Search Engine Volume Will Drop 25% by 2026 (and 50% By 2028) Gartner Predicts – Are You Ready?

The Changing Face of Search in 2024

What’s Happening to Search?

The year 2024 marks a significant turning point in the world of online search. For over two decades, when people needed information, they typically turned to search engines like Google, typing in a few keywords and scrolling through pages of results. However, this familiar landscape is undergoing a radical transformation.

Artificial Intelligence (AI) is revolutionizing how we interact with information online. Instead of typing keywords into a search box, more and more people are now asking questions to AI-powered chatbots and virtual assistants. These AI tools, like ChatGPT, Google’s AI, or smart home devices, can understand and respond to questions in natural language, often providing direct answers without the need to click through to a website.

This shift is not just a minor change in user behavior; it’s a fundamental reimagining of how information is accessed and consumed online. For businesses and website owners, this means rethinking everything they know about making their content discoverable online.

The Big Prediction

In the world of technology and marketing, few predictions have caused as much stir as the one made by Gartner, a respected research and advisory company. They’ve forecasted that by 2028 – just a few years from now – websites could see their traffic from organic search (that’s traffic that comes from unpaid search results) drop by 50% or more.

Why? Because consumers are embracing AI-powered search tools that often provide answers directly, without the need to visit a website. This prediction has sent shockwaves through the digital marketing community.

To put this in perspective, imagine if half of the customers who currently find your business through Google suddenly stopped coming. For many businesses, especially those that rely heavily on search engine traffic, this could be a seismic shift.

However, it’s important to note that this is a prediction, not a certainty. The digital world is notoriously difficult to predict with precision. That said, even if the actual drop in traffic is less dramatic, the trend towards AI-assisted search is undeniable and requires preparation.

New Ways to Measure Success

Moving Beyond Keywords

In the traditional world of search engine optimization (SEO), one of the fundamental metrics has been keyword volume – essentially, how many times people search for a particular word or phrase each month. This metric helped businesses understand what potential customers were looking for and how to tailor their content to match these searches.

However, as we move into the era of AI-assisted search, this metric is becoming less relevant. Here’s why:

1. Conversational Queries: People tend to interact with AI assistants more conversationally. Instead of typing “best restaurants New York,” they might ask, “What are some highly-rated restaurants in New York that offer vegetarian options and have a romantic atmosphere?” This shift means we need to think less about individual keywords and more about the intent and context behind queries.
2. Query Length: As people become more comfortable with AI assistants, they’re likely to use longer, more detailed queries. We need to track not just what people are asking, but how they’re asking it.
3. Intent Understanding: AI systems are getting better at understanding the intent behind queries, even if the exact wording varies. This means we need to focus more on addressing user needs comprehensively rather than targeting specific keyword phrases.

To adapt to this new reality, marketers and SEO professionals are developing new metrics. One approach is to analyze the trend in the number of words per query over time. This can be done by looking at data from paid search campaigns (where such data is still available) and tracking how the average query length changes month over month.

Another approach is to focus on topic clusters rather than individual keywords. This involves creating comprehensive content around broad topics and tracking how well this content performs in addressing a wide range of related queries.

Watching the Competition

In the world of traditional search, keeping an eye on the competition often meant checking where your website ranked for important keywords compared to your competitors. You might use tools to see who was ranking in the top spots for searches relevant to your business.

With the rise of AI-assisted search, this competitive landscape is changing dramatically. Now, the game is less about where you rank on a search results page and more about how often your brand gets mentioned in AI-generated answers.

Here’s how this new competitive analysis might work:

1. Question Analysis: Start by compiling a list of questions that are relevant to your industry or product. These should be the kinds of questions a potential customer might ask an AI assistant.
2. AI Response Monitoring: Run these questions through various AI assistants (like ChatGPT, Google’s AI, or others) and analyze the responses. Are your competitors being mentioned? How often? In what context?
3. Share of Voice: Instead of tracking keyword rankings, you might track your brand’s “share of voice” in AI-generated responses. This could involve calculating the percentage of relevant queries where your brand is mentioned compared to competitors.
4. Context Analysis: It’s not just about being mentioned, but how you’re being mentioned. Are you being recommended? Are specific products or features being highlighted? This qualitative analysis is crucial.
5. Tracking Over Time: By repeating this process regularly, you can track how your brand’s presence in AI-generated responses changes over time, especially as you adjust your digital strategy.

This new approach to competitive analysis requires creativity and often manual effort, as there aren’t yet established tools for tracking performance in AI-generated responses. However, it’s crucial for understanding your brand’s visibility in this new search landscape.

Tracking Visibility in AI Responses

Just as we need new ways to watch the competition, we also need new methods to measure our own visibility in AI-generated responses. This is challenging because, unlike traditional search engines, AI assistants don’t provide readily available data on how often they reference different sources.

Here are some strategies being developed to tackle this challenge:

1. Scaled Question Testing: This involves creating a large set of relevant questions and systematically running them through AI assistants. The responses are then analyzed to see how often your brand, products, or website are mentioned. This can be partially automated using tools that integrate with AI APIs, allowing for regular, large-scale testing.
2. Sentiment Analysis: Beyond just counting mentions, it’s important to analyze the context and sentiment of these mentions. Are you being portrayed positively? Are your key selling points being highlighted? This often requires a combination of automated sentiment analysis tools and human review.
3. Link Monitoring: While AI assistants often provide information without linking to sources, when they do provide links, it’s crucial to track these. Some are developing tools to monitor when and where AI assistants link to their websites.
4. User Surveys: Another approach is to directly survey your target audience about their use of AI assistants. Questions might include whether they’ve encountered your brand through these tools and in what context.
5. Proxy Metrics: In the absence of direct data, some are using proxy metrics. For example, if your website traffic from traditional search is stable but overall brand awareness or direct traffic is increasing, it could indicate growing visibility in AI-assisted search.

These new visibility metrics are still evolving, and there’s no one-size-fits-all solution. The key is to experiment with different approaches and find what provides the most meaningful insights for your specific business and industry.

Adapting to AI-Powered Search Features

Google’s New Search Experience

Google, the dominant player in traditional search, isn’t standing still in the face of the AI revolution. They’re testing and rolling out new features that incorporate AI into the search experience. One of the most significant of these is the Search Generative Experience (SGE).

Here’s what you need to know about SGE:

1. AI-Generated Summaries: Instead of just showing a list of links, SGE often presents an AI-generated summary at the top of the search results. This summary aims to directly answer the user’s query.
2. Interactive Follow-ups: Users can ask follow-up questions directly from the search results page, creating a more conversational search experience.
3. Integration with Traditional Results: These AI-generated elements are integrated with traditional search results, creating a hybrid experience.

For businesses and content creators, SGE presents both challenges and opportunities:

• Reduced Click-Through Rates: With more information provided directly on the search results page, users might be less likely to click through to websites.
• New Visibility Opportunities: Content that’s used to generate these AI summaries might gain a new form of visibility, even if it doesn’t result in a direct click.
• Emphasis on Structured Data: Websites that provide clear, structured data may be more likely to be used as sources for these AI-generated summaries.

To adapt to this new search experience, consider the following strategies:

1. Monitor SGE Results: Regularly check how your key search terms are being presented in SGE. Are your competitors being featured more prominently?
2. Optimize for Direct Answers: Structure your content in a way that directly and concisely answers common questions in your field.
3. Use Schema Markup: Implement structured data on your website to help search engines (and their AI systems) better understand your content.
4. Create Comprehensive Content: Since AI systems draw from multiple sources to create summaries, aim to create content that covers topics thoroughly.
5. Track New Metrics: Beyond just ranking and click-through rates, start tracking how often your content is cited or used in these AI-generated summaries.

Understanding ChatGPT’s Impact

ChatGPT and similar AI chatbots have taken the world by storm, offering a new way for people to access information. Unlike traditional search engines, these tools engage in conversational interactions and can provide detailed, contextual answers to complex queries.

Here’s how ChatGPT and similar tools are impacting search and what it means for businesses:

1. Information Access: ChatGPT can quickly synthesize information from its training data, providing users with comprehensive answers without the need to visit multiple websites.
2. Query Complexity: Users tend to ask ChatGPT more complex, nuanced questions compared to traditional search engines.
3. No Direct Traffic: Unlike Google, which sends users to websites, ChatGPT typically provides information without linking to sources, potentially reducing direct website traffic.
4. Rapid Evolution: The capabilities of these AI tools are evolving rapidly, with new features and improvements being added regularly.

To adapt to the impact of ChatGPT and similar AI chatbots:

1. Monitor AI Responses: Regularly test how ChatGPT responds to queries relevant to your business. Is it providing accurate information about your industry or products?
2. Create AI-Friendly Content: Focus on creating clear, factual content that AI systems are likely to draw upon when generating responses.
3. Build Brand Authority: Work on establishing your brand as an authoritative source in your field. While ChatGPT doesn’t currently provide sources, future versions of AI assistants might prioritize information from recognized authorities.
4. Leverage ChatGPT for Research: Use these AI tools to understand the types of questions people are asking in your industry. This can inform your content strategy.
5. Prepare for Voice Search: As AI assistants become more prevalent, optimize your content for voice search queries, which tend to be more conversational.
6. Focus on Unique Value: Since AI can provide general information easily, focus on offering unique insights, personal experiences, or specialized knowledge that AI can’t replicate.

Remember, while ChatGPT and similar tools are powerful, they also have limitations. They can sometimes provide outdated or incorrect information. By staying informed about these tools’ capabilities and limitations, you can better strategize how to complement and compete with them in providing value to your audience.

Educating Company Leaders

Explaining the New Search Landscape

One of the biggest challenges in adapting to the AI-driven search landscape is getting buy-in from company leadership. Many executives are familiar with traditional SEO metrics like keyword rankings and organic traffic. Explaining why these metrics may no longer tell the whole story – and why new investments might be necessary – can be crucial for securing resources and support for new initiatives.

Here’s how to approach this education process:

1. Start with the Basics: Begin by explaining in simple terms how AI is changing the way people search for information. Use relatable examples, like asking Siri for the weather instead of checking a weather website.
2. Highlight Changing User Behavior: Share data on the increasing use of voice search, AI assistants, and chatbots. This helps illustrate that the change is already happening.
3. Explain the Limitations of Traditional Metrics: Show how metrics like keyword rankings don’t capture visibility in AI-generated responses. You might demonstrate this live by comparing a Google search result with a ChatGPT response to the same query.
4. Present Case Studies: If possible, share examples of companies in your industry that have already been impacted (positively or negatively) by these changes.
5. Connect to Business Outcomes: Always tie the discussion back to business results. How might these changes affect lead generation, sales, or customer acquisition costs?
6. Propose a Phased Approach: Rather than suggesting a complete overhaul of existing strategies, propose a phased approach to incorporating new AI-focused tactics alongside traditional methods.
7. Ongoing Education: This isn’t a one-time conversation. Plan for regular updates as the AI search landscape evolves rapidly.

Remember, the goal is not to create panic, but to foster understanding and proactive planning. Position your team as forward-thinking problem-solvers ready to navigate this new terrain.

Creating New Performance Indicators

With the search landscape changing, the metrics we use to measure success need to evolve too. This involves developing new Key Performance Indicators (KPIs) that capture performance in an AI-driven search world.

Here’s an approach to creating these new performance indicators:

1. AI Visibility Score: Develop a score that combines various factors indicating visibility in AI-generated responses. This might include:
   • Frequency of brand mentions in AI responses to relevant queries
   • Sentiment of these mentions (positive, negative, neutral)
   • Accuracy of information about your brand in AI responses
   • Presence of key messages or unique selling points in these responses
2. Query Complexity Trend: Track the average length and complexity of queries that lead users to your site. An increase could indicate more engagement with AI-assisted search.
3. Content Utility Index: Measure how often your content is used as a source for AI-generated responses. This might require partnerships with AI companies or the development of proxy metrics.
4. AI-Driven Conversion Rate: Track conversion rates specifically for traffic that comes from interactions that likely involved AI assistance (this can be challenging and may require user surveys or advanced analytics setups).
5. Brand Authority Metric: Develop a score that combines traditional authority metrics (like backlinks) with new factors that might influence AI systems, such as frequency of citation in academic papers or trusted news sources.
6. Voice Search Readiness Score: If your business has a local component, create a score that measures how well you’re optimized for voice searches, which are often AI-assisted.
7. AI Chatbot Engagement: If you’re using AI chatbots on your own site, track metrics like user satisfaction scores, successful query resolution rates, and chatbot-assisted conversion rates.

Creating a Composite AI Search Performance Score:

Consider combining these various new metrics into a single, easy-to-understand score that gives an overall picture of your performance in the AI search landscape. This could be weighted based on the relative importance of different factors to your specific business.

For example:

AI Search Performance Score = (AI Visibility Score x 0.3) + (Content Utility Index x 0.25) + (AI-Driven Conversion Rate x 0.25) + (Brand Authority Metric x 0.2)

The weights can be adjusted based on what’s most important for your business.

Implementing New Reporting Structures:

With these new KPIs, you’ll need to adjust your reporting structures:

1. Dual Reporting: Present both traditional SEO metrics and new AI-focused metrics side by side to show a complete picture.
2. Trend Analysis: Focus on trends over time in these new metrics, as absolute numbers might be less meaningful initially.
3. Competitive Benchmarking: Where possible, include competitive data to provide context for your performance.
4. Regular Review and Adjustment: Given the rapid pace of change in AI technology, plan to regularly review and adjust these metrics to ensure they remain relevant.

By developing and tracking these new performance indicators, you’ll be better equipped to understand and improve your position in the evolving AI-driven search landscape. Remember, the goal is to create metrics that meaningfully tie back to business outcomes and can guide strategic decisions in this new era of search.

Optimizing Content for AI

Writing for AI Understanding

As AI systems become more prominent in delivering information to users, it’s crucial to optimize content not just for human readers, but also for AI understanding. This doesn’t mean writing robotic, keyword-stuffed content. Instead, it means creating clear, well-structured, and informative content that both humans and AI can easily interpret.

Here are some strategies for writing AI-friendly content:

1. Comprehensive Coverage: AI systems often pull information from sources that thoroughly cover a topic. Instead of creating many short, specific pages, consider developing longer, more comprehensive content that covers a topic from multiple angles.
2. Clear Structure: Use a logical structure with clear headings and subheadings. This helps AI systems understand the organization of your content.
3. Structured Data: Implement schema markup to provide explicit clues about the meaning of your content. This could include things like product information, FAQs, or event details.
4. Natural Language: Write in a clear, natural style. AI systems are getting better at understanding natural language, so there’s no need for awkward, keyword-heavy phrasing.
5. Question and Answer Format: Where appropriate, include a Q&A section. This aligns well with how many people phrase queries to AI assistants.
6. Factual Accuracy: Ensure your content is factually accurate and up-to-date. AI systems may cross-reference information across sources, and consistent, accurate information may be favored.

7. Cite Sources: When presenting data or claims, cite reputable sources. This can help establish the credibility of your content in the eyes of both AI systems and human readers.
8. Use Descriptive Meta Data: While traditional keyword-stuffed meta descriptions may be less important, clear, descriptive meta titles and descriptions can still help AI systems understand your content’s purpose.
9. Incorporate Relevant Entities: AI systems often use entity recognition to understand content. Include relevant people, places, organizations, and concepts in your content where appropriate.
10. Address User Intent: Focus on thoroughly addressing the user’s intent behind a query. AI systems are increasingly adept at understanding and matching user intent, so content that comprehensively answers user needs may be favored.
11. Regular Updates: Keep your content fresh and updated. AI systems may prioritize more recent information, especially for topics that change frequently.
12. Multimodal Content: As AI becomes better at understanding images, videos, and audio, consider incorporating a variety of content types to provide a richer information source.

Remember, the goal is to create content that is valuable and accessible to both human readers and AI systems. By doing so, you increase the chances of your content being used as a source for AI-generated responses, even if it doesn’t result in a direct website visit.

Technical Improvements

While content is crucial, the technical aspects of your website also play a significant role in how well AI systems can understand and utilize your information. Here are some key technical improvements to consider:

1. Site Speed Optimization: Fast-loading pages are crucial not just for user experience, but also for AI crawlers. Implement techniques like:
   • Image optimization
   • Minification of CSS, JavaScript, and HTML
   • Leveraging browser caching
   • Using a Content Delivery Network (CDN)
2. Mobile-First Design: With the majority of searches now happening on mobile devices, ensure your site is fully responsive and provides a great mobile experience.
3. Implement Structured Data: Use schema.org markup to provide explicit clues about the meaning of your content. This can include:
   • Organization and local business information
   • Product details
   • FAQ content
   • How-to guides
   • Event information
4. Improve Site Architecture: Create a logical, hierarchical structure for your website that AI crawlers can easily navigate and understand.
5. Optimize for Voice Search: As many AI assistants are voice-activated, consider optimizing for voice search queries, which tend to be more conversational and question-based.
6. Use of Natural Language Processing (NLP) Techniques:
   • Implement latent semantic indexing (LSI) keywords
   • Use synonyms and related terms naturally throughout your content
   • Structure content with clear headings that directly answer potential user queries
7. XML Sitemaps: Ensure you have updated XML sitemaps that help AI crawlers understand the structure of your site and find all important pages.
8. Improve Internal Linking: Create a robust internal linking structure that helps establish content relationships and topic relevance.
9. Implement AMP (Accelerated Mobile Pages): While not directly related to AI, AMP can improve mobile performance, which is increasingly important.
10. Use of AI-Powered Tools: Consider using AI-powered tools for technical SEO audits, which can help identify issues that might not be apparent through traditional methods.
11. API Development: If appropriate for your business, consider developing APIs that allow AI systems to access your data directly in a structured format.
12. Implement Semantic HTML: Use HTML5 semantic elements (like <article>, <nav>, <header>) to provide more context about your content’s structure and meaning.

By implementing these technical improvements, you’re making it easier for AI systems to crawl, understand, and utilize your website’s content. This can increase the likelihood of your information being used in AI-generated responses and improve your overall visibility in the evolving search landscape.

Looking to the Future

Continuous Learning

In the rapidly evolving world of AI and search, staying informed and continuously learning is not just beneficial – it’s essential. Here’s how to approach continuous learning in this field:

1. Stay Updated with AI Developments:
   • Follow AI research publications from major tech companies and universities
   • Attend AI and search-focused conferences and webinars
   • Join online communities and forums dedicated to AI and search
2. Experiment with AI Tools:
   • Regularly interact with various AI assistants and chatbots
   • Test how different types of queries are handled by these systems
   • Pay attention to how the capabilities of these tools evolve over time
3. Engage in Ongoing Training:
   • Encourage team members to take online courses in AI and Machine Learning
   • Consider partnering with local universities for specialized training programs
   • Implement a knowledge-sharing system within your organization
4. Monitor Industry Case Studies:
   • Pay attention to how other companies in your industry are adapting to AI-driven search
   • Learn from both successes and failures in AI implementation
5. Develop a Culture of Curiosity:
   • Encourage team members to ask questions and challenge assumptions
   • Foster an environment where experimentation and learning from failures is valued
6. Cross-Functional Learning:
   • Promote learning across different departments (e.g., SEO teams learning from data science teams)
   • This interdisciplinary approach can lead to innovative solutions
7. Predictive Analysis:
   • Try to anticipate future trends based on current developments
   • Regularly revisit and update your predictions as new information becomes available

Remember, the goal of continuous learning is not just to accumulate knowledge, but to apply it practically to your strategies and tactics.

Balancing Old and New

While adapting to AI-driven search is crucial, it’s equally important not to completely abandon traditional SEO practices. The key is to find the right balance. Here’s how to approach this:

1. Audit Current Strategies:
   • Evaluate which traditional SEO tactics are still effective
   • Identify areas where traditional methods may be losing effectiveness
2. Gradual Integration:
   • Introduce AI-focused strategies gradually alongside traditional methods
   • Monitor the performance of both and adjust the balance accordingly
3. Hybrid Approach:
   • Develop strategies that cater to both traditional search engines and AI assistants
   • For example, create content that ranks well in traditional search but also provides clear, concise answers for AI systems
4. Diversified Traffic Sources:
   • While adapting to AI search, also invest in other traffic sources (social media, email marketing, etc.)
   • This diversification can help mitigate risks associated with search Algorithm changes
5. User-Centric Focus:
   • Remember that both traditional SEO and AI-driven strategies should ultimately serve user needs
   • Prioritize creating value for your audience, regardless of the channel
6. Regular Performance Reviews:
   • Consistently analyze the performance of both traditional and AI-focused strategies
   • Be prepared to shift resources based on what’s driving the best results
7. Maintain Technical SEO:
   • Many technical SEO best practices (like site speed optimization) benefit both traditional and AI-driven search

By maintaining this balance, you can ensure that you’re prepared for the future of AI-driven search while still capitalizing on current traffic sources and established best practices.

Ethical Considerations

As AI becomes more integral to search and content strategies, it’s crucial to consider the ethical implications of these technologies. Here are some key ethical considerations:

1. Transparency:
   • Be clear about when and how you’re using AI in your content creation or customer interactions
   • If you’re using AI-generated content, consider disclosing this to your audience
2. Data Privacy:
   • Ensure that any AI tools you use comply with data protection regulations (like GDPR)
   • Be transparent about how user data is collected and used in AI-driven processes
3. Bias and Fairness:
   • Be aware that AI systems can perpetuate or amplify biases present in their training data
   • Regularly audit your AI-driven processes for potential biases
4. Accuracy and Misinformation:
   • Take steps to ensure that AI-generated or AI-curated content is accurate
   • Implement fact-checking processes, especially for sensitive topics
5. Human Oversight:
   • While leveraging AI, maintain human oversight in critical decision-making processes
   • Understand the limitations of AI and when human judgment is necessary
6. Job Displacement Concerns:
   • Consider the potential impact of AI adoption on your workforce
   • Look for ways to retrain and upskill employees as roles evolve
7. Accessibility:
   • Ensure that your AI-driven solutions don’t exclude or disadvantage certain user groups
   • Strive to make your content and services accessible to all
8. Environmental Impact:
   • Be aware of the environmental costs associated with large-scale AI processing
   • Consider ways to minimize the carbon footprint of your AI implementations
9. Ethical Use of Persuasive Technologies:
   • If using AI for personalization or recommendation systems, ensure these are used ethically and don’t exploit user vulnerabilities
10. Compliance with Emerging Regulations:
    • Stay informed about developing AI regulations and ensure compliance
    • Participate in industry discussions about ethical AI use in marketing and search

By actively considering these ethical dimensions, you can ensure that your adoption of AI technologies in search and marketing is responsible and sustainable. This ethical approach can also build trust with your audience and differentiate your brand in an increasingly AI-driven landscape.

Final Thoughts

The rise of AI in search represents a paradigm shift in how people access information online. While this presents challenges for traditional SEO and content strategies, it also opens up exciting new opportunities for businesses to connect with their audiences in more meaningful ways.

Success in this new era will require a combination of technical knowledge, creative thinking, and a deep understanding of user behavior. By staying adaptable, focusing on providing genuine value to users, and leveraging the power of AI in your own strategies, you can not only survive but thrive in the age of AI-powered search.

Remember, the core principle remains the same: connecting users with the information they need. As long as you keep this goal at the forefront of your efforts, you’ll be well-positioned to succeed, regardless of how the technology evolves.

The future of search is dynamic and exciting. Those who embrace these changes, innovate accordingly, and maintain an ethical approach will find themselves at the forefront of the next generation of digital marketing. The key is to start preparing now, stay informed, and be ready to adapt as the landscape continues to evolve.