Enterprise brands just got their very own AI engine.
Adobe Inc. today introduced AI Foundry, a new service that rebuilds its flagship creative‐AI suite Adobe Firefly from the ground up for each client—yes, really customised, not just fine-tuned.
With major names already onboard—including The Home Depot and Walt Disney Imagineering—the move signals that generative AI has entered the “scale for brands” phase.
Key Takeaways
- Adobe’s AI Foundry lets companies reconstruct Firefly with their brand IP and style.
- Foundry versions go beyond image generation—multimodal models (video, audio, graphics) are part of the pitch.
- Enterprises won’t do the heavy‐lifting themselves: Adobe embeds teams to manage data ingestion, tagging and “continuous pre-training.”
- Foundry is positioned above Adobe’s existing “Custom Models” for Firefly (which target single-concept fine‐tuning).
- This service underscores the rising premium on brand control, IP‐safe compliance and scale in generative-AI adoption.
Adobe AI Foundry is a new enterprise service that rebuilds the Adobe Firefly generative-AI model around a company’s own brand assets and style—creating multimodal, bespoke models for image, video and graphic output, managed with embedded Adobe teams and aimed at scaling on-brand content creation.
What is AI Foundry?
In today’s announcement, Adobe described AI Foundry as a “deep-tuning” service for its Firefly AI stack. Rather than simply fine-tuning an existing model on some customer data, Adobe says it will rearchitect Firefly models for each enterprise, embedding that enterprise’s brand tone, image and video style, product set, services, and IP.
Where existing Firefly Custom Models (already available) focus on one “concept” (e.g., a single object or character) and are image-only, Foundry versions are designed to handle multiple concepts and multiple modalities—image, video, audio and graphics.
Adobe will roll the Foundry solution out via its existing Firefly Services API infrastructure.
How it works
According to Adobe’s VP of GenAI New Business Ventures, Hannah Elsakr, the pipeline begins with embedded Adobe staff working with the enterprise to select, secure, tag and ingest that company’s assets. Then the company’s IP is kept separate from Adobe’s base model, and a “continuous pre-training” run is applied to overweight enterprise-specific signals. She calls this “deep tuning” (rather than mere fine-tuning).
Adobe says these rebuilt models are not pared-down or distilled—in fact, with added data they may enlarge Firefly’s parameter scope.
Early customers
- The Home Depot: “We are always exploring innovative ways to enhance our customer experience and streamline our creative workflows. Adobe’s AI Foundry represents an exciting step forward…” — Molly Battin, SVP & CMO.
- Walt Disney Imagineering is cited as another early adopter.
Why it matters
For brands
Until now, most enterprises using generative AI were either working with off-the-shelf models, or doing internal fine-tuning jobs themselves. AI Foundry marks a shift: brands want scale + control + safety, and are willing to partner with AI platform providers to deliver it.
Brands face growing pressure to generate large volumes of visual, video and audio content—across channels and markets—while maintaining their visual identity and protecting IP. According to Adobe’s internal data, 71% of marketers expect content demand to increase more than five-fold by 2027. AI Foundry is pitched as a way to meet that demand without losing brand fidelity or risking IP/licensing landmines.
For the AI ecosystem
This move signals that generative-AI vendors are moving from “models for everyone” to “models for enterprise, custom built”. That means higher margins, longer sales cycles—but also higher stakes (compliance, governance, operation).
Adobe is staking its creative-software legacy on becoming the enterprise creative-AI platform—not just a vendor of image-and-text generators—but a full workflow platform. That’s a different business than “model API calls”.
For competitors
Other players (e.g., OpenAI, Google DeepMind, Microsoft Corporation) may focus on general models, developer tooling, or on-premises foundation models. Adobe’s strength is integrating generative AI into creative and marketing workflows; AI Foundry leverages that. It raises the bar for what enterprise clients expect from model vendors (brand safety, IP control, workflow integration).
How does this compare to Firefly Custom Models?
| Feature | Firefly Custom Models (standard) | AI Foundry |
| Scope | Fine-tune on single concept (object/character) + images only. | Re-architect multi-concept, multimodal model (image + video + audio) built around brand IP. |
| Ownership | Enterprise uses model, Adobe says custom models won’t train base model on enterprise data. | Corporate IP is ingested, but kept separate from base model; enterprise owns output. |
| Implementation | Customer or team can train using Adobe tools. | Adobe works closely as advisor/embedded team, managing the build. |
| Modalities | Primarily image generation. | Image, video, audio, graphics – full creative stack. |
| Positioning | “Accessible customisation” for enterprise creative teams. | “Next-level bespoke models” for strategic brand workflows. |
Risks & open questions
- Cost & complexity: Enterprises may face significant investment (both dollars and internal time) to supply assets, govern workflows and adopt the new model.
- Time to value: These are not plug-and-play; deep tuning will likely take weeks or months to deploy effectively.
- IP and data governance: While Adobe emphasises IP separation and commercial safety, enterprises must still ensure asset rights, ethical uses and governance frameworks.
- Model update path: How will enterprise models keep pace with base-model advancements? Will brands become locked-in?
- Measurement & ROI: Adobe claims major gains via Firefly and its enterprise offerings, but each bespoke project may vary significantly.
What happens next?
For companies evaluating generative-AI strategy:
- Decide whether “do-it-yourself” fine-tuning suffices or you need a fully customised foundation (like AI Foundry).
- Audit your brand-asset library: Do you have licence-clean, high-quality imagery, video and audio for training?
- Address governance: Training multimodal models tied to brand IP implies cross-function collaboration (legal, marketing, creative).
- Evaluate workflow integration: The model is only useful if embedded into your creative supply chain (asset management, review cycles, delivery).
- Monitor results: Track metrics such as time-to-asset, consistency of brand tone, number of variants produced, cost savings.
Conclusion
Adobe’s AI Foundry marks a critical shift in enterprise generative AI—moving from “let’s tweak a model ourselves” to “let’s rebuild a brand-specific model with the vendor”. For big brands with complex creative demands, that’s a meaningful upgrade: scale, brand fidelity and governance. But it also raises the stakes: higher investment, longer cycles and tougher outcomes. As generative AI becomes mainstream, the winners will be those who treat these models as full-scale workflows, not experimentation tools.