In a recent InPublishing AI Special "AI in practice: Image workflows", Fotoware Chief Product Officer Janniche Moe wrote an article how publishers are approaching AI in image workflows. 

Takeaways from the article: 

How are publishers using AI in image workflows?

Publishers are primarily using AI to automate metadata tasks rather than generate or manipulate editorial images. AI can identify objects, people, locations, or events in a photo and automatically add keywords, descriptions, and metadata. This helps teams organize content, improve searchability, and reduce manual work.

Many publishers are also using AI alongside workflow automation, such as routing images to the correct archives, desks, or publication systems based on recognized content.

Are publishers using AI to create news images?

Most publishers are approaching AI-generated imagery cautiously. While AI can support parts of the workflow, many news organizations are placing a greater emphasis on proving image authenticity and documenting content provenance.

The focus is increasingly on demonstrating that an image is genuine, where it came from, and what happened to it throughout its lifecycle.

What is image provenance?

Image provenance is the documented history of a digital image from capture to publication.

It includes information such as:

• Where the image originated
• Who created it
• What edits were made
• Which tools were used
• Whether AI was involved
• When changes occurred

Provenance helps publishers verify authenticity and build trust with audiences.

Image provenance vs AI detection: What's the difference?

Image provenance is evidence-based, whereas AI detection relies on probability. 

AI detection tools estimate whether an image may have been generated or altered using AI, but their conclusions are based on probability.

Image provenance is evidence-based, providing a verifiable record of where an image came from, how it was handled, and what changes were made throughout its lifecycle. For organizations that depend on trust, provenance offers stronger proof than detection alone.

What is C2PA?

C2PA (Coalition for Content Provenance and Authenticity) is an open standard for documenting the origin and history of digital content.

It uses cryptographically signed metadata to create a record of how a file was created and modified. This allows publishers, agencies, and audiences to inspect provenance information and understand whether content has been altered.

Why is C2PA important for publishers?

C2PA helps publishers address growing concerns about misinformation, manipulated images, and deepfakes.

By preserving provenance information throughout the content lifecycle, publishers can provide greater transparency about where images come from and how they have been edited.

This supports editorial integrity and helps rebuild trust in visual media.

Can an image still be authentic if it has been edited?

Yes.

Normal editorial adjustments such as cropping, color correction, or resizing do not automatically make an image inauthentic. The important factor is transparency.

Publishers should be able to show what modifications were made, when they occurred, and which tools performed them. A provenance record provides this context.

How does a DAM support image authenticity?

A Digital Asset Management (DAM) system helps preserve, manage, and expose provenance information throughout the content lifecycle.

A DAM should:

• Preserve provenance records and cryptographic signatures
• Track image edits and workflow actions
• Document AI involvement
• Make provenance information easy to inspect
• Enable filtering based on authenticity or provenance status

This gives editors confidence that they are working with trustworthy content.

Should a DAM track AI involvement?

Yes.

AI can be involved in many parts of an image workflow without directly changing the image itself. For example, AI may generate keywords, captions, descriptions, or metadata.

Organizations need visibility into where AI was used, what role it played, and how it contributed to the final asset. Transparency helps support compliance, editorial standards, and audience trust.

How can publishers document AI use in image workflows?

Publishers should maintain a clear record of when and where AI was used.

This could include:

• AI-generated keywords
• Automated metadata enrichment
• AI-assisted classification
• AI-generated descriptions
• AI-based workflow decisions

The goal is not simply to detect AI, but to document its role and make that information available for review.

How can publishers build trust when using AI?

Trust starts with transparency.

Organizations should clearly document AI involvement, maintain provenance records, and ensure that editorial teams can inspect how content was created and processed.

When audiences, editors, and partners can understand where content came from and how it was handled, confidence in the content increases.

What should publishers consider before implementing AI?

Publishers should evaluate AI as part of a broader content ecosystem rather than as a standalone tool.

Successful implementations typically:

• Follow common standards across systems
• Preserve provenance information
• Maintain metadata consistency
• Support transparency and accountability
• Integrate with existing workflows

A fragmented approach can make it difficult to track content history and AI involvement.

How can AI and workflow automation work together?

The greatest efficiency gains often come from combining AI with workflow automation.

For example, AI can identify people, objects, or events within an image. Automated workflows can then use that information to classify content, enrich metadata, route files, or trigger downstream processes.

When provenance records are preserved, organizations can document both the AI analysis and the resulting workflow actions.

Why is transparency about AI important?

Transparency is increasingly becoming both a business requirement and a trust requirement.

Publishers rely on credibility. If audiences cannot determine whether content is authentic or understand how AI was used, confidence can be undermined.

Documenting AI involvement and maintaining content provenance helps organizations demonstrate accountability and protect trust over time.

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Image provenance in Digital Asset Management

Organizations need more than AI tools. They need systems that preserve provenance, maintain metadata integrity, and provide transparency throughout the content lifecycle.

Content Authenticity
Metadata Management
Workflow Automation
Fotostation
DAM Platform