With the help of DAM, AI, and a group of skilled data analysts, we can track, visualize, and analyze valuable data along the digital asset workflow to extract highly sought after insights such as:
DAM User Interactions
- Which users interact with the DAM the most across the asset lifecycle?
- Which resources have the skillsets and experience to be matched with this next campaign?
- How long does the average digital asset end-to-end workflow take? Where are the bottlenecks and how can we improve the process?
Digital Asset Metadata
- How many assets will expire in the next month? Is it more cost effective to re-license these assets or produce them in-house?
- Where is the asset of the woman with the hat sipping a cocktail?
- The asset I want to use has been overused, which assets are similar but have been used less than 10 times and never on the website?
Digital Asset Lifecycle
- Which individuals worked on this asset?
- How much was spent on it? What was the total cost of production?
- How quickly was this asset delivered to the customer?
- Where has it been used?
- When does the asset expire and when do we need to take it down from our channels?
- How effective was this campaign?
- How effective was this specific piece of content?
- How well did this image of someone using our products perform in comparison to a product shot?
- How do we measure the impact of our assets?
- Can we understand WHAT is working? And WHY?
- Which content is most effective for each audience/region/market?
- Which variant is most effective? Which is least effective?
- Can we save money on production by focusing the marketing budget on the most effective content?
- Which channels and content have the greatest impact on revenue?
Ultimately, CMOs and organizations want to know which marketing tactics are contributing to conversations and sales revenue. Marketing attribution programs can only work if data is gathered and normalized across the full creative content workflow, which includes DAM and a myriad of data from all platforms across the lifecycle.
With all of this data at our fingertips, we can start to make more informed decisions about our digital asset strategy, saving time and money while making our marketing efforts more efficient and effective. So if you’re feeling bogged down by tedious tasks or struggling to make sense of your digital asset data, consider enlisting the help of a DAM system with AI capabilities to make your life a little easier. Trust me, your future self will thank you.
Things To Consider Before Getting Started With AI in DAM
By automating tasks, increasing efficiency, and improving accuracy, AI can be a powerful tool for enriching data and metadata in digital asset management. However, it’s foolish to believe that AI is a magic wand that will simply apply the correct metadata that humans forgot to enter, automatically deliver the assets to us by reading our minds, or provide answers and insights into the effectiveness of our assets.
On the contrary, AI technologies require careful consideration, selection, strategy, and ongoing management, resourcing and funding.
As DAM systems increasingly incorporate AI, businesses are asking themselves how they can use AI to improve their creative processes. Before you dive into AI to enrich the data and metadata on your digital assets, here are five tips on how to get started.
1. Build a shared strategy and vision: Investing in AI can be costly and without a clear strategy and vision for the purpose of using AI, businesses can quickly run down the budget and be left with little return on investment (ROI). Before investing time, resources, and money in a problem that does not exist, an organization should consider the issue it is trying to solve, why AI technology is the right solution to the problem, and what the organization plans to achieve.
2. Choose wisely and test before you buy: Not all AI tools are created equal and there is no one-size-fits-all tool. That’s because there are many types of AI technologies available and each one is specialized to help you solve a different problem. For example, while you may be able to train an AI tool to identify the difference between cereal and dog food in your image assets, you will need to train a different AI technology to identify the executive speaking in your corporate videos. Make sure to test out different AI tools before making a purchase to find the one that fits your needs. Once you’ve decided on the AI tool that is best suited to helping you solve a challenge, we encourage you to speak with your DAM provider to consider integration possibilities.
3. Start small and simple: Don’t try to use AI for everything at once. Begin by identifying specific tasks within your digital asset management system that can be automated or improved with the help of AI. And apply AI to simple tasks before exploring complex tasks. Start by automating simple tasks that can save you time and improve accuracy.
4. Be patient as your AI grows up: Depending on the complexity of the tasks which you want the AI tool to accomplish, you will need to invest time, resources, and clean datasets to “train” machine learning and deep learning technologies. It takes time to train AI tools and to feed them the correct data. Working with a data scientist can help your organizations to feed clean, accurate, unbiased, and comprehensive training assets to your AI tools and continuously improve the outputs from your preferred AI technologies. Don’t expect AI to be perfect from the start.
5. Educate your team: The more your team knows about how to use AI tools, the more productive they will be. Make sure to provide adequate training on how to use these tools in combination with your DAM. Collaborating with robots can be a significant change in the way people are working and having a change management resource and plan to support user adoption is key to your success.
By taking the time to consider these factors upfront, you can set yourself up for success in using AI to improve your creative processes.
What Does the Future of AI in DAM Look Like?
Every major sector and industry has been affected by artificial intelligence in one way or another, but the digital asset management marketplace is still in its infancy when it comes to exploring the applications of AI. However, some innovative DAM providers are already experimenting with advanced AI applications to address common issues in managing huge volumes of data and metadata relating to their creative content.
There is a significant amount of untapped potential in the future for vendors and end-users of AI in DAM, especially around metadata enrichment, asset discoverability and accessibility, creative content production, content personalization at scale, workflow automation, and analytics to power content, creative, and marketing functions.
As more industries and vendors trial new technologies and share their successes with the community, we look toward a future where we can realize the full value and potential of the powerful combination of AI and DAM.