AI in DAM has many exciting implementations, including smart search, automating the tedious meta-tagging process, and providing real-time analysis of trending topics and images. As the technology improves, AI will continue to help simplify marketing processes, says, Byung Choi, CEO, MarcomCentral.
Artificial intelligence, better known as AI, has been the trendy new innovation in the Martech space for the past few years, The digital asset management (DAM) industry has similarly, been eager to tout how AI applications and integrations can take its technology into the future. However, some of the ways AI has been implemented in DAM technology still remains primitive and limited, needing intensive human supervision.
Nevertheless, the great potential that AI presents for the DAM industry as a whole, warrants a closer look. Gartner, in its top technology trends of 2020, predicts that by the end of 2024, 75% of organizations will move from piloting to operationalizing AI. In this article, we explore some of the future possibilities for this technology, as AI enablement in DAM continues to innovate and improve, and the benefits that might bring for marketers.
Learn More: Top 10 Digital Asset Management (DAM) Software Solutions for 2020
Optimizing and Streamlining Search Functions Within DAM
Marketing teams that need to store and create large amounts of visual assets, like heavy imagery and video files see huge value in DAM tools. In order to search through large quantities of assets, DAM systems require diligent data management and archival. Making sure assets are correctly tagged with descriptive and specific metadata terms is imperative to help make the assets easily searchable and discoverable. Here is where AI comes in. AI-driven visual recognition technology can help with this tedious process of identifying and tagging content assets with the right metadata, freeing up precious marketing man hours for other priorities. AI relies on pattern recognition, using millions of past examples to draw predictions on how to tag an image or video, so the process does not have to be handled manually.
Despite its potential usefulness, a common criticism of AI-generated metadata is that current solutions on the market are still too generic and broad, which becomes a more pronounced problem when the assets are nuanced, similar, or industry-specific. An example of this could be a pet photography portfolio, where the same keyword ‘dog’ might appear on every single asset – not very helpful. Most AI tools are able to identify a storefront in an urban setting but may not be able to accurately tag if the photo is of the company’s flagship retail store in Manhattan.
To mitigate this issue, assets that have been auto-tagged should be periodically and systematically checked by humans, to be able to add that layer of context that might be missing. In addition to this regular quality check, metadata created by AI should be classified on its own, as if by a separate user, and tracked separately. This allows teams to differentiate which metadata has been contextualized by humans, while still benefiting broadly from AI’s automation and time saving benefits. Over time, as you add context to and correct existing tags on marketing assets, and with enough data, machine learning algorithms will improve and adjust to the point that they will be able to add the contextual specificity that you need, requiring less human supervision.
Another interesting use case is how AI can use speech recognition technology to extract searchable text from audio and video assets. This can be a real boon for marketers that have long videos or podcasts to be able to tag topical keywords, without having to spend huge amounts of time going through the assets themselves.
Create and Distribute With Ease and Confidence
Armed with your detailed and descriptive metadata and library of assets, it is easy to create new materials by pulling together some existing assets you already have on hand. Consider investing in more advanced DAM tools that allow for easy customization and work within an existing brand template or brand lockup, with the dual purpose of being user-friendly and brand compliant. For example, hotel chains have their overall master brand, but individual hotel properties might also need to run their own promotions and ads relevant to their locale and unique holidays. By using a DAM tool with a customizable drag-and-drop template, individualhotel managers will be able to create their own ads by choosing from available assets within the repertoire. This allows individual locations to autonomously manage their marketing campaigns, while brand teams have the assurance that hotel teams are staying brand compliant throughout.
Learn More: Build Your Brand’s Bottom-Line With Cloud-Based Digital Asset Management (DAM) Solutions: Q&A With Widens
Real-Time Analysis of Trending Topics and Images
By observing current news and social media conversations, AI can also help identify larger trends happening in the world, which can help marketers create more timely and relevant marketing assets. For instance, as we all adapt to the new normal of working in a world affected by COVID, it would be strange to see ads of children in a school setting, employees seated closely around a conference table, or large groups of people congregating together. AI can help to identify trending topics and zero in on the content with images of people wearing masks, or practicing social distancing, for your next campaign.
While certain AI functionality within the DAM space still remains nascent, it would be foolish to write it off as a passing fad or failed experiment. With more investment funneled into AI technology within DAM, and with more custom business-specific solutions being created, we predict AI will continue to get smarter and more efficient, helping to optimize, automate and simplify marketing processes.