Artificial intelligence is rapidly transforming the advertising landscape, offering unprecedented opportunities for efficiency and creative innovation. However, this acceleration in AI adoption by advertisers is occurring amidst a widening perception gap with consumers, particularly Gen Z and Millennials. While ad executives increasingly leverage AI in creative processes, The AI Gap Widens, new research from IAB and Sonata Insights reveals that consumers hold significantly less positive views on AI-generated advertising than advertisers assume. For senior marketing and customer experience (CX) leaders, addressing this disconnect through strategic disclosure and robust governance is critical to maintaining brand trust and ensuring measurable campaign effectiveness.
The Widening Disconnect: Consumer Skepticism Versus Advertiser Optimism
Enterprises are rapidly integrating AI into their advertising workflows, driven by the promise of enhanced efficiency and creative output. The report highlights that 83% of ad executives now utilize AI in the creative process, a substantial increase from 60% in 2024. Generative AI has become a cornerstone for video ad creation, with 86% of buyers either using or planning to use it. Advertisers cite cost efficiency as the top benefit (64%), followed by creative innovation (61%).
Despite this enthusiasm, consumer sentiment presents a stark contrast. Only 45% of Gen Z and Millennial consumers feel positive about AI-generated ads, a figure nearly half of the 82% of ad executives who believe consumers feel positive. This perception gap has widened from 32 points in 2024 to 37 points in 2026. Gen Z consumers exhibit particular skepticism, with 39% expressing negative sentiment, nearly double that of Millennials (20%). Consumers are also significantly more likely to associate negative attributes like “manipulative” (20% of consumers versus 10% of executives) and “unethical” (16% of consumers versus 7% of executives) with brands that use AI in advertising. This divergence underscores a significant risk to brand equity and campaign efficacy if not proactively managed.
Disclosure as a Strategic Imperative: Building Trust and Driving Engagement
The IAB and Sonata Insights study identifies clear disclosure as a powerful mechanism to mitigate consumer skepticism, improve ad engagement, and positively influence purchase likelihood. While 89% of advertisers using generative AI for ads disclose at least sometimes, less than half always do, an inconsistency that undermines trust. This intermittent approach misses a strategic opportunity.
Clear disclosure ranks as the third-highest driver of attention for AI-generated ads (35%), just behind high-quality visuals (40%) and funny content (39%). Importantly, disclosure also impacts purchase consideration: 36% of Gen Z and Millennial consumers report being more likely to purchase a product or service if they know an ad was created with AI, while 37% indicate no difference in purchase likelihood. This suggests a net-positive or neutral impact, indicating disclosure carries more upside than downside for these key demographics.
Consumers express a strong desire for disclosure across various AI-generated elements. More than half want disclosure for 100% AI-generated ads, AI video, and AI images. Nearly half also seek disclosure for AI voices or AI avatars. Beyond fully AI-generated content, significant minorities desire disclosure for digitally adjusted images or video (35%), manipulated photo techniques (31%), and even the use of stock photos (23%). This preference for transparency is particularly pronounced in highly regulated sectors such as pharmaceutical/healthcare, political advertising, and financial services, where consumers and advertisers largely agree on the importance of disclosure.
What to Do:
- Establish Clear, Enterprise-Wide AI Disclosure Policies: Define precise conditions under which AI usage must be disclosed. Examples include: ads entirely generated by AI, ads featuring AI-generated video, images, or voices, and any significant AI-driven manipulation of content.
- Integrate Disclosure into Creative Workflows: Implement processes to embed disclosure mechanisms directly into creative review and deployment stages. This may involve metadata tagging, on-screen labels (e.g., “AI-Generated Content”), or clear audio cues.
- Define Standards for Manipulated Content: Develop internal guidelines for disclosing content that has undergone substantial digital adjustment or manipulation beyond standard retouching, irrespective of whether AI was explicitly used.
- Prioritize Disclosure in Regulated Industries: For sectors like financial services, healthcare, and telecom, proactively implement robust disclosure frameworks to align with both consumer expectations and potential future regulatory requirements.
- Train Marketing and Legal Teams: Ensure all relevant personnel understand the disclosure policies, their rationale, and the technical requirements for implementation.
What to Avoid:
- Assuming Implicit Understanding: Do not presume that consumers can identify AI-generated content or that they are indifferent to its provenance.
- Prioritizing Short-Term Efficiency Over Trust: While AI offers cost savings, neglecting disclosure can erode long-term brand trust, ultimately harming campaign effectiveness.
- Inconsistent Disclosure Practices: Avoid varied disclosure approaches across different campaigns, channels, or geographic regions. Inconsistency confuses consumers and diminishes credibility.
- Treating Disclosure as an Afterthought: Integrate disclosure as a fundamental component of the creative development process, not as a final, superficial addition.
Operationalizing Ethical AI in Advertising: Governance and Measurable Outcomes
To effectively leverage AI in advertising while building consumer trust, enterprise leaders must implement robust governance frameworks and define clear operating models. This approach ensures AI capabilities translate into responsible, measurable outcomes.
Operating Model and Roles:
- AI Ethics Council/Committee: Establish a cross-functional body comprising representatives from Marketing, Legal, Compliance, CX, and Data Science. This council is responsible for setting AI usage policies, reviewing AI creative guidelines, and managing associated risks.
- Creative AI Leads: Designate specific roles within marketing teams responsible for overseeing AI tool selection, developing prompt engineering best practices, and ensuring strict adherence to AI disclosure standards.
- Data Governance Team: Task this team with ensuring ethical data sourcing for AI models, managing consumer consent, and ensuring compliance with privacy regulations (e.g., GDPR, CCPA) for AI-driven personalization.
Governance and Risk Controls:
- Guardrails and Policies: Implement clear, documented AI usage policies (e.g., “No AI-generated content depicting vulnerable populations without explicit human review and consent,” “All synthetic media must carry a visible and audible AI disclosure”).
- Disclosure Thresholds: Define quantitative or qualitative thresholds for when AI disclosure is mandatory. For instance, requiring disclosure if 25% or more of an ad’s visual or audio elements are AI-generated, or if any AI-driven deepfake or synthetic character is used.
- SLAs and Escalation Paths: Develop Service Level Agreements for the review and approval of AI-generated assets, ensuring timely legal and brand safety checks. Establish clear escalation paths for any content that deviates from policy or poses significant brand risk.
- Red-Teaming and Audits: Regularly conduct internal “red-teaming” exercises to identify potential biases, factual inaccuracies, or brand safety vulnerabilities in AI-generated ads before deployment. Perform periodic audits of AI-generated content to ensure ongoing compliance and ethical alignment.
What ‘Good’ Looks Like (Metrics and Targets):
- Consumer Sentiment: Aim to improve positive consumer sentiment towards AI-disclosed ads (e.g., reduce negative attribute associations like “manipulative” by 10% within 12 months). Track CSAT and NPS for segments exposed to disclosed AI content.
- Engagement Rates: Achieve higher engagement rates for appropriately disclosed AI ads (e.g., 5% higher click-through rates or video completion rates compared to non-disclosed AI ads in A/B tests).
- Purchase Likelihood: Maintain or increase purchase intent for products advertised with disclosed AI content (e.g., ensure that at least 70% of surveyed consumers report no decrease in purchase likelihood when AI is disclosed).
- Compliance Adherence: Target 95% or higher compliance with internal AI disclosure policies across all advertising channels and campaigns.
- Creative Quality Benchmarking: AI-generated assets should consistently meet or exceed human-generated creative benchmarks in terms of aesthetic appeal, emotional resonance, and message clarity, as measured by brand lift studies and qualitative consumer feedback.
Immediate Priorities (First 90 Days):
- Conduct an Internal AI Ad Usage Audit: Map out all current AI integration points within creative, media, and marketing operations.
- Form an AI Advertising Ethics Task Force: Convene key stakeholders from Legal, Marketing, CX, and Product to draft initial enterprise-wide AI disclosure guidelines.
- Pilot Disclosure Testing: Launch controlled A/B tests with target Gen Z and Millennial audiences to evaluate the impact of various disclosure methods (e.g., text overlays, audio cues, logos) on engagement and sentiment.
Summary
The rapid advancement of AI in advertising presents enterprise leaders with both immense potential and significant challenges regarding consumer trust. The widening perception gap highlighted by IAB and Sonata Insights necessitates a proactive, trust-centric approach. By implementing clear, consistent disclosure practices, establishing robust governance frameworks, and prioritizing creative quality over mere cost efficiency, organizations can navigate this evolving landscape successfully. Strategic disclosure is not a compliance burden but a powerful enabler of brand equity and sustainable growth in an AI-driven marketing future. Ignoring consumer sentiment risks undermining the very effectiveness AI promises to deliver.
Source: IAB and Sonata Insights, “The AI Ad Gap Widens,” January 2026.
