The article discusses the transformative impact of large language models (LLMs) on the customer journey, emphasizing the need for businesses to adapt their marketing strategies. As LLMs become integral to consumer decision-making, with AI agents soon making purchases autonomously, brands must rethink whom to influence and how. Marketers have used generative AI for campaign optimization, but many overlook the critical shift toward AI-driven decisions. Key statistics highlight the increasing prevalence of LLMs in shopping contexts. The piece outlines the necessity of ensuring websites are accessible to LLMs, optimizing content for AI, and preparing for interactions between business and consumer AI agents. Overall, it urges marketers to focus on AI Visibility Optimization (AEO) and prepare for a future where both human and AI customers are pivotal in the buying process.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| B2AI Transition | Businesses are unprepared for the shift towards influencing AI systems instead of just consumers. | Shift from traditional marketing to influencing AI agents in decision-making. | Businesses will increasingly rely on AI systems to make marketing decisions and manage customer relationships. | The rise of AI technology and consumers consulting AI before making purchases. | 4 |
| Generative AI Adoption | Growing use of Gen AI in the B2B purchasing process, rapidly increasing over time. | Move from limited AI use in shopping to widespread integration in B2B and B2C. | Generative AI will become a primary tool for conducting business and making purchasing decisions. | The effectiveness and efficiency of AI systems in processing information and offering recommendations. | 4 |
| AI Overviews (AIO) Impact | AI Overviews are significantly changing how searches result in user engagement. | Transitioning from traditional click-throughs to consuming information directly from AI responses. | Consumer behavior will shift to accepting AI-generated content without visiting websites, altering web traffic dynamics. | Consumers prefer immediate answers and convenience over extensive browsing. | 5 |
| AI Agents for Purchase Decisions | Envisioning AI agents that make autonomous purchase decisions for users. | Transitioning from human decision-making to AI-driven negotiations and purchases. | AI agents will handle most consumer and enterprise purchasing without human intervention. | Advancements in AI technology and consumers’ desire for convenience. | 5 |
| Machine Readability Compliance | Growing importance of making website content machine-readable for LLM crawling. | Shift from SEO to AEO, focusing on AI accessibility of content. | Websites will be optimized for AI understanding, changing content creation dynamics. | The necessity for competitive advantage in an AI-driven marketplace. | 4 |
| AEO Practices | Emerging practices for optimizing visibility in AI-generated content and responses. | Shift from traditional SEO strategies to AI Engine Optimization techniques. | Content strategy will be heavily centered around AI representations and machine learning interpretations. | The increasing reliance on AI for content sourcing and recommendation. | 3 |
| Market Attribution Challenges | Difficulties in measuring the direct impact of AI-driven traffic on businesses. | Advent of fuzzy attribution metrics in assessing AI interactions with brands. | Attribution methods will evolve to better reflect AI’s influence on consumer purchasing. | The complexity of interactions between AI tools and consumer behavior. | 3 |
| Emerging Standards for AI Discovery | Implementation of llms.txt files to help LLMs discover website content effectively. | From robots.txt exclusive usage to incorporating llms.txt for AI discoverability. | Standards and protocols for website accessibility to AI will be commonplace in digital marketing. | The need for businesses to adapt to new AI-driven content discovery methods. | 4 |
| name | description |
|---|---|
| Loss of Human Agency in Decision Making | As AI agents start making decisions on behalf of consumers, individuals may lose their ability to influence purchase decisions actively. |
| Dependence on AI for Brand Representation | Brands may risk dependency on AI’s interpretation of their presence in training data, leading to marketing strategies heavily reliant on AI accuracy. |
| Bias in AI Training Data | The way brands are represented in AI training data could lead to biased recommendations, potentially disadvantaging lesser-known brands. |
| Access Issues for AI Crawlers | Improper configurations in websites may block LLM crawlers, limiting visibility for brands in AI-generated content and overall digital presence. |
| Shifts in Consumer Behavior | Increased reliance on AI tools for purchase decisions may alter traditional marketer-consumer relationships and require new strategies. |
| Changing Metrics of Success in Marketing | As AI influences visibility and recommendations, traditional metrics may no longer suffice, demanding a reevaluation of marketing effectiveness. |
| Unequal Visibility Among Brands | The competition for AI recommendation may create disparities in visibility, favoring brands already prominent in training datasets. |
| Potential Decrease in Click-Through Rates | As AI answers directly provide information, traditional click-through to websites may decline, affecting web traffic and revenue models. |
| Ethical Considerations of AI Agents | The advent of AI-to-AI transactions raises ethical questions about transparency and accountability in AI decision-making processes. |
| Impact on SEO Strategies | With AI technologies reshaping search behavior, traditional SEO strategies may become less effective, necessitating new optimization approaches. |
| name | description |
|---|---|
| AI-Centric Decision Making | Consumers increasingly consult LLMs for purchase decisions, leading to AI-driven decision-making processes. |
| AAI (AI-to-AI) Commerce | Future commerce where buyers’ AI agents negotiate and make purchases from sellers’ AI agents, transforming traditional transactions. |
| Generative Engine Optimization (GEO) | Marketers optimize content for AI systems, aiming for visibility and citation in AI-generated results. |
| Machine Readability Enhancements | Businesses enhance website content using structured data to improve accessibility for LLM crawlers. |
| AI-Powered Marketing Strategies | Marketers are developing strategies to influence both human consumers and AI systems equally in their campaigns. |
| Content Formatting Experiments | Businesses are experimenting with content formats, such as placing important information early to cater to LLM behavior. |
| Brand Representation in LLMs | The way brands are portrayed in LLM training data influences consumer perception and decision-making. |
| Data Accessibility for LLMs | The importance of ensuring that websites are accessible to LLM crawlers to increase brand visibility in AI interactions. |
| name | description |
|---|---|
| Large Language Models (LLMs) | AI models that can understand and generate human-like language, transforming customer interactions and decision-making processes. |
| Generative AI | AI that generates content such as text, images, or audio, significantly impacting marketing strategies and consumer engagement. |
| AI Agents | Autonomous AI systems that make purchasing decisions and negotiate on behalf of users, redefining the buying process. |
| AI Overviews (AIO) | Summarized answers generated by AI in search results, changing the way consumers access information and make decisions. |
| Generative Engine Optimization (GEO) / AI Engine Optimization (AEO) | Strategies for brands to improve their visibility and engagement within AI-generated content and recommendations. |
| llms.txt files | A new standard for websites to improve discoverability by LLMs, facilitating better indexing and understanding of content. |
| Structured Data (Schema Markup) | Machine-readable tags added to web content to enhance understanding and visibility in AI systems. |
| name | description |
|---|---|
| B2AI Marketing Shift | Transition from traditional marketing to targeting AI systems as consumers increasingly rely on AI for purchase decisions. |
| Generative Engine Optimization (GEO) | New practices for optimizing brand visibility specifically for AI models instead of traditional search engines. |
| AI Agent Interactions | Emerging necessity for brands to interact with AI agents representing consumers, transforming the purchase journey and influence strategies. |
| AI Overviews (AIO) Influence | Changes in how consumers receive information through AI Overviews, impacting click-through rates and brand visibility. |
| Structured Data Importance | Growing significance of structured data for machine readability, influencing how brands are ranked in AI responses. |
| AI-to-AI Commerce | Future commerce model where AI agents autonomously interact, requiring new brand strategies and AI interpretative capabilities. |
| Content Accessibility for AI Crawlers | Need for businesses to ensure their digital content is accessible to AI crawlers to improve discoverability in AI environments. |
| Data Representation in AI Training | The way brands are portrayed in AI training data significantly influences their visibility and recommendation by LLMs. |
| Evolution of Consumer Decision-Making | Consumers increasingly relying on AI for decision-making, necessitating shifts in marketing and customer experience strategies. |