For decades, archives, museums, and archaeological organizations have relied on trained staff and volunteers to transcribe historical documents—sometimes line by line, sometimes word by word. It’s patient, meticulous work. But it’s also slow. In 2025, with pressure mounting to digitize and share collections, many teams are asking: can AI really do this better?

The answer isn’t simple. But it’s getting clearer.

There’s a reason institutions have relied on manual transcription for so long. A human can read between the lines—spot irregularities, decipher faded ink, and understand context that machines struggle with. When you’re working with 18th-century field notes or Indigenous language dictionaries, that kind of judgment matters. Here’s a breakdown of both approaches and what heritage professionals need to consider when choosing the right path forward.

But that kind of expertise is expensive. And rare.

Manual transcription can take upwards of 30 minutes per page and take weeks (or months) to complete even a modest collection. If you’re dealing with thousands of pages, you’re facing a real bottleneck.

Let’s say you have 5,000 pages of archaeological field reports. You could hire a team and have them transcribe the set over a few months. Or, you could run it through ArchAI overnight and have structured CSV output ready the next morning.

Speed means grants are easier to fulfill. It means collections become searchable. And most importantly, it means your team spends time curating, interpreting, and sharing—not typing.

For small, high-detail projects, manual transcription may still play a role.

But for any institution dealing with scale, backlog, or a limited budget, AI transcription is the clear choice in 2025.

It’s not just faster. It’s a strategic shift toward accessibility, discoverability, and impact.