Part 1: AI Concepts Marketers Need to Know

Marketing leaders are being asked by CEOs and Boards about their strategies and execution plans for AI. Previous articles have discussed strategic aspects and techniques for supporting implementation and project management. Delving into AI – regardless of which specific flavor of the technology – a whole new language of terms that comes with it can be unsettling. This can particularly be true if marketing teams don't have experience with marketing environments that are immersed in technology. ?

This article is the first of two on AI related terminology likely to be a topic of discussion when executing an AI plan. The list is not exhaustive. Such lists are out there and can be found easily through search. These articles focus on 10 plus a bonus concept that build upon basic concepts such as understanding what a prompt or LLM is.? ?

In this part, we focus on data management and generation tools, which are fundamental for building and improving AI systems. Part two will highlight key aspects related to deployment and compliance. So, let’s dig in. ?

PART ONE: DATA MANAGEMENT & GENERATION TOOLS

These tools focus on managing, generating, and enhancing data, which are fundamental for building and improving AI systems, their reliability and minimizing erroneous outputs known as hallucinations. ?

1. AI REGISTRIES

Definition: An AI registry is a repository that tracks, monitors, and manages AI models, datasets, and related metadata throughout the AI’s lifecycle. ?

Key Characteristics?

• Transparency: ensures visibility into AI models' origins, performance, and updates.?

• Governance: facilitates compliance with regulatory standards.?

• Accessibility: provides centralized access to AI resources for developers and stakeholders.?

Provider Examples?

Neptune.ai: Helps data scientists manage the building, evaluating, and deploying machine learning models by providing tools to systematically track, query, compare and reproduce model experiments.?

Credo.ai AI Registry: Is a centralized database to catalog all AI systems, manage compliance, risk identification, mitigation and project prioritization.?

Use Cases in Marketing

• Campaign Optimization: Tracking the performance of various AI models used for predicting customer behavior and adjusting marketing strategies accordingly. Poor output leads to poor decisions.?
• Regulatory Compliance: Ensuring all AI-driven marketing tools comply with consumer data protection laws. Lack of compliance can lead to negative consequences in the marketplace. ?

2. SYNTHETIC DATA?

Definition: Synthetic data is artificially generated data that mimics real-world data in structure and statistical properties, but does not correspond to actual events or individuals. ?

Key Characteristics?

• Privacy: no personal data is used, reducing privacy concerns.?

• Scalability: easily generated in large quantities to suit various needs.?

• Versatility: applicable in numerous testing and training scenarios.?

Provider Examples?

Mostly AI: a leading synthetic data platform that specializes in generating high-quality, privacy-safe synthetic data. Designed to democratize data and accurately preserve the statistical properties and patterns of the original data.?

Gretel: a provider of multimodal synthetic data tailored to the needs of developers. Targeted to the enterprise and capable of on-demand data generation, model training and validation with features that prioritize privacy and security.?

Use Cases in Marketing?

• Market Research: Using synthetic data to model potential market scenarios without compromising customer privacy.?
• A/B Testing: Generating large volumes of synthetic user data to test different marketing strategies.?

3. PRIVATE LARGE LANGUAGE MODELS (LLMs)?

Definition: Private LLMs are customized large language models trained on proprietary data to meet specific organizational needs while maintaining data privacy and security. They are intended to provide enhanced security, control, and customization for organizations.?

Key Characteristics

• Data Isolation and Protection: minimizes risks of data breaches and aids in compliance with regulatory requirements.?

• Customization and Fine-tuning: ?enhances the model’s accuracy and relevance of outputs in specialized domains.?

• Control and Autonomy: allows organizations to adapt strategies and workflows according to their evolving needs and priorities such as ethical guidelines and policies.?

Provider Examples?

Practicus.ai: offers a unified AI platform allows organizations to host and train private LLMs within their own secure environments. The approach ensures sensitive data remains confidential.?

Cohere: Offers enterprise-grade language models for various business applications. They are a major player touting features such as customization, data safeguards, scalability and performance for enterprise customers.??

Use Cases in Marketing?

• Personalized Content Creation: Generating tailored marketing content based on proprietary and protected customer data.?
• Customer Interaction: Automating customer support with highly specific and contextual responses for specific industries and applications.?

4. RETRIEVAL AUGMENTED GENERATION (RAG)?

Definition: RAG combines the strengths of retrieval-based and generation-based approaches by using external documents to enhance the context for generating more accurate and relevant responses. ?

Key Characteristics?

• Access Up-to-Date Information: retrieval of information from external sources that may be more current or accurate. ?

• Enhanced Contextual Understanding: accessing context-specific information helps the model better understand nuances of a query providing better outputs.?

• Improve Scalability: integration of multiple knowledge sources can reduce hallucinations and bias?inherent in LLMs at scale.?

Provider Examples:?

Meta AI's RAG: A model that integrates retrieval with generative capabilities to answer complex queries. It’s strength is in improving the precision of AI responses without retraining the entire model.?

Haystack by Deepset: An open-source framework for building production ready LLM applications that include RAG systems. Application includes chatbots, multimodal question answering, and information extraction.??

Use Cases in Marketing?

• Customer Insights: Generating comprehensive reports from disparate data sources to inform marketing strategies.?
• Content Enhancement: Improving the relevance and accuracy of marketing content by integrating real-time data retrieval.?

5. LANGHCHAIN?

Definition: LangChain is an open-source framework that provides a modular and flexible architecture allowing developers to integrate LLMs with various data sources and external tools, enabling the creation of sophisticated natural language processing (NLP) applications.??

Key Characteristics:?

• Integration with LLMs: supports various LLMs and provides a standard interface for these models. Their modular structure enables customization for applications.?

• Data-Aware and Agentic: connects with diverse data sources for richer experiences and enables dynamic interactions within its environment.?

• Conversational Memory: incorporates a memory module that enables management, alteration and recall of conversations essential for chatbots.?

Provider Examples?

LangChain is an open-source framework supporting various LLMs from major players. A few of these providers not named OpenAI, Microsoft, AWS or Google include:?

Hugging Face: Allows integration with models and Hugging Face hub to leverage a large repository of pre-trained models.?

Wolfram Research: Provides powerful computational and data visualization functions for sophisticated mathematical capabilities.?

Use Cases in Marketing?

• Integrated Campaigns: Developing sophisticated marketing tools that combine language models with up-to-date market data, unique capabilities, and output formats.?
• Automation: Automating complex marketing tasks by integrating LLMs with CRM and other marketing systems to deliver real-time customization based on behaviors, preferences and audience attributes.?

?Wrapping Up Part One?

In this first part of the series, we've explored essential data management and generation tools that are necessary to enhance AI systems in marketing.??These tools and techniques enable marketers to handle vast amounts of data efficiently, ensure compliance, and generate high-quality synthetic data for testing and analysis. Stay tuned for part two, where we will dive into deployment and compliance tools, further expanding on how AI can be effectively integrated and regulated within your marketing strategies.?

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