AI-powered DAM: the key to gaining a competitive advantage now

This scenario has played out repeatedly across numerous major brands, including well-known names like Unilever, NBCUniversal, JPMorgan Chase, Burberry, and Spotify, among others. These companies have successfully leveraged early AI adoption to gain a competitive edge in their respective industries.

This is the power of AI in DAM, but where do you start? In this year’s inaugural research on the topic (download the research here), we heard from companies using AI in their DAM strategies today and gained insights into how they plan to attain that competitive advantage in the future.

In this article, we’ll explore how you can achieve similar success by embracing AI early, selecting the right tools, defining use cases, developing a strategy, and championing AI within your organization.

Define use cases: understand the specific problems you aim to solve with AI

Clearly defining AI use cases is essential for effectively leveraging AI in your DAM system. It ensures that AI applications are aligned with your organization's goals and address specific pain points.

Steps to define AI use cases

• Identify pain points: conduct a thorough analysis of your DAM workflows to identify pain points and inefficiencies. Engage with users to understand their challenges and gather insights on areas where AI can add value.
• Set objectives: define clear objectives for each AI use case. For example, if improving search and discovery is a priority, set specific goals such as reducing search times or increasing the accuracy of search results.
  
  The top 3 outcomes companies are aiming to achieve with AI-powered DAM include:
  1. Boost efficiency and productivity
  2. Time savings and speed-to-market
  3. Cost savings
• Prioritize use cases: prioritize AI use cases based on their potential impact and feasibility. Start with high-impact, low-effort use cases to demonstrate quick wins and build momentum for further AI adoption.
  
  The top 5 AI capabilities brands are currently seeking to be able to manage content better include:
  1. Improved search and discovery
  2. Content management and moderation
  3. Automated metadata tagging
  4. DAM analytics and insights
  5. Automatic cropping, editing, or format optimization

Embrace AI early: start experimenting with AI-enhanced DAM

Embracing AI early in your Digital Asset Management journey allows your organization to stay ahead of the curve, gain a competitive edge, and drive innovation. Already today, 52% of brands and organizations say they are experimenting with AI*. Early experimentation helps identify potential benefits and challenges, fostering a deeper understanding of AI's impact on your workflows.

Steps to start experimenting

• Pilot projects: begin with small-scale pilot projects to test AI capabilities in a controlled environment. Focus on specific tasks such as automated metadata tagging or content recommendations.
• Learning and iteration: use the insights gained from pilot projects to refine your AI strategy. Embrace a culture of learning and iteration, continuously improving your AI applications based on feedback and results.
• Stakeholder engagement: involve key stakeholders early in the process to gain buy-in and support. Demonstrate the potential benefits of AI through tangible results from pilot projects.

Seek optimal tools: find and integrate the right AI tools for your needs

Choosing the right AI tools is crucial for maximizing the benefits of AI in your DAM system. It's essential to align AI capabilities with your organization's specific needs and goals.

How to find optimal AI tools

• Assess needs and goals: conduct a thorough assessment of your organization's needs and goals. Identify the specific challenges you aim to address with AI, such as improving search and discovery, enhancing content management, or automating repetitive tasks.
• Research and compare: research available AI tools and compare their features, capabilities, and compatibility with your DAM system. Look for tools that offer robust functionality, ease of integration, and scalability. And remember to think beyond what is available in your DAM. Currently, 41% of organizations are experimenting with AI tools natively integrated into their DAM systems, while 59% are also experimenting with other AI tools*.
• Vendor collaboration: collaborate with both your DAM vendor and independent AI vendors to understand their offerings and how they can meet your specific requirements. Request demonstrations, trials, and case studies to evaluate the effectiveness of their AI solutions.

Integration considerations

You may think you're done once you've identified the right tools, but don't forget about what it will take to integrate the tool with your content or your DAM directly. When identifying the right tools for your needs, it's important to evaluate the integration approach, associated effort, timelines, and cost to ensure a successful and efficient implementation that aligns with your organizational goals.

• Seamless integration – ensure that the AI tools you choose can be seamlessly integrated into your existing DAM system. Consider tools with native integration capabilities or pre-built plugins for minimal configuration.
• Custom integrations – if off-the-shelf solutions don't meet your needs, consider custom integrations tailored to your specific requirements. This may involve developing bespoke API integrations to fully customize AI capabilities and workflows.
  
  

Develop a strategy: create a framework to navigate AI integration challenges

A well-defined AI strategy is fundamental for navigating the complexities of AI integration in DAM. It provides a roadmap for implementation, ensures alignment with organizational goals, and addresses potential challenges. Given that 62% of organizations already have an AI strategy or plan*, you'd better get going if you don't have one yet.

Steps to develop an AI strategy

• Articulate vision and goals: start by articulating a clear vision for AI integration. Define specific goals and objectives that align with your organization's overall strategy.
• Data strategy: develop a data strategy that outlines the types of data needed, data sources, and data quality requirements. Ensure that your data is clean, relevant, and ready for AI training.
• Governance and ethics: establish governance frameworks and ethical guidelines for AI use. Define policies for data privacy, bias mitigation, and transparency in AI decision-making. Nearly half of brands either acknowledge the risks associated with AI but lack proper governance and ethical frameworks, or are completely unaware of the potential for AI bias*.
• Build a cross-functional team: assemble a cross-functional team with AI specialists, data scientists, DAM experts, and key stakeholders. Ensure that the team has the necessary skills and expertise to drive AI integration.
• Roadmap and milestones: create a detailed roadmap with clear milestones and timelines. Prioritize early experimentation and learning, and be realistic about the timelines for achieving each milestone.

Addressing challenges

Developing an AI-enhanced DAM strategy and roadmap is essential, but it is equally important to ensure that these plans can be effectively implemented, adopted, and maintained into the future. Be sure to plan for the future with:

• Resource allocation: allocate sufficient resources for AI projects, including budget, technology, and talent. Consider partnering with external consultants or vendors if in-house expertise is limited.
• Change management: implement change management strategies to support AI adoption. Communicate the benefits of AI, provide training, and address any concerns or resistance from users. Set up long-term sustainable change management support for AI-powered DAM.

Champion AI: gain expertise in AI to drive innovation within your organization

At the start of your AI adventure, there's a golden opportunity for you to step up and become the champion who leads your company through its AI transformation. Becoming an AI champion involves gaining expertise in AI technologies and leading efforts to drive AI innovation within your organization.

Steps to become an AI champion

• Continuous learning: invest in continuous learning and professional development in AI. Attend industry conferences, participate in AI training programs (check out this free Google course on How to Create an Image Captioning Model), and stay updated on the latest AI trends and technologies.
• Network and collaborate: build a network of AI professionals and collaborate with peers, vendors, and industry experts. Engage in knowledge sharing and learn from the experiences of others. Join Kristina’s DAM best practice newsletter to receive DAM, AI, and AI-powered DAM news straight to your inbox.
• Advocate for AI: advocate for AI adoption within your organization. Share success stories, demonstrate the benefits of AI, and inspire others to embrace AI technologies.
• Lead by example: lead by example by actively participating in AI projects and initiatives. Showcase your expertise and drive AI innovation through hands-on involvement.

By embracing AI early, seeking optimal tools, defining use cases, developing a strategy, and championing AI, organizations can effectively integrate AI into their DAM systems and create an AI-enhanced DAM stack, drive innovation, and achieve significant business benefits.

* Based on data taken from the 2024 research study The state of AI in DAM