Solution · Marketing
AI agents for marketing: more output, same brand.
Marketing was the first department to try generative AI and the first to learn its limit: more drafts, not more results. Agents change that by running whole workflows, from brief to published asset and from raw campaign data to the Monday report, while your editors and your legal team approve what matters.
Where agents pay off in marketing
Marketing teams rarely lack ideas or tools. They lack hours: for briefs, versions, localisations, reports and the unglamorous upkeep of the CRM.
A chat assistant gives you a draft. An AI agent runs the workflow around it: it reads the brief, pulls product facts and brand rules from your systems, writes and localises, checks its output against your claims list, prepares the CMS entry and stops where an editor or a lawyer needs to decide. That is the difference between a tool your team uses occasionally and a capacity you can plan with.
Why now: current models write long-form, multilingual copy at a usable level, the connectors to marketing systems have matured, and search itself is changing. Buyers increasingly ask AI assistants instead of typing keywords, so visibility in AI answers (often called GEO or AEO) is becoming a channel with its own research and content needs.
We design agents around your editorial process rather than replacing it. The goal is production, not another pilot: a workflow that runs every week, with a baseline, a metric and a named owner.
- Typical entry pointWeekly campaign reporting or one content pipeline
- SystemsCMS, DAM, analytics, ad platforms, marketing automation, CRM
- Human checkpointEditorial approval, claims and legal review, publishing
- First pilotTypically 6–8 weeks
Use cases
Six workflows we see in most marketing teams. Each names the systems involved and the point where a person decides.
Content operations agent
From brief to publishable asset: the agent drafts outline and copy from your brief, product pages and brand guide, localises with a glossary, and runs brand-voice, banned-term and claims checks before an editor sees it. Editors approve in their usual tool; the agent prepares the CMS entry and DAM assets. Nothing publishes without a human click.
Campaign performance reporting agent
Every Monday the agent pulls spend, impressions, conversions and pipeline from ad platforms, analytics, marketing automation and the CRM, reconciles the numbers, drafts commentary and flags anomalies against plan. The marketing lead reviews and annotates before the report reaches leadership.
SEO and answer-engine research
The agent tracks what buyers ask search engines and AI assistants, checks whether your pages are cited in those answers, finds gaps against competitors and drafts content briefs with sources. Your content strategist decides what to pursue. Classic SEO and visibility in AI answers (GEO/AEO) are covered together.
Personalisation and segment research
From CRM, product-usage and campaign data the agent proposes segments, explains what distinguishes them and drafts message variants for email, landing pages and ads. It checks the consent basis of the data, and a marketer approves segments and copy before anything runs.
Lead enrichment and intent hand-off
New leads are enriched from permitted sources, scored on fit and intent signals (pages visited, downloads, event attendance), summarised in three sentences and routed to the right salesperson with a reason. Sales confirms or rejects the routing, which sharpens the rules. The other side is described under AI agents for sales.
Marketing ops and market monitoring
The agent keeps CRM and marketing automation clean (duplicates, missing fields, UTM and asset naming), prepares bulk changes for approval, and watches competitors, pricing pages and industry news for a weekly digest. Bulk changes to customer records always need human approval.
Worked example
A content production pipeline with approval gates
A typical scenario: a B2B company publishes in German and English across web, email and LinkedIn, and every asset passes through five inboxes. An agentic pipeline handles the same work like this, with a person deciding at each gate that matters.
- Brief. A campaign manager files the brief in the usual template: audience, goal, key message, channels, deadline. The agent checks it for completeness and asks for what is missing.
- Context. The agent gathers product pages, previous pieces on the topic, the brand voice guide, the approved claims list and persona notes from your marketing platform.
- Outline (gate 1). It proposes an outline and two angles. The content lead picks one or edits it. Nothing long is written before this decision.
- Draft and checks. The agent writes the source-language draft and checks brand voice rules, banned terms, every claim against the approved list, links and readability. Results travel with the draft as a checklist.
- Localisation. Further markets are produced with your glossary and market notes. Native reviewers see a side-by-side view instead of a blank page.
- Editorial approval (gate 2). An editor edits inline in your CMS or document tool. Every change is logged and fed back as an example, so the next draft is closer.
- Claims and legal review (gate 3, where required). Comparative, regulated or sustainability claims go to legal with their sources. Where policy or law requires it, AI-generated content is labelled.
- Publishing. The agent prepares the CMS entry, metadata and DAM assets with licence checks, and schedules. In the first months a person presses publish.
- Feedback. Performance flows back to the brief, and the weekly reporting agent picks it up.
Guardrails and risks in marketing
Brand voice drift
Models default to a generic register. We encode your voice as rules and annotated examples, check every draft automatically and keep an editor as the last word. More in our guide to guardrails for agents.
Claims, comparisons and legal exposure
An agent will happily write “the fastest on the market”. Every factual claim is checked against an approved list; comparative, health, financial or environmental claims go to legal, with the sources attached.
Personal data and consent
Segmentation, personalisation and enrichment run only on data with a valid legal basis under the GDPR: no scraping of personal profiles, data minimisation in prompts, EU hosting where required, a data-protection review before the pilot. Process design, not legal advice.
Disclosure of AI-generated content
The EU AI Act sets transparency duties for AI-generated and manipulated content, and platforms add their own rules. We build labelling into the workflow where it applies and recommend an internal policy checked against the current legal status.
Vanity metrics
More assets is not the goal. We baseline cycle time from brief to publication, cost per asset, first-pass approval rate and pipeline contribution before the pilot, so you can see what changed.
How we start
Marketing assessment
We map your content and campaign workflows, systems and volumes, interview the team and pick the first use case with a measurable baseline. Data protection and brand rules are reviewed here.
Workflow and gate design
We design the workflow with its approval gates, encode brand voice and claims rules, connect the systems and agree the governance with marketing, legal and IT.
Pilot
One team, one workflow, real campaigns. Weekly reviews of quality and metrics against the baseline, gates tuned, editors trained to work with the agent.
Scale and operate
Roll-out to more channels, markets and teams, further use cases from the list above, and managed AI operations to keep prompts, rules and integrations current.
Frequently asked questions
Will the content sound generic or obviously AI-written?
Not if the workflow is built properly. The agent drafts from your briefs, product facts and an encoded brand voice, automated checks catch the usual tells, and an editor has the last word. Edits are fed back as examples, and we track the first-pass approval rate to show the trend.
How do you handle GDPR when the agent works with customer data?
We start with a data map: which fields the agent actually needs, where they come from and on what legal basis. Personal data stays out of prompts unless a use case needs it, tools are hosted in the EU where required, and your data-protection officer reviews the design before the pilot. The legal assessment stays with your counsel.
What happens to our content and campaign team?
Agents remove tasks, not usually roles. Retyping, versioning, reformatting and manual reporting disappear; briefing, editing, judgement about what to publish and maintaining the agent's rules grow. We plan that change with the team lead, including training, and are honest where a role does change.
Do we have to label AI-generated content?
In some cases, yes. The EU AI Act contains transparency duties for AI-generated and manipulated content, advertising and platform rules add their own, and obligations are being phased in. We build labelling into the workflow where it applies and recommend checking the current status with your legal team; our EU AI Act guide gives an overview.
Which tools do you use? We are on HubSpot and a headless CMS.
We are vendor-neutral. Agents connect to marketing automation, CMS, DAM, analytics and ad platforms through their APIs or the Model Context Protocol, and we evaluate the native AI features of your platforms before adding anything new. What we bring is workflow design, guardrails and integration work.
Related pages
Agentic workflow automation
Multi-step business processes automated end to end by LLM-powered agents across ERP, CRM, ticketing and email, with human approval steps built in.
AI governance, EU AI Act & GDPR
An AI register, risk classification under the EU AI Act, GDPR-aligned processes and a usage policy your teams will actually follow, built together with your lawyers and your data protection officer.
AI agents for sales
Inbound qualification, account research, CRM updates from calls and emails, quote and tender drafting, and compliant outreach that reps approve before it leaves.
E-commerce & retail
Product content at scale, customer service for order status and returns, claims processing and catalogue data cleaning, built for peak season.
Let's find the first workflow worth automating.
A 30-minute intro call, no slides and no obligation. We listen, ask about your processes, and tell you honestly where AI agents would pay off and where they would not.