The Rise of "AI Washing"
Exaggerated AI Claims
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Everyone is doing it, and you should, too. Organizations have embraced it for years now, in some instances, for well over a decade. Of course, I'm talking about AI, and of course, this is a gross exaggeration. OpenAI released an early demonstration of ChatGPT on November 30, 2022. Since then, investors across industry sectors have been agog. As a graduate student at Stevens Institute of Technology, I experimented with neural networks. Specifically CNNs or Convolutional Neural Networks, a type of machine learning (a subset of artificial intelligence), to perform image recognition tasks. AI existed almost exclusively within research labs and academia for a long time. And yet, ever since ChatGPT burst onto the scene, there has been a proliferation of "experts" claiming to enhance existing engineering efforts and boost bottom lines through AI innovation. These claims are dubious and harken to an earlier trend when companies claimed they were "going green." Making false or misleading statements about the environmental benefits of a product or practice was called "Greenwashing." Such claims aimed to boost sales by exposing altruistic and moralistic desires to save the planet by being more eco-conscious. Fast forward to today, many companies are claiming to use AI to return greater shareholder value. However, in many instances, AI is simply being used as a buzzword. This practice, which goes by a similar name, "AI Washing," has been gaining popularity since the end of 2023 and the beginning of 2024.
Why are organizations exaggerating their AI usage? Past studies have found that startups mentioning "AI" attract 15% to 50% more investment than those that don't. This hype environment tempts companies to rebrand even traditional analytics or simple automation as "AI-powered solutions." Tech writer Bernard Marr notes that while AI is genuinely transformative, "it’s also clear that there is a lot of hype and hot air around the subject!" Aside from investor hype, analysts also point to cultural factors. A California Management Review insight blames a lack of technical literacy at the top and a fear of falling behind. Many executives don’t fully understand AI’s capabilities or limitations yet feel pressure to proclaim their company is “AI-driven.” These claims can lead to over-promising. Internally, an overly aggressive tech culture can incentivize teams to label routine analytics as AI to get projects greenlit or draw attention. Essentially, a company’s culture and leadership may foster AI washing – intentionally or not – by valuing the appearance of innovation as much as actual results.
A 2025 McKinsey report states, “Almost all companies invest in AI, but only 1% would call their AI initiatives truly mature,” meaning fully integrated into the business with substantial outcomes. In practice, most organizations remain at the proof-of-concept or limited-use phase. For example, a bank might use a basic machine-learning model for credit scoring, or a retailer might add a chatbot for customer service. These are valuable, but they’re often incremental improvements using existing data analytics techniques (like regression, decision trees, or rule-based automation) rather than breakthrough “intelligent” systems. If a system can not genuinely analyze data, learn from it, and make autonomous decisions based on that learning, it's a continuation of data science and business intelligence efforts.
The result of all this posturing is a credibility gap. Businesses talk up AI, but experts and regulators increasingly scrutinize whether there's real machine learning behind the claims or just conventional data analytics. What should your company do instead? Invest in genuine AI development that aligns with business needs and ensure the supporting data and talent are in place (many AI initiatives fail due to data quality or skill gaps, not just the algorithms). If your business doesn't require AI, using your data instead to perform data analysis for business intelligence and insights is the pragmatic approach.
Al Haddi, Hanan. “AI Washing: The Cultural Traps That Lead to Exaggeration and How CEOs Can Stop Them.” California Management Review, Dec. 2024.
SEC Enforcement – Longo, Amy J., et al. “Decoding the SEC’s First ‘AI-Washing’ Enforcement Actions.” Harvard Law School Forum on Corporate Governance, Apr. 18, 2024.
Lumenova AI. “AI in Finance: The Rise and Risks of AI Washing.” Jan. 7, 2025.
Federal Trade Commission. “FTC Announces Crackdown on Deceptive AI Claims and Schemes (Operation AI Comply).” Press Release, Sept. 2024.
Wells, Anna. “AI Maintains Buzzword Status, But Other Tech Tools Reign Supreme.” Industrial Equipment News, May 31, 2024.
Stanford University – AI Index 2024 Report, Chapter 3 (AI Industry and Policy).
Kalyani, Aakash, et al. “AI Hype or Reality? Shifts in Corporate Investment after ChatGPT.” St. Louis Fed – On the Economy Blog, Oct. 3, 2024.
National University. “131 AI Statistics and Trends for 2025.” (Compilation of AI stats, citing Exploding Topics), Jan. 2024.
McKinsey & Co. “Superagency in the workplace: Empowering people to unlock AI’s full potential” (Report), Jan. 28, 2025.
Marr, Bernard. “Spotting AI Washing: How Companies Overhype Artificial Intelligence.” Forbes, Apr. 25, 2024.
Marr, Bernard. "A Short History Of ChatGPT: How We Got To Where We Are Today." Forbes, May 19, 2023.