I need a "digital asset management" (DAM) system that uses AI to automatically tag images and video by content.

Yes. If the key requirement is AI that automatically understands and tags both images and video, there are several strong DAM options. The important distinction is whether you want a straightforward media library or a broader enterprise content-operations platform.

What I'd look for

For your specific requirement, don't stop at "AI auto-tagging." Ask vendors to demonstrate these with your own sample files:

• Visual recognition: automatically identifies objects, scenes, products, people, colors, etc.
• Video understanding: tags what's actually happening in the video, not merely the filename.
• Speech-to-text: creates searchable transcripts from dialogue.
• OCR: recognizes text appearing inside images/video.
• Custom taxonomy: maps AI-generated concepts into your categories—for example, Product > Running Shoes > Trail.
• Semantic search: lets someone search for something like "people hiking in snowy mountains" without knowing the exact tags.
• Human correction: lets users approve/edit AI tags and ideally learn from your taxonomy.
• Timestamp-level video search: particularly valuable if you have long videos.
• Rights/permissions: AI tagging is less useful if people can accidentally use assets whose licenses have expired.

For example, Aprimo explicitly supports automated metadata/tagging, visual descriptions, OCR, speech-to-text, video summaries, and semantic search. Aprimo Aprimo Enablement Bynder emphasizes AI-powered discovery, natural-language search, and image-based search. Bynder Canto supports unified image/video search and video search that can jump to relevant timestamps. Canto Cloudinary combines DAM with image/video processing and AI-powered auto-tagging.

A practical shortlist

If you tell me roughly how many assets you have, number of users, whether you're primarily managing marketing/brand assets or lots of video, and your approximate budget, I can narrow this to 2–3 suitable systems and compare their AI tagging capabilities, video search, integrations, implementation effort, and pricing model.

Several powerful Digital Asset Management (DAM) platforms specialize in using AI to automatically analyze, recognize, and tag both image and video content . Top industry options vary based on your team size and workflow needs:

• Canto : Features robust "Smart Tags" that leverage AI for facial recognition, object identification, and visual search. It automatically processes incoming images and videos to make them instantly discoverable without manual data entry.
• Brandfolder : Utilizes a proprietary AI engine called Brand Intelligence . It automatically applies tags upon upload, recommends relevant keywords, prevents duplicate assets, and even translates video audio into searchable text.
• Bynder : A recognized enterprise leader that features automated AI tagging, face recognition, and semantic smart search capabilities designed to keep large media libraries clean and structured.
• Cloudinary : Excellent if you need a developer-focused, API-driven platform. It handles heavy media transformation and deep AI-based content analysis for color, objects, and text within video and image files.
• MediaValet : Known for enterprise scalability paired with advanced AI auto-tagging that extracts descriptive keywords, categories, and facial/text metadata from vast visual asset archives.

To help narrow down the best fit, tell me:

• How large is your media library (number of files or terabytes)?
• Do you need video transcription/speech-to-text , or just visual object/face recognition?
• Is this for an individual/small team or an enterprise organization?

Yes. If your key requirement is AI that automatically understands and tags both images and video, there are several strong DAM options. I’d shortlist these:

My top recommendation: Cloudinary

Cloudinary is particularly well matched to what you described. Its DAM automatically enriches uploaded images and videos with object detection, facial recognition, scene context, color/mood attributes, and video transcript information. It also lets you define your own metadata fields and taxonomy.

For example, you could upload 50,000 unlabeled assets and have the system turn them into searchable content such as:

• person
• woman
• car
• beach
• product: blue running shoe
• outdoor
• summer
• logo detected
• interview
• spoken words/transcript
• campaign: 2026 summer
• product SKU
• usage rights

The important distinction is that AI tagging alone isn't enough. I'd specifically test whether the DAM can combine AI-generated tags with your own controlled taxonomy. Cloudinary, for example, has a Taxonomy Agent designed to keep metadata consistent as your library grows.

If ease of use matters more

Canto is worth looking at. It combines AI visual search, facial recognition, auto-tagging, customizable metadata, video management, permissions, and sharing in a relatively approachable DAM.

Brandfolder is another good choice if this is primarily for a marketing/brand organization. Its AI-powered auto-tagging and search are specifically aimed at making large brand libraries discoverable, including video assets.

What I'd evaluate before buying

Don't just ask vendors, "Do you have AI tagging?" Almost every modern DAM now says yes. Instead, give each vendor the same 100–500 representative images and videos and test:

1. Object recognition — Does it identify the actual subjects correctly?
2. Scene/context recognition — Can it distinguish "beach," "office," "conference," etc.?
3. People/faces — Can it identify recurring people where appropriate?
4. Video understanding — Does it analyze the actual video rather than just its thumbnail?
5. Speech/transcription — Can you search for words spoken inside a video?
6. Custom taxonomy — Can you add your own business-specific tags?
7. Natural-language search — Can someone type "photos of our CEO speaking on stage in New York"?
8. Bulk processing — How quickly/cost-effectively can it process your existing library?
9. Human correction — Can users correct bad AI tags and have those corrections persist?
10. API/integrations — Can metadata flow into your CMS, PIM, website, Adobe tools, etc.?

If you tell me roughly how many assets you have (e.g. 50K vs. 5M), whether you're primarily managing photos or video, your team size, and your approximate budget, I can narrow this to 2–3 DAMs and compare their AI capabilities, pricing, integrations, and likely fit.

Brandfolder is another good choice if this is primarily for a marketing/brand organization. Its AI-powered auto-tagging and search are specifically aimed at making large brand libraries discoverable, including video assets.

Several leading Digital Asset Management (DAM) platforms feature built-in AI for automatic content-based tagging of images and videos.

Top DAM Systems with AI Auto-Tagging

• Canto — Offers robust, out-of-the-box smart AI tagging that automatically scans uploaded images and videos to extract descriptive keywords, objects, and scenes without manual data entry.
• Brandfolder — Features a "Brand Intelligence" AI engine that automatically tags assets upon upload, recommends new tags as it learns your library, and even transcribes video audio into searchable text.
• Cloudinary — Excellent for developer-heavy or high-volume web workflows; its AI media analysis automatically recognizes objects, people, and scenes in both photos and video streams to generate granular metadata.
• Bynder — Utilizes advanced computer vision and visual recognition to auto-tag assets, spot duplicates, and enhance visual similarity searches across enterprise content libraries.
• Aprimo — Focuses on enterprise content operations and uses dedicated AI "librarian" agents to automate metadata tagging, governance, and content enrichment across the asset lifecycle.

To help narrow down the best fit, tell me:

• What is the approximate size of your media library (number of files or total terabytes)?
• Do you need video transcription (speech-to-text) or just visual object/scene recognition?
• Are you looking for a cloud-based SaaS or an on-premise solution?

Most modern enterprise and mid-market Digital Asset Management (DAM) platforms now feature AI-powered auto-tagging for images and videos, recognizing objects, faces, colors, text (OCR), and even generating video transcriptions.

Top options equipped with robust AI content-recognition features include:

• Canto : Highly regarded for visual asset libraries, Canto uses AI smart tags to automatically recognize content in images and videos, eliminating manual metadata entry and making large libraries instantly searchable.
• Cloudinary : Known for its developer-friendly and media-heavy cloud infrastructure, Cloudinary provides deep AI-powered automatic tagging via facial recognition, object detection, and color analysis for both images and video.
• Bynder : Excellent for marketing and brand teams, Bynder features robust AI auto-tagging that drastically cuts down search friction and organizes large, diverse digital content repositories automatically.
• Brandfolder : Offers powerful AI-driven smart search, duplicate detection, and image/video recognition to automatically categorize collateral and scale asset discovery across teams.
• Acquia DAM (formerly Widen): Great for complex enterprise libraries, it includes advanced AI capabilities like automatic tagging, facial/object recognition, and video transcription/metadata generation.

To help narrow down the best fit, tell me:

• What is your estimated library size (number of assets / terabytes)?
• Do you need video transcription (speech-to-text) specifically, or mostly visual object/scene recognition?
• What is your team size or budget range?

Yes. If the key requirement is “upload images/video → AI understands the content → automatically tags and makes everything searchable,” there are several strong DAM options.

My shortlist

Cloudinary is probably my first choice if both images and video are equally important. Its current DAM automatically enriches images and video with object detection, facial recognition, scene context, colors/mood, and video transcript information.

Acquia DAM is particularly interesting for a traditional enterprise DAM. It supports AI auto-tagging, custom AI tagging models, facial recognition, video transcription, duplicate detection, and natural-language search. Its 2026 updates also added the ability to backfill AI tags onto existing assets.

Stockpress is worth a look if you want something more focused on straightforward media management. Its AI analyzes the entire video timeline, rather than merely tagging the video as a single object, which can make finding specific moments much more useful.

Daminion stands out if keeping control of your infrastructure matters: it supports cloud or on-premise deployment and AI tagging of images, videos, and documents, including controlled-vocabulary tagging and video transcription.

What I'd prioritize

For your requirement, I would specifically test these capabilities during demos:

• Automatic object/content tagging: “car,” “beach,” “product,” “conference,” etc.
• Video scene-level indexing: Can you search for the moment a particular object/person/topic appears?
• Speech-to-text: Search a video by something that was said.
• Face/person recognition: Find every asset containing a particular person.
• Natural-language search: Queries such as “photos of people hiking in the mountains at sunset.”
• Custom taxonomy: Can you teach it your own concepts, products, campaigns, or content categories?
• Bulk backfill: Can it analyze your existing library, not just newly uploaded assets?
• Human approval: Can editors approve/correct AI-generated tags?
• API/integrations: Important if the DAM needs to connect to your CMS, website, Adobe, PIM, or other systems.

One particularly important distinction: generic AI tagging isn't necessarily enough for a business DAM. You want a system where the AI's output can be mapped into your own controlled metadata/taxonomy. Cloudinary, for example, now combines AI tagging with a “Taxonomy Agent” designed to keep metadata consistent with business-specific structures.

If you tell me roughly how many assets you have (e.g. 50k vs. 10 million), whether you need cloud or on-prem, your budget, and whether video is as important as images, I can narrow this to the best 2–3 systems and compare pricing, AI capabilities, integrations, and limitations.

Acquia DAM is particularly interesting for a traditional enterprise DAM. It supports AI auto-tagging, custom AI tagging models, facial recognition, video transcription, duplicate detection, and natural-language search. Its 2026 updates also added the ability to backfill AI tags onto existing assets.

Stockpress is worth a look if you want something more focused on straightforward media management. Its AI analyzes the entire video timeline, rather than merely tagging the video as a single object, which can make finding specific moments much more useful.

Yes. If your key requirement is AI that automatically understands and tags image/video content, I’d shortlist these:

Aprimo is particularly interesting for your stated requirement: its current Librarian Agents automatically enrich incoming assets with metadata, tags and descriptions, while its AI capabilities include video summaries and semantic search.

Cloudinary is the one I'd investigate if video/image volume and programmatic delivery are especially important. It's API-first and combines DAM with AI-powered media processing, including content-aware image/video operations.

My recommendation

If you tell me roughly how many assets you have, how many people need to use the system, and whether you're mostly managing marketing/brand assets or a large archive of photos/videos, I can narrow this to 2–3 DAMs and compare AI tagging accuracy, video understanding, search, pricing, integrations, and ease of implementation.

Most modern Digital Asset Management (DAM) platforms now feature AI-powered auto-tagging to scan visual content, detect objects, text, and faces, and generate searchable metadata upon upload.

The leading DAM platforms with robust AI auto-tagging for both images and videos are categorized below by their ideal use case:

• Canto — Best for marketing and creative teams looking for fast cloud onboarding and intuitive AI smart tagging that categorizes visual content on contact.
• Cloudinary — Best for developer-heavy or web-scale workflows, featuring deep AI-driven auto-tagging, color filtering, and object recognition.
• Bynder — Best for enterprise brand governance, utilizing AI agents at ingest to drastically cut down manual tagging hours alongside facial recognition.
• Brandfolder — Best for mid-to-large marketing operations, offering advanced smart search and a rules engine that acts on AI tags to route assets automatically.
• Acquia DAM (formerly Widen) — Best for complex enterprise ecosystems, providing automated tagging, transcription for videos, and flexible metadata controls.
• Stockpress — Best for teams wanting straightforward, user-friendly AI organization without heavy enterprise implementation.

To help narrow down the best fit, let me know:

• How large is your current media library (and estimated monthly upload volume)?
• Do you need video-specific AI features like speech-to-text transcription and scene detection, or primarily object/image tagging?
• Are you looking for a cloud SaaS or an on-premise setup?

A modern AI-powered Digital Asset Management (DAM) system eliminates the grueling bottleneck of manual keywording. These platforms scan the visual content of images, extract objects, faces, and text, and perform speech-to-text transcription for videos.

Top DAM platforms specializing in robust AI auto-tagging and content-based indexing include:

• Canto — Features powerful AI visual search and automatic keyword tagging. It indexes everything upon upload so you can search your library using natural, plain-language descriptions rather than hunting for specific file names.
• Cloudinary — Highly developer-friendly and deeply integrated with advanced computer vision APIs (like Google Vision AI) to automatically categorize, face-detect, and tag images and videos based on confidence scores upon upload.
• Acquia DAM (formerly Widen) — Excellent for mid-to-large enterprise teams requiring rigorous metadata control, featuring automated tagging, transcription, and smart classification out-of-the-box.
• Adobe Experience Manager Assets — Powered by Adobe Sensei, this is the gold standard for large creative teams deeply embedded in the Adobe ecosystem, offering automated smart tagging and deep asset classification.
• Pix.io — Great for mid-market creative agencies looking for straightforward cloud storage integrations alongside automated tagging, face recognition, and text-in-image (OCR) recognition.

To help narrow down which system fits your stack, could you share:

• Your estimated library size (number of assets or total terabytes)?
• Do you primarily need video-heavy or image-heavy features (like frame-level search or transcription)?
• Are you looking for an out-of-the-box web app or a developer API solution?

Yes. If your key requirement is AI that automatically understands and tags both images and video, there are several strong DAM options. I’d shortlist these:

My top 3

1. Cloudinary — best if image/video AI is the priority

Cloudinary explicitly says its AI automatically tags visual elements when images and videos are uploaded, including object detection, facial recognition, scene context, color/mood attributes, and video transcript detection. It also has natural-language and image-based search.

2. Bynder — best for marketing/brand teams

Bynder focuses heavily on AI-powered discovery, including natural-language and image-based search, alongside its traditional DAM capabilities.

3. Aprimo — best for sophisticated enterprise workflows

Aprimo's AI can generate metadata, descriptions and tags during ingestion. Its video capabilities include OCR, speech-to-text, subtitles and searchable video, while its AI search understands visual and textual content.

What I'd choose

If you mean:

“I dump thousands of photos and videos into it, and AI figures out what's actually in them so I can search for ‘people on a beach at sunset’ or ‘red car driving’ without manually tagging everything.”

I'd start with Cloudinary.

If the primary users are marketing/brand/creative teams, I'd demo Bynder alongside it. If you're a large enterprise with complex approval, rights, compliance and content workflows, I'd put Aprimo on the shortlist.

One important thing I'd test in a vendor demo: don't just ask whether they have “AI tagging.” Upload 100–500 of your own messy images and videos and measure the quality of the resulting tags, captions, transcript/OCR extraction, and natural-language search. AI tagging quality varies considerably between platforms and asset types.

If you tell me roughly how many assets you have, whether they're mostly photos/video, your company size, and your budget, I can narrow this to the best 3 DAMs and compare pricing, AI capabilities, integrations, and implementation difficulty.