Assess client-safe workflows first
A 200-person marketing agency needs an AI readiness assessment that distinguishes internal productivity from client-risk workflows. HubSpot State of Marketing and Salesforce State of Marketing show how quickly AI is entering marketing work, while PwC Responsible AI survey points to the governance burden that comes with client data, brand standards, and quality control.
Start with workflows that have clear source material and human review: brief intake, competitive research summaries, campaign reporting, internal knowledge retrieval, first-draft production, and QA checklists. Do not start with unsupervised client-facing output.
Map data and approval boundaries
NIST AI Risk Management Framework gives a useful readiness structure: map intended use, measure risk, manage controls, and govern accountability. For an agency, that means client data permissions, brand guidelines, content approval rules, model usage policies, and retention standards need to be visible before rollout.
The assessment should also inspect which teams are already using AI informally. Shadow usage is a readiness signal, not just a policy problem. The goal is to turn scattered experimentation into governed workflow improvement.
Measure margin and quality together
McKinsey State of AI research reinforces that value capture depends on operating redesign. For a marketing agency, the scorecard should include production cycle time, rework, account-team adoption, client-feedback quality, QA defects, and margin impact by service line.
Use AI for sales and marketing and AI governance and training to move from readiness assessment into a client-safe implementation path.
