Is it possible to make money on a “digital twin” metaverse, on the empowerment of workers not normally empowered, on the binding of consumerism to consumers in a better way? I’ve talked in past blogs about the technology of such a metaverse, and I’ve brushed a bit at the things that it could benefit, but we need to be hard-headed here. Can we see a digital-twin metaverse creating an ecosystem that would be profitable enough to encourage participation? If not, technology is irrelevant.
The essential notion of digital twinning is to create a computer model of a real-time system, in order to better assess its overall state and support its operation and use. The information used to create the model is presumed to be gathered from sensors, which means IoT. The “support” could take the form of assisting human workers or driving automated systems through direct control.
From an architecture perspective, such a system would consist of four parts. First, the model, which is presumably a dataset that collects not only real-time information but also information inferred from that primary source. It’s the “digital twin” itself. Feeding it is a collection of sensors and the associated software, and that’s the second part. Third, a set of processes running off the model would be assigned the mission of establishing state and matching goals to the state. Finally, a set of processes running off the model would interface the model to external elements, which to me would include both humans and automated processes.
None of these pieces are particularly challenging. I contend that a reasonably qualified team of a dozen programmers/architects could produce a prototype of this system in six to nine months. Would it be comprehensive, merchandisable? Surely not at that point, but it would be enough to demonstrate a credible model and attract support, providing of course that it was offered by a credible player. But would such a credible player emerge without having the support that requires a credible model to build? That’s the challenge here.
Breaking the chicken-and-egg loop here would likely require a clear addressable opportunity. To do that, we start by saying that you could divide the labor force you’d want to consider into three groups, the “office” workers, the “retail/service” workers, and the “product and production” workers. Roughly half of all workers work at a desk and so could be considered “office” workers. The remainder are fairly evenly divided between the other two groups.
Most of the currently active empowerment strategies target the office group and a limited number can also target workers in other groups. It’s hard to gather data on this except anecdotally but my contacts suggest that 70% of office workers are targeted and about 8% of each of the other groups, which means that we actually work to empower about half of workers with current technology. I argue that it’s the other half that need the digital-twin metaverse, and that would mean that we could use it to open an IT opportunity as large, in worker population, as we already target. That should surely be enough to gain credibility.
Obviously it has not, at least not in an effective way. Enterprises with a lot of workers in the second and third groups tell me that there are three issues. First, they’re not actively promoted by vendors or providers of service to target these two groups. Second, what applications and services they see are specialized to a job and/or industry, intersecting a small part of those two groups within their companies. Third, technologies and practices for addressing workers in these groups are different, offering no resource or management economies.
What nobody mentions is the difference in the way that various jobs and industries have to be assessed and empowered. For example, retail and service workers typically have lower unit values of labor, which means that in order to recover a given investment in empowerment you’d have to empower a larger number of workers through any contemplated project. In all the major job classifications recognized in the US, there are some that can provide a relatively high unit value of labor, some that have large numbers, but interestingly none that have both. That makes efforts at empowerment a balancing of the two factors, and every job and industry seems to require its own balancing. In some cases, this means taking a fairly innovative view of how you might address them.
I had an interesting chat with the CIO of a restaurant chain, who reached out to me to understand the way “digital-twinning” might work for a restaurant. It turns out that rather than considering a worker as a target, you have to consider the restaurant as the target, meaning you model the restaurant as an ecosystem. That shouldn’t have been a surprise given that this would be true for empowering assembly-line workers as well, but it points out that worker empowerment doesn’t necessarily mean twinning a worker, and in fact probably doesn’t. It means twinning the real-time system that workers are a part of, and that’s in my view the underlying problem with addressing those two underserved sectors.
Creating a model for a workplace, which is really what we’re talking about here, is a three-level challenge. First, it takes somebody who has a detailed understanding of the workplace and the work to do that, which means a line person. That person isn’t likely to understand the process of twinning, so you also need a model specialist to translate the workplace/work insights into a model. Then you need the actual modeling tools, the model, and the interfaces between the model and the real world. That means that somebody has to support the general notion of digital twinning, and that seems intuitively like a heavy lift for a vendor who’s likely trying to pick some opportunity low apples.
Years ago, I worked on software for a company who was having a major problem with fixed asset tracking. They had computers, typewriters, desks, chairs, tables, and so forth, and of course when people moved around some of these moved with them. Other stuff stayed where it was, and some stuff disappeared. The company had tagged the asset types with serial numbers, and annually they did an inventory, sending people into each space to record what was there. They wanted to do this faster and more often, and their thought was to put an RFID tag on each asset so the process of recording it would be quicker. They envisioned an inventory type walking to a room, shooting a room tag with an RFID gun, then entering and shooting the asset. From that, they could construct a “digital twin” of the spaces and contents.
The problem was that the twin quickly lost connection with reality, which meant you had to do inventory more often, which was more costly. The solution was to put an RFID scanner at the entry points to each space, so when something was taken out or brought in it was recorded. Now the map was up to date. However, you can see that this application, having been developed from scratch and targeting this problem, wouldn’t create much that could be leveraged for other “twinning” missions.
Most of the applications that could be addressed through digital twinning, even those being developed today, aren’t built on a general digital-twin model. I think the biggest challenge we face in addressing new empowerment opportunities, even opportunities in consumer empowerment, is that of silo development. We’ve probably spent ten times what it would have cost to create a general model that could have propelled the whole space forward, and we still don’t have that model.
We also don’t have broad recognition of an almost-as-important truth, which is that a digital-twinning model is a general case of a social metaverse, and that metaverses and digital twinning are two faces of the same coin. That means we’re at risk to directing metaverse development into the creation of another set of silos. Any virtual reality has to start with a model of the reality. Any participatory virtual reality has to integrate the real world with the model. Whether we visualize the result or use it to empower workers or automate processes is just the way we manage the output. The rest is all the same—a digital twin model.