Digital asset management (DAM) teams are under more pressure than ever: shrinking staff, rising expectations, rapidly evolving platforms, and a growing belief that AI should be able to replace human work. Many DAM leaders are being asked to move faster with fewer resources, while also figuring out how to integrate tools that change weekly.
This article is about what’s really happening beneath that pressure. It looks at why the Great AI Divergence matters specifically for DAM, how platform promises and support shifts are changing the role of DAM leaders, and how teams can use AI in a way that protects their value rather than erodes it. Most importantly, it’s about how to move forward imperfectly, human-first, and with clarity in a moment that isn’t slowing down.
In this piece, we’ll explore:
- How AI is impacting DAM today
- What the growing divide between AI adoption and hesitation means for DAM teams, and
- How DAM leaders can adapt as platforms, expectations, and support models continue to change.
It is written for practitioners navigating real constraints, not hypothetical futures.
The Great AI Divergence: What It Means for DAM
For DAM teams, The Great AI Divergence is here. We’re past theoretical discussion. It's already happening.
We are seeing DAM teams cut in half, dramatically downsized, or in some cases, nearly eliminated altogether. The expectation placed on the teams that remain is almost impossible to overstate. “Do more with less” has become the default operating mode, but at this point, that phrase barely captures reality. What is really being asked is this: do exponentially more, with fewer people, faster, and don’t fall behind.
AI is quickly becoming the dividing line.
DAM professionals are being put into a position where learning how to effectively use AI is no longer optional. It’s not a nice-to-have skill or something to explore when there’s time. The unspoken, and sometimes very spoken, reality is simple. If you don’t figure out how to use AI to amplify your work, someone else will. And that is increasingly seen as an acceptable replacement strategy.
This is the uncomfortable edge of The Great AI Divergence. When technology replaces jobs, we are often told it will create new ones. Historically, that’s been true. However, those new roles are vague, undefined, or inaccessible to the people being displaced. DAM leaders are being asked to adapt in real time, without clear paths forward, while still delivering business-critical results.
The result is a widening gap between teams that are integrating AI into their workflows and those that are overwhelmed, under-resourced, or unsure where to begin. In DAM, that gap has very real consequences for job security, team morale, and the long-term health of programs.
Signals Everywhere: Policy, Layoffs, and Unease
The pressure DAM teams are feeling is part of a much larger shift playing out across government policy, corporate strategy, and day-to-day work culture. The signals are everywhere.
At the federal level, the White House has begun publishing guidance on the workforce impacts of AI, acknowledging both its productivity potential and its likelihood of disrupting jobs at scale. AI is no longer treated as a future concern. It’s being actively addressed as a present-day force reshaping work.
At the corporate level, the signals are even harder to ignore. Companies like Amazon are carrying out layoffs at a scale not seen in decades while investing heavily in AI and automation. Personally, this stopped me in my tracks. I remember when Amazon was just selling books. Now, many of us rely on it almost daily. Seeing a company so deeply embedded in everyday life make workforce reductions of this magnitude is unsettling, not just because of the numbers, but because of what it signals about where work is heading.
Culturally, the tension is palpable.
People are being told to embrace AI, but are rarely shown how to do so in a meaningful, sustainable way. They’re encouraged to experiment, but expected to maintain, or increase, output while figuring it out. In DAM, teams are being handed increasingly powerful platforms with AI baked in, yet left to connect the dots on their own.
This is where the Great AI Divergence becomes very real for DAM leaders.
Some teams are finding ways to integrate AI into their workflows. Others are overwhelmed or hesitant to move without clearer guidance. We’re not seeing resistance to change, but a lack of support in the process. These are symptoms of a system in transition. Platforms are evolving quickly. Organizations are recalibrating expectations just as fast. DAM leaders are left navigating the space in between.
Acceptance vs. Paralysis
One of the clearest fault lines in The Great AI Divergence is how people are responding to it.
Some teams are accepting that AI is part of their reality, and that it’s changing fast. They’re experimenting, testing features, and trying things that don’t always work, knowing what fails today might work next week after an update. That flexibility, the ability to ebb and flow with rapid change, is becoming a critical skill.
Other teams are stuck at the starting line.
Often, this isn’t because they don’t care. It’s because the pace of change feels overwhelming. AI capabilities evolve weekly. Platform roadmaps shift constantly. DAM leaders already stretched thin are also expected to decipher what’s sales messaging is versus what is actually ready, functional, and reliable.
The stakes are incredibly high. DAM platforms are expensive. Migrating off one system and onto another is costly, disruptive, and not easily undone. Leaders are being asked to commit based on future promises while managing very real present-day risk. That ambiguity makes decision-making difficult and deeply stressful.
Paralysis isn’t about resistance to the change, but about uncertainty and being overwhelmed.
Acceptance doesn’t mean blind adoption; it means acknowledging AI is here, it will keep changing, and learning how to work alongside it imperfectly and iteratively is now part of the job. The teams making progress arethe ones willing to start, adjust their plans, and keep going, even if it’s not perfect.
Platforms Will Move Forward - With or Without You
One thing feels inevitable. DAM platforms will continue embedding AI, whether their customers are ready or not.
As platforms race to innovate, support models are shifting. Hands-on guidance is replaced with documentation. One-to-one support gives way to generalized resources. The assumption becomes that customers will adapt as the platform evolves.
For many DAM teams already operating with fewer people and higher expectations, this creates drift. The platform moves forward. Features change. AI capabilities expand. Meanwhile, teams are left translating possibility into practice without additional time or support.
Over time, that drift compounds. Without intervention, the promise of AI-enhanced DAM risks becoming another source of frustration rather than empowerment.
What This Means for DAM Leaders
DAM leaders are being asked to do three jobs at once. Maintain programs with fewer staff. Keep up with rapidly evolving technology. Still deliver on the day-to-day work that keeps organizations running.
There’s also an unspoken reality. Leaders are being asked to continually prove their value in environments where AI is increasingly seen as a substitute for human work. Getting to a place where you can operate confidently in this reality does not happen overnight, but it is one of the most effective ways to protect your role and your team.
The leaders gaining traction are learning how to offload the right tasks to AI while intentionally reserving their time and energy for work that still requires human judgment, context, and relationships.
It comes down to knowing what you can change, what you can’t, and having the wisdom to know the difference.
Where Stacks Comes In
This growing gap between platform capability and day-to-day reality is exactly where Stacks does its best work.
Platforms live in roadmaps and release notes. DAM teams live in deadlines, staffing constraints, and competing priorities. Stacks helps DAM leaders translate possibilities promised by platforms into practice for their actual teams. We help teams understand what their platforms can actually do today, how AI fits into real workflows, and how to adopt new capabilities without burning people out.
We also help leaders get the executive buy-in their teams need. DAM leaders are often the strongest advocates their teams have, and they need to be equipped to explain why people still matter. AI is always in support of humans. It’s a toolkit to help us work better, faster, and stronger, not a replacement for expertise.
This article itself is an example. I couldn’t have produced it in this timeframe, doing everything manually. AI helped me communicate more efficiently without replacing my thinking or my voice.
Ultimately, Stacks helps DAM leaders build the understanding and adaptability needed to succeed long-term, technically, emotionally, and strategically. We help teams stay flexible, welcome change, and confidently decide where AI makes sense and where human touch is essential.
Personal Insight & Timing Note
I started reading the article that sparked this post at 1:12 PM on Tuesday, January 27, 2026.
It triggered something in me. I’ve been wanting to tell the Stacks audience we see what’s happening, that platforms and their support models are shifting, expectations on DAM teams are changing, and this divergence isn’t going away.
AI helped me get this message out. Not by replacing my voice, but by supporting it. I also ran it against our SEO results so that it could better perform. This would have easily taken a few days without the tech support.
I sent this to our publishing team at 3:54 PM on Tuesday, January 27, 2026, and I even had a meeting in the middle of this time block. I may come back to it again, because learning, adapting, and refining are part of this moment too.
The divergence is real.
The work is evolving.
And no one should have to navigate it alone.
What Comes Next
DAM will continue to evolve with AI faster than most teams can plan for. The question isn’t whether DAM programs will change, but whether leaders will have the clarity, support, and confidence to guide that change responsibly. The teams that succeed won’t chase every new capability. They’ll build the judgment to know what’s useful, what’s not, and where people still matter most.
