Driving Innovation Through AI Strategy & Emerging Technology

An AI marketing strategy is a comprehensive approach to leveraging artificial intelligence technologies to enhance marketing efforts. It encompasses using AI tools for customer data analysis, content creation, personalization, automation, and performance optimization.

The importance of AI marketing strategy lies in its ability to deliver more personalized customer experiences, improve efficiency through automation, provide deeper insights from data analysis, and ultimately drive better ROI on marketing investments in today's highly competitive digital landscape.

Marketing utilizes several types of AI models, each designed for specific use cases:

• Large Language Models (LLMs): Generate human-like text for content creation, chatbots, and personalized messaging
• Computer Vision Models: Analyze images and videos for visual search, content moderation, and creative asset generation
• Predictive Analytics Models: Forecast customer behavior, campaign performance, and market trends
• Recommendation Systems: Suggest products or content based on user preferences and behavior patterns
• Natural Language Processing (NLP): Analyze customer sentiment, feedback, and conversations

"Share of model" is an emerging concept in AI branding that refers to how prominently and accurately a brand is represented within AI models' outputs, particularly in large language models (LLMs) and generative AI systems.

Just as brands previously competed for "share of voice" in traditional media or "share of search" in digital marketing, they now need to establish a strong "share of model" to ensure they're properly represented when customers interact with AI systems.

This matters because as consumers increasingly use AI interfaces to discover information and make purchase decisions, brands with stronger presence in AI models will gain competitive advantage in visibility and accurate representation.

Brands can optimize their "share of model" through several strategic approaches:

• Digital Presence Enhancement: Maintain comprehensive, accurate information across authoritative web sources that AI models use for training
• Structured Data Implementation: Use schema markup and structured data to make brand information more accessible to AI systems
• Brand Language Consistency: Develop and maintain consistent brand voice, terminology, and descriptors across all platforms
• AI-Ready Content Creation: Produce clear, informative content that addresses common customer questions about your products or services
• AI Platform Partnerships: Explore direct partnerships with AI platforms to ensure accurate representation

AI influences consumer brand perception in multiple ways:

• AI as Information Gatekeeper: As consumers use AI assistants to discover products and information, these systems mediate brand exposure
• Personalized Experiences: AI enables highly tailored brand interactions that can strengthen emotional connections
• Service Quality: AI-powered customer service (chatbots, voice assistants) directly impacts brand perception
• Content and Recommendations: AI-generated content and personalized recommendations shape how consumers encounter brands
• Brand Innovation Image: A brand's use of AI can itself influence perception as innovative or cutting-edge

AI can now produce a wide variety of marketing content:

• Written Content: Blog posts, social media updates, email copy, ad copy, product descriptions
• Visual Content: Images, designs, logos, data visualizations, video concepts
• Audio Content: Podcast scripts, voice-overs, music, sound effects
• Interactive Content: Chatbot conversations, personalized recommendations, dynamic messaging
• Data-Driven Content: Reports, analytics summaries, performance insights, trend analysis

While AI can generate impressive content, human oversight remains essential for brand alignment, factual accuracy, and creative quality.