Business productivity improvement should be the target for a lot of the tech initiatives we hear about, including things like 5G, AI, and edge computing. In a PRIOR BLOG I talked about the importance of business productivity enhancement in the 5G business case. At some future point, I’ll dig into the details of how and where such enhancements should be targeted. Here, I want to explore the technical framework for enhancing productivity at the next level.
My modeling shows that the next wave in productivity enhancement for workers has to shift the focus of “empowerment”. Today, we think of empowering workers via IT as establishing an IT-centric framework for a job, which we do by giving workers “applications”. The workers’ task is then to do the job by doing the application. This is logical in a lot of ways, but it’s not optimum in most cases, and it’s downright impossible in others.
The new empowerment focus has to be integrating IT into the work, not the other way around. In order to do this, we have to provide the worker with an information appliance that’s handy, which is where smartphones (or networked tablets) come in. We’re culturally tuned to having a smartphone in our pocket/purse, and so there’s no great transitional training effort needed to exploit that in the work context. In fact, “mobile empowerment” has been a driver of a lot of stuff already, including the cloud.
We can call “Phase One” of mobile empowerment an information portability approach. The applications and data involved are largely the same as that already in use at desks or other fixed locations, but the platform to which they’re delivered is now the smartphone. This model of empowerment doesn’t require anything new in the way of technology, if we define “new” as meaning beyond what we already have. It’s a good on-ramp to other more specialized approaches to empowerment, but it’s not revolutionary, nor is it likely to increase IT budgets significantly.
It could still play a role in later phases, though. We have cobbled together a series of “cloud front-end” approaches to mobile empowerment, and these are aimed at creating a better user experience through either a browser or app interface. It would have been great to have seen a model of this front-end development be suitable for later phases, but since we haven’t anticipated those later phases, that would happen only through serendipity.
Phase Two of mobile empowerment is what we could call the “co-presence” phase. Workforces are just what the name suggests, meaning groups of workers. Most people don’t work in their own little world, they share projects with others and play their own role. That implies coordination and collaboration, and so the second phase of mobile empowerment is to create a useful collaborative framework.
Collaboration isn’t impossible today, even with mobile workers. Phones have voice, text, and video calling and can also interact with web collaboration sites. The problem that needs to be solved here isn’t creating a collaborative relationship, as much as preventing the collaborative relationship from interfering with (rather than supporting) the work. Smartphone screen real estate is limited, so having a full-screen web conference kind of rules out looking at data at the same time. That’s particularly true when you consider that collaborating with a mobile worker, in my surveys, is almost certain to involve sharing some data or visual focus.
The enterprises that I’ve surveyed, and who had actually looked at better mobile empowerment, suggested that what was needed was a “panel” or “mashup” approach. The goal would be to first select the kind of communication that was actually needed by the worker. Video calling commits both parties, to a degree, to video. In most cases, the collaborating parties didn’t need to see each other, they needed to see what the other was seeing, or more directly, what the worker was working on. The panel or mashup means that a visual “job frame of reference” is established to contain what any of the parties needs to see/hear/read. The parties are then allowed to set their own “viewer” to select from the referenced items, and in some cases to direct the viewer of the other party to focus on something specific.
There’s a need for additional front-end visual mashup tools, building the frame of reference and managing the viewers. There’s also likely to be an increase in the mobile traffic required, but the most significant difference in this phase is that the tighter integration between worker, partner, and information means that reliability of the entire application ecosystem is more critical. Loss of the tools means a major shift in worker behavior and a loss of productivity rather than a gain. Thus, this phase of mobile empowerment could generate incremental spending.
Phase Three moves IT closer still to the work processes themselves. We’ll call this the “Hal” phase, because it would involve the injection of artificial intelligence into worker empowerment. While this is a distinct phase of empowerment strategy, it’s divided into steps, and the order in which these are taken may vary.
One step is to have AI create the job frame of reference based on past experience with the job, a machine learning mission perhaps. Similarly, AI could generate the viewers for the parties involved and keep the viewer information synchronized and relevant to activity. Speech recognition is likely to be useful in this mission, but so is the ability to “see” where any of the collaborators are focusing their attention, by knowing what the information content of a given viewer window was.
AI could also be used to initiate the collaboration, meaning to generate the job frame of reference, make collaborative connection(s), and set viewer contexts based on a worker’s information viewing, speech, buttons, etc. Think of this process as starting with a panel that lays out a series of steps and asks for confirmation of each, in positive or negative form. The AI process would look like a kind of finite state machine, leading the worker and initiating collaborative relationships as needed.
Another step would be to have AI be the collaborating party. This goes beyond basic speech recognition to what might be called “contextual interpretation”. Remember that the worker is using a job frame of reference, and is signaling via worker focus the specific thing they’re exploring. Speech would be interpreted within a series of set contexts, based on the job frame of reference and the focus. The AI element would interpret questions and then use some form of AI, including expert systems or neural networks, to frame a response. The response could include refocusing the viewers, but also giving directions via voice or triggering a video or textual response.
Augmented reality could also be introduced at this point. If a worker had AR goggles, for example, it could be possible to “see” what the worker sees, either in the sense of sharing that view with the collaborator(s) or interpreting the view. The view could then be augmented either by superimposing a reference image or by having a collaborator “draw” or indicate something, which would then be added to what the worker sees. This step could be eased into if the worker, holding the phone up, could see the image the camera captured as well as any superimposed annotations or images.
And then there’s Phase Four, which is the integration of real-world context with worker activity. This phase is where IoT information is critical, because the goal is to create that “virtual world” from information, then synchronize it with the real world via IoT-contextual sensor information.
Almost all work involves getting the worker into proximity with what they’re expected to work on, or with. Much of it also relates to understanding the ambient conditions (including lighting, temperature, humidity, noise level) and inputting them into the real-world simulation, so that the empowerment processes, particularly those involving AI/ML, can accommodate them.
The big difference in this phase versus the earlier ones is the significant injection of information from outside the worker and outside current IT. Most of that information is aimed at providing a virtual image of real-world conditions so that applications can interpret conditions rather than forcing workers to do that. True automation is about doing something for the worker, not pushing the worker along a path of doing something, and this is the phase that accomplishes that.
Obviously, the integration of the real and virtual world implies that empowerment processes and tools are coupled more closely to work processes and the worker. That means they have to be highly available and scalable, but also that the latency associated with the empowerment workflows have to be short enough that empowerment synchronizes with the target worker(s). Add this to the fact that there is considerably more front-end interpretation and manipulation than before, and you have the reason why my models have said that this phase is the real start of “cloud-native” applications. This is where the benefits that can justify the shift can be reaped.
The cloud is the consistent beneficiary of the empowerment shift, and if there is indeed a new IT cycle coming along, driven by mobile empowerment, the cloud will both lead it and reap the rewards. In point of fact, the cloud will drive the bus on all of this, as I hope my descriptions have showed. That’s why I’m frustrated (and even disgusted) by the constant prattle that “edge-will-do-this” or “IoT-will-do-that”, or “5G-will-do-everything.” None of them will do anything, or even develop much, without the support of the cloud, and the cloud won’t support them if cloud applications aren’t developed to realize these phases of empowerment growth.
I believe the four phases I’ve outlined here are logical, and that each of the phases can be incrementally justified by the benefits it creates. I also believe the benefits will build from phase to phase, to the point where they could generate over $700 billion in incremental cloud-related revenue. Finally, I believe that the software that’s created to support each phase could build the base for the next phases, and also build a framework for things like edge applications, IoT applications, AI applications, and even 5G applications. This would be the optimum way to get to where all these technologies (and their proponents) want us to be.
Software architects will surely see the shape of the tools and features needed for this, and surely see the value in developing an architecture for Phase Four that’s not excessive for Phase One, but rather can be enhanced into later phases without requiring everything be redone. That should be our goal; it’s my goal for sure.