AI in artwork management: solving problems or creating new ones?

AI has quickly become a default topic in conversations about artwork and packaging workflows. For many organisations, it is no longer a question of if they should adopt AI, but how fast they can implement it.

But this urgency often comes from pressure rather than clarity.

Yes, the challenges are real: growing product portfolios, increasing regulatory complexity, and constant updates across markets. However, the narrative around AI as a universal solution tends to oversimplify the problem.

Instead of asking what AI can realistically solve, many teams start from the assumption that it should solve everything — from compliance checks to localisation and error detection.

This gap between expectation and reality is where most problems begin.

The reality of AI in artwork management: expectations vs execution

Artwork and packaging teams are under more pressure than ever. SKU counts continue to grow, product portfolios are becoming more complex, and global expansion requires the same product to exist in dozens of language and regulatory variations.

What used to be a manageable number of variants has turned into dozens per brand. And the workload does not grow linearly — it compounds. Every new flavour, size, or formulation triggers a cascade of updates across packaging, marketing materials, and supporting assets.

At the same time, regulatory pressure is increasing. Labelling errors are becoming more expensive, leading to reprints, recalls, fines, and reputational damage. Manual checking processes that worked a few years ago are no longer sufficient at today’s scale.

Sustainable packaging adds another layer of complexity. New materials, evolving recycling requirements, and environmental claims mean that artwork is in constant revision. There is no longer a “final” version — only the latest one.

In this environment, AI appears to be the perfect solution. It promises speed, automation, and reduced workload. Unsurprisingly, expectations around it are growing rapidly — often unrealistically.

The illusion of full automation

Today, many organisations expect AI to do things that are, in reality, not fully achievable. For example:

• fully automate artwork compliance
• detect all possible errors in packaging
• automatically adapt designs for every market
• translate content and instantly validate it against local regulations

In practice, these expectations are far from realistic.

A significant part of artwork management involves context, interpretation, and constantly changing regulatory nuances. These cannot be fully formalised or reliably automated. AI can assist, accelerate, and highlight risks — but it cannot replace human expertise.

There will always need to be a human in the loop to review and validate outcomes.

What often happens instead is automation for the sake of automation — AI implementation because it is seen as necessary, not because it is clearly solving a well-defined problem.

The hidden cost of AI implementation

Another commonly overlooked factor is cost.

Complex processes like artwork validation require large volumes of data and significant computational resources. This translates into:

• high token and infrastructure costs
• integration with existing systems
• ongoing model tuning and maintenance
• involvement of internal experts

Many organisations start AI initiatives without fully understanding the long-term financial impact. Pilot projects may look promising, but scaling them across the business can become prohibitively expensive.

Data security risks

Artwork workflows often involve sensitive information: product formulations, ingredient lists, and future product launches.

Introducing AI — especially external tools — raises critical data security questions:

• Where is the data stored?
• Who has access to it?
• Is it used to train external models?
• Does it comply with regulatory requirements?

These risks are frequently underestimated in the rush to adopt AI quickly.

The fast-moving AI landscape

The AI market is evolving extremely fast. A tool chosen today, integrated into workflows, and paid for on an annual contract may become outdated within months.

New models, better tools, and shifting regulations can quickly change the landscape. As a result:

• previously selected solutions may lose relevance
• unexpected limitations may appear
• organisations may become locked into suboptimal tools

In some cases, even geopolitical or regulatory factors can influence which AI tools remain viable.

The risks of moving too fast

Under pressure, organisations tend to accelerate decision-making. With AI, this often leads to premature adoption — before the necessary foundations are in place.

One critical question is often overlooked:
Is your data actually ready for AI?

When AI is implemented on weak or inconsistent data, the consequences can be worse than doing nothing:

• errors get scaled instead of reduced
• inconsistencies turn into system failures
• compliance gaps go unnoticed

Instead of solving problems, organisations end up adding another layer of complexity — while still needing to fix the underlying data issues.

Conclusion

The organisations that succeed with AI are not the ones that move fastest. They are the ones that:

• invest in data quality
• understand the limitations of AI
• treat it as a tool, not a replacement for expertise

AI is not a magic solution. It is an amplifier — it amplifies both order and chaos. And that is why preparation matters far more than speed.