Are you unlocking the power of AI in DAM or still waiting for the dust to settle?

AI as enabler instead of a threat

Although we are only beginning to see the possibilities and impact of AI in digital asset management, it's interesting to find out how others navigate its complexities. In a recent webinar, organized in honor of the launch of the survey report, attendees were asked how confident they are about using AI in creating marketing copy and images. More than 3 out of 10 attendees (33%) stated they were somewhat confident, 27% was neutral, and 10% was not confident.

A healthy mix of fear and positivity, in line with the outcome of the survey, in which 66% of the participants reported feeling positive about AI and being hopeful that it will make their jobs easier. Over half (52%) of brands and organizations say they are already experimenting with AI, so it's safe to say that the majority of DAM professionals see AI as an enabler instead of a threat! But which AI capabilities are they interested in, or currently testing? Are they ready and able to start integrating AI into their digital asset management, much like other DAM integrations are and have already been implemented by many companies?

Top 3 most used/ tested AI in DAM features

It won't come as a surprise that automated data tagging topped the list of 16 AI capabilities listed in the survey, followed by content generation and content recommendations. Interestingly enough, though, AI features for content generation (ChatGPT, Midjourney, etc.) are features that a lot of DAM users are actively testing and experimenting with for the sake of content creation, but we don't see these features integrated into our DAM solutions so much, yet. This might be because most use their DAM platform to manage content, and other tools to create and produce content and assets.

What is holding companies back to experiment with AI in DAM?

Jumping on the AI bandwagon seems to be the key to competitiveness: those who started early are already excelling compared to those who are behind. But what makes it so hard to stop talking about AI and actually start doing something with it? The challenges and risks. Newsflash: those can be combatted!

1. Data quality and accuracy issues of AI generated metadata
   As far as the quality and accuracy of AI output is concerned, the answer lies in training your AI tools (with the help of consultants or other specialists) and to use humans to moderate, review, and validate AI generated content.
   
   
2. How do we go about the scarcity of in-house AI expertise in order to meet the need for skilled human supervision, moderation, and resources to train AI?
   Although AI can deliver many tangible benefits, we still need people to ensure its proper operation. We're only just starting to see what AI can do (for us), so real AI experts are still a rarity. How can you secure enough skilled human supervision?
   
   By creating safe environments that encourage learning and trigger curiosity. Investigate and address the impact of AI on individuals. Provide training and education and teach people to work with AI assistants. This may involve learning new skills necessary to effectively collaborate with and utilize AI-driven assistants or tools. And, finally, develop a network of AI champions to inspire and support others.
   
   
3. Ethical considerations and concerns about biases in AI
   Addressing this challenge starts with creating awareness on what the risks are – and those risk are real, just check the everyday news. The second step is to establish clear guidelines on how to go about AI in your DAM, followed by extensive testing and training of your AI tool.
   
   
   Fig 2: survey results show that 11% of the participants is not aware of AI-related risks
   
   
4. Uncertainty about the cost of AI investments
   Most AI experiments are done with free or trial versions of available tools. When you are planning for an AI investment to solve a challenge for your business, consider these elements:
   
   • Monthly AI licenses or volume charges
   • Implementation and maintenance costs
   • Integration costs
   • Staff costs
   • Costs for training your AI tool

Embracing the challenge

As we navigate the ever-evolving landscape of AI in DAM, the possibilities are as vast as they are transformative. While uncertainty and fear naturally accompany new technologies, the true potential lies in our willingness to try them. By taking that first step, we can expand our understanding of what AI can do for us and pave the way for AI-based innovation and growth.

As AI in DAM is still mainly unchartered territory, everyone out there has the opportunity to become an AI champion. It starts with understanding the problem: why do you need AI and how is it going to help you? Then, learn as much as you can and seek out the tools that work for you. And above all: don't wait till the dust has settled, but embark on the AI journey now. It's OK to make mistakes and it's important to be realistic: you don't have to generate significant results and benefits right from day one.

Do you want to get started with AI in your organization, but you don't know where to start?
Then take advantage of WoodWing Scienta's AI Governance handbook, which offers you the tools to use AI responsibly in your organization.