Filtering Out AI Washing in Today’s Noisy AI Market
With over 70,000 companies worldwide claiming artificial intelligence (AI) capabilities, separating genuine innovation from marketing hype has become a critical business challenge. This post draws parallels to historical instances of greenwashing and the dot-com boom to reveal how AI washing follows the same deceptive patterns. For business leaders evaluating AI investments, the key is prioritizing measurable business outcomes over technological sophistication by asking the right questions to identify real AI solutions while avoiding exaggerated vendor claims.
Listen to: “Filtering Out AI Washing in Today’s Noisy AI Market” Podcast
There are now over 70,000 companies worldwide claiming AI capabilities. Of these, 26,000 AI startups have received $330 billion in funding over the past three years alone. Yet, industry analysts estimate that only 130 out of thousands of “agentic AI” products will actually deliver on their promises. For business leaders evaluating AI investments, this creates a critical challenge: while genuine AI innovation can drive competitive advantage, the wrong choice wastes resources and delays transformation.
The question becomes:
“How do you separate breakthrough technology from unproven marketing tactics?”
The Pattern Behind AI Washing
In 1986, environmentalist Jay Westerveld coined the term “greenwashing” to describe companies claiming environmental friendliness without evidence to back up those claims, like Chevron’s 1980s campaigns featuring employees protecting sea turtles all while the company violated clean air laws and spilled oil into wildlife refuges.
During the dot-com boom, companies similarly added “.com” to their names and claimed that they had revolutionary online business models in order to attract investors, despite having traditional operations with minimal digital innovation.
“AI washing follows the same pattern, whereby companies rebrand existing products as “AI-powered” despite their lack of meaningful machine-learning capabilities.”
AI washing follows the same pattern, whereby companies rebrand existing products as “AI-powered” despite their lack of meaningful machine-learning capabilities. The difference today is that while AI tools have genuinely democratized development capabilities, they’ve also made it easier to create convincing facades of innovation. Just because practically anyone can now build AI applications through approaches like vibe coding doesn’t guarantee business success.
The key distinction isn’t between real and fake AI, but between AI that solves meaningful business problems and AI that applies impressive technology to non-problems. Smart technology buyers focus less on AI sophistication and more on measurable business outcomes.
What Smart Buyers Ask Before Investing in AI
Gartner estimates that of the thousands of agentic AI vendors in the marketplace, only 130 deliver genuine capabilities. According to Anushree Verma, Senior Director Analyst at Gartner, “Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied. This can blind organizations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production.”
The result? “Over 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.”
This post captured AI washing perfectly: “It’s the equivalent of calling a PowerPoint presentation immersive storytelling or renaming a basic dashboard a machine-learning engine.”
Smart buyers cut through this noise by asking AI-specific questions:
How will AI create tangible value and deliver ROI for the organization?
What specific AI capabilities does this actually use?
Can this product demonstrate measurable improvement over non-AI alternatives?
What happens when the AI makes mistakes and how frequently does that occur?
What data do you need from us and how will you protect it?
In an earlier post, we identified how strategic questions drive competitive advantage in AI and why companies tend to rush to adopt AI by focusing just on tech as opposed to simply asking the right questions.
Avoiding the AI Washing Trap
AI capabilities are evolving rapidly, making it tempting to believe sensational vendor claims. However, it is crucial for business leaders to maintain strategic discipline when evaluating AI investments. CTO Magazine identifies these key red flags:
Vague claims: If vendors use terms like “AI-powered” without explaining specific capabilities, be suspicious
Missing Evidence: No case studies, benchmarks, or third-party validation of claimed performance
Unrealistic promises: Claims the AI product can solve every business problem (real AI has clear limitations)
Static Performance: AI that doesn’t improve over time isn’t learning, it’s likely rule-based software that has been rebranded as AI
AI washing can have a negative impact on brands, resulting in hindering or stifling true innovation, creating negative brand perception and diluting investor confidence.
The stakes are rising. The Federal Trade Commission now warns that companies making exaggerated AI claims face regulatory action for “overpromising and underdelivering.” For buyers, this creates both risk and opportunity, because while AI washing damages vendor credibility, it also helps smart buyers identify genuine innovation partners.
Bridging the Gap Between Skepticism and Opportunity
While established providers like OpenAI, Microsoft Copilot, Google Gemini, Anthropic, and Meta Llama have proven track records and transparent capabilities, business leaders should apply extra scrutiny to specialty large language models (LLMs) and emerging providers, where credibility is less established.
Yes, the AI landscape is evolving rapidly, but smart business leaders don’t need to chase every new development. Instead, they should focus on building internal AI evaluation frameworks that separate genuine innovation from marketing hype. The companies that master this skill by asking the right questions, demanding proof of performance and maintaining strategic discipline will gain a sustainable competitive advantage while others get distracted by shiny objects.
The question isn’t whether AI will transform your industry; it’s whether your organization will choose the right AI partners.
What AI evaluation mistakes have you seen in your industry?
Questions? Email me here. As always, thank you for reading and/or listening.
