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I’ll Have My Digital Twin Email Your Digital Twin

We're about a year and a half into the big AI boom, and we've reached the point where the people responsible for technology spending are asking, "What will AI really do?" No matter how advanced a technology purports to be, if it doesn't reduce labor while simultaneously boosting outcomes -- it's not a great technology.

Since one of the alleged benefits of AI has been to "improve productivity" and liberate employees to do other, presumably more rewarding -- and certainly more lucrative -- work, this raises an entirely reasonable question: How are we measuring productivity here?

It helps if we back up and take a look at the type of work AI vendors are targeting when they say they want to transform work. This work is often "knowledge work," i.e. tasks that require, and cannot be completed without, the worker's education or knowledge. Knowledge work can be tricky, because it requires the worker to be responsive to highly specific situations or information, to be continually iterating, and to have a high degree of autonomy while doing all that responsive, iterative conversion of knowledge from one state (in the worker's head) to another (their deliverable).

Therefore, it's easier for AI and productivity vendors to focus not on knowledge work itself but on the perceived barriers to doing that work -- such as the laborious collection of that data that is foundational to so many knowledge worker tasks. As Enterprise Connect general manager Eric Krapf recently wrote:

The current assumption among many in the AI world seems to be that all we have to do to unlock maximum employee productivity is clean our enterprise data and get it into the LLM (or LLMs) that drive the AI use cases. Then employees will have a single source of truth that can answer their questions, write their reports, and take care of other routine, boring tasks – freeing up their time for still more productive pursuits.

But there's also the presumption that AI can eliminate other presumably-onerous tasks that may not seem to be part of knowledge work, and this is where we begin to hear about "digital twins." No Jitter contributor Shelia McGee-Smith recently explained how digital twins could be used to handle queries during a customer experience journey. So far, so good, right? We've all done the cut-and-paste emails to people in a group when we've gotten variations on the same question five, ten, thirty times. That's digital twin work.

Then Zoom CEO Eric Yuan recently gave an interview in the Verge where he advanced the idea that your digital twin will just go to meetings and make decisions on the days you can't be bothered to turn on your camera and click the "join" link. Here's his vision:

Let’s say the team is waiting for the CEO to make a decision or maybe some meaningful conversation, my digital twin really can represent me and also can be part of the decision making process … All of us, we will have our own LLM. Essentially, that’s the foundation for the digital twin. Then I can count on my digital twin. Sometimes I want to join, so I join. If I do not want to join, I can send a digital twin to join. That’s the future.

Here's what we should focus on: Just the vision of this technology is an implicit admission that the most valuable component of knowledge work is focused, responsive attention from the worker. In this example, the worker (or his digital stand-in) is required because whatever knowledge they hold is necessary for a collaborative decision.

We are a long way off from digital twins synthesizing our experiences, memories, and recallable subject-specific information and producing the same result we would if we were engaged in collaborative problem-solving. And given the chaotic and ever-growing challenges in enterprise data -- we may never get there. But that doesn't mean that generative AI tools aren't going to be combing through your emails and meeting participation transcripts to give it a try. Expect digital twinning in rote tasks to come to a productivity suite near you soon.