This content has been automatically translated and may include minor variations.
AI is no longer experimental. It is operational.
That is one of the clearest findings from Kristina Huddart’s latest report, 2026 State of AI in DAM and Content Operations. Nearly 80% of organizations are already using AI within their business operations, but only 54% say they are successful with it.
For me, that gap is the real story — and it reflects something I am seeing play out across the market right now. The hype is giving way to harder questions about measurable value and tangible results. Enthusiasm alone is no longer enough.
As Product Director at Papirfly, I speak with marketing and brand teams every week who are under pressure to scale content faster, localize campaigns, and maintain brand consistency across more channels than ever before. But the organizations seeing meaningful results are not simply adopting AI tools. They are building AI-ready content operations — with strong foundations, quality data, and clear internal policies in place before AI ever enters the picture.
That distinction matters.
Why AI adoption isn’t the same as AI content success
One of the strongest themes in the report is the shift from enthusiasm to operational reality.
In 2024, most organizations were still experimenting with AI. By 2026, 79% are actively using it. But widespread usage has also exposed a difficult truth: many businesses are not structurally prepared for AI at scale.
79%
of organizations actively use AI in business
Source: Huddart Consulting, 2026
79%
of organizations actively use AI in business
Source: Huddart Consulting, 2026
I think part of the problem is that organizations are moving from pure experimentation toward AI adoption without clear intent. Leaders need to be asking what specific pain points they are solving and what return on investment looks like — not simply applying AI to every process because it is available.
AI can accelerate content creation, metadata tagging, search, and workflow automation. But when organizations lack clean metadata, connected systems, approval structures, or governance, AI simply amplifies operational chaos faster.
Organizations with fully embedded AI are achieving 77% success rates, while those still experimenting report only 35% success. The difference is not access to AI tools. The difference is operational maturity.
How DAM becomes the foundation for scalable AI workflows
AI in content operations does not live in one platform. Modern marketing ecosystems are deeply interconnected, spanning DAM systems, campaign management tools, CMS platforms, localization workflows, and creative production environments. AI operates across that entire stack.
The challenge is that 48% of organizations are still using standalone AI tools with no integration into their core systems. That creates short-term outputs, but not scalable operational outcomes.
This is where Digital Asset Management software becomes far more strategic than many organizations initially realize. A modern DAM is no longer just a storage system — it is the operational foundation for AI-ready content operations. And the quality of data within it matters enormously. In our own testing, generic or overly broad metadata significantly reduces the relevancy of AI search results, while specific, niche metadata improves the machine’s ability to interpret and match content accurately.
That means investing in:
- Centralized asset governance
- Consistent, specific metadata structures
- Approval and workflow management
- Controlled brand access across regions and teams
- Searchable, AI-enriched content libraries
- Integration between DAM, campaign workflows, and content creation
These are operational requirements — not optional enhancements.
Where AI is delivering real results in DAM today
The strongest AI results are happening in operational workflows: metadata tagging and enrichment, workflow automation, search and discovery, and content creation support.
58%
of organizations report productivity and efficiency gains from AI
Source: Huddart Consulting, 2026
58%
of organizations report productivity and efficiency gains from AI
Source: Huddart Consulting, 2026
But I think the more interesting shift is in how DAM itself is evolving. It is moving from a place of record to a place of action. Rather than requiring teams to manually search through vast content libraries, AI should be acting as a creative partner — suggesting relevant assets, checking for brand consistency, and prompting users during the creation process. That is the direction we are building toward at Papirfly.
For global organizations managing large volumes of localized content, this matters even more. The goal is to empower local teams to move quickly while governance and brand consistency are maintained automatically — not manually policed.
The biggest challenge is not AI. It is trust.
Metadata quality, governance, integration complexity, and unclear ownership all point toward the same issue: organizations do not yet fully trust their AI content operations software.
That hesitation is understandable — and it mirrors something we have seen before. When the industry pushed hard to become “data-driven,” many companies had the ambition but not the trust in their own data infrastructure to act on it confidently. AI adoption is following the same pattern.
AI systems are only as reliable as the operational structures behind them. Poor metadata leads to poor recommendations. Weak governance increases compliance risk. Disconnected workflows create inconsistency. Organizations need clear standards around asset ownership, metadata consistency, workflow approvals, brand governance, AI usage policies, and user access controls.
AI cannot create operational clarity on its own. It depends on it.t is exposing the strengths and weaknesses already there in your content operations.
What do AI‑ready content operations actually look like?
Kristina Huddart’s report is refreshingly practical. And its core finding aligns with what I believe: the organizations that succeed with AI will not necessarily be the ones with the most tools. They will be the ones that have done the harder, less glamorous work of building strong operational foundations.
That also means being intentional about what you finish, not just what you start. AI makes it easier than ever to prototype and spin up new initiatives — but teams that chase every capability risk ending up overwhelmed and underdelivering. The focus has to be on completing what actually delivers value.
AI is not replacing content operations. It is exposing which organizations have built content operations capable of scaling into the future.
See how Papirfly supports AI‑ready content operations
Connected DAM, governance, and scalable workflows.
See how Papirfly supports AI‑ready content operations
Connected DAM, governance, and scalable workflows.
Connected DAM, governance, and scalable workflows.
FAQs
What is AI in content operations?
AI in content operations refers to the use of artificial intelligence to automate, enhance, and scale the processes involved in creating, managing, and distributing content. This includes tasks like metadata tagging, workflow automation, search and discovery, and content creation support.
Why are so many organizations struggling to get results from AI?
Widespread AI adoption has exposed a gap between using AI tools and being operationally ready for them. Organizations without clean metadata, connected systems, or strong governance find that AI amplifies existing inefficiencies rather than solving them.
How does a DAM system support AI content workflows?
A modern DAM acts as the operational foundation for AI-ready content operations. It centralizes assets, structures metadata, governs usage rights, and connects workflows — giving AI the clean, organized environment it needs to deliver consistent results.
What does AI-ready content governance look like?
AI-ready governance includes clear standards around asset ownership, metadata consistency, workflow approvals, brand guidelines, AI usage policies, and user access controls. Without these structures in place, AI outputs become unreliable and difficult to scale.
What’s the difference between using AI tools and having an AI-ready content operation?
Using AI tools means adding AI capabilities to existing workflows. An AI-ready content operation means the underlying systems — DAM, metadata, governance, integrations — are structured to support AI at scale. The first creates isolated outputs; the second creates sustainable operational outcomes.
Table of contents: