AI Strategy for Marketing
Where AI genuinely helps a marketing team, where it quietly costs more than it saves, and how to put the useful parts into daily work.
Überblick
Marketing teams are being sold AI for everything, and the results are uneven. Some tasks genuinely collapse from hours to minutes. Others produce output that takes longer to fix than it would have taken to write.
We look at what your team actually spends its week on, test where the technology holds up against your own material, and put working practice around the parts that survive. The unglamorous wins are usually the real ones.
Für wen das gedacht ist
- Marketing teams under pressure to use AI without a clear plan for it
- Companies worried about brand voice and accuracy in generated output
- Teams already using AI informally with no standards or oversight
Unser Vorgehen bei AI Strategy for Marketing
Die konkreten Arbeitspakete eines typischen Projekts. Der Umfang steht vorab fest, nichts davon taucht später als Überraschung auf der Rechnung auf.
Task audit
Where the team's hours go, and which of those tasks are actually suited to automation. Frequently not the ones people assume.
Tool evaluation
Tested against your content and your data, not against a vendor demo. Most tools look identical until you use your own material.
Brand voice control
Prompt patterns, reference material and review steps so generated copy sounds like you rather than like everyone.
Workflow integration
Where AI sits in the existing process, who reviews the output, and what never goes out without a human reading it.
Governance and disclosure
What data may be put into which tool, retention terms, and your position on disclosure. Worth settling before it is tested by an incident.
Team training
Practical sessions on your own work, including the judgement about when not to use it, which is the harder half.
Vom ersten Gespräch zum gemessenen Ergebnis
Immer dieselbe Reihenfolge, damit Sie wissen, was als Nächstes kommt.
Audit
Two weeks understanding where the time goes and where quality problems currently come from.
Trial
Test candidate tools on real tasks with real material, and measure against how long it takes today.
Embed
Build the surviving uses into the workflow with review steps, templates and reference material.
Train
Bring the team up on the new practice, then review after a month to see what stuck and what quietly reverted.
Ergebnisse statt Aktenordner
Ein Stapel Dokumente ist kein Fortschritt. Das hier sind die Veränderungen, die die Arbeit bewirken soll.
Time back on real tasks
Measured against a before figure, so the saving is a number rather than a feeling.
Consistent voice
Reference material and review steps mean output does not drift into generic phrasing.
Fewer subscriptions
Testing against your own work usually eliminates most of the shortlist, which pays for the engagement on its own.
A defensible position
Written rules on data, review and disclosure, so the question has an answer when someone senior asks.
Häufige Fragen zu AI Strategy for Marketing
Was Kundinnen und Kunden fragen, bevor sie sich melden. Fehlt Ihre Frage, stellen Sie sie uns direkt.
How is this different from AI strategic consulting?
This is scoped to the marketing function: content, campaigns, reporting and the team's working practice. The broader engagement covers AI across the whole company, including operations and product.
Will AI content hurt our search rankings?
Unedited, generic output will, because it is indistinguishable from everyone else's. Search engines reward usefulness rather than authorship, so the review step and genuine expertise are what matter.
Our legal team is nervous. Can you help with that?
Yes. Data handling, tool terms, retention and disclosure are part of the work, and we would rather involve legal early than present them with a fait accompli.
What if we conclude AI is not worth it for us?
Then you have saved the subscriptions and the disruption, and you have a documented reason. That is a perfectly good result.
AI Strategy for Marketing im Kopf?
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