How to Kickstart a Marketing Knowledge Base to Power AI-Enabled Growth

Marketers face growing pressure to harness a wave of AI tools without losing creativity, control, or results. At the heart of the AI-forward marketing is the Marketing Knowledge Base, a well-governed single source of truth that can be used to power personal and composable marketing outcomes.

Tl;DR

AI-enabled marketing depends on a governed Marketing Knowledge Base, a structured source of truth that feeds accurate, composable content into every channel.

Key points:

• A Knowledge Base organizes brand, product, pricing, proof, and operational data into clean tables.

• AI tools transform these tables into reusable “insights” that power personalized, consistent content.

• Syncing insights to readable docs improves performance in AI research assistants.

• Keeping data portable (Sheets, Airtable, databases) prevents vendor lock-in and preserves control.

• A strong Knowledge Base enables speed, accuracy, and creative consistency across the entire marketing engine.

Inputs and Outputs

Key Inputs

• Product Marketing research

• Meeting transcripts

• Performance data apps

Key Outputs

• Structured tables (see below)

• Granular marketing insights (via AI-summarized rows)

• Marketing reports e.g. knowledge base to docs or dashboards

Knowledge Base Structure

Identify Informational Sub-Domains

A Knowledge Base is a structured source of truth that supplies information to a modern Content Engine. It captures the essential data that an organization can use (and reused) to tell consistent, factual stories about its products, people, and mission.

The information is typically hosted in spreadsheets or databases and organized in tables that reflect the key domains within the marketing function. This data provides the raw material for AI systems to compose more accurate, consistent, and personalized content.

A few of the more critical marketing tables include:

• Market: Geographic, industry, category

• Product Marketing Research: Positioning, personas, pricing, value props, win/loss etc.

• Brand: Identity, tone, mission, values, visual + narrative rules.

• Proof & Credibility: Testimonials, case studies, awards, press, analyst mentions

• Pricing & Packaging: Tiers, bundles, licensing, promotions

• Programs & Promotions: Loyalty schemes, offers, sustainability/buy-back, incentives

• Legal & Compliance: Disclaimers, brand usage rights, data governance, accessibility

• People: Profiles of Founders, internal experts, advocates, partners, influencers

• Insights: Trends, patterns, anomalies powered by interviews + AI Notetakers

• Messaging & Storytelling: CTAs, narratives, tone/voice examples

• Operations & Performance: Channels, KPIs, campaigns, experiments, dashboards

• Partnerships & Ecosystem: Integrations, co-marketing alliances, tech stack relationships

• Prompt Library: Saved prompts for different marketing use cases

Collaborate Where Possible

Ownership of Knowledge Base tables should be shared wherever it make sense. For example, as subject matter experts, Product Marketing folks should own the Product Playbook (which is actually multiple tables). Similarly, if resources are limited, Finance might be able to manage the Pricing table.

Ultimately, data should be owned and managed by the people who understand how to improve and govern it best.

Transform Information Into Insights

In an AI-Enabled marketing function, insights are concise, factual summaries drawn from structured marketing information.

Most spreadsheets (e.g. Google Sheets with Gemini, Excel with Copilot) and databases (e.g. Airtable) integrate AI capabilities that can be leveraged to summarize single or multiple records. This is a fast and efficient way to create composable “insights,” concise factual summaries that can be assembled and reassembled to generate accurate, compelling, and personalized content at scale. They are the genes that make up your content DNA.

Note: Insights are conceptually similar to the reusable “blocks” used in CRM or CMS systems, but they differ in purpose and flexibility. Insights are designed to support the generation of draft content as part of a prompt, helping to scale accurate and context-aware drafts. Blocks, by contrast, are static components limited to specific formats such as emails or web pages.

Sync Insights To Docs

Tables are excellent for organizing structured data, but most Generative AI tools still perform best when reading natural language. Turning structured rows into readable text helps bridge that gap.

A simple way to do this is to sync your Knowledge Base insights to documents that can be connected or uploaded to AI Research Assistants such as NotebookLM. A lightweight Google Apps Script or workflow platform like N8N can automate the process.

Note: Enterprise teams with access to Vector Database technology may be able to streamline this even further.

The alternative is to build a “doc-centric” Knowledge Base from the start, but large files and folders tend to introduce more errors, duplication, and drift over time. Keeping your data structured and automatically exported as text gives you the best of both worlds.

Stay Structured, Move Fast

It’s tempting to publish content ad hoc, but without a structured Knowledge Base as your foundation, scale and consistency will quickly break down.

Without it, achieving the benefits of an AI-enabled Marketing System, including speed, accuracy, and creative consistency, is nearly impossible.

Whether or not your business intends to invest in a large content marketing team, this is the path forward.

Avoid Vendor Lock-In

Your Marketing Knowledge Base is a strategic asset. Treat it like one.

Many CRM, email, and CMS platforms offer dynamic content capabilities, but with so many AI tools in today's martech landscape, marketers should be cautious about hosting their Knowledge Base in a dedicated app rather than a more flexible data management environment like a spreadsheet or database.

Some issues to consider:

• Restrictions on table design, which will limit blocks

• Difficulty in exporting data e.g. Custom content bots

• Only support one or two content formats e.g. email and blog only

• Challenges with access e.g. access ties to subscribers only

• Issues with governance

Maximizing the ownership and control of critical marketing data is generally advised for marketers.

About Friends Electric

Friends Electric is a private AI marketing consultancy founded by Chris McLellan, a Certified Chartered Marketer and contributing member of the AI Marketing Committee at the Canadian Marketing Association.

He's also founder and producer of the Ask AI podcast, and serves as Technical Committee Lead and co-author at the Digital Governance Council for the Data Collaboration Framework, a Canadian and international standard governing human-ai innovation.

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