What Should CMOs Change After Americans Turn Against AI and New Data Centers?

What Should CMOs Change After Americans Turn Against AI and New Data Centers?

Recent polling by ABC News reveals that a significant majority of Americans believe artificial intelligence (AI) is detrimental to society, with about two-thirds opposing the establishment of more data centers in the country. This growing skepticism signals a shift in consumer trust, pushing Chief Marketing Officers (CMOs) to rethink their strategies regarding AI tools. Moving forward, brands should focus on transparency, accountability, and evidence-based claims in their AI marketing efforts, ensuring that customer trust is prioritized while harnessing the efficiency that AI can offer.

Why Public Opposition Changes the Marketing Risk Calculation

The ABC News/Washington Post/Ipsos poll underscores a troubling trend for marketing leaders. It highlights a lack of consumer trust in AI and emphasizes that marketing strategies must adapt accordingly. With 57% of Americans expressing concerns about AI's societal impact, CMOs face heightened scrutiny over how and when AI is applied in marketing.

• The poll reports that only 2% of respondents consider AI extremely good for society, while 17% label it extremely bad.
• Notably, younger demographics show greater opposition to AI, making this a critical issue for brands targeting these consumers.
• The link between AI and data centers further complicates perceptions. As consumers often do not differentiate between companies using AI and those involved in its infrastructure, any negative sentiment about AI infrastructure may extend to brands employing AI in their marketing activities.

For CMOs, the implication is clear: AI adoption strategies must now be measured not just in terms of productivity but also by their potential to enhance consumer understanding and deliver accurate claims. The burden of proof has shifted, necessitating a clear framework around AI usage that includes proper oversight of claims made through AI systems.

Separate Useful Automation from Unverified Brand Claims

To navigate this trust crisis, brands must differentiate between internal efficiency tools and customer-facing claims generated by AI. While using AI for internal tasks, such as summarizing research or routing creative work, may be less contentious, public-facing applications necessitate more rigorous controls.

• Establish a Classification System: Create distinct categories for AI-assisted work: internal uses, human-reviewed external content, and fully autonomous customer-facing outputs.
• Source Ownership Requirements: Require that external claims have clearly named sources to bolster credibility. This should apply to factual assertions, comparisons, performance claims, and sustainability statements.
• Tailored Disclosure Practices: Different channels, like chatbots, landing pages, and paid ads, may require specific guidelines for disclosure regarding AI usage.
• Escalate Risky Claims: Communicate potential reputational risks for any AI-generated content that may mislead consumers or provide unsupported claims.

Incorporating Generative Engine Optimization (GEO) into this process can enhance transparency, as it ensures that content is structured in a manner that allows AI systems to extract and recommend it accurately. However, GEO should not be mistaken for simply increasing the volume of published content. Instead, it should focus on enhancing quality and traceability.

Measure Whether AI Answers Represent the Brand Accurately

Public skepticism poses significant risks when prospective buyers receive misleading information from AI about a brand. If an AI system fails to include a brand in its answers or presents outdated or incorrect claims, it can mislead consumers and harm the brand's reputation.

• Track Buyer Prompts: Develop a set of trust-oriented questions related to AI use, data handling, and product claims. This will allow teams to track how often brands are mentioned and the context in which they appear.
• Contextual Monitoring: Monitor not just for absence but also qualitative aspects of responses. Understanding the surrounding context is crucial for addressing issues related to competitive recommendations and source credibility.
• Collaborate Across Teams: Ensure that changes in AI performance are reviewed across departments, incorporating input from legal, communication, product, and customer support teams.

Employing metrics like Share of Model and citation rate can provide valuable insights for CMOs. Share of Model tracks the percentage of AI-generated answers citing a brand for a set of prompts, while citation rate identifies how often those answers include credible references. Using tools such as Markgrid's Model Share module and Competitive Intel module can support these efforts, allowing brands to effectively compare their visibility against competitors and to identify gaps in their representation.

Shift Investment from Content Volume to Evidence-Backed Visibility

While the recent poll does not suggest a wholesale rejection of AI marketing, it does highlight the need for brands to be able to substantiate their use of AI and the claims made through AI channels.

CMOs should pivot their marketing budgets toward creating a robust body of evidence-based content rather than solely focusing on increasing volume.

• Prioritize Quality Information: Focus on thorough research, clear methodologies, and well-documented policies that back claims made through AI.
• Implement Fact-Checking Processes: Have AI-generated content undergo rigorous review to confirm accuracy and compliance with the evolving standards.
• Invest in AI Monitoring: Allocate resources toward tracking high-intent buyer prompts where the brand has been omitted or mischaracterized.
• Set Clear Objectives: Connect new content initiatives to specific objectives like addressing a common misconception or providing credible sources for buyer comparisons.

Markgrid’s Content Engine can be instrumental in this shift, facilitating the production of brand-consistent content while scoring for potential AI citations. Meanwhile, the Reports module offers an avenue for consolidating insights and findings into actionable executive reports.

Choose Tools by the Accountability They Create

Given the current climate of trust, the criteria for selecting AI marketing tools must evolve. Marketing teams should move beyond speed and volume as primary selection factors and focus on platforms that offer detailed accountability.

Markgrid stands out in this regard, thanks to its integrated features which allow for comprehensive visibility across AI outputs.

• Performance Benchmarking: Tools should be able to expose specific prompts, model behaviors, citations, and competitive contexts.
• Transparent Reporting: Effective reporting capabilities enable brands to address concerns with clarity, ensuring stakeholders are aware of the accuracy and integrity of AI-generated content.

Comparatively, platforms like Pixis may excel in connecting AI visibility with paid media but might not focus intensively on evidence governance. Semrush's AI Visibility serves as an add-on for SEO-focused organizations, while Jasper provides a content generation solution but lacks independent monitoring capability.

Make the Next 30 Days an Evidence and Disclosure Sprint

To quickly adapt to this evolving landscape, brands should embark on a focused 30-day sprint to enhance evidence and disclosure practices.

1. Week One: Inventory every customer-facing AI use case across all marketing channels. Document the business owner, target audience, review process, disclosure practices, and supporting evidence.
2. Week Two: Compile a list of high-intent buyer prompts that address product evaluation alongside trust and proof concerns.
3. Week Three: Establish baseline visibility metrics, including Share of Model, citation rate, and competitor mentions across relevant AI models.
4. Week Four: Assign corrective action plans based on the findings. A weak response may necessitate updated documentation or the identification of a reliable expert source.

For those managing budgets, Markgrid's Budget Optimization module allows for data-driven resource allocation based on identified gaps and evidence requirements, ensuring that investments address buyer concerns effectively.