AI in Marketing 03 - Strategic AI Transforming Marketing with the Right Blend of AI.

Artificial Intelligence (AI) is revolutionizing the way businesses operate. In this article We will be covering: ?

• Overview, exploring the various intelligent designs of AI, their advantages, and the pivotal role they play in shaping marketing strategies.
• Uncover the diverse designs of AI and grasp their individual benefits and limitations.
• Gain insights into how these intelligent designs elevate marketing practices, enhancing efficiency and effectiveness.
• Explore the defining characteristics within each level, providing a roadmap for businesses to evolve and thrive in the AI-driven landscape.

Let's explore the distinct types of AI:

Mechanical AI: Streamlining Repetitive: Actively performs repetitive tasks, enhancing efficiency and productivity in real-world applications.

Mechanical AI stands as the foundation of artificial intelligence, leveraging its power in routine and repetitive tasks at the lowest level of AI intelligence. Learns and adapt minimally, but excelling in routine marketing tasks.

• Examples of Technologies: Remote sensing, machine translation, classification algorithms, clustering algorithms, and dimensionality reduction

Mechanical AI Benefits:

• Process Standardization: Ideal for scenarios demanding process standardization, Mechanical AI thrives in tasks like fast-food ordering, delivery, self-service, and routine customer service.
• Consistency and Reliability: Tasks involving high consistency and outcome reliability, such as collaborating robots in packaging or drones aiding in distribution.
• Cost saving: Firms emphasizing cost benefit from operational excellence achieved through the automation of production and service processes.

Mechanical AI Limitations:

• Poor in Contextual Data: Struggles with contextual data, especially emotional data, capturing and analyzing individual-specific and context-dependent information.
• Loss of Context: Emotional data often lose context during interactions, impacting applications like customer service where understanding the emotional state requires nuanced context.
• Machine-to-Machine Strength: Excelling in machine-to-machine interactions, like ATMs getting authorization for cash withdrawal, Mechanical AI may compromise customer intimacy when human interaction is minimized.

Thinking AI: Data-driven Decision Making: Tasked with processing data, arrives at new conclusions, possibilities for advanced decision-making and strategic planning.

Designed to process unstructured data and make informed decisions using capabilities like:

• Data Processing Expertise: Thinking AI processes unstructured data, leveraging methods like machine learning, neural networks, and deep learning to draw new conclusions and decisions.
• Pattern Recognition: Recognizes patterns and regularities, find applications in text mining, speech recognition, facial recognition, and various decision-making systems like IBM Watson and recommender systems.

Thinking AI Benefits:

• Personalization Powerhouse: Ideal for personalization, available customer data is used for well-defined predictions like, customer preferences for new services.
• Analytical Advantage: The analytical subtype uncovers meaningful patterns in data, laying the foundation for personalized recommendations and optimized decision-making, especially in complex scenarios like shopping decisions.
• Strategic Market Insights: Use case: Service providers can leverage Thinking AI when the nature of the service task is data-based, analytical, and predictive. It proves valuable in creating high-quality, functional, and high-tech service offerings, enabling market prediction, new service creation, customer prospecting, and service customization.

Thinking AI Limitations:

• Transparency Challenges: The opacity in how Thinking AI arrives at recommendations poses challenges for marketers. The current machine learning approach lacks transparency, making it difficult for marketers to explain the reasoning behind AI-generated decisions.
• Bias and Accountability Issues: Thinking AI is not neutral, and biased input can lead to biased output. Even unbiased algorithms may unintentionally produce discriminatory results. Marketers must be vigilant about potential biases in AI systems, understanding how AI learns to mitigate biases and avoid accountability issues.

Feeling AI: Decoding Human Emotions: Feeling AI delves into human emotions, offering insights into sentiments and paving the way for empathetic interactions.

Feeling AI, the next frontier in artificial intelligence, transforms the landscape by learning and adapting from experiences. A potential application in personalized relationship building, customer satisfaction, and retention.

• Learning from Experience: Transcends mechanical and thinking AI capabilities by applying them to experience-based data, evolving through learning from interactions and emotions.
• Ideal for Relationalization: Ideal choice for relationalization, creating personalized relationships where interaction, communication, understanding, and experience play pivotal roles.
• Polarized Applications: From virtual agents and chatbots providing mechanical AI-like customer service at the low end, to high-end applications such as automatic speech emotion recognition and sophisticated chatbots like Sophia, Feeling AI spans a spectrum of possibilities.

Feeling AI Benefits:

• Relationalization Advantages: Personalizes relationships by recognizing and responding to emotions, making it suitable for marketing functions requiring interaction, communication, and relational benefits.
• Diverse Marketing Functions: Various marketing functions, including customer satisfaction, handling complaints, understanding customer moods, and incorporating emotions into advertising, can leverage Feeling AI for enhanced effectiveness.
• Strategic Service Applications: use case: Service providers can benefit from Feeling AI when the service task is experience-based, emotional, requires interaction, and when the service offering emphasizes sensory, fun, high-touch benefits, or higher customer lifetime value.

Feeling AI Limitations:

• Perceived Capability Inflation: Marketers using lower intelligence AI for feeling functions may inflate the perceived capability of AI in understanding customer emotions. Overreliance on these technologies could lead to customer disengagement.
• Customer Readiness Concerns: Some customers may not be ready for interactions with Feeling AI, as evident in instances where customers hang up on call-out marketing chatbots once they realize they are interacting with bots.
• Awareness Challenges: The Technology Readiness Index reveals that only 10% of individuals consider Feeling AI to have had the most significant impact on their jobs in the past five years, indicating a lack of awareness about AI's potential emotional capabilities.

From the foundational prowess of Mechanical AI streamlining repetitive tasks to the intellectual might of Thinking AI driving data-driven decisions, and the empathetic touch of Feeling AI personalizing relationships, each facet brings unique capabilities to the marketing realm.

If you find this topic interesting Follow me for exploration of our previous and next article within the AI in Marketing series: ?"AI Maturity: The Process of Transition". We'll be delving into the crucial stages that businesses navigate on their journey to AI maturity, uncovering the strategic transitions that redefine marketing landscapes.