well-structured data
• A clear understanding of the problem space
• Collaboration between domain experts and data scientists
• Ongoing evaluation and iteration

But when the problem is complex, dynamic, and data-rich, AI may be essential to staying relevant.

How We Help Clients Cut Through the Noise

At Latitude 40, we don't lead with buzzwords. We lead with questions:

• What problem are you trying to solve?
• What decisions need to be made?
• What data do you have?
• What would success look like?

Sometimes the answer is AI. Sometimes it's automation. Sometimes it's just a better process. Our job is to help you figure that out and build something elegant, maintainable, and effective.

​AI is a tool. Automation is a tool. So is a thoughtfully designed workflow. The key is knowing when to use which and having a partner who can help you decide.

Final Thought

If you're evaluating a product or planning a new system, don't start with "we need AI." Start with the problem. Then find the simplest, smartest way to solve it.

That's how we work. And that's how we help our clients move faster, with or without AI.

About Latitude 40

Latitude 40 helps businesses achieve operational excellence and long-term business agility through tailored software solutions and expert guidance. By embedding into client teams, Latitude 40 delivers elegant, maintainable software while teaching Agile practices that foster sustainable growth. Latitude 40 builds with clarity, purpose, and a deep respect for the people who maintain and evolve code.

Have questions about incorporating AI into your custom applications? Let’s talk.

About the Author

Andrew Anderson is President of Latitude 40 Consulting and a seasoned software architect with over two decades of experience in developing Agile solutions. He's worked globally as a developer, analyst, and instructor, and is passionate about writing maintainable code and helping teams grow through clean architecture and the Agile mindset. Andrew shares insights from the field to help developers and leaders build better software, and better teams.