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Putting Content in Context with AI

Running a compelling campaign comes down to putting the right content out to the right audience. Unfortunately, many marketers struggle to find content for campaigns, wasting precious time searching for images, videos, audio, documents, design files, and other types of content.

Research by Nuxeo shows that the average person spends just under an hour every day looking for content assets associated with their job. Similarly, in 60% of the cases where an asset can’t be found, it will be recreated.

The time lost searching for content adds up very quickly, with an estimated $30,000-$50,000 in costs associated with each asset. For maximum efficiency, marketers need to view searchable images, tagged with the correct metadata. In other words, marketers need content in context.

Many enterprises attempt to manage metadata manually, but this approach often results in inaccurate tags and time wasted tagging assets. The only viable option for tagging and contextualizing content at scale is with Artificial Intelligence (AI).

Content in Context: The Smart Way

AI offers enterprises an extremely efficient way to manage the metadata of creative assets. With AI, a company can use machine learning models to identify image attributes and tag assets with consistent metadata automatically. The result is a more effective search process for the end-user.

For example, a hotel chain looking to advertise a new property could use AI to assign metadata to creative assets, which the user can easily find and add to online or print promotions. If the same hotel chain were to take the manual approach, a human-user would need to create and categorize thousands of photographs.

The manual approach is not only inefficient but also leads to inaccurate tags. While many companies use DAM platforms to search for creative assets, a lack of accurate metadata makes it difficult to find the right content.

Using AI also can connect information from other disparate systems throughout the digital supply chain and eliminate siloing. No more image hide-and-seek means that marketers can select the best creative assets for a campaign instantly.

Making AI Work for You

When adopting AI to enhance your asset management process, you can choose between using a generic AI service or developing custom machine learning models that align with your business needs. Building your own ML models is essential for developing sophisticated insights.

While a generic AI service may be able to identify that a vehicle shown in an image is red, more tailored machine learning models could provide you with more in-depth information, and identifying the same vehicle as a 2019 model Ford F150.

Building machine learning models from scratch helps you trust in the output, and gives you peace of mind that you have access to the right information. The short-term challenge of creating custom machine learning pays dividends in the form of sophisticated insights over the long term.

A Better Way to Search for Content

If you want to streamline the management of creative assets, then AI is a must-have. When leveraged correctly, AI can apply rich metadata models to creative assets and connect information located in multiple systems throughout the supply chain. Being able to find assets faster enables you to focus on your marketing.

The manual approach of tagging images and managing them through a DAM platform isn’t a scalable solution for companies maintaining hundreds of thousands of assets (particularly if they don’t have the right metatags).

Adopting a Product Asset Management solution that has AI driven by custom ML models will give you all the benefits of a searchable repository of content without any of the administrative headaches or image hide and seek that comes with manual tagging.

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