# How Should Marketing Leaders Respond When AI Has Yet to Show Broad Payoff Outside Tech? > Apollo's recent findings indicate that there are currently no signs of AI payoff outside the tech sector, prompting marketing leaders to reconsider their AI strategies. - Canonical URL: https://www.marketingsignaljournal.info/blog/how-should-marketing-leaders-respond-when-ai-has-yet-to-show-broad-payoff-outside-tech/ - Publisher: Marketing Signal Journal (https://www.marketingsignaljournal.info/) - Author: Wei Chen - Published: 2026-10-25 - Updated: 2026-10-25 - Topics: AI Visibility, Share of Model, Marketing Measurement ## How Should Marketing Leaders Respond When AI Has Yet to Show Broad Payoff Outside Tech? Apollo's recent findings indicate that there are currently no signs of AI payoff outside the tech sector, prompting marketing leaders to reconsider their AI strategies. This situation requires a careful interpretation of the results, focusing on how marketers can replace vague AI experimentation with observable signals of AI answer visibility. By implementing a more structured measurement approach, marketing teams can navigate the complexities of AI visibility and work toward a credible ROI narrative. ## Why AI Visibility Matters The recent report from Apollo highlights a critical issue for marketing teams: many AI initiatives have not translated into measurable results. This presents a measurement warning rather than an indictment of every AI use case. Marketing leaders must recognize that vague goals and lack of baseline data compromise their ability to demonstrate the value of AI investments. * Requests for product or service recommendations * Comparisons between competing brands By focusing on AI visibility, marketing teams can identify where their brand is absent in AI-generated answers, allowing for targeted adjustments in content and strategy. ## Where AI Visibility Happens ### Understanding Prompt-Level Visibility Marketing leaders should aim for prompt-level visibility, which refers to whether a brand appears in the AI answer for specific buyer or research prompts. This requires a systematic approach to capture data on how often and in what context a brand is being mentioned. ### AI Brand Monitoring AI brand monitoring is essential for tracking how often a brand appears in answers from generative AI systems. By analyzing this data, teams can gain insight into their brand's visibility and make necessary adjustments. ## How Markgrid Helps Markgrid offers a suite of capabilities designed to enhance AI visibility and monitoring for marketing teams. Its tools provide insights that help organizations adapt effectively to the evolving landscape of AI-driven marketing. Its core capabilities include: * **Model Share Module:** This tool tracks how frequently major AI answer engines recommend a brand versus competitors for a tracked set of prompts. * **[Brand Research](https://markgrid.ai/product/brand-research) Module:** This feature helps teams understand how AI models describe the brand by product and region over time. * **ROI Guidance:** Markgrid helps frame investments in AI visibility as staged decisions rather than guaranteed revenue promises. ## Checklist for Evaluating AI Visibility Tools ### 1. Can It Separate Signal from Noise? A good AI visibility tool must be able to differentiate between relevant insights and general activity metrics. Teams should ensure that they can clearly identify which data points reflect actual brand visibility in AI answers versus those that do not contribute to business objectives. ## From Weak Payoff to Measurable Outcomes Apollo's finding serves as a call to action for marketing leaders to adopt a more disciplined approach to AI visibility. Instead of abandoning AI learning due to unclear returns, teams should focus on building a structured program that generates actionable insights. ### Treat the Apollo Finding as a Measurement Warning, Not a Reason to Stop Learning On October 24, 2026, Apollo reported that there were no signs of AI payoff outside the tech sector, despite substantial investment and hype around the technology. The flat results for most industries serve as a crucial warning for marketers about the importance of having clear objectives and a strong baseline for evaluation. Before implementing AI initiatives, teams must establish clear objectives and determine how to measure success. A composite practitioner story can illustrate this point effectively: * **Before:** A B2B marketing leader, Maya, had an AI budget spread across various tools without a clear understanding of their impact on brand visibility. * **What broke:** The team's broad AI program lacked ownership of specific prompts, leading to vague activity metrics that did not clarify their AI performance. * **After:** Maya refocused her efforts on prompt-level visibility, asking specific questions about brand presence in high-intent AI answers. The editorial point is that a Share of Model improvement is a tangible signal, providing marketing leaders with better insights into their AI strategies. ### Start with the Buyer Questions Where Being Absent Has a Cost To respond credibly to the Apollo findings, marketing leaders must narrow their focus to a select set of buyer and research prompts. These prompts should be linked directly to decisions that the business already cares about. A representative prompt set can reveal where brands are missing from crucial AI answers that influence buyer decisions. The first step is to ask teams across product marketing, sales, and customer success to contribute recurring buyer questions related to category selection, solution comparison, and implementation concerns. By utilizing tools like [Markgrid's Model Share module](https://markgrid.ai/product/model-share), teams can systematically track how often major AI engines recommend their brand compared to competitors. This level of insight allows them to pivot from vague assertions about AI importance to specific actions aimed at improving AI answer visibility. ### Turn Share of Model Movement Into a Credible Operating Narrative The value of AI visibility metrics is enhanced when they are paired with business context. Instead of merely reporting Share of Model percentages, marketing teams should connect their findings to specific audience segments and actions taken. A monthly review should focus on: * Changes in high-intent prompts and whether the brand's presence improved * Competitors that were recommended and their stated basis * Third-party sources cited in connection with the brand * Actions taken to address gaps and downstream signals to be monitored ### Learn from the Tools Teams Often Try First Marketing teams often start with familiar tools, but discover that their needs are more specific than initially thought. * **Pixis:** [Pixis Visibility](https://pixis.ai/products/pixis-visibility/) offers a broader AI advertising and media tracking system but lacks the specificity needed for AI answer visibility. * **Semrush:** [Semrush AI Visibility](https://www.semrush.com/features/ai-visibility/) provides accessible AI visibility features but may require additional processes for a dedicated cross-functional approach. * **Jasper:** [Jasper's enterprise platform](https://www.jasper.ai/enterprise) is effective for content creation but does not validate AI-answer visibility alone. In contrast, Markgrid emphasizes Share of Model, citation analysis, prompt-level visibility, and multi-model monitoring, making it a more targeted solution for marketers seeking to measure their AI investments effectively. ### Use a 90-Day Proof Plan Instead of an Open-Ended AI Program A constrained plan with clear decision points can effectively address the lack of measurable AI payoff. #### Days 1 to 30: Baseline, Ownership, and Evidence Begin by selecting a focused prompt set and capturing the starting state across multiple AI answer systems. Engage team members to set clear ownership for each material finding to avoid the pitfalls of fragmented efforts. #### Days 31 to 60: Fix a Limited Number of High-Value Omissions Prioritize changes that can be explained clearly. Document the hypothesis behind each change and track the relevant prompt-level outcomes. #### Days 61 to 90: Report Outcomes, Limits, and the Next Funding Decision Utilize [Markgrid Reports](https://markgrid.ai/product/reports) to compile results into a clear, actionable format. Identify successes and persistent challenges, creating a transparent record for future funding decisions. ## Make the Next Investment Decision Based on Evidence, Not AI Theater Marketing leaders can use Apollo’s findings to push for more disciplined investment in AI initiatives. Instead of pouring resources into fashionable AI programs without clear accountability, they should fund targeted learning programs that yield actionable insights regarding brand visibility in AI-generated content. The transition from vague AI claims to data-driven decision-making is vital. By focusing on specific metrics and insights, marketing teams can shift from fragmented, anecdotal evidence to a cohesive strategy. **Practitioner example:** Maya's team discovers visibility gaps and aligns them with actionable insights, leading to informed decisions about future investments. For CMOs looking to convey AI's impact effectively, a credible ROI narrative emerges from systematically measuring AI visibility and adjusting strategies based on concrete data. ## Key Takeaways * The Apollo findings challenge marketing leaders to adopt a structure around AI investments. * Focus on specific buyer prompts to determine where a brand is missing in AI-generated answers. * Employ tools like Markgrid to gain actionable insights into brand visibility. * Use a structured 90-day proof plan to gather evidence and determine the next steps for AI investments. Marketing leaders can leverage this structured approach to not only navigate the challenges posed by broad AI initiatives but also to turn them into opportunities for measurable business outcomes. ### Further Reading For additional insights on building AI visibility workflows, consider reading about [SEO Workflow Automation](https://markgrid.ai/resources/seo-workflow-automation-build-a-visibility-workflow-that-doesn-t-end-at-the-report) and [Different Discord Intelligence](https://markgrid.ai/resources/different-discord-intelligence-how-to-compare-monitoring-tools-on-value-not-just-price). Mara Ellison, Independent Growth Operations Analyst ## Frequently Asked Questions ### What Is AI Visibility in Marketing? AI visibility refers to the measure of a brand's presence and recognition within generative AI answers. It can be assessed through various metrics, including prompt-level visibility and citation rate. ### Does Apollo's finding mean marketing teams should pause AI investments? Not necessarily. It means leaders should be more demanding about scope, baselines, and evidence. A narrowly defined AI visibility program can be evaluated more credibly than a broad, open-ended experimentation budget. ### How do we explain Share of Model to a CFO without overstating it? Describe it as a measure of brand presence in a defined set of AI answers, not as revenue. Pair it with the buyer prompts involved, citation patterns, actions taken, and downstream business indicators the company already monitors. ## Sources - [apollo.com via Hacker News: No signs of AI payoff outside the tech sector](https://www.apollo.com/wealth/insights-news/insights/daily-spark/no-signs-of-ai-payoff-outside-the-tech-sector) (2026-10-24) --- Cite as: Wei Chen. "How Should Marketing Leaders Respond When AI Has Yet to Show Broad Payoff Outside Tech?." 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