The Other Side of Scarcity: Rethinking DAM in an Age of Abundance

As AI makes content creation increasingly accessible, the value of digital asset management is shifting. The question is no longer simply how much content we can produce and manage, but how intelligently we decide what to create, use, and deliver. 

At Henry Stewart DAM New York 2026, gateB CEO Sarah Iskander opened the conference with a deceptively simple question:

“What happens when scarcity disappears?”

Her keynote, The Other Side of Scarcity: Rethinking DAM in an Age of Abundance, challenged attendees to reconsider the fundamental economics that have shaped digital asset management for decades. 

The premise was simple: when technology removes one constraint, it doesn’t necessarily eliminate the problem. It moves the bottleneck somewhere else. And when the bottleneck moves, value moves with it. 

From 36 exposures to 22,640 photographs

Remember when taking a photograph required a little thought?

A roll of film gave you 36 exposures. Every time you pressed the shutter, you made a decision: Was this moment worth capturing?

Today, Sarah has 22,640 photographs on her phone. Some are meaningful memories. Others are screenshots she’ll never revisit, countless pictures of food, and a dozen nearly identical sunsets. 

We’ve effectively eliminated the scarcity of photography. But in doing so, we’ve created a different problem. The challenge is no longer taking the photograph. It’s knowing which photographs matter. The same shift is happening in marketing. 

For decades, producing high-quality content required significant investments of time, money, and creative resources. Photoshoots, video production, localization, and campaign adaptations were expensive. DAM emerged, in large part, to help organizations maximize the value of those investments: store the asset, find it, reuse it, distribute it.

The more effectively organizations could reuse their content, the greater the return on their production investment. 

But the economics are changing. AI and automation are dramatically reducing the marginal cost of producing additional content. One image can become 10 variations. A single campaign can be adapted across dozens of markets, audiences, and channels. The initial creative investment still matters. But producing the next version is becoming faster, easier, and less expensive. 

Content is becoming abundant.

And that raises a much more interesting question:

When content itself is no longer the primary constraint, where does the value move?

1. Attention: What deserves to be seen?

For years, marketing organizations have invested in producing content at scale. More channels. More formats. More personalization. More localization. More content. 

That investment made sense when production capacity was a limiting factor. But what happens when every brand can create content at scale?

When generating a thousand variations becomes relatively easy, the ability to produce those variations is no longer much of a competitive advantage. Because while the supply of content can grow exponentially, the audience’s capacity to consume it cannot. 

As Sarah put it during her keynote:

“Your customer’s day didn’t get any longer. Their feed didn’t get any less crowded. Their brain didn’t suddenly gain more processing capacity because you implemented generative AI.”

More content does not equal more attention. In an environment where audiences are overwhelmed by competing messages, simply increasing content output is unlikely to deliver a corresponding increase in engagement. This changes the question marketers need to ask. 

Instead of:

How do we create enough content?

We need to ask:

How do we create something worth someone’s attention?

That distinction matters for DAM. The next generation of content operations cannot be measured solely by how many assets an organization can produce, manage, or distribute. Those capabilities remain important. But they are increasingly table stakes. 

The more meaningful measure is whether the content ultimately delivered is relevant enough to earn attention. And that leads to the second shift. 

2. Decision: What should we do?

Think about how we consume music. Not long ago, our music libraries were limited to the CDs we owned. If we wanted something new, we had to find it, purchase it, or borrow it. Access was the constraint. 

Then streaming changed everything. Today, services like Spotify and Apple Music give listeners access to catalogs containing millions of songs. But having access to more music isn’t necessarily what makes the experience valuable. The real value increasingly comes from helping listeners decide what to play. A recommendation that fits your taste, your mood, or what you’re doing at that moment can be more useful than access to another million songs. 

When access became abundant, the value shifted toward intelligent recommendation.

Sarah argued that a similar shift is underway in digital asset management. Historically, one of DAM’s most important contributions was helping organizations answer two questions: What do we have? And can we find it? 

Those questions remain relevant. But when organizations can create new assets and variations almost instantly, simply finding another option becomes less differentiating. The more valuable question becomes: What should we do? 

Which asset should we use? For which audience? In which market? On which channel? Should we reuse something that already exists, adapt it, or generate something new? And what information should inform that decision?

Context. Audience preferences. Brand guidelines. Usage rights. Historical performance. Business objectives. None of these concepts is new. DAM professionals have been discussing metadata, governance, personalization, and content relevance for years. What is changing is their relative value. 

As creation gets cheaper, decision becomes more valuable.

The ability to generate more options is useful. But the ability to identify the right option is increasingly where organizations can differentiate themselves. For DAM leaders, this suggests a shift in how we evaluate technology. Rather than asking only whether a platform helps users find assets faster, we should ask: Does it help the organization make better decisions about which assets to use, when, where, and why? 

From Digital Asset Management to Digital Asset Intelligence

The DAM community has been debating the relevance of its own acronym for years. As digital experiences evolve, the boundaries of what constitutes an asset continue to expand. Content is no longer always a static file. It can be dynamically assembled, personalized, adapted, or generated in response to a specific need. And as the nature of assets changes, so does the role of the systems designed to manage them. 

During her keynote, Sarah proposed a different way to think about the category:

What if DAM evolved into DAI: Digital Asset Intelligence?

Not AI as in artificial intelligence. Intelligence in the broader sense: understanding what deserves attention and helping organizations make better decisions.

That isn’t simply about adding an LLM to a DAM interface or introducing AI-powered search. It’s about using the information surrounding digital assets to inform what happens next. 

What DAI looks like

Consider the difference: a traditional DAM might tell you how your organization has 200 approved product images available for a campaign. An intelligent content ecosystem should increasingly help you determine which image is most appropriate for a particular audience, channel, market, or business objective. It should understand relevant context, respect usage rights and brand requirements, and draw on available performance information to support that recommendation. And it should help teams decide whether the best course of action is to reuse an existing asset, adapt one, or create something entirely new. 

That intelligence doesn’t come from AI alone. It depends on the foundations DAM teams have been building for years: metadata, taxonomy, governance, integrations, and increasingly, the ability to connect assets with their business context and outcomes. 

AI can help put that information to work at a scale that was previously difficult to achieve. But technology alone doesn’t make an organization intelligent. The opportunity is to evolve from systems that primarily manage what we have to systems that help us decide what to do with it. 

Looking ahead

For organizations evaluating their DAM strategies, that’s a meaningful change in perspective. The next era of DAM isn’t about managing more. The move toward content abundance doesn’t make DAM less important. It changes what makes it valuable. 

Organizations will still need reliable repositories, governance, rights management, distribution capabilities, and integrations. But as content production becomes easier, the greatest opportunities will lie beyond simply increasing the volume of content those systems can handle. 

The focus shifts toward two questions:

Attention: Are we creating and delivering something worth noticing?

Decision: Are we making better choices about what to create, use, and deliver?

There aren’t new challenges. They’re familiar challenges viewed through a different economic lens. And that was the central message Sarah left with attendees at DAM New York:

“When scarcity disappears, the bottleneck moves. And value moves with it.”

The future of DAM may not be defined by how much more content organizations can produce or store. It may be defined by how much smarter they become about what they already have, what they choose to use, and what they create next. 

The bottleneck has moved. The opportunity is to recognize where it went.