What does a marketing organization for the agent era look like?
Many CMOs have encountered the same dilemma: as product iteration keeps accelerating, the marketing department often fails to keep up the pace. Even if AI is introduced for copywriting and graphic design, the efficiency improvement at individual points remains limited. The real solution is not to add more tools, but to build an agentic marketing organization, reconstruct workflows with brand code, enable human-AI collaboration, and break out of the endless fire-fighting cycle.
"We are ready to go live."
"The marketing side is not ready yet."
No Chief Marketing Officer (CMO) wants to become a business bottleneck, yet a growing number of managers in this position are trapped in such an awkward situation. The root cause is that AI is accelerating the work rhythm of the entire company, the demands the marketing department needs to take on are continuously increasing, and the existing operation model can no longer support such demands.
AI was first implemented in engineering and data workflows, bringing the most intuitive and easily quantifiable results, so most enterprise AI deployments are concentrated in these fields. Research from Anthropic shows that the software engineering field currently accounts for nearly 50% of all agent task volume. As a result, the product R&D cycle has been greatly shortened, and teams have shifted from quarterly delivery to continuous iterative launch. This change directly increases the number of launch projects that marketing needs to undertake and the speed of their advancement. At the same time, AI has also expanded the support boundary of marketing, covering more segmented audiences, channels and markets.
Marketing teams have already reaped the benefits of AI in scenarios such as copy generation, image creation and personalized content. But these improvements are limited to individual links. Marketing work itself is cross-departmental and highly collaborative. Embedding AI into the original workflow cannot eliminate the underlying collaboration friction. Faster output does not mean faster overall implementation.
The problem does not lie in the tools, but in the operation model. The vast majority of marketing workflows are still restricted by serial processes and siloed systems, which cannot match the response speed required by current business operations.
In our practice of leading marketing teams, providing AI implementation consulting and building AI-driven systems, we have witnessed this phenomenon repeatedly. But we also observe that some successful enterprises have adopted a completely different approach: Instead of simply stacking AI into existing processes, they build a brand-new model oriented to human-agent collaboration, combining autonomous workflows with a unified intelligent underlying layer. We call this organizational form an agentic marketing organization.
The core of this model is brand code: a set of machine-readable knowledge base. Key information such as brand strategy, product experience, customer insights and business rules is recorded in a unified format, which can be understood by both enterprise employees and AI agents to carry out work accordingly. Brand code is the cornerstone of the entire system, which stipulates the decision-making mechanism, content generation logic, and the way to shape the experience of each user touchpoint. It can be understood as a set of permanently onboarding guidelines, and both humans and agents rely on it to complete their own work.
With this underlying foundation, agents can execute and coordinate all kinds of work; humans can focus on strategy, judgment and control, design and guide this system, instead of manually controlling every single thing step by step.
Enterprises such as HubSpot and AWS have already started to implement this model. In practical projects, enterprises have achieved quantifiable results: the speed of adapting and adjusting marketing materials has been increased by up to 98 times, the unit cost has been reduced by 80%, and the click-through rate has been increased by up to 17 times. Large-scale research from Boston Consulting Group also confirms this value: after enterprises integrate agent AI into marketing workflows, the return on investment, campaign promotion efficiency and content output scale can be increased up to three times the original level.
The following part will present a practical framework for building an agentic marketing organization.
Build the Platform: A Team System Composed of Multiple Digital Teams
In an agentic marketing organization, the platform is not a collection of scattered individual tools, but a system with intelligent layered connection capabilities.
1. Bottom Layer: Brand Code
It is used to implement shared intelligence, ensuring that all output results — no matter from which channel, product, market or team — follow the same underlying logic and specifications. It encodes information into structured forms such as classification systems, prompt templates, decision trees and labeled datasets, which agents can directly retrieve and interpret in workflows.
Brand code will keep iterating in continuous use. After the marketing campaign goes live, performance data flows back to the system to optimize communication copy, audience definition and decision logic. Over time, the system will become more accurate and adaptive, and every round of execution will continuously optimize the working method. It can also solve a common pain point: knowledge loss caused by employee turnover. Brand code precipitates this experience and knowledge, so that experience can be shared and retained for a long time.
2. Execution Layer (Above the Brand Code)
This layer deploys agents for different work modules. Each type of agent is specifically responsible for one type of task, such as content creation, localization adaptation, and performance testing, and completes corresponding work relying on existing tools, datasets and team resources.
Take the experimental testing workflow as an example: multiple dedicated agents process different links in the process in parallel, and automatically collaborate once new information is generated. Some agents generate creative material variants, some build testing frameworks, some are responsible for multi-channel delivery, and the rest count data and output analysis reports.
3. Orchestration Layer (Above the Execution Layer)
This layer coordinates the work of all kinds of dedicated agents: managing task dependencies, defining task priorities, distributing output content, and triggering subsequent actions. Matters that used to be completed by project schedules, progress meetings and manual handover are now dynamically scheduled by the system. The orchestration layer is responsible for sorting, distributing and controlling the circulation of work; the execution layer undertakes the specific implementation work.
Continuing with the experimental testing example: agents in the orchestration layer can judge when to generate material variants, when to launch tests, who the results will be analyzed by, and when to start the next round of execution based on the obtained conclusions. Once manual judgment is required, such as content approval and direction setting, the system will push the pending negotiation items and decision items to the human in charge.
4. Interaction Layer
Marketers interact with the system here: set goals, review outputs, and make decisions when prompted by the system, with all permissions matched to corresponding positions. The interaction entrance is embedded in the tools that everyone uses daily, such as Slack, WhatsApp or Teams. Marketers can operate the system in the original working environment without switching between multiple platforms back and forth, and no additional training is required.
These four layers of architecture jointly build the marketing platform into a collaborative system composed of agents, operating on shared intelligence, with human decision-makers grasping the overall direction.
Workflow Reconstruction: Five Agent Workflows
After the system is built, the question shifts from "where to apply AI" to "how to reconstruct the work itself". Our experience shows that high-value systems are suitable for building on high-volume, repeatable, and measurable tasks. Content adaptation, experimental testing, data reporting, and localization are all common starting scenarios. The value of the agent system is reflected in the fact that various tasks are connected to form a complete workflow.
Under this model, marketing work is naturally broken down into five collaborative workflows:
1. Intelligence Insight and Creative Conception
It replaces the front-end part of the traditional planning process. Agents continuously aggregate market signals, competitor intelligence, user behavior and performance data, and output structured strategic directions: prioritized opportunity points, business assumptions, and directly implementable work briefs. Human work shifts from collecting information to evaluating opportunities, setting priorities, and determining strategic goals.
2. Content Creation
It implements the strategy. Agents generate content across formats, channels and audiences, and the first draft can meet the requirements of the brief relying on brand code. There is no longer a need for repeated production and repeated review, and content can be generated and adjusted on a large scale under established specifications. The focus of human work shifts from content production to direction guidance: formulating standards, anchoring creative intentions, and elevating content quality in links that are still difficult for agents to reach.
3. Research and Testing
It enables continuous learning. Agents use real or simulated audiences to design and carry out experiments across channels, and aggregate and analyze results. Testing is no longer a phased action, but embedded in the workflow. Humans no longer perform tests manually, but define learning goals: what to test, what the significance is, and how test results guide the strategy.
4. Distribution and Delivery
The explosive growth of channels has made distribution one of the most complex links in marketing operations. Agents are responsible for completing content adaptation, scheduling and launch across channels, markets and audiences; humans focus on channel strategy and cooperation decisions.
5. Performance Monitoring and Reporting
It forms a closed business loop. Instead of relying on post-event reviews, agents continuously monitor data, mark anomalies, mine patterns, and feed experience back to the system in near real time. Reporting work shifts to continuous optimization, and performance data feeds back to the decisions of all workflows. Humans no longer simply output reports, but interpret data: weigh trade-offs, identify patterns, and guide system iteration.
Redefine the Role of Marketers
Marketers have always been responsible for strategy and decision-making, but a lot of their time is spent writing positioning copy, managing materials, and following up cross-post handover. In the agent system, more marketers transform into work leaders: set goals, review outputs, and make decisions as needed in teams composed of humans and agents. The focus of value shifts from output delivery to professional judgment, where marketers define "qualification standards", evaluate system outputs, design input instructions, and guide subsequent results.
This requires marketers to have a whole new set of capabilities. Excellent marketers can clearly convey strategic intentions, think from the perspective of workflows rather than functional departments, and understand how a decision in one part of the system will affect the final results of other links. They work in an iterative way, testing, learning and continuously adjusting together with the system.
This transformation is not simply adding new tasks. Many marketers build their professional confidence and sense of competence by delivering results in person; making tangible works by hand can bring a sense of satisfaction in itself. Under the agent system, the focus of control shifts from hands-on execution to direction control. The challenge is not only to learn new skills, but also to let go of the instinct of intervening at any time and operating in person.
This shift will also profoundly affect the enterprise's talent recruitment, training and management models.
Enterprises need marketing talents with system thinking, not only focusing on individual tasks. They need to be able to design workflows, set parameters, and interpret data quickly. Such talents do not need to be proficient in underlying technologies, but need to understand system orchestration and be familiar with the iterative system design ideas.
The management model also needs to evolve. When execution is completed by agents, the focus of managers' work shifts from reviewing deliverables to reviewing the entire system: do the outputs conform to the strategic direction? Is the feedback loop effective? Is the brand code kept updated and accurate?
Finally, enterprises need to invest corresponding resources in this transformation. Teams need to learn how to write high-quality briefs for the AI platform, evaluate results against strategic standards, and judge which links require human intervention. Enterprises can arrange senior marketers to work collaboratively with the agent system in real projects — this is not a pilot attempt, but a brand-new way of working. Marketers who adapt the fastest are not necessarily proficient in technology; they can judge the quality of results combined with business scenarios, distinguish whether the output is qualified and where needs to be optimized, and promote the continuous iteration of the system based on practical observations.
In practical work, marketers who adapt to the change the fastest will follow the rapid iteration rhythm of the system, adjust its operation mode, make flexible decisions, and precipitate the accumulated experience into an optimization mechanism that the system can use for a long time.
In an agentic marketing organization, when the business team says "ready to go live", the marketing work has already been carried out synchronously. The materials are ready, tests are ongoing, and all kinds of data signals flow back to the product and R&D team in real time; the CMO is responsible for grasping the overall direction, and no longer has to rush to catch up with progress. Enterprises that embark on this transformation path early will not only be more agile in action, but also can define the operation mode of marketing in the next era and gain compound growth.
Keywords: #Marketing
Michelle Taite, John Winsor, Will Fernandez | Text
Michelle Taite is a marketing executive who provides enterprises with consulting on AI transformation, agentic marketing, and modern go-to-market operation models. She serves on the Global CMO Growth Council of the American Association of Advertising Agencies, and was selected to Business Insider's 2023 list of the most innovative CMOs. John Winsor is a researcher at the AI Institute of Harvard Business School, whose research focuses on the intersection of AI, organizational design and the future of work, and provides transformation consulting for executives and enterprises. Will Fernandez is a marketing executive and entrepreneur, focusing on the practice of large-scale AI implementation for leading big brands.
Zhou Qiang | Editor
This article is from the WeChat official account "Harvard Business Review" (ID: hbrchinese), the author is HBR-China, and 36Kr is authorized to distribute it.