Practical Guide to Building an AI-Enabled Marketing Function
- Chris McLellan
- Oct 24
- 6 min read
Updated: Nov 4
This guide introduces a practical framework for marketing teams to collaborate with Generative and Agentic AI across every stage of the marketing function. Designed for continuous improvement, it connects human knowledge and creativity with AI inference and scale to drive productivity and better customer outcomes. Each module represents a stage of the system, from Knowledge, Planning, Production, Orchestration, Analysis, and Collaboration, forming an open, composable model that can adapt to different marketing strategies and tech stacks.
This is the anchor post for an open research initiative focused on developing a practical framework for building an AI-Enabled marketing function. Marketers, vendors, and other stakeholders are invited to contribute. To participate as an attributed collaborator, email: chris@friendselectric.ca
Quick Overview
An AI-enabled marketing function enables humans, Generative AI systems, and Agentic AI systems to collaborate on the core functions, including marketing plans, product marketing, brand marketing, performance marketing, and content marketing.
This post focuses on how such systems can support the creation, expansion, tracking, and optimization of marketing content to help achieve key growth objectives, including lead generation.
This post provides a summary of each module in the AI-enabled marketing function:
Module 4: Production
Module 5: Orchestration
Module 6: Tracking
AI-Enabled vs. AI Native
The framework incorporates Generative AI and AI Agent technology, but it does not run itself. The use of the term 'AI-Enabled' is a deliberate choice as 'AI Native' does not yet accurately describe the current design, something that could change as it evolves.
But whatever the technical architecture, until AI starts selling to AI (something that's been predicted), a successful marketing system will require active engagement from human marketers who are informed, creative, and precise at every stage.
From planning to analysis, human oversight and judgment keep the AI-Enabled Marketing system honest, relevant, and responsible.
Humans ARE The Loop
One of the main purposes of an AI-enabled marketing function is to give marketers more time to focus on essential tasks:
Researching customers, competitors, and products
Identifying insights from customers, colleagues, and partners
Finding creative ways to reach prospects, communicate value, and enable teams
As marketers, the most valuable skills we possess are active listening, creative problem solving, and communication. Without these human contributions, AI-enabled marketing function will not deliver long-term results.
The future of marketing belongs to those who understand how to leverage AI for its powers of inference and scale while protecting the human spark, intuition, and media savviness that makes content resonate with audiences.
Built for Every Type of Marketer
An AI-Enabled Marketing Function should support any approach to marketing, and not focused around a single strategy or channel. Instead, it provides a flexible structure that connects human creativity, data, and AI reasoning across different disciplines.
The framework can be adapted by:
Content marketers to plan, produce, and distribute campaigns efficiently.
Brand marketers to manage research, storytelling, and creative consistency.
Product marketers to translate product insights into clear positioning, messaging, and go-to-market plans.
Performance marketers to optimize spend, measure impact, and scale automation.
Inbound marketers to attract, engage, and nurture audiences through continuous insights.
Each team works from the same core modules: Knowledge, Collaboration, Planning, Production, Orchestration, and Analysis.
They apply them in ways that match their goals, tools, and metrics. This flexibility allows the framework to fit any marketing approach while maintaining a collaborative, insight-driven foundation.
How To Build An AI-Enabled Marketing Function
The following modules can be set up as standalone processes or as part of a more complete marketing system.
Module 1: Establish Marketing Knowledge Base
The Marketing Knowledge Base is the informational foundation of the AI-enabled marketing system. It captures brand, product, customer, market, and operational data in connected tables and documents. AI tools then summarize that information into short, reusable insights that power better decisions and more coordinated execution.
Inputs include product research, stakeholder feedback, pricing plans, campaign data, and other marketing information.
Outputs are verified insights written in AI-readable formats, ready to support every stage of the system from planning and production to orchestration and analysis.
The Knowledge Base provides the raw materials that enable the entire system to insightful, composable, and personalized marketing outcomes.
Module 2: Create Human-AI Collaboration Space
The Collaboration Space creates a shared digital space where marketers and AI systems work together. It enables teams to explore insights, develop ideas, plan activities, and improve operations through conversation and co-creation.
Inputs include marketing insights from the Knowledge Base, templates, operational guides, and performance data.
Outputs include refined ideas, structured prompts, improved workflows, and updates to system operations.
Human-AI collaboration also happens across the various tools and agents connected to the system, but this module serves as the central workspace where shared understanding and alignment take shape.
Module 3: Create Smart Plans
Smart Plans help translate insights into clear, coordinated action. This stage of the process supports the creation of multiple types of plans, including yearly strategies, content plans, marketing plans, campaign plans, and project plans. Each outlines what needs to be done, and when.
They are informed by collaboration and can be managed in an integrated AI marketing platform like Notion or in stand-alone task management tools such as Google Sheet or Asana.
Inputs include ideas and updates powered by the Collaboration Space.
Outputs include detailed, realistic plans that include milestones and activities that have assigned timing, ownership, dependencies, and deliverables.
Smart Plans keeps the AI-enabled marketing function organized, focused, and adaptable. As new insights and performance data flow back in, plans can be updated quickly to stay aligned with business goals and revenue opportunities.
Module 4: Establish AI-Enabled Production Tools
The AI production tools focus on generating the assets, materials, and workflows needed to execute plans. This can include text, graphics, video, sales tools, and ad copy as just a few examples.
AI tools such as Jasper, Custom GPTs, or Canva can support drafting, design, and production of essential content.
Inputs include structured prompts that define format, medium, tone, brand as well as approved templates.
Outputs include ready-to-use designs and assets.
The goal is to combine human creativity with AI speed and scale to produce high-quality work efficiently and consistently.
[Full Post Coming Soon]
Module 5: Implement AI-Enabled Orchestration Layer
The Orchestration Layer manages how marketing activities are executed across channels and systems. It connects plans and assets to distribution channels through AI workflows and agents.
Inputs include context from the Knowledge Base, activities from Smart Plans, assets from the Production Tools, performance data, and workflow logic.
Outputs include multi-stage, multi-system workflows that execute paid campaigns, social media page content, email blasts, blog posts, hosted events, and other marketing activities.
This is where reasoning agents become most valuable. They draw on connected information from across the system to make informed decisions and optimize how marketing activities are sequenced and executed. These agents help ensure every task, asset, and message is delivered at the right time, through the right channel, and with the right intent.
[Full Post Coming Soon]
Module 6: Close The Loop With Marketing Analysis
The Analysis Module closes the loop by turning marketing performance data and feedback into new insights that improve decision-making across the system.
Inputs include audience engagement data such as clicks, views, comments, shares, and conversions, as well as feedback from customers and other stakeholders. Outputs include updates to the Knowledge Base, plans, and operational guides that strengthen overall system performance.
AI tools and assistants can help interpret results, surface insights, and identify opportunities for improvement. This continuous feedback process ensures the system learns, adapts, and improves over time.
[Full Post Coming Soon]
Join This Research Project
For many marketers, AI is simultaneously everywhere and nowhere.
Teams are testing its capabilities, but few are adopting operational systems to actually guide its implementation, mainly because so few frameworks actually exist.
This research is all about learning in public and building smarter and more productive marketing functions through controlled human-AI collaboration.
If you have expertise in marketing, creative strategy, knowledge management, system design, or AI agents and would like to become an attributed co-author of the design and Build Notes doc, please get in touch.
Email: chris@friendselectric.ca
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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