Here’s a stat worth sitting with: 86% of agencies use AI for brainstorming. Only 20% use it for strategy.
That’s not a technology gap. That’s a courage gap.
Most agencies are using AI the way most people use a gym membership — showing up just enough to say they’re doing it, without doing anything that actually changes the outcome. AI writes the first draft. A human edits it for an hour. The AI-assisted time saving gets presented to the client as an innovation story. Nothing structurally changes.
A real agentic AI agency is something different. It’s not AI as a writing assistant. It’s AI as a workflow participant — making decisions, executing tasks, handing off to other systems, and looping back with results. The humans aren’t removed. They’re repositioned: from doing the work to directing it.
This is where we operate. And it changes everything about how agency work gets done.
What “Agentic” Actually Means
The word gets used loosely. Let’s be specific.
A traditional AI tool responds to a prompt. You ask, it answers. One input, one output, done. This is what most people mean when they say they “use AI” — ChatGPT, a Canva Magic Write button, a grammar checker.
An AI agent is different. It has a goal, not just a prompt. It can take actions — searching the web, reading files, writing code, calling APIs, triggering other agents. It can evaluate its own output, decide whether it’s sufficient, and keep working until the goal is met. It operates across steps, not just within one.
A multi-agent AI system takes this further: multiple specialized agents, each handling a domain, coordinating with each other through defined handoffs. One agent does competitive research. Another synthesizes the findings into a strategy brief. A third writes the content. A fourth schedules and publishes it. A fifth analyzes performance and feeds the data back to the first.
That’s not a chatbot. That’s a workflow.
Why Most Agencies Are Stuck at 20%
It’s not that agency leaders don’t understand the potential. It’s that integrating AI into strategic and executional workflows requires rebuilding how work gets done — not just adding a new tool to the existing process.
That’s genuinely hard. It requires rethinking job functions, redesigning approval chains, deciding which decisions can be automated and which require human judgment, and building the infrastructure to connect AI systems to real data sources and real platforms.
According to McKinsey’s 2024 State of AI report, only 21% of companies have embedded AI into more than one business function. The majority are running isolated experiments — one team, one tool, one use case — rather than integrating AI into how the organization actually operates.
The agencies that crack this aren’t just more efficient. They’re operating at a different quality ceiling. More research, more testing, more iteration — all at a cost structure that lets them reinvest in the thinking that actually moves the needle.
What the Agentic Workflow Looks Like in Practice
Here’s a concrete example. A client needs a content marketing program — 12 pieces of content per month, across blog, social, and email.
Traditional Agency Workflow
Account manager briefs a strategist. Strategist writes a content plan. Plan goes to the writer. Writer produces drafts. Drafts go to a creative director for review. Revisions happen. Finalized content goes to a designer. Designer produces assets. Assets go to a scheduler. Scheduler publishes. Somewhere in there, someone checks analytics occasionally and reports on it monthly.
Timeline: three to four weeks for the first deliverable. Heavy on coordination costs. Bottlenecks at every handoff.
Agentic AI Workflow
A research agent pulls competitive content data, keyword opportunity analysis, and audience intent signals. A strategy agent synthesizes this into a topic and angle brief — flagged for human review and approval. Once approved, a content agent produces drafts to spec. A quality agent evaluates each draft against defined criteria and returns a pass/fail with specific feedback. A production agent prepares assets and schedules publication. A performance agent monitors results and surfaces what’s working to feed the next cycle’s strategy agent.
The human team’s job: set direction, review strategy, make judgment calls on anything the system flags as ambiguous, and focus creative energy where it genuinely matters.
Timeline: significantly compressed. Quality: higher, because every piece goes through a defined quality evaluation before it ever reaches a human reviewer. Consistency: locked in by the system itself.
What This Means for You as a Client
If you’re hiring an agency, ask how they use AI. Not whether they use it — everyone uses it. Ask where in the workflow it operates, who or what makes which decisions, and how quality is maintained when production velocity increases.
Bad answer: “We use AI to help with first drafts and ideation.”
Better answer: “We have defined agent workflows for research, strategy, content production, and performance analysis. Here’s how they connect and where humans are in the loop.”
Agentic AI workflows don’t just affect speed — they affect the depth of work an agency can do for you. When the routine execution is systematized, the humans on your account can think harder about strategy, positioning, and the stuff that actually requires judgment.
The Guardrails Matter as Much as the Capability
A common concern: if AI is making more decisions, who’s accountable?
This is the right question. In a well-designed agentic system, accountability is clearer, not murkier — because every decision point is defined in advance. The system doesn’t have discretion beyond its scope. When something falls outside the defined parameters, it escalates to a human. The handoff points are explicit, documented, and testable.
The agencies that get this wrong are the ones building agentic systems without clear scope boundaries and human review gates. The result is autonomous systems doing autonomous things without oversight — which is how you end up with AI-generated content that’s technically publishable and strategically wrong.
The agencies that get it right treat the human review layer as non-negotiable. Automation handles scale. Humans handle judgment. That distinction doesn’t blur — it sharpens.
Where DM+ Operates in This Space
We’ve built agentic AI systems into our production workflows across every service we offer. Research, content strategy, copy production, performance analysis — each has defined agent workflows with explicit human checkpoints.
Our agentic AI services are also available as a standalone offering — for brands and agencies that want to build this capability into their own operations. We design the system architecture, build the agent workflows, define the quality gates, and train your team to operate at the director level, not the executor level.
This isn’t a pitch for AI replacing your marketing team. It’s a pitch for your marketing team being able to do work that actually matters — because the volume work is handled.
Frequently Asked Questions
What’s the difference between an AI agency and an agentic AI agency?
An AI agency uses AI tools to assist human work — faster copy, better research, smarter targeting. An agentic AI agency has built AI systems that execute multi-step workflows autonomously, with humans directing and reviewing rather than executing. The outputs can look similar. The operational model underneath is fundamentally different.
How do I know if my business is ready for agentic AI workflows?
If you have repetitive, high-volume work — content production, lead research, reporting, campaign monitoring — you’re ready. The question isn’t complexity. It’s volume. Agentic systems earn their value when the work is too consistent to require human time but too important to do poorly. That’s most marketing operations.
Does agentic AI work for small teams?
Especially for small teams. When you can’t hire a five-person marketing department, an agentic workflow gives you research, production, and analysis capacity that would otherwise require headcount you don’t have. The ROI is highest for teams where one person is currently doing five jobs — which describes most startups and growing businesses.
The 80% of agencies still using AI just for brainstorming are building the same deliverables, slightly faster. The 20% building agentic systems are building a different kind of agency. Let’s talk about what that looks like for your business — and whether it’s the right move for where you’re trying to go.