AI washing exposed: how empty promises destroy marketing credibility

The marketing industry confronts a credibility crisis rooted in systematic exaggeration of artificial intelligence capabilities. New academic research identifies two interconnected phenomena—AI washing and AI booing—that together threaten consumer trust in marketing technologies and the brands deploying them.

Selcen Ozturkcan and Ayşe published research examining how companies overstate AI capabilities for marketing advantage while facing public backlash from unmet expectations. The paper, submitted to an academic journal, introduces formal definitions for emerging patterns that marketing professionals experience daily but rarely name explicitly.

According to the research, AI washing represents "the deliberate or negligent exaggeration of a system's artificial intelligence capabilities, typically by presenting rules-based or pre-programmed functionalities as autonomous, adaptive, or ethically governed systems." This practice mirrors greenwashing in environmental marketing, where companies make misleading sustainability claims.

The phenomenon extends beyond simple marketing hyperbole. Firms increasingly claim their platforms fully automate complex tasks such as video production and market research. These systems frequently continue to rely on significant human oversight to maintain quality, interpret nuanced contexts, and ensure accurate outcomes, according to the research.

AI booing describes the inevitable response when these inflated promises collapse under scrutiny. The paper defines it as "public disapproval or backlash against AI technologies, often triggered by incidents of bias, opacity, surveillance concerns, or perceived ethical breaches."

The research documents specific cases where this dynamic plays out. Coca-Cola faced criticism for claiming artificial intelligence co-created a new drink, Y3000, without clear explanation of the technology's actual involvement. The campaign appeared more innovative than reality supported.

The U.S. Securities and Exchange Commission charged two firms for misleadingly stating their investment strategies were driven by artificial intelligence. In insurance, findings from Sprout.ai revealed a rise in AI-altered fraudulent claims, a trend that some insurers have overstated in their reliance on artificial intelligence while downplaying the necessity of human oversight.

The research identifies several warning signs that marketing claims cross into AI washing territory. Claims about transformative impact frequently lack substantiated evidence or detailed case studies. Companies rely on vague promises rather than specific, data-backed examples.

Exaggerated portrayals of superiority over traditional tools often lack clear or proven functionality. When technical specifics are missing—such as explanations of machine learning or natural language processing—the audience receives an inflated impression of capabilities.

The consequences extend beyond individual companies to the entire marketing technology ecosystem. Marketing professionals increasingly question AI reliability as deployment challenges mount, with practitioners reporting widespread issues across platforms.

Tom Goodwin, keynote speaker and consultant, stated on October 26, 2025, that "Gen AI is what happens when you ship something about 8 years too early and hope it doesn't catch up with you." His post resonated with professionals experiencing similar frustrations, receiving 115,500 views within hours.

WordStream research published July 10, 2025, found that 20% of artificial intelligence responses to pay-per-click advertising questions contained inaccurate information. Google AI Overviews demonstrated the poorest performance with 26% incorrect answers.

The academic research positions these problems within trust theory frameworks. Trust in marketing involves willingness to be vulnerable based on perceptions of competence, integrity, and benevolence. It is a dynamic psychological construct shaped by values, attitudes, and past experiences.

According to the research, consumers who feel misled lose faith in both the technology and the brand. The paper emphasizes that trust and mistrust are not simply opposites on a linear continuum. Individuals may simultaneously trust an entity's abilities but remain skeptical of its intentions, leading to partial trust or careful distrust.

The cyclical nature of AI mistrust forms when AI washing leads to AI booing, creating a boom-and-bust pattern where initial enthusiasm deteriorates into skepticism. Early adopters—such as consultancies and business media—often drive hype without fully considering practical viability, according to the research.

This process leads to inflated expectations. When unmet, these expectations create disappointment and erode trust in both specific vendors and the broader category of AI-driven marketing solutions.

Data governance challenges expose fundamental AI confidence crisis facing enterprises. Publicis Sapient's 2026 Guide to Next industry trends report, released in November 2025, exposes a reality where organizations fail at artificial intelligence not because algorithms are flawed, but because the data feeding them is inconsistent, fragmented, and ungoverned.

The research identifies specific mechanisms through which AI washing operates. Companies add "go-faster stripes" to products—superficial enhancements that capitalize on AI hype without real technological advancement. This practice stifles genuine breakthroughs, erodes consumer trust, inflates expectations, and sets unrealistic goals for investors who struggle to identify valuable projects.

The rise in AI ethical guidelines has drawn criticism as "ethics washing," according to the research. High-minded principles lack enforcement and practical application, diverting focus from potential systemic harms. This pseudo-ethical positioning can lead to biased decisions and false promises of "AI for good" initiatives that may conflict with ethical standards when companies sell surveillance technology to questionable buyers.

The academic paper proposes a framework for responsible AI adoption centered on ethical data management, human agency, stakeholder collaboration, and transparency. Companies must emphasize transparency, utilize bias detection tools, and adhere to comprehensive guidelines that ensure data integrity and foster consumer trust.

Regulatory frameworks play crucial roles in enforcing accountability for AI practices. However, these regulations must be dynamically updated to keep pace with the rapidly expanding field while avoiding stifling innovation.

IAB Europe's comprehensive AI whitepaper released July 7, 2025, addresses growth, guardrails and policy for European digital advertising. More than 80% of marketers worldwide now integrate some form of artificial intelligence into their online activities, while over half of marketing and advertising professionals in Europe report using generative AI for content creation.

The research emphasizes that addressing issues such as AI bias and opacity requires treating artificial intelligence not merely as a tool but as an extension of human agency. Development and application must actively align with human intentions and ethical responsibilities.

Building participatory marketing cultures can support accountability by involving consumers in brand values, transforming traditional relationships into ones of shared values and transparency. Incorporating key ethical principles into AI systems—such as autonomy, the right of explanation, and value alignment—ensures operations are technically proficient and ethically responsible.

The financial implications extend beyond technical performance. According to practitioners documenting experiences, "The time and money that go into 'guardrailing,' 'safety layers,' and 'compliance' dwarfs just paying a human to do the work correctly."

Most companies still pilot AI programs despite widespread adoption. McKinsey released research on November 9, 2025, revealing that while 88% of respondents report regular AI use in at least one business function, most implementations remain stuck in pilot or experimental phases.

Only approximately one-third of respondents report their companies have begun scaling AI programs across the enterprise. This scaling challenge arrives despite dramatic increases in adoption rates.

The research documents how corporate messaging around artificial intelligence capabilities continues despite documented limitations. Reddit discussions included multiple responses from practitioners describing similar experiences. One commenter with an "AI engineer" title stated, "I see almost no ability for it to automate, at least until robotics and FSD advances."

According to the academic research, AI washing in marketing manifests through several comparative dimensions. The claim type focuses on technological capability such as autonomy and AI use. The nature of deception involves functional inflation. Target audiences include investors, consumers, and regulators. Motivation centers on hype generation and market valuation.

This differs from greenwashing, which makes environmental sustainability claims through moral exaggeration targeting consumers and regulators for reputation and legitimacy. Ethics washing involves ethical commitment claims like fairness and privacy through ethical posturing targeting the public and civil society to avoid scrutiny.

The research identifies transparency as essential for overcoming both AI washing and AI booing. Regulatory pressure, ethical standards, and consumer advocacy are crucial to breaking the cycle of mistrust. These forces can encourage companies to adopt more responsible AI marketing strategies.

For marketing professionals, the implications are immediate. IAB Europe research found that 91% of digital advertising professionals have either embraced or experimented with generative AI technologies, highlighting rapid market penetration.

The whitepaper identifies AI revenue forecasts growing from roughly $200 billion in 2023 to approximately $1.4 trillion by 2029. For European markets specifically, firms adopting AI early experience up to 3.1 percentage-point faster annual worker-productivity growth.

However, these projections assume responsible implementation rather than the AI washing patterns the academic research documents. The gap between stated readiness and actual governance represents the primary challenge facing marketing organizations.

Investment patterns reflect industry momentum despite implementation challenges. McKinsey's Technology Trends Outlook 2025 documented that artificial intelligence attracted $124.3 billion in equity investment during 2024, representing the highest funding levels among 13 analyzed trends.

The research emphasizes that trust serves as both a barrier and an enabler of AI adoption in marketing. Practical applications must align with promises to prevent trust erosion, as gaps between expectations and real-world performance fuel consumer skepticism.

Ethical risks such as bias, deception, and lack of transparency must be proactively mitigated to foster responsible AI engagement. Operationalizing trust requires calibrated transparency, governance mechanisms, and ethical AI practices that ensure fairness and accountability.

Berkeley researcher Michael I. Jordan proposed collectivist approaches to artificial intelligence development, arguing the marketing and technology industries should embrace perspectives that treat social welfare as fundamental rather than an afterthought.

The research submitted on July 8, 2025, directly challenges Silicon Valley's dominant approach to AI development. Jordan contends that current systems neglect humans' fundamentally social nature, stating that "Humans are social animals, and much of our intelligence is social and cultural in origin."

The academic paper on AI washing concludes with recommendations for marketing professionals and policymakers to mitigate the cycle of AI mistrust and establish more credible AI integrations in marketing. The study calls attention to the need for transparency, human agency, stakeholder collaboration, and ethical data management to foster responsible AI practices.

Transparency challenges extend beyond technical standards to fundamental questions about accountability. Check My Ads Institute submitted formal comments on October 20, 2025, regarding Media Rating Council Digital Advertising Auction Transparency Standards.

According to the submission, transparency standards can only restore trust if they are mandatory, independently audited, publicly disclosed, and consistently enforced. Without these elements, voluntary suggestions mislead market participants.

The research framework positions responsible AI integration in marketing across four dimensions. Ethical data management addresses how biases in AI data affect consumer perceptions and trust, along with best practices for maintaining data integrity in AI-driven marketing.

Human agency examines how oversight mitigates risks associated with autonomous AI in marketing. The impact of AI-driven automation on human decision-making in marketing requires careful consideration to ensure ethical use without stifling innovation.

Stakeholder collaborations investigate how partnerships with regulatory bodies foster ethical AI practices. Cross-sector relationships play essential roles in addressing AI washing. Cross-cultural perspectives shape AI ethics and regulation for global marketing.

Sustainable practices explore ways artificial intelligence can contribute to sustainable marketing without compromising performance and consumer engagement. The research emphasizes that responsible AI adoption requires moving beyond compliance toward transparency, human agency, and stakeholder collaboration as strategic imperatives.

The timing matters for the marketing community as platform consolidation accelerates and major advertising technology providers integrate AI capabilities across their ecosystems. Meta CEO unveiled personal superintelligence vision on August 1, 2025, with comprehensive advertising automation plans that envision businesses providing only objectives and budgets while artificial intelligence handles creative development, targeting, and optimization.

This philosophical positioning addresses concerns about artificial intelligence's societal impact. Rather than replacing human economic participation, Meta's vision emphasizes augmenting individual capabilities across creative, professional, and personal domains.

The academic research warns that such ambitious visions risk exacerbating AI washing patterns unless companies maintain rigorous distinctions between current capabilities and future aspirations. The announcement acknowledges safety considerations while maintaining commitment to democratized access.

Industry-wide patterns suggest widespread recognition of these challenges. McKinsey research on agentic AI published July 27, 2025, positions artificial intelligence systems capable of autonomous planning and execution as the most significant emerging trend for marketing organizations.

The report notes that agentic AI represents a shift from chatbot interactions to virtual coworkers that can independently manage complex workflows. However, the technology enables marketing teams to automate tasks like campaign optimization, audience targeting, and performance analysis without constant human oversight.

According to McKinsey data, $1.1 billion in equity investment flowed into agentic AI in 2024, with job postings related to this technology increasing 985% from 2023 to 2024. While interest levels remain relatively low compared to established AI technologies, growth rates exceed all other technology trends.

The research on AI washing emphasizes that these rapid adoption patterns increase risks of exaggerated claims. As companies rush to integrate artificial intelligence capabilities, the temptation to overstate functionality intensifies.

Adverity launched Adverity Intelligence on September 12, 2025, marking expansion beyond its established data integration platform into AI-powered analytics capabilities. The London-based company built its new intelligence layer on AI and Model Context Protocol technology.

According to Chief Product Officer Lee McCance, "Adverity Intelligence isn't just about AI. We've been embedding AI in our platform for some time and have already seen the benefits to our customers." This statement illustrates how companies navigate the tension between highlighting AI capabilities and avoiding AI washing patterns.

The academic research identifies several research questions that marketing professionals should consider. How do biases in AI data affect consumer perceptions and trust? What are the best practices for maintaining data integrity in AI-driven marketing?

How can artificial intelligence help identify and reduce dark patterns in digital marketing, and what ethics are needed to prevent AI from enabling these tactics? What methodologies can be developed to measure the real impact of AI on consumer decision-making processes in controlled versus uncontrolled marketing environments?

These questions matter because they address fundamental tensions between technological capability and marketing practice. The research emphasizes that treating AI as merely a tool rather than an extension of human agency contributes to both AI washing and subsequent AI booing.

The regulatory environment continues evolving in response to these challenges. Commission released AI Act guidelineson July 18, 2025, clarifying obligations for providers of general-purpose artificial intelligence models under EU regulation.

The 36-page framework targets four key areas: model classification criteria, provider identification, open-source exemptions, and enforcement procedures. For the advertising industry, implications extend beyond immediate compliance requirements to fundamental shifts in how AI-powered tools can be developed and deployed.

Documentation requirements under these guidelines will influence how AI-powered advertising tools integrate with major platforms. Marketing organizations using general-purpose AI models for content creation, customer targeting, or campaign optimization must now demonstrate compliance with detailed transparency and safety requirements.

The academic research positions the cycle of AI mistrust within broader patterns of technological hype and backlash. Early adopters drive enthusiasm without fully considering practical viability. This creates inflated expectations that, when unmet, generate disappointment and erode trust.

Regulatory pressure, ethical standards, and consumer advocacy become essential for breaking this cycle. Companies must adopt more responsible AI marketing strategies that align stated capabilities with actual functionality.

The paper concludes that transparency and stakeholder collaboration are essential for overcoming both AI washing and AI booing. By recognizing factors that contribute to AI mistrust, marketers can develop more effective strategies to build and maintain consumer trust.

For the marketing community, the research provides a conceptual framework for understanding dynamics that many professionals experience but struggle to articulate. The formal definitions of AI washing and AI booing enable more precise discussion of these phenomena.

The study extends understanding of how cycles of exaggerated claims and public backlash shape debates on technological legitimacy, institutional signaling, and responsible innovation in AI-driven marketing. From a managerial perspective, it underscores that hype-driven or symbolic approaches to artificial intelligence carry reputational, legal, and consumer trust risks.

Responsible AI adoption requires moving beyond compliance toward transparency, human agency, and stakeholder collaboration as strategic imperatives for sustaining trust and competitive advantage. The research emphasizes that these are not abstract principles but practical requirements for maintaining credibility in an increasingly skeptical marketplace.

The marketing technology sector faces mounting pressure to demonstrate genuine value rather than merely claiming it. Industry data supports concerns about implementation challenges. Financial implications extend beyond technical performance to encompass the entire value proposition of AI-driven marketing solutions.

Organizations must balance innovation aspirations with realistic assessments of current capabilities. The gap between what artificial intelligence can do and what companies claim it can do threatens to undermine the entire category's credibility.

The research identifies that trust is far from an abstract concept. It is the dynamic currency of responsible AI in marketing, one that can be gained or lost instantly. Careful stewardship is essential for aligning technological promise with societal expectations.

As transparency standards clash in programmatic advertising and auction transparency challenges mount, the broader ecosystem grapples with fundamental questions about accountability and trust.

The academic research on AI washing and AI booing provides a theoretical framework for understanding these practical challenges. By naming these phenomena and analyzing their mechanisms, the study enables marketing professionals to recognize patterns, avoid common pitfalls, and build more sustainable approaches to artificial intelligence integration.

The ultimate test will be whether the industry can move from cycles of hype and backlash toward steady implementation grounded in realistic capabilities and transparent communication. The research suggests this transition is possible but requires conscious effort from companies, regulators, and marketing professionals to prioritize trust over short-term competitive advantage.

Timeline

• 1998: Children's Online Privacy Protection Act establishes federal requirements for children's online privacy
• 2022: Congress enacts INFORM Consumers Act as part of Consolidated Appropriations Act of 2023
• 2023: FTC begins combating fake reviews and testimonials with new rule potentially saving consumers $6.64-$17.52 billion annually
• December 2023: ANA Programmatic Media Supply Chain Transparency Report reveals only 36% of budgets reach valid impressions
• January 2025: Academic research on AI washing and AI booing submitted examining systematic exaggeration of AI capabilities
• April 22, 2025: New COPPA rules take effect June 23, 2025 with major advertising changes
• July 7, 2025: IAB Europe releases whitepaper on AI in digital advertising showing 91% of professionals have experimented with generative AI
• July 8, 2025: Berkeley researcher proposes collectivist approach to artificial intelligence development challenging industry focus
• July 10, 2025: WordStream research finds 20% of AI responses to PPC questions contain inaccurate information
• July 18, 2025: Commission releases AI Act guidelines clarifying obligations for AI model providers
• July 27, 2025: McKinsey positions agentic AI as most significant emerging trend for marketing organizations
• August 1, 2025: Meta CEO unveils personal superintelligence vision with comprehensive advertising automation plans
• August 27, 2025: IAB Technology Laboratory declares Prebid transaction ID changes violate OpenRTB specification
• August 30, 2025: Transaction ID standards clash raises programmatic transparency concerns
• September 8, 2025: Temu pays $2 million in first INFORM Act enforcement case
• September 12, 2025: Adverity debuts AI-powered intelligence layer for marketing analytics
• September 29, 2025: FTC finalizes restrictions on Omnicom's acquisition of IPG preventing coordination to deny advertising dollars
• October 2, 2025: Trade Desk launches OpenAds to counter supply chain manipulation
• October 20, 2025: Check My Ads challenges auction transparency standards
• October 26, 2025: Industry consultant states "Gen AI is what happens when you ship something about 8 years too early"
• October 28, 2025: Marketing professionals question AI reliability as deployment challenges mount
• October 29, 2025: Media Rating Council issues draft standards for digital ad auction transparency
• November 2025: Publicis Sapient's 2026 Guide to Next reveals organizations fail at AI due to data governance gaps
• November 9, 2025: McKinsey survey shows most companies still pilot AI programs despite widespread adoption
• November 27, 2025: IAB Australia releases affiliate program compliance framework
• December 10, 2025: IAB Australia reveals 16 audience segmentation methods for retail media

Summary

Who: Researchers Selcen Ozturkcan and Ayşe, alongside marketing professionals, regulatory bodies including the FTC and European Commission, major platforms like Google and Meta, and industry organizations including IAB Europe and McKinsey documenting patterns of exaggerated AI claims and subsequent consumer backlash.

What: Academic research identifies AI washing—deliberate or negligent exaggeration of AI capabilities where companies present rules-based systems as autonomous technologies—and AI booing, the public backlash against AI technologies when promises don't match reality. The study documents how these phenomena create cyclical mistrust threatening consumer confidence in marketing technologies.

When: Research submitted in January 2025 analyzes patterns emerging from 2020-2025 period, with significant enforcement actions and industry developments accelerating throughout 2025 including FTC settlements, regulatory guidelines, and documented reliability issues affecting marketing professionals.

Where: Global marketing technology ecosystem with particular focus on United States FTC enforcement, European Union AI Act implementation, and digital advertising platforms where 91% of professionals have experimented with generative AI according to July 2025 IAB Europe research, representing markets expected to grow from $200 billion in 2023 to $1.4 trillion by 2029.

Why: The marketing community should care because AI washing creates reputational, legal, and consumer trust risks that undermine the entire category's credibility. McKinsey data shows 88% of organizations use AI regularly but only one-third have scaled programs successfully, while WordStream research finds 20% of AI responses contain inaccurate information. Responsible AI adoption requires moving beyond compliance toward transparency, human agency, and stakeholder collaboration as strategic imperatives for sustaining competitive advantage in increasingly skeptical marketplace where trust serves as dynamic currency that can be gained or lost instantly.