Assess operating readiness before scaling tool usage
A 250-person marketing agency probably already has teams using AI in pockets. The leadership question is whether that usage is controlled, measurable, and connected to margin. The RSM middle-market AI survey shows middle-market AI adoption moving quickly, while the OECD report on AI adoption by small and medium-sized enterprises makes clear that adoption depends on skills, data, governance, and process maturity.
The readiness assessment should score eight areas: workflow value, client-data boundaries, source quality, QA standards, tool access, adoption friction, pricing impact, and measurement clarity. The goal is not to stop experimentation. It is to decide which client or internal workflow can safely become an operating standard.
Use the SMB AI readiness assessment as the base structure. Agencies need the same discipline, with extra attention to client confidentiality, brand QA, and billing-model implications.
Choose workflows where quality and economics can both improve
The best agency candidates are research briefs, creative brief preparation, content repurposing, campaign QA, performance summary drafting, and internal knowledge search. These workflows are repeated, reviewable, and tied to client delivery cadence. They also expose whether AI changes profitability or simply makes teams produce more low-margin work.
The NIST AI Risk Management Framework helps leadership separate useful controls from vague AI policy. For an agency, controls include approved data sources, client-specific restrictions, human review, retained workpapers, and clear escalation when output quality is uncertain.
Finance should pair readiness scoring with real AI ROI measurement. The agency should measure reduced rework, faster handoffs, better QA consistency, and capacity that can be redeployed into higher-value client work.
Convert readiness into one production workflow
The Deloitte State of AI report argues for process change over tool access, which is exactly the issue for a mid-market agency. A successful first release has a named owner, defined work type, client-data rules, review checklist, training plan, and weekly measurement cadence.
The Gartner agentic AI project forecast is a useful reminder to avoid agentic complexity before value, data quality, and controls are clear. A marketing agency should prove one governed workflow before connecting AI to cross-client systems or automated publishing paths.
The next step is workflow discovery for AI automation. Use it to select the first agency workflow that is ready for production discipline.
