When AI Won't

The creation and subsequent rapid integration of artificial intelligence (AI) into almost every business and industry sector has been both revolutionary and, for many, overwhelming.

AI-powered solutions now underpin countless products and services, offering remarkable efficiencies and insights.

However, as we increasingly use this technology, it’s crucial to remain vigilant against exaggerated claims (AI washing) and resist the temptation to implement AI where instead of adding value, it actually creates problems.?

The Benefits of AI in Media Asset Management

AI has already transformed media asset management (#MAM) and digital asset management (#DAM) by empowering businesses with tools that streamline processes and enhance productivity. Early adopters have already reaped substantial benefits from:

• Auto-tagging: Automating metadata generation to simplify content discovery.
• Visual-based searches: Enabling users to find content faster using image recognition.
• Quality assurance: Detecting flaws or inconsistencies in assets.
• Duplication detection: Identifying and removing redundant files.
• Generative AI: Creating content or enhancing existing assets with innovative tools.

These advancements save time, reduce operational costs, and give creative teams more time and freedom to focus on what they do best.

Challenges and Limitations of AI

While AI has proven its value, it is not a silver bullet. There are instances where AI over-promises and under-delivers, particularly in areas like content tagging.

Anyone who has relied solely on AI for auto-tagging can attest to its occasional errors.

This isn’t due to a failure of the technology itself but rather a lack of understanding about its limitations. AI is excellent at analysing patterns and processing large volumes of data but struggles with:

• Nuance and context: An image of a beach may be tagged as "vacation," missing its connection to an environmental campaign.
• Subjective judgment: Interpreting creative content or making decisions that require human intuition.

Moreover, the quality of AI outputs is only as good as the data it learns from. Poorly curated or biased datasets can result in inaccuracies, rendering AI more of a hindrance than a help. Over-relying on AI without human oversight can lead to frustration, inefficiencies, and even project failure.?

Finding the Right Balance:

The key to maximising AI's potential lies in striking a balance between digitalisation and human expertise. Here’s how:

• Evaluate specific needs: Understand where AI can genuinely add value to your operations.
• Tailor AI solutions: Choose systems designed to address your unique requirements.
• Leverage human oversight: Use skilled professionals to validate AI outputs, ensuring accuracy and relevance.
• Set realistic expectations: Be transparent about what AI can and cannot do to avoid disappointment and build trust.

At Greatstock, by aligning #AI with human skills, we ensure that our clients get the best of both worlds.

AI is a powerful tool, but its success depends on how well it is understood, implemented, and integrated. While it offers immense potential for efficiency and innovation, human expertise remains irreplaceable for adding context, refining outputs, and ensuring strategic alignment.

With the right approach, businesses can harness AI’s capabilities without falling victim to its limitations—turning it into a genuine asset rather than a liability.

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