Table of Contents
- The Rise of AI Washing
- Why Does AI Washing Happen?
- Examples of AI Washing
- How to Identify Genuine AI Use
- Ethics as a Competitive Advantage
Artificial Intelligence (AI) is undoubtedly the most talked-about technology of our time. It’s at the heart of every tech conversation and is hailed as the cornerstone of innovation, competitiveness, and digital transformation. But in today’s landscape, a key question arises: Are we truly witnessing the widespread adoption of AI, or is the term being overused for marketing purposes?
As the saying goes, “AI is like parsley — it’s in everything.” Or at least, that’s how it seems. Just look at the post-2022 market: nearly every product, service, or tech startup now boasts the label “AI-powered” — often without any real technological substance behind it.
The Rise of AI Washing
This brings us to the concept of AI Washing: the misleading or false claim of using AI technologies, when in reality the adoption is minimal or even non-existent. Much like greenwashing in the sustainability space, AI washing is a risky play between aggressive marketing and the erosion of trust.
In recent years, being associated with AI has become synonymous with innovation and appeal. Startups branded as “AI-based” attract between 15% and 50% more funding than their traditional counterparts. That’s a powerful incentive — and one that explains why many companies inflate, or even fabricate, their AI capabilities.
Yet the technical complexity of AI makes it difficult — even for seasoned investors and journalists — to distinguish between genuinely innovative projects and superficial ones. The result is a growing skepticism and a dangerous loss of confidence in the true value of innovation.
Why Does AI Washing Happen?
There are several reasons behind this trend:
1. Terminological Ambiguity
“AI” has become a catch-all term, encompassing machine learning (ML), natural language processing (NLP), deep learning (DL), and more. This semantic vagueness allows for opportunistic interpretations — even basic automation can be marketed as “advanced artificial intelligence.”
2. Access to Incentives and Funding
Research grants, tax benefits, and venture capital often prioritize “AI-driven” projects, encouraging companies to overstate their AI involvement without any substantial technological foundation. Many public funding applications and startup pitches include “AI-powered” claims without clear explanations.
3. Defensive Market Strategies
To stay competitive, some companies label their products as “AI-powered” even if their internal capabilities are still under development. While this may seem like a strategic move, it undermines transparency with customers and stakeholders.
Examples of AI Washing
Here are some notable cases where AI was more marketing gimmick than real technology:
- Intelligent Document Processing (IDP): Many solutions advertised as AI-based turn out to be basic OCR tools with a few added rules, far from the intelligent automation they promise.
- Smart Devices: From refrigerators to thermostats, numerous products are marketed as “smart” when they are simply internet-connected, without any true learning or decision-making abilities.
- Content Creation Tools: Several platforms claim to autonomously generate text or video, yet still require heavy human input to produce usable results, undermining their AI claims.
The Coca-Cola Case: The “Y3000” flavor was marketed as being “co-created” with AI, but the company never clarified what role the technology actually played, raising questions about the claim’s validity.
How to Identify Genuine AI Use
In a world where AI is often more declared than applied, developing a critical eye is essential. Here are a few indicators to look for:
- Demand Transparency:
Serious companies have no problem disclosing which models and algorithms they use (e.g., convolutional neural networks, transformer models for NLP, etc.). Technical clarity is a strong sign of authenticity. - Evaluate Bias Handling:
Thoughtful management of algorithmic bias is another sign of maturity. Responsible AI projects acknowledge the risk of bias and have active procedures to address it. - Review the Documentation:
Solid case studies, white papers, and technical reports should be concrete and accessible. Vague or overly polished language often signals more storytelling than substance.
Look at Performance Metrics:
Metrics like accuracy, recall, and F1 score offer objective ways to assess the real value of an AI solution.
Ethics as a Competitive Advantage
AI washing may generate short-term visibility, but it undermines long-term trust. In an increasingly mature and discerning market, transparency about the real use of AI becomes a true competitive edge.
At Neodata, we believe that real innovation stems from integrity and expertise — values essential for building a more sustainable, responsible, and collaborative tech future.
Our AI initiatives go beyond buzzwords: we focus on truly advanced AI solutions, methodological transparency, and ethical responsibility. This ensures tangible and lasting results for our clients and partners.
Because at Neodata, innovation isn’t just about using AI — it’s about using it the right way.
AI Evangelist and Marketing specialist for Neodata
