From coffee makers to investment funds, the term “AI-powered” has become an inescapable prefix attached to nearly every product and service. This ubiquity promises a future of unprecedented efficiency and innovation, driven by intelligent systems. However, it has also created a growing and problematic gap between marketing hype and technological reality.
This phenomenon is known as “AI washing”—the exaggeration and misrepresentation of AI capabilities to influence investors, consumers, and regulators. As companies scramble to appear on the cutting edge, they often stretch the definition of artificial intelligence to its breaking point, blurring the lines between genuine machine learning and conventional software. This creates a noisy, confusing landscape where it’s difficult to separate substance from spin.
This article cuts through that noise to reveal five surprising and impactful takeaways about the current state of AI washing. We will explore how consumers actually perceive the “AI” label, how ethics is being used as a new marketing tool, and how regulators are finally cracking down on deceptive claims.
For more on how AI is reshaping the SaaS industry, see our analysis on The AI Funding Apocalypse.
1. The “AI” Label Isn’t the Magic Bullet Marketers Think It Is
The common assumption in marketing is that labeling a product “AI” automatically makes it seem more advanced, innovative, and trustworthy to users. The term is treated like a magic wand, waved to bestow an aura of sophistication on everything from chatbots to data analytics tools. However, recent research suggests this assumption may be fundamentally flawed.
In an experimental study on “AI Washing: The Effect of Framing,” researchers tested how different labels influenced user trust and behavior. Participants were asked to estimate a car’s price and were then given advice from an advisor. Crucially, the advice was identical across all groups, but the advisor was framed with different labels to signal varying levels of expertise.
The key result was counter-intuitive. While participants predictably trusted a human “industry expert” more than a “student,” they showed no difference in their trust or how they used the advice when it came from an “artificial intelligence” versus a “statistical model.” This finding implies that trendy buzzwords alone don’t sway users. People appear to be more influenced by a system’s actual performance and functional attributes than by a label.
Simple AI washing without real substance does not work.
While consumers may be savvy enough to ignore empty labels, corporations are doubling down on a more sophisticated form of spin: “ethics washing.”
2. “Ethics Washing” Is the New Greenwashing
Just as “greenwashing” emerged to describe companies making unsubstantiated environmental claims, a new analogue has appeared in the technology sector: “ethics washing,” also referred to as “machinewashing.” It is a strategy organizations adopt to create the surface illusion of positive change and ethical engagement while leaving underlying issues of bias, fairness, and accountability unaddressed.
In a Boston Globe article that coined the term “machinewashing,” researchers from the MIT Media Lab described it as a direct response to public anxiety over AI’s downsides:
“Addressing widespread concerns about the pernicious downsides of artificial intelligence (AI)—robots taking jobs, fatal autonomous-vehicle crashes, racial bias in criminal sentencing, the ugly polarization of the 2018 election—tech giants are working hard to assure us of their good intentions surrounding AI. But some of their public relations campaigns are creating the surface illusion of positive change without the verifiable reality.”
This has led to a proliferation of AI ethics guidelines, with a 2023 study identifying over 200 such documents. The core problem, however, is the immense challenge of translating these high-level principles into concrete technical and organizational measures. This gap allows companies to use ethics as a “reputational asset,” projecting legitimacy while sidestepping the genuine transformation and binding regulation required to ensure responsible AI development. For an analysis of ethics in AI governance, see the academic research on digital ethicswashing.
This gap between ethical posturing and genuine practice has not gone unnoticed, creating a prime target for regulators and litigators.
3. Regulators Are Cracking Down and Lawsuits Are Piling Up
The consequences of AI washing are no longer theoretical. What was once a marketing concern has escalated into a significant legal and financial risk, with regulators and investors taking aggressive action against deceptive claims.
U.S. federal agencies have begun a wave of enforcement, signaling that unsubstantiated “AI-powered” claims will no longer be tolerated.
The SEC:
- The Securities and Exchange Commission has settled charges against the restaurant technology company Presto Automation for “making materially false and misleading statements about critical aspects” of its AI product.
- In 2024, it also brought enforcement actions against two investment advisers for misleading disclosures about their use of AI. See DLA Piper’s analysis.
The FTC:
- The Federal Trade Commission has filed lawsuits against multiple business opportunity schemes, including Ascend Ecom, Ecommerce Empire Builders, and FBA Machine.
- These companies were accused of defrauding consumers by falsely claiming their tools were “AI-powered” and could generate thousands of dollars in passive income. See the FTC’s AI enforcement page.
The scale of this litigation is staggering: According to the Stanford Law School Securities Class Action Clearinghouse and Cornerstone Research, 53 Securities Class Actions (SCAs) with allegations relating to AI were filed in the period leading up to June 30, 2025. This makes AI-related claims the largest single class of event-driven SCAs, exceeding those related to cryptocurrency, Covid-19, or cybersecurity. This sharp rise in enforcement and litigation demonstrates that regulators are actively targeting deceptive AI marketing. See WTW’s analysis of AI securities litigation.
For SaaS CEOs, these developments underscore why authentic AI capabilities matter. See our article on Builder.ai’s Collapse: Lessons for Seed-Stage SaaS CEOs for a cautionary tale.
With legal and financial risks now a reality, the focus is shifting inward, revealing that the greatest barrier to genuine AI adoption is not external scrutiny, but internal company culture.
4. The Real Bottleneck Isn’t Technology—It’s Company Culture
As the initial excitement around AI tools begins to mature, many companies are realizing that simply implementing new software is not enough to unlock its potential. An emerging consensus suggests that organizations must first build an “AI-ready culture” before they can reap the benefits of the technology.
This cultural deficit is starkly illustrated by a key data point from Dean Guida, CEO of Infragistics, who notes that currently, only 23% of employees feel completely educated and trained on AI. (See the Slingshot 2024 Digital Work Trends Report.) To bridge this gap, organizations are shifting their focus from technology acquisition to internal readiness.
There are two key components to “readying” an organization for AI:
- Training and Education: Companies must provide employees with the proper skills and knowledge to use AI tools to their full potential, transforming them from novelties into essential business support.
- Data Readiness: AI thrives on comprehensive, high-quality data. Organizations must centralize their data, which is often spread across multiple systems, channels, and gatekeepers. Giving AI a holistic view of the organization is essential for generating valuable insights.
For practical guidance on implementing AI effectively, see our article on The 7 Deadly Mistakes Every B2B Company Makes When Implementing AI.
The key takeaway is clear: the companies that slow down to focus on building a strong cultural and data foundation before adding more AI tools will be the ones who see the most significant benefits in 2025.
This necessary focus on cultural and data groundwork is setting the stage for a market-wide shakeout, where investors will finally separate the prepared from the pretenders.
5. The Investment Frenzy Is Ending, and an Accountability Reckoning Is Coming
The initial hype cycle that fueled a massive investment frenzy in any company with “AI” in its pitch is drawing to a close. As we move into 2025, the investment landscape is shifting from speculative excitement to a demand for tangible results. It will no longer be enough for a company to simply “adopt AI”; CIOs and CTOs will demand hard ROI metrics to prove its value before approving new investments.
This shift is predicted to trigger a period of mass consolidation in the market. As AI startups begin to run out of cash, they will be acquired by traditional companies—a trend predicted by Phil Lim of Diligent. According to Gartner’s analysis, this will lead to significant market correction, and a widespread lack of education and poor governance will cause many organizations to over-invest in dubious promises that lack a long-term competitive advantage. See also Crunchbase’s analysis of startup consolidation.
“The party won’t end in 2025, but the cover charge will get a lot higher.” — Jeremy Burton, CEO of Observe
As this market correction unfolds, a reckoning is coming. Firms that have relied on deceptive marketing and AI washing will face a cascade of regulatory fines and investor lawsuits. In contrast, those that have made a “serious effort” to integrate genuine AI and can demonstrate quantifiable outcomes will be best positioned for sustainable success.
For a deeper dive into how the AI funding landscape is reshaping SaaS, see our comprehensive analysis: The SaaS Exit Crisis: A Survival Guide for CEOs Navigating the AI Era in 2025.
Conclusion: From AI Hype to AI Accountability
The era of unchecked AI hype is giving way to a new era defined by AI accountability. The narrative is shifting from a blind faith in buzzwords to a rigorous demand for proof. Authenticity, regulatory compliance, and measurable value are quickly becoming the new cornerstones of success in the algorithmic economy.
As AI becomes more deeply integrated into our business and personal lives, the most important question for any organization is no longer “Are you using AI?” but rather, “Can you prove it?”
Additional Resources from DevelopmentCorporate
For more insights on AI, SaaS strategy, and M&A, explore these related articles:
- Build vs. Buy Isn’t Dead—It Just Got a Third Way to Fail — Why the “15-minute AI fix” is actually a recipe for unmaintainable technical debt
- Enterprise SaaS: A Comparative Analysis of AI in Software Sales — How leading firms leverage AI to transform sales operations
- Autoflation: How AI Is Rewriting the Economics of Work for SaaS Founders — Understanding the economic implications of AI automation
- The Hidden Search Engine Your B2B Buyers Are Using — Why SaaS CEOs need an AI optimization strategy
- Europe’s AI SaaS Startups in 2025: Pre-Seed and Seed VC Trends — Insights from the Q1 PitchBook Report
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About DevelopmentCorporate LLC
DevelopmentCorporate LLC is an M&A advisory and strategic consulting firm specializing in early-stage SaaS companies. With over 30 years of enterprise software experience, we help pre-seed and seed-stage CEOs with competitive intelligence, win-loss analysis, pricing studies, and acquisition strategies. Learn more at developmentcorporate.com.
