After 25 years of change, image quality matters more than ever. Here’s why.

Over the past quarter century, one aspect of imaging has changed faster than anything else: volume.

What used to take days is now expected in minutes. What used to be edited by hand, is now handled by automation. And what used to be a few hundred images is now millions—in fact, we now process 147 million images per day. 

As we reach this 25-year milestone, we’re reflecting on how much the expectations around image quality, speed, and consistency have evolved, and what it takes to keep up in a world of emerging technologies.

Way more than outputs

The shift from manual workflows to automated pipelines is obvious, but the deeper change is how imaging is used.

Images are no longer just outputs, but are part of the product, the experience, and the revenue stream.

• High-volume photography operations need consistent results across thousands of images
• Content pipelines demand speed without adding headcount
• Platforms and partners expect seamless integration into existing workflows

At the same time, AI has moved from experimentation to expectation. Automation is no longer a differentiator on its own. 

The fundamentals that remain

People still notice when something looks off. Skin tones, color balance, glass glare, and natural detail still matter. Trust in the output still matters. Consistency across a set still matters. And accuracy—from technology such as our Real Color Photography—is more important than ever.

This creates tension. As volumes increase and timelines shrink, the margin for error becomes smaller, not larger.

Automation has to do more than process images. It has to get them right.

Where many approaches fall short

In the push toward speed and scale, it is easy to lose sight of quality.

We see four common mistakes:

• Prioritizing throughput over accuracy
• Treating AI as a black box with no control or predictability or true science inside the black box
• Failing to account for edge cases that appear at scale
• Assuming one-size-fits-all models work across different use cases

These issues rarely show up in small test sets. They show up in production, where consistency matters most.

What we learned about building better automation

25 years as a business—plus over 500 years of experience collectively on our team—informs how we approach image processing.

It is not just about building models. It is about understanding how images behave across real workflows, across industries, and across edge cases.

A few principles stand out:

• Quality must be measurable and repeatable
• Automation needs to be controllable, not just automatic
• Domain expertise matters as much as model performance
• Scalability includes consistency, not just speed

AI is powerful, but without structure and experience behind it, it can introduce as many problems as it solves. That’s why we back our technology with 500 years of Real Science. We won’t just throw images into an AI mixer and let the model spit something out. We apply math to gain an understanding of how to incorporate machine learning in the process. This is where we’re different from our competitors.

The next era of image processing

Looking forward, we see the focus shifting again. 

It is no longer just about automation. It is about intelligent automation that adapts to context, integrates easily into workflows, and maintains quality at scale.

We see three clear directions:

• More context-aware processing that takes scenes and subjects into account 
• Greater control for businesses that need predictable outputs
• Evolving beyond standalone tools to a deeper integration into production pipelines

The expectation is not just faster workflows. It is better outcomes without added complexity.

Closing

After 25 years, the core challenge remains the same: delivering consistent, high-quality images at scale. The tools have changed, but the standard has not.

The companies that succeed will be the ones that balance automation with control, speed with accuracy, and innovation with real-world experience.