The Future of Information Science in Digital Advertising: AI-Driven Transformation of Google Ads, SEO, and Marketing Strategy
In our previous installments, we explored how Library and Information Science (LIS) principles form the foundation of digital marketing technologies and cloud infrastructure. This third part examines the evolving relationship between information science, artificial intelligence, and digital advertising—particularly Google Ads and SEO. Despite being a cornerstone of the digital economy, paid advertising often receives less scholarly attention than other technological domains. Yet it stands at the precipice of profound transformation as information science principles and AI capabilities converge to reshape how businesses connect with audiences.
The Evolution of Information Science in the AI Era
From Classification to Machine Learning
The trajectory of information science in the burgeoning AI era reveals a profound evolution in information organization methodologies, moving from rule-based systems to sophisticated, data-driven paradigms. This transition, as delineated below, signifies a pivotal shift in how information is structured, accessed, and leveraged.
Era: Pre-digital
Information Organization Approach: Manual classification and indexing relied on human intellect to categorize and assign subject headings to information resources. This approach was characterized by hierarchical structures and controlled vocabularies.
Technologies: The primary tools were physical card catalogs for indexing library holdings and printed indexes for navigating periodical literature. These systems were labor-intensive and inherently limited in scalability and search precision.
Era: Early digital
Information Organization Approach: The advent of digital technologies introduced database-driven categorization, enabling more structured and searchable information repositories.
Technologies: Online Public Access Catalogs (OPACs) digitized library catalogs, allowing keyword searching. Electronic databases provided access to bibliographic information and full-text articles, enhancing search capabilities but still largely relying on predefined fields and Boolean logic.
Era: Web 1.0
Information Organization Approach: The early internet era saw the emergence of metadata tagging and hyperlink analysis as key organizational principles. Information was linked through hyperlinks, and descriptive metadata was embedded in web pages.
Technologies: Search engines began to index the web based on keyword matching and link analysis. Directory services offered hierarchical categorization of websites, reflecting a blend of manual and automated organization.
Era: Web 2.0
Information Organization Approach: The participatory nature of Web 2.0 fostered collaborative tagging (folksonomies) and recommendation systems. Users actively contributed to information organization through tagging, and algorithms suggested content based on user behavior and preferences.
Technologies: Social bookmarking platforms allowed users to collectively tag and organize web resources. Collaborative filtering algorithms analyzed user interactions to generate personalized recommendations.
Era: AI era
Information Organization Approach: The current AI era is characterized by neural embedding and graph-based representation. Information is encoded into high-dimensional vector spaces (embeddings), capturing semantic relationships. Knowledge graphs represent information as interconnected entities and relationships.
Technologies: Vector search enables retrieval based on semantic similarity rather than exact keyword matching. Knowledge graphs provide a structured representation of knowledge, facilitating complex queries and inferences. Large language models (LLMs)possess an inherent understanding of language and context, enabling sophisticated information organization and retrieval.
This historical progression underscores a fundamental paradigm shift. Earlier eras depended on explicit, predetermined categorical structures crafted by human expertise. The AI era, conversely, leverages machine learning to derive dynamic, context-sensitive information representations directly from the data itself. This evolution has profound implications for information retrieval, enabling more nuanced and semantically relevant search results, and for advertising targeting, allowing for more precise and contextually appropriate audience segmentation based on deeper understandings of user interests and behaviors.
Semantic Understanding vs. Keyword Matching
Traditional information retrieval systems rely heavily on lexical matching—finding documents containing search terms. Modern systems increasingly employ semantic understanding:
Lexical paradigm: Match document terms to query terms
Semantic paradigm: Match document meaning to query intent
This transition from lexical to semantic paradigms manifests in:
Query understanding systems: Interpreting user intent beyond keywords
Document representation models: Capturing meaning rather than just term frequency
Matching algorithms: Connecting queries with content based on semantic similarity
The implications for advertising are significant: systems can now match advertisements to user intent rather than merely keywords, potentially increasing relevance while reducing reliance on exact keyword targeting.
From Document Retrieval to Knowledge Synthesis
The most profound evolution in information science is the shift from retrieving documents to synthesizing knowledge:
Traditional paradigm: Present relevant documents for human interpretation
Emerging paradigm: Extract, combine, and present relevant information directly
This transformation is enabled by:
Knowledge extraction: Identifying facts, entities, and relationships from unstructured text
Knowledge integration: Combining information across multiple sources
Knowledge representation: Structuring information for machine reasoning
Large language models (LLMs) represent the current frontier of this evolution—systems that not only retrieve information but generate contextually appropriate responses based on patterns learned from vast corpora.
AI's Impact on Information Discovery and Advertising
Generative AI as Information Mediator
AI systems increasingly mediate information access, changing how users discover content and products:
Search-based discovery: User actively queries for information
AI-mediated discovery: AI systems proactively present relevant information
This shift has significant implications for content visibility and advertising placement:
Attention bottlenecks: AI systems may present summarized information, reducing exposure to original sources and traditional advertisements
Recommendation dominance: Content discovery increasingly influenced by AI recommendation rather than active search
Answer engines vs. link engines: Search interfaces evolving toward direct answers rather than link lists
For advertisers, this represents both challenge and opportunity. While traditional placements may receive less visibility, integrated advertising that aligns with AI-mediated information flows may achieve unprecedented relevance.
Multimodal Information Representation
Information science increasingly addresses multimodal content—combining text, images, audio, and video:
Cross-modal retrieval: Finding relevant information across different modalities
Multimodal understanding: Interpreting meaning from combined modalities
Unified representation: Developing common embedding spaces for diverse content types
Advertising applications include:
Contextual placement in multimodal content: Advertisements that relate to visual and auditory elements, not just text
Cross-modal creative optimization: Developing advertisements that maintain consistent messaging across modalities
Multimodal targeting: Identifying audience interests through varied content consumption patterns
These capabilities enable more sophisticated advertising that responds to the full spectrum of user engagement, not just text-based signals.
Personalization vs. Privacy: The Information Ethics Challenge
Information science has long balanced information access with privacy protection—a tension now central to advertising:
Maximum personalization: Using all available data for precise targeting
Privacy preservation: Limiting data collection and profile development
Contextual relevance: Finding middle ground through context rather than identity
Emerging approaches include:
Privacy-preserving targeting: Techniques like federated learning and differential privacy that enable personalization without centralized data collection
On-device processing: Moving targeting decisions to user devices rather than central servers
Cohort-based approaches: Grouping similar users rather than tracking individuals
These developments will reshape digital advertising, potentially shifting emphasis from individual targeting toward contextual relevance—a return to advertising fundamentals through advanced technology.
The Transformation of Google Ads Through Information Science
From Keyword Bidding to Intent Fulfillment
Google Ads has evolved from simple keyword auctions to sophisticated intent-matching systems:
EraPrimary Targeting MethodUnderlying TechnologyEarly AdWordsExact keyword matchingDirect term matchingExpanded matchingBroad match modifiersSynonym expansionSmart campaignsIntent-based targetingMachine learning modelsPerformance MaxAI-driven placementMultimodal understanding
This evolution represents the application of increasingly sophisticated information science principles:
Query expansion: Identifying related terms and concepts
Intent classification: Categorizing queries by underlying purpose
Contextual understanding: Interpreting query meaning within session context
For advertisers, this transition demands a strategy shift from keyword lists to comprehensive intent coverage—understanding not just what terms users search for but why they search and what information needs those searches represent.
The Knowledge Graph and Entity-Based Advertising
Google's Knowledge Graph—a semantic network of entities and relationships—increasingly influences both organic search and advertising:
Entity recognition: Identifying mentions of people, places, events, and concepts
Relationship mapping: Understanding connections between entities
Knowledge panels: Presenting entity information directly in results
These capabilities enable entity-based advertising approaches:
Product entity targeting: Advertising based on product attributes rather than just keywords
Brand entity association: Building connections between brands and relevant entities
Entity-based audience development: Creating audience segments based on entity interests
For businesses, this shift demands thinking beyond keywords to the entities relevant to their products and services—and building content and advertising strategies that establish these connections.
Automated Bidding and the Performance Max Paradigm
Google's automated bidding systems and Performance Max campaigns represent the application of advanced information science principles to advertising optimization:
Signal processing: Integrating diverse signals into unified bidding decisions
Predictive modeling: Estimating conversion probability based on historical patterns
Multi-objective optimization: Balancing competing goals within campaigns
These systems implement concepts from:
Information value theory: Assessing the value of different information signals
Decision theory: Making optimal choices under uncertainty
Utility maximization: Optimizing for business outcomes rather than intermediate metrics
For advertisers, these developments reduce the importance of manual bid management while increasing the importance of providing complete business context—conversion values, customer lifetime value, and profit margins—that enable algorithms to optimize toward meaningful outcomes.
Strategic Approaches for Businesses in the Evolving Information Landscape
Developing First-Party Information Assets
As third-party data becomes less available and less effective, businesses must develop proprietary information assets:
Customer data platforms: Unified repositories of customer information
Content knowledge bases: Structured repositories of product and service information
Interaction history databases: Records of customer engagements across touchpoints
These assets should implement information science principles:
Structured organization: Clear taxonomies and relationship models
Consistent metadata: Standardized descriptive attributes
Accessibility mechanisms: Systems for appropriate information retrieval
For advertising effectiveness, these assets enable:
More accurate audience targeting
More relevant creative messaging
Better measurement of advertising impact
Information-Rich Creative Strategy
As advertising systems become more intelligent about placement, creative content becomes the primary differentiator:
Information density: Providing substantive, valuable content rather than empty claims
Structural clarity: Organizing information for easy comprehension
Multimodal reinforcement: Ensuring text, image, and audio elements complement each other
Effective implementation includes:
Creative information architecture: Structuring advertisements to prioritize key information
Semantic markup: Using schema.org and similar frameworks to clarify meaning
A/B testing for information effectiveness: Testing which information arrangements drive results
This approach transforms advertising from interruption to information service—providing value through content while still achieving business objectives.
Integration of Paid and Organic Information Strategies
The artificial separation between SEO and paid advertising becomes increasingly counterproductive as both channels respond to similar information science principles:
Unified intent mapping: Identifying and addressing user intents across channels
Content ecosystem development: Creating complementary content across owned and paid media
Cross-channel measurement: Assessing the combined impact of organic and paid visibility
Practical implementation strategies include:
Shared keyword intelligence: Using insights from each channel to inform the other
Content gap analysis: Identifying opportunities for paid coverage where organic is weak
SERP feature targeting: Developing specific strategies for knowledge panels, featured snippets, and other information-rich results
This integrated approach recognizes that users don't distinguish between paid and organic results—they seek information regardless of its commercial classification.
Practical Google Ads Strategies for the Information Science Era
Intent-Based Campaign Structure
Rather than organizing campaigns by product lines or keywords, structure campaigns around user intents:
Informational campaigns: Addressing research-stage questions
Navigational campaigns: Capturing branded and direct-intent searches
Transactional campaigns: Focusing on purchase-ready queries
Commercial investigation campaigns: Targeting comparison and evaluation searches
Implementation requires:
Intent classification framework: Consistent categorization of query types
Intent-specific landing experiences: Content designed for each intent type
Intent-aligned measurement: Success metrics appropriate to each intent category
This structure aligns advertising delivery with information-seeking behavior, improving relevance and performance.
First-Party Data Activation in Google Ads
Leverage proprietary data assets within Google's advertising platforms:
Customer Match: Uploading customer lists for targeting and analysis
Enhanced conversions: Improving measurement accuracy through transaction data
Offline conversion import: Connecting in-store and offline activities to digital advertising
Strategic applications include:
Customer lifecycle targeting: Different messaging for acquisition, growth, retention
Value-based bidding: Optimizing based on expected customer value
Look-alike audience development: Finding new customers similar to high-value segments
These approaches transform customer information into advertising advantage—using proprietary data to enhance targeting precision and measurement accuracy.
Automation-Ready Campaign Design
Design campaigns that work effectively with Google's AI systems:
Comprehensive asset coverage: Providing multiple headlines, descriptions, and images
Clear performance objectives: Specifying business goals rather than proxy metrics
Robust conversion tracking: Implementing reliable measurement across the customer journey
Best practices include:
Testing asset variations: Providing meaningful alternatives rather than minor variations
Regular performance analysis: Identifying patterns and opportunities in automation reports
Strategic constraint application: Using targeting constraints judiciously to guide automation
This approach treats automation as a partnership rather than a replacement—providing clear direction while allowing AI systems to optimize tactical execution.
The Future Integration of SEO and Paid Advertising
Unified Information Strategy
The distinction between SEO and paid advertising will continue to blur, requiring integrated information strategies:
Content inventory management: Cataloging all content assets regardless of channel
Unified keyword taxonomy: Consistent categorization across organic and paid efforts
Cross-channel attribution: Understanding how channels interact to drive outcomes
Implementation approaches include:
Combined reporting dashboards: Viewing organic and paid performance together
Shared content calendars: Coordinating content development across channels
Integrated testing programs: Using paid channels to validate content approaches for organic development
This integration recognizes that both disciplines ultimately address the same challenge: connecting business offerings with user information needs.
Entity Optimization Across Channels
As search systems shift toward entity-based understanding, both SEO and advertising must adapt:
Entity identification: Determining relevant entities for the business
Entity association development: Building connections to those entities
Entity authority establishment: Becoming the authoritative source for certain entities
Tactical approaches include:
Schema markup implementation: Clarifying entity relationships in content
Entity-focused content development: Creating definitive resources on key entities
Entity-based advertising targeting: Using entity categories in audience development
This approach positions the business within the knowledge graph that increasingly shapes both organic visibility and advertising relevance.
Preparing for AI-Mediated Discovery
As AI systems increasingly mediate information access, both SEO and advertising strategies must evolve:
Answer optimization: Structuring content to serve as direct answers
Conversation design: Preparing for dialogue-based information exchange
Multimodal presence: Ensuring visibility across text, image, and audio contexts
Forward-looking strategies include:
Featured snippet targeting: Optimizing for position zero in search results
Voice search preparation: Addressing natural language queries directly
Visual search optimization: Ensuring image assets are discoverable and meaningful
These approaches recognize that future visibility depends not just on appearing in search results but on being selected by AI systems as the optimal information source.
Conclusion: The Information-Centric Business
The convergence of information science, artificial intelligence, and digital advertising demands a fundamental rethinking of marketing strategy. Businesses that succeed in this environment will:
Prioritize information quality: Viewing content as a primary business asset rather than marketing collateral
Integrate information systems: Breaking down silos between marketing, product, and customer service data
Develop information governance: Establishing principles for information collection, use, and measurement
Embrace semantic thinking: Moving beyond keywords to entities, intents, and meaning
Invest in information architecture: Structuring content and data for both human and machine consumption
This information-centric approach doesn't replace traditional marketing objectives but transforms how they're achieved—recognizing that in an AI-mediated information environment, the businesses that organize and present information most effectively will gain disproportionate visibility and influence.
The future of Google Ads, SEO, and digital marketing broadly lies not in tactical optimization but in strategic information management—building information assets that create value for both customers and algorithms, and deploying those assets through increasingly intelligent systems that connect information seekers with information providers at unprecedented scale and precision.
DigiCompli holds credentials in Information Science with specialization in digital information systems. DigiCompli’s research focuses on the application of information organization principles to emerging digital marketing technologies, cloud architectures, and artificial intelligence systems.
