Is your content AI-ready? Probably not

Most enterprise AI conversations focus on the models, the compute or the governance frameworks. Far fewer focus on the content those models are supposed to learn from  —  the images, videos, documents, contracts and other assets scattered across dozens of systems.

That gap is becoming expensive. Enterprises that want AI to draft marketing copy, surface medical insights or automate compliance reviews are discovering that their content infrastructure was built for humans retrieving files, not for models reasoning over them.

Brian McLaughlin has a stake in how that plays out. He’s spent the better part of two decades using technology to untangle complex problems. For the past 10 years, that work has centered on AI, analytics and emerging technologies.

Since joining Orange Logic as CEO just over two years ago, he has turned his attention to digital asset management and repositioning the company to harness the potential of the AI age.

He spoke with TechInformed about why content without context is increasingly a liability, what CIOs should be asking of their digital asset management (DAM) platforms and where he sees the boundary between “file storage” and “AI infrastructure” moving next.

For those unfamiliar, what is digital asset management — and what does ‘AI-ready content’ actually mean for an enterprise?

At its core, DAM is a platform that helps organizations manage and orchestrate all their content. For large enterprises, that could include marketing assets, compliance materials, patient data, videos, 3D images. Really, any kind of content.

What we do is bring all of that into a single framework; apply context through metadata, taxonomy and structure; and then enable that content to be used effectively.

AI has accelerated everything. The volume of content has exploded, along with the risks if it’s not managed properly. Without strong orchestration and context, companies can’t fully leverage AI, and they risk compliance issues or brand damage.

For example, if you’re using images from a photoshoot, you need to track usage rights — where, how long and on which channels they can be used. Without that context across thousands of assets, you create serious legal and operational risks.

Why is content quality such an important foundation for enterprise AI?

Content without context is dangerous. You won’t know how it should be used, and it won’t be structured in a way that AI models can effectively learn from.

DAM is in a strong position because it helps structure and activate content. It’s a very exciting time in this space.

How have enterprise DAM needs evolved over the past two years?

The biggest shift is that DAM has evolved from a passive repository into an active system. It used to be more like a database, but now it’s a dynamic platform that manages, distributes, governs and activates content.

We’ve also seen a huge expansion in use cases. DAM can now be applied across industries from healthcare to marketing to manufacturing.

For example, it can support surgical video analysis in hospitals, manage large-scale marketing campaigns across hundreds of agencies or bring together text-based knowledge systems.

Where are you seeing the most meaningful impact from DAM and AI right now?

One example that stands out is working with a large charity that supports sick children and families. We’re helping them transform their fundraising and donor engagement using AI.

We’re also working with hospital systems to capture and analyze surgical video data, applying AI to identify patterns and improve outcomes.

On the enterprise side, we support large manufacturers and tech companies handling petabytes of data, acting as the context layer that feeds their AI strategies.

There are also creative use cases, like event production companies distributing content to the press in near real time, or cultural institutions making performances accessible online.

What DAM developments do you expect in the next couple of years?

Two big areas: autonomy and adaptability.

DAM systems will become increasingly autonomous, learning from patterns and automating routine tasks. That allows teams to focus on more strategic work.

We’re also investing heavily in AI agents that automate workflows and connect across systems. That’s how DAM evolves from a passive tool into an active, intelligent platform.

Is ‘dark data’ still a challenge?

Very much so. We think of it in terms of data sustainability. The growth of data — and the energy required to store and process it — has real environmental implications.

We focus on intelligent storage: deciding what data to keep, what to archive and how to minimize duplication. For example, instead of storing multiple versions of the same image, you can dynamically adapt a single asset for different uses.

We’re also enabling “file on demand” systems, where users can work with content without downloading it locally — reducing storage and improving efficiency.

How do you personally use AI in your day-to-day work?

I’ve actually built a few AI agents to act as my administrative assistants. They help manage my schedule, prioritize emails and streamline communication.

They’re not perfect yet but they’re already saving a lot of time.

Across the business, we’re using AI in coding, QA, support and knowledge management. It’s helping us scale efficiently without adding unnecessary overhead.