AI representative:
Tasks, competencies & advantages for companies
Today, marketing teams can create visual worlds in hours that used to require shoots, agencies and long coordination loops. This is both an opportunity and a risk.
The short answer:
AI image generation in marketing is worthwhile if you need to produce repeatable assets (e.g. social ads, newsletters, product variants) and establish clear quality and approval rules. Use Midjourney for creative image ideas and strong aesthetics, DALL-E for fast iteration directly in the chat and Stable Diffusion if data control and brand consistency are a priority. Legally, transparency (EU AI Act, Art. 50 – from 02.08.2026), clean usage rights for input and a documented approval process are crucial.
The opportunity: faster campaigns, more variants, better personalization.
The risk: “AI look” instead of brand image, shadow use without approval and legal uncertainty, which in case of doubt ends up as a warning letter in the mailbox.
5 key takeaways
- In 2025, AI image generation will no longer be a creative toy, but a production mode: those who need variants, speed and personalization will win, but only with clear quality and approval rules
- The choice of tool is a risk and operational decision, not just a question of aesthetics: Midjourney delivers look, DALL-E/Gemini accelerate iteration and editing, Stable Diffusion brings maximum control – Firefly scores with enterprise workflow and compliance proximity.
- Legal certainty is not created by “the right tool”, but by process: clean input rights, documented use (account/plan/prompt/date) and a labeling strategy for reality-like content (EU AI Act Art. 50 from 02.08.2026).
- The biggest operational bottleneck is brand consistency: without references, a prompt library and a “brand pass”, you get more output faster – but dilute the brand identity with a generic AI look.
- Shadow AI is the standard problem in marketing teams: bans are of little help; a tool traffic light, secure standard workflows and measurement across three KPI levels (efficiency, impact, risk) are effective so that scaling remains controllable.
What does "AI image generation in marketing" really mean and when is it worthwhile?
“AI image generation” sounds like a feature. In practice, it is a new production mode: you swap part of the photo/CGI/illustration for a system of prompting, variant creation and post-processing.
This is particularly rewarding in three situations:
- Firstly, if you need many variants (formats, target groups, countries, A/B tests).
- Secondly, when speed is a real competitive advantage (trend reaction, launch communication).
- Thirdly, if your traditional production is a bottleneck (budget, resources, delivery times).
A sober reality check: AI does not replace your marketing thinking. It replaces waiting time and parts of execution. This is precisely why process design is more important than “the best model”.
What signals show that your team is already using shadow AI?
When designs are suddenly available more quickly, but no one can say how they were created, AI is often already in play. This is not a moral problem, but a governance problem: data and rights migrate out of the company without anyone having made a conscious decision.
Bitkom figures show how widely AI has now arrived in companies and that marketing/communication is one of the most common areas of application.
AI training for your employees?
Which marketing assets can be reliably created with AI image generators?
In marketing, reliable means: “fast enough”, “close enough to the CI” and “without nasty surprises” in the output. Three asset classes work particularly well:
- Social media banners and ad variants: strong impact through image idea, composition and texture.
- Product mock-ups and scenic images: “Product in context” without having to photograph every setting.
- Email headers and campaign visuals: consistent series that are not reinvented every time.
Less suitable (or only with clear guard rails):
- photorealistic people who look “like real people” if you don’t have a labeling/transparency strategy.
- motifs that are sensitive under trademark law (well-known figures, logos, lookalikes).
- anything that is legally or reputationally rem iniscent of “evidence images” (e.g. before and after, medical promises of efficacy).
Mini-FAQ: Assets & Quality
Question 1: Do I still need designers at all?
Yes, but differently. The role is shifting to creative direction, selection, post-production, production capability and quality control.
Question 2: Is AI only for “performance marketing”?
No. It can also be used for brand work, only there brand consistency (recognizability) is the hard currency.
Question 3: How do I prevent “generic AI aesthetics”?
With references (style/structure), fixed prompt modules and a release routine that checks CI criteria, not just “I like it”.
How do you choose the right tool in terms of risk, integration and quality?
A simple thought helps here: you are not just buying image quality. You are buying an operating model.
Midjourney: creatively strong, but in need of explanation on the company side
Midjourney is often used for its aesthetics and image look. At the same time, it is often cumbersome from an enterprise perspective: images are publicly visible (depending on the plan/settings), “private mode” is not the default, and classic admin/SSO mechanisms are limited.
To the tool: https://www.midjourney.com/home
DALL-E in ChatGPT: fast in the workflow, good for iterating "in conversation"
The advantage is the dialog: Briefing → image → correction request → next version. This fits well with marketing processes in which stakeholders provide feedback. How the provider handles business data is also important for companies (e.g. “no training by default” for enterprise offerings).
To the tool: https://openai.com/de-DE/index/dall-e-3/
Stable diffusion: maximum control, but you're really running it
Stable diffusion is attractive if you want to keep data within the company (self-hosted) and if you need to repeat brand objects consistently (e.g. via LoRA). The price is complexity: setup, security, checking models/licenses, operation and responsibilities are up to you.
To the tool: https://stablediffusionweb.com/de
Google Gemini (Nano Banana / Nano Banana Pro): strong in editing, consistency and text in the image
Nano Banana is Gemini’s fast image model (“Fast”) for rapid generation and, above all, practical image editing (local edits, combining photos, character consistency). Nano Banana Pro (“Thinking”) focuses on more control and “pro” features such as better text rendering quality, more precise edit controls (light/camera/aspect) and higher resolution. This is particularly interesting if you want to derive many clean variants from an existing asset (instead of “reinventing” each image). Google also mentions visible watermarks and invisible SynthID watermarking for AI images. This can help with transparency and governance approaches.
To the tool: https://gemini.google/lu/overview/image-generation/?hl=de
Adobe Firefly: Compliance and workflow standard (if Adobe is already your ecosystem)
To the tool: https://www.adobe.com/de/products/firefly.html
Mini-FAQ: Tool & Operation
Question 1: Why is IP indemnification suddenly so important?
Because it contractually answers the question “Who bears the risk?” – and thus makes procurement/legal/marketing more capable of acting.
Question 2: Can I just “allow everything” and clean up later?
This almost always ends in Shadow AI. Better: a green/yellow/red tool traffic light and a fast, secure standard workflow.
How do you create social media banners with Midjourney (step by step)?
Goal: A banner that works in series, not a single “wow” image.
Step 1: Write a briefing that AI understands
Write a mini-briefing:
- Brand/CI (colors as words, style adjectives),
- Target group,
- Message,
- Context (LinkedIn, Instagram, Display),
- format (e.g. 1:1 or 16:9),
- No-gos (e.g. no “stick aesthetic”, no exaggerated faces).
Step 2: Prompt template (can be copied)
Use a template that you later standardize as a prompt library:
“Marketing banner for ⟪Product/Service⟫, target group ⟪…⟫, image idea ⟪Metaphor/Scene⟫, style ⟪…⟫, light ⟪…⟫, background ⟪…⟫, space for headline top left, clean negative space, no writing in the image, high contrast, ⟪aspect ratio⟫”
If you work with style references: Midjourney documents style reference mechanics (e.g. sref) that are interesting for consistent series.
Step 3: 3 rounds instead of 30 variants (discipline beats luck)
Round A: 4 variants based on image idea/composition only.
Round B: select one variant, then fine-tune style/lighting.
Round C: “Brand Pass”: colors, mood, negative space, “does this suit us?”.
Step 4: Export + post-processing (the part that is often forgotten)
The last 15% is almost always done outside: cropping, text, logo, contrast, safe area for mobile. Plan for this – otherwise AI looks “finished” but is not ready for marketing.
Mini-FAQ: Midjourney in the corporate context
Question 1: Are midjourney images publicly visible?
Depending on the plan/setting, generations can appear publicly in galleries; “Stealth/Private” is not automatically standard. You should clearly regulate this in the policy.
Question 2: Can I use this commercially?
This depends on the respective terms/plans. Clarify this centrally (Legal/Procurement) and document which account/plan was used.
How do you create product mockups with DALL-E (step by step)?
Goal: “Product in context” without the product “mutating”.
Step 1: Define product as "source of truth"
If you have a real product: Where possible, work with reference images and masking/editing rather than “purely from text”. The more the model has to guess, the more often shapes/details are invented.
Step 2: Prompt in three levels
Level 1 (Fixed): Product features, material, shape, perspective.
Level 2 (Variable): Scene, background, props, light.
Level 3 (Marketing): Space for copy, eye guidance, “clean” instead of “busy”.
Example prompt (template):
“Create a realistic product mockup for ⟪Product⟫. Keep the shape and proportions exactly the same. Scene: ⟪Use case⟫. Light: ⟪soft studio light / daylight⟫. Background: ⟪clean / gradient / environment⟫. Style: ⟪premium, minimal⟫. Do not distort logos, do not generate additional text, space for headline on the right.”
Step 3: Variant logic for marketing
Create variants based on a hypothesis:
“Which scene increases conversion?” (e.g. usage vs. still life)
“Which color scheme suits segment A vs. B?“
This way, AI becomes an experimental tool instead of just an “image machine”.
Step 4: Compositing as a safety belt
When logos/details are critical: Use AI for background/scene and place product/logo over it as a real asset. This is often the quickest way to “sleep soundly on the legal and brand side”.
Weniger Diskussionen.
Mehr Umsetzung.
Wir bringen Struktur rein und starten mit dem sinnvollsten Schritt.
What do you have to consider legally in Germany or the EU (copyright, licenses, GDPR, EU AI Act)?
This is not a substitute for legal advice, but a practical map for marketing decisions.
1) EU AI Act: Transparency obligations (Art. 50) - relevant from 02.08.2026
The EU AI Act contains transparency obligations, including for deepfakes or artificially generated or manipulated content that must be disclosed. It is crucial that information is provided in a clear and distinguishable manner (see AI Act)
For marketing, this means that if you use “realistic” images that make people/situations appear real, you need a labeling strategy (e.g. disclaimer, metadata/content credentials, internal documentation).
Important: The AI Act will apply in stages; the starting point “applies from” is generally 02.08.2026 (with earlier partial applications for certain chapters).
2) Copyright law in Germany: thinking separately about input and output
Input (training/usage rights):
There are barriers and opt-out mechanisms for text and data mining in German law; at the same time, the practice is in flux due to case law and concrete design (e.g. machine-readable reservation of use). A recent decision by the Higher Regional Court of Hamburg(December 2025) has been widely received in the context of training/data mining and shows that companies should not treat “data origin and opt-out” as a detail.
Output (Who “owns” the image?):
In Germany, copyright protection is typically linked to human creation. Pure AI outputs can therefore be without classic copyright protection, depending on the design; in practice, post-editing/compositing is often used to create a clearly “human-influenced” work. (In case of doubt: legally check and document how the output was created).
3) Contractual reality: IP indemnification can be a deal breaker
One very specific lever is contractual indemnification. Adobe describes Firefly IP Indemnification and defines when it applies (e.g. depending on the feature/export).
For C-Level, this means that tool selection is not just marketing preference, but risk allocation.
4) GDPR & trade secrets: Prompts are data flows
The most common glitch is not the finished image. It’s the briefing in the prompt: product roadmaps, customer data, internal figures, unpublished motifs.
For enterprise offerings, it is relevant whether business data is used for training by default or not ( OpenAI describes “no training by default” for business offerings)
Mini-FAQ: Law & Labeling
Question 1: Do I have to label every AI image?
Not every one. It becomes critical with “reality-like” content/deepfakes (AI Act Art. 50). In practice, a graduated policy makes sense: always label internally, externally depending on realism/risk of confusion.
Question 2: What is the quickest remedy against the risk of a warning letter?
Clean input rights + no copying of protected figures/brands + approval process + documented tool usage (account/plan/date/prompt).
Question 3: What if courts evolve?
Then you benefit from processes that generate evidence (provenance/logs) instead of gut feeling.
How do you set up governance, brand consistency and provenance to make the whole thing scalable?
This is where “pilot theater” separates itself from real production capability.
1) Governance: tool traffic light, roles, approvals
Define minimal:
- Permitted tools/plans (green/yellow/red),
- which data should never be included in prompts,
- who releases (marketing + legal/brand),
- where assets are stored (DAM) and how they are labeled.
The goal is not bureaucracy. The goal is speed without later damage limitation
2) Brand Consistency: References + Prompt Library + "Brand Pass"
If you “re-promote” per campaign, you get a style break.
Better: 10-20 approved “basic recipes” (prompt building blocks), plus reference images. Structure/composition reference functions are documented for Firefly that address precisely this repeatability.
3) Provenance/transparency: content credentials and C2PA
C2PA is an open standard that is intended to map origin and processing as (tamper-evident) metadata. Adobe describes content credentials as a “nutrition label” for content and integrates them into workflows.
This does not solve every platform issue (metadata is sometimes removed). But it is a structured way of making transparency verifiable – both internally and externally.
What does it cost and which KPIs are really useful?
Costs are rarely just license costs. Calculate with three blocks:
- Tool costs (plans, credits, infrastructure if applicable)
- Human time (prompting, selection, post-processing, approval)
- Risk/quality costs (corrections, recalls, brand inconsistency, legal clarification)
KPI set that works in practice
Measure three levels in parallel:
- Efficiency (throughput time, cost per asset),
- Impact (CTR/CVR, CPL/CPA, pipeline contribution),
- Quality/risk (correction rate, brand compliance, policy violations).
A pragmatic pilot: 4-6 weeks, one workflow, clear basis for comparison “before/after”.
Why "cost savings" is only half the story
Some companies communicate savings and scaling effects through generative image processes. Otto, for example, describes generative AI for product images and mentions effects such as higher production volumes and significant cost reductions in certain setups.
For decision-makers, the most important thing is: Can you test and learn faster – without diluting the brand?
Do you need help with your AI strategy?
2 practical examples (clear, realistic, close to SMEs):
Below we show two fictitious but practical examples.
Practical example 1: B2B SME (mechanical engineering, 800 employees) - "Launch in 3 weeks, not 3 months"
Initial situation: Product launch, but no time for a new shoot in all formats/countries.
Procedure:
- Midjourney for mood boards and key visual variants (idea phase, 48 hours).
- DALL-E/Chat-Workflow for fast iteration with product manager + marketing (feedback in dialog).
- Final seatbelt: product photo remains “real”, AI creates backgrounds/scenes; compositing in Photoshop.
- Governance: Tool traffic light (no sensitive briefings in consumer accounts), approval by Brand + Legal.
Result: More variants per channel, significantly shorter loops without the product becoming “wrong”.
Practical example 2: E-commerce (DACH brand, 40 employees) - "Weekly newsletter series without a break in style"
Initial situation: Newsletter needs a header every week that fits the brand, but the design team is small.
Procedure:
- Stable diffusion (self-hosted) for a defined visual language; 3 series templates (season, sale, editorial).
- LoRA (optional) for recurring brand object (e.g. icon/mascot) so that it remains consistent.
- Content Credentials/C2PA, where possible, for internal verifiability and subsequent transparency.
Result: predictable, recognizable series – less “creative stress”, more process.
How do you build up AI expertise in a team instead of stacking pilot projects?
AI will only be stable in everyday life if teams know how to evaluate results and understand limitations. This includes training for specialist departments (interpretation, process integration), IT (operation, monitoring) and governance (data protection, security, approvals).
Plan enablement not as “training at the end”, but in parallel with the pilot. This not only produces a result, but also the ability to operate and develop it further.
Conclusion and outlook
AI image generation in marketing will no longer be a “gimmick” in 2025 , but a production lever if you treat it like production: with tool selection as a risk profile, with brand standards and with a process that generates evidence.
If you are setting up the topic internally: Start with a clear pilot workflow, a tool traffic light and a prompt library. After that, AI will not become “more work”, but a controllable output channel
- Pascal Smoluchowski
- December 17, 2025
FAQ
Usually yes, but the details depend on the tool T&Cs, plan and your input (e.g. whether you use protected templates). Document the account/plan used and clarify rights centrally, not per employee.
Depending on the plan/setting, generations can be publicly visible; “stealth/private” is not automatically the default. This is a classic policy point, because otherwise briefings can be indirectly leaked to the outside world.
Transparency obligations apply to “realistic” synthetic content/deepfakes (EU AI Act Art. 50); in practice, you should define a graduated labeling rule. It is also relevant that the AI Act is generally applicable from 02.08.2026 (with staggered partial deadlines).
In Germany, copyright is typically linked to human creative work. For pure AI outputs, this can mean weaker protection – hence often the workaround of human post-editing/compositing and clean documentation.
Shadow AI: Employees use private accounts/tools, copy briefings into them and create assets without a chain of custody and approval. This is not so much “evil” as a system error that you can rectify with secure standard tools and clear rules.
Work with references (style/structure), fixed prompt building blocks and a brand pass (checklist) that checks each series. Structure/composition reference functions that support repeatability are explicitly described for Firefly.
This is a contractual indemnification: the provider assumes (under certain conditions) legal risks arising from use. In enterprise environments, this is often a selling point because it makes risk predictable.
On-prem helps with data protection and data sovereignty, but not automatically with license and model issues. You need to set up model/weight licenses, data sources and governance properly yourself.
C2PA is an open standard for provenance/authenticity; content credentials are a common metadata approach to make provenance and processing transparent. This is not a panacea, but it is an important building block for verifiability and trust.
Take a workflow (e.g. social ads), define 3 KPIs (efficiency/impact/risk) and build a prompt library with 10 approved recipes. Then scale up to the next asset type.
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