Artificial intelligence has become an essential topic in business discussions. It's present in many areas, from email filtering to optimizing logistics processes and supporting strategic decisions. However, there is a significant issue: not everything that claims to be AI truly is. It has become crucial for decision-makers to distinguish between genuine value creation and empty promises.
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AI washing: When artificial intelligence is just a marketing gimmick
Artificial Intelligence has become an indispensable part of business discourse - yet not everything that glitters is AI. AI washing occurs when companies (either intentionally or due to a lack of technical readiness) exaggerate or misrepresent their products' capabilities to gain a better market position or win investor confidence.
This phenomenon is not new: we have seen similar waves with blockchain, big data, and IoT. However, the market pressure is immense - today, an AI-free pitch can be a significant disadvantage in the race for funding.
When regulators step in
AI washing is not just an ethical issue - it is a serious legal risk. In March 2024, the U.S. Securities and Exchange Commission fined two investment advisory firms a total of $400,000 for deceptive AI claims. Delphia, for instance, claimed to use algorithms to predict market trends—only for it to be revealed later that neither the necessary datasets nor the advertised technology actually existed.
The three greatest risks
Loss of trust: Unreliable claims erode brand reputation and corporate integrity over the long term.
Stifling innovation: Market disappointment breeds skepticism, which hinders the adoption of genuine, value-creating innovations.
Waste of resources: Capital and time invested in "empty" solutions result in a direct competitive disadvantage.
Cutting through the noise – 4 quick tests
Before committing to a solution, it is worth asking the following questions:
Technical depth: Is there a concrete technical description (model type, architecture), or are we just getting empty buzzwords?
Data strategy: Is the source and quality of training data clear? Is there a defined mechanism for updating the models?
Cost-Value Ratio: Is the price realistic considering the high computational and expertise requirements of genuine AI development?
Actual Necessity: Is the use of AI truly justified, or would a simpler, rule-based system be more efficient and cost-effective?
Summary: There is a massive gap between "hype-riders" and true innovators. It is the responsibility of leaders to look behind the "technological fog" and invest in solutions that don't just sound good but deliver real efficiency gains and lasting competitive advantages.
What is AI washing?
AI washing refers to the phenomenon where a company exaggerates or presents the artificial intelligence capabilities of its products or services misleadingly, either intentionally or due to a lack of understanding. The primary aim is often to enhance market position or gain investor confidence.
This concept closely resembles "greenwashing" in the sustainability sector, where companies adopt a trendy and positive-sounding label without genuinely innovative technological content behind it.
This strategy is not new - similar patterns have emerged throughout tech history during the waves of "blockchain," "big data," and "IoT." The market pressure is immense - if a startup or growth-stage company does not include AI in its pitch, it may already face disadvantages in securing funding. However, market bubbles inevitably burst, and only those who create real business value with genuine technology will survive.
When regulators step in
AI washing is not just an ethical concern - it poses significant business and legal risks. Misleading claims can result in disappointed investors and potential legal repercussions.
In March 2024, the U.S. Securities and Exchange Commission set a precedent by imposing a total fine of $400,000 on two investment advisory firms for AI washing. The cases of Delphia (USA) Inc. and Global Predictions Inc. demonstrated that authorities are no longer ignoring false claims about technology.
Delphia claimed to predict market trends by analyzing client data using algorithms, but later admitted that they did not actually have the necessary datasets or algorithms.
Similarly, Global Predictions marketed itself as a "regulated AI financial advisor" but failed to provide any evidence to support its performance claims.
Why is this critical at the leadership level?
AI washing presents three main risks for companies:
Erosion of trust: Overblown claims can damage a brand's reputation in the long run.
Hindering adoption: Disappointment from false promises fosters skepticism, which can obstruct the implementation of truly valuable technologies.
Waste of resources: When a company invests in ineffective solutions that are falsely labeled as AI, it risks losing its competitive edge.
Not all AI is equal: The importance of categorization
The team at Bobcats Coding, specialists in AI implementation and product development, established a clear framework for evaluating technological content. This framework helps leaders discern where marketing concludes and engineering performance begins.
AI Integration Categories
Understanding different levels of AI integration in software
Category
Characteristics
Example
AI-Native
AI is the foundation of the product, utilizing proprietary models and extensive datasets.
Autonomous vehicle control
Generative AI-Driven
The system is based on external models, such as OpenAI, but places a strong emphasis on the use of AI as a central value.
Specialized, industry-specific content generator
Generative AI-Assisted
AI serves as a supplementary feature; the product continues to function effectively without it.
Quiz evaluation module on an educational platform
Traditional software
AI is simply a convenience tool, such as predictive text input.
Modern email client
AI-Native
Characteristics
AI is the foundation of the product, utilizing proprietary models and extensive datasets.
Example
Autonomous vehicle control
Generative AI-Driven
Characteristics
The system is based on external models, such as OpenAI, but places a strong emphasis on the use of AI as a central value.
Example
Specialized, industry-specific content generator
Generative AI-Assisted
Characteristics
AI serves as a supplementary feature; the product continues to function effectively without it.
Example
Quiz evaluation module on an educational platform
Traditional software
Characteristics
AI is simply a convenience tool, such as predictive text input.
Example
Modern email client
How can you filter out AI washing? (Leadership checklist)
Based on the Bobcats Coding study, here are four questions to quickly assess the credibility of any AI solution:
Lack of substance: If the vendor or internal team relies heavily on buzzwords like "disruptive" or "transformative" without being able to clearly explain the architecture, such as which large language model they are using, the structure of their prompts, and the reasoning behind choosing that particular model, this should raise suspicions.
Data strategy: AI systems learn from data. If there is no clear explanation of where the data comes from, how it is stored, and how it is updated, then the AI solution may lack a solid foundation.
Budget anomalies: Developing true AI solutions requires significant resources. If a seemingly "revolutionary" solution is offered at an unusually low price, it is likely just a simple API wrapper, essentially a straightforward repackaging of an external service.
The “Why does this need AI?” test: If a task can be accomplished more cheaply and reliably using traditional, rule-based software, then incorporating AI may serve no real purpose and could be seen as unnecessary embellishment.
Summary
There is a significant difference between "hype riders" and true innovators. Leaders have the responsibility to see beyond the buzzwords and invest in solutions that not only look good in press releases but also provide real efficiency gains and a competitive advantage.
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