Q&A: Debunking Common AI Misconceptions and What That Means for the Future of DAM

Nearly half (46%) of organizations say marketing is leading the charge with artificial intelligence and machine learning to enhance their daily workflows. Yet, many still believe commons myths surrounding AI or are unsure of how AI within a DAM can actually benefit them. 

In our webinar “Work Smarter, Not Harder” with Forrester Senior Analyst, Nick Barber, we tackled the challenges that marketers and creatives face when it comes to managing thousands of creative assets and how AI can lessen administrative work while providing opportunities for more complex projects.

Following the webinar, we asked Nick to address some of the questions we heard from the audience around common AI misconceptions and how more advanced AI within DAMs can offer brand-specific opportunities. 

Below are his responses. 

Q: Do I really need AI and will it help me with my library of creative assets? 

Barber: Artificial intelligence offers a lot of promise for companies of all sizes because it can enrich a large library of content where metadata governance has been historically not very good. If you have a library of a few thousand assets, it might either be a monumental—or potentially an impossible—task for humans to tag all of the assets. AI offers “helper bots” to fill in those gaps in metadata with a much more efficient process than using humans. 

Q: I generally understand AI, but how will having AI in a DAM really benefit my organization?

Barber: Many organizations are swimming in content. They have it across cloud storage, flash drives, adjacent systems like content marketing platforms and Web CMSes, and elsewhere. When organizations centralize their content into a DAM, many quickly discover their metadata strategy wasn’t very good. Artificial intelligence can enrich a library of assets with metadata that would otherwise be a very inefficient process for humans. With additional metadata that is product and brand-specific, users of the DAM can find assets more easily and efficiently. 

Q: Other DAMs provide generalized AI from Google and Amazon services; how can I use more advanced models to benefit my organization?

Barber: It’s true that many DAMs today offer AI capabilities from Microsoft, Amazon, or Google, and most of the time those capabilities offer generic metadata for assets. For example, if you upload an image of a car, AI might tag it as “vehicle, transportation, automobile,”etc. But, if I’m a car company, then I have a lot of images of cars in my DAM and having those generic tags doesn’t help me much. Instead, I want more business-specific information around a specific make or model. By using an advanced DAM with trainable AI and machine learning, I can teach the DAM to recognize the differences between cars and tag them with business-specific metadata that is much more valuable than the generic kind. The DAM can then surface content based on the real intent behind the search query vs. just generic cars that might not be relevant for marketing campaigns or sales material. 

Q: How much time and energy do I need to invest in AI within a DAM? (i.e. Do I need additional teams, like data science, operations, or IT, to help train the models or monitor the health of my DAM?)

Barber: Artificial intelligence and machine learning often scare organizations because they think they’ll need to hire a team of data scientists with specialized training, but that’s not the case. Today’s trainable AI systems need a relatively small sample set of content. For example, thinking of the car company example above, if we wanted to train the AI to recognize the difference between a pick-up truck and convertible, we might need 15-20 images of each of those cars in order to train the AI system to recognize the difference. So, you don’t need a big budget, a long-time frame, or specialized skills as you once did.  

Q: Will advanced AI eventually replace the jobs of some of my employees?

Barber: For low complexity, high-volume tasks—like tagging assets or organizing your taxonomy—one bot could do the work of three to four full-time employees. That doesn’t mean those employees are out of a job, but it means they can move to more complex tasks like creating content or understanding which content to leverage for specific campaigns and materials. Using AI to tag assets means that a nearly impossible task could get completed more efficiently. 

Want more insight into how AI within a DAM can improve your findability of assets, reduce content waste, enhance workflows, and provide actionable insights for more personalized campaigns? Contact a DAM expert to learn more.