AI readiness
What Is an AI Readiness Assessment? What Agencies Should Evaluate Before Investing
What a useful readiness assessment should inspect before an agency commits to implementation, automation or agents.
Insights
Insights for Replatforming an Agency for the AI Era
Practical guidance for agency leaders moving from disconnected AI use toward a more deliberate operating model.
AEA Insights covers the questions behind Agency Replatforming: AI readiness, operating-model design, knowledge and context, workflows, automation, governance, QA, implementation, measurement and the choices agency leaders have to make as AI becomes part of everyday delivery.
The goal is not to publish AI news for its own sake. Each article should help an agency understand a decision, diagnose an operating problem, compare approaches or prepare for implementation.
Launch library
Seven practical starting points
AI readiness
AI Readiness Checklist for Marketing and Ecommerce Agencies
An evidence-oriented checklist for knowledge, workflows, systems, governance, QA, adoption, measurement and scope.
Operating model
What Is an AI Operating Model? A Practical Guide for Agencies
How knowledge, workflows, people, systems, governance and AI fit together as an operating model rather than a tool stack.
Agency Replatforming
Why AI Tools Alone Do Not Make an Agency AI-Enabled
The practical difference between individual AI adoption and organizational AI capability.
Governance
AI Governance for Agencies: What Needs Rules, Ownership and Human Control
A practical agency governance model for tools, data, permissions, source authority, QA, escalation and accountability.
Automation
AI Automation for Marketing Agencies: What to Automate, What Not to Automate
A decision framework for where AI assistance, deterministic automation, tool-using AI and agents fit.
Roadmap
AI Transformation Roadmap for a 10-20 Person Agency
A practical sequence from evidence and priorities through operating design, implementation, adoption and measurement.
Agency Replatforming & AI Operating Models
Start here if the bigger question is what makes an agency meaningfully AI-enabled and how the operating model changes when AI becomes part of the business.
AI Readiness & Roadmaps
Readiness work helps leadership understand the current state before committing to an implementation path.
Knowledge, Context & Workflow Foundations
These articles focus on the operating material AI-supported work depends on: authoritative sources, reusable context, documented workflows, ownership, handoffs and the difference between adding AI to a task and redesigning the workflow around it.
See What We BuildAutomation, Agents & Implementation
Automation and agents can be useful when the underlying work is stable enough to support them. This section examines where they fit, what should come first and how implementation choices interact with context, systems, exceptions and human accountability.
Governance, QA & Human Control
AI-supported work still needs explicit decisions about access, sensitive information, ownership, review, escalation and quality.
Pricing & Buyer Questions
Some of the most useful content is commercial: what an assessment should include, how to compare providers, what changes scope, and how to distinguish a useful operating-model engagement from generic AI consulting.
How AEA approaches Insights
Articles should be direct about what is known, what is operator perspective and what still depends on the agency's specific conditions. External facts should be sourced when they are current, numerical, comparative or material to a decision. AOC experience should be identified as affiliated proving-ground context rather than independent client validation.
AEA will favor fewer distinct, useful articles over publishing overlapping posts to chase close keyword variants. Every article should have a clear reader question, an original agency-operating contribution, and a relevant path into the commercial page that owns the next step.
Want to assess your own agency instead of only reading about the problem?
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