For decades, searching for information on the internet followed a fairly consistent model - you typed keywords into a search box, and the engine returned a ranked list of relevant web pages. This process of looking for answers by querying keywords and sifting through websites is being fundamentally reshaped by the rise of generative AI.

Large language models like ChatGPT can now directly provide human-like answers and content in response to natural language queries. This AI revolution is catalyzing a seismic shift from the paradigm of “searching” to simply “asking” - with profound implications for search engines, websites, e-commerce, and digital business models.

The Shift from Search to Ask

The first major impact of generative AI is the transition from using fragmented keywords to conversational, natural language for queries. Instead of typing “best cameras 2023”, you can simply ask “What are the highest rated cameras I should consider for travel photography this year?”

AI models can understand the intent behind the full question, the context around photography and travel usage, and provide a tailored, coherent response pulling in relevant information. This represents an evolution from the constrained “10 blue links” search model to an “ask experience” powered by advanced language AI.

Examples like Google’s Search Generative Experience, Microsoft’s integration of ChatGPT into Bing, and the AI-powered search engine Perplexity.ai demonstrate how major search platforms are pivoting to this new paradigm. Perplexity.ai completely reimagines the search interface as an interactive conversational experience fronted by an AI assistant that can engage in back-and-forth dialogs to better understand and meet query intents. Gartner predicts that by 2025, 40% of new enterprise applications will include integrated AI capabilities like natural language queries.

But the shift goes far beyond just conversational search interfaces. The rise of AI virtual agents and assistants allows users to simply “ask” an AI to perform a wide variety of tasks - writing emails or essays, analyzing data, generating code, creating images, and more.
 AI assistants are becoming ubiquitous for personal, work, and creative activities. A McKinsey study found that generative AI like ChatGPT could automate 20-25% of current work activities across industries. The lines are blurring between searching for information and directly delegating cognitive work to an artificial agent. This “askification” of computing represents a profound societal shift in how we interact with and leverage technology.

Impact on Website Traffic and Digital Publishing


One major disruption is the potential decline in website traffic as more searches get directly answered by AI without users needing to click through websites. Some estimates predict as much as 25% of website traffic could get cannibalized .

This poses an existential threat to the digital publishing and content ecosystem that has relied on driving search traffic as the core revenue model through advertising. Billions of dollars in ad revenue could get wiped out, causing major disruption and consolidation in the industry. While some websites may actually see increased traffic, many are bracing for steep drops that undermine their business.

According to eMarketer, global digital ad spending surpassed $521 billion in 2022 , with search ads representing over $245 billion. Even a 15% hit due to generative AI disrupting search could equate to $36 billion in lost ad revenue annually.

To adapt, websites and publishers will need to reduce their dependence on search traffic by focusing more on other channels like email, social media, video platforms, and owned channels. There will be an urgency to establish first-party data strategies and diversify revenue beyond advertising into areas like e-commerce, subscriptions, and services.

The Continued Role of Websites and Search Engines

However, it would be premature to declare search engines and websites obsolete in this new “ask” era. While generative AI can directly provide answers, it still relies heavily on retrieving information from the open web that has been crawled and indexed by search engines.

Even the most advanced AI models like ChatGPT utilize retrieval-augmented generation (RAG) to interface with search engine APIs and databases to source information and construct responses. No single AI model can ingest and have direct access to the entirety of the open web.

This means high-quality, authoritative websites will remain crucial for supplying AI models with reliable data to generate accurate, trustworthy outputs. Establishing expertise, authoritativeness, and trustworthiness (E-E-A-T) will become even more essential beyond just simplistic answer matching.

Websites will need to optimize their content strategy for how people ask natural language questions, not just keyword matching. New success metrics like impressions and visibility may take precedence over simplistic clickthrough rates.

At the same time, search engines themselves must adapt their ranking algorithms and systems to better cater to AI and machine consumption of websites, not just human readability. Google, Microsoft, and others are actively working on integrating generative AI capabilities to evolve their search products.

For example, Google has announced their own AI chatbot called “Bard” to compete with ChatGPT, as well as incorporating generative AI into core search. Microsoft has gone all-in on a “AI-powered Microsoft 365 Copilot ” to infuse AI assistants across its productivity software

Ultimately, a symbiotic relationship will persist between AI models that can provide direct answers and the open web ecosystem of websites and search engines that supply the underlying data. The most powerful experiences will blend AI-generated content with human-authored websites and multimedia.

Future: The Rise of AI Agents

Looking ahead, we can envision the emergence of ubiquitous AI agents that act as personalized assistants to automate many of our search and research activities. Today, we routinely gather information for work/school projects, look for products and services, compare options across providers, find entertainment recommendations, and more.

In the future, an AI agent that deeply understands our preferences, context, and query intents could streamline and enhance these activities in powerful ways:

• Act as a personalized research assistant to gather data, insights, and create reports/analysis for any project
• Proactively surface tailored information and content based on our interests without even asking
• Provide personalized product/service recommendations and comparisons based on our needs
• Book travel, make restaurant reservations, place orders, and complete transactions seamlessly through conversational “asking”
• Suggest new movies, shows, songs, and entertainment content we’d enjoy based on our tastes

AI Agents as Shopping Assistants

A major role for AI agents will be to handle the entire shopping journey - from initial product research and discovery, to comparing options across brands/retailers, all the way through to making the final purchase decision and transaction.

Rather than us manually scouring e-commerce sites, reading reviews, filtering by our criteria, and deciding what to buy, an AI agent could comprehensively manage this workflow. We could simply ask it to “Find me the best OLED TV for my budget and room size” and have it scour relevant product data sources to analyze every option.

The agent would factor in our preferences, past purchases, price constraints, and any other contextual details from our query. It could then present a shortlist of personalized recommendations with pros/cons and even autonomously place the order.

This represents a profound shift for e-commerce companies. Historically, they have optimized their product data, marketing, and entire experience for human shoppers. But in the future, they will also need to optimize for AI-based product discovery and purchase decision making.

Just as e-commerce sites today invest in Search Engine Optimization (SEO), optimizing for visibility on marketplace platforms, or their App placement in the App Store, they may need to focus on “AI Optimization” strategies. This could include:

• Providing accurate, structured product data formatted for AI ingestion
• Aligning product descriptions with natural language queries
• Enabling AI agents to seamlessly integrate with their platforms
• Developing AI-friendly schemas for features, specs, compatibility
• Signaling trust/authority signals that AI models can detect

E-commerce companies that successfully court AI assistants as an influential channel could gain a significant competitive advantage in the “ask” era of shopping.

Rather than us having to manually search across disparate websites and apps, an AI agent could act as the centralized interface understanding our needs and preferences. It could handle tedious research and data gathering in the background, then surface summarized insights, analysis, and recommendations.

We could simply ask our AI assistant to “Plan my summer vacation in Italy” or “Find a new computer for video editing” and have it comprehensively complete the entire multi-step workflow through natural language interactions.

This could unlock significant productivity gains by offloading cognitive work to the AI. A PwC study estimated that AI could contribute $15.7 trillion to the global economy by 2030.\But it will require a careful balance of automation/convenience with transparency into sources and preserving data privacy/control.

Takeaways


The rise of generative AI catalyzes a monumental shift in how we seek information and leverage the internet. We are transitioning from an era of looking for answers through manual searching and browsing, to an era of simply asking AI for insights, answers, and task completion.

This “askification” of search creates tremendous opportunities but also risks for websites, e-commerce businesses, advertisers, and the open web ecosystem. While AI can directly answer many queries, it paradoxically increases the importance of authoritative websites and sources to fuel these AI models.

Brands and publishers must optimize their content strategy for both human readers and algorithmic, AI consumption. This means evolving content format, style, and structured data to align with how people ask natural language questions, establish topical credibility for AI to recognize expertise, and provide detailed sourcing/citations that AI ingestion requires.

Businesses will need to invest in AI technologies, conversational interfaces, and enterprise knowledge models to power both internal and customer-facing AI experiences. Ecommerce companies have a prime opportunity to deploy generative AI for personalized shopping journeys from product discovery to checkout.

However, this must happen within a balanced approach that preserves the open web’s democratized value rather than enabling walled gardens controlled by a few centralized AI companies. We may see a rise of decentralized AI agents that act as knowledgeable intermediaries curating and vetting trusted open web sources.

Regardless of how it unfolds, businesses and organizations must start prioritizing an AI and algorithmic content strategy now to stay ahead of this tectonic “ask” shift. The future is moving rapidly from an internet of looking for answers, to simply asking AI to understand our needs and complete our goals - a future that will be shaped by the decisions we make today.

[Original article authored for Forbes ]