Communication is great, but too much communication can become toxic.
Over the years I've seen a number of products that provide message transaction analytics to identify requests versus responses or to identify if you're just filling somebody's buffer and unlikely to receive a timely response and need to walk over and ask that question directly. I remember losing a job once and a "fat bonus", because I didn't realize the manager I was communicating with could only handle emails containing three bullet points. I've learned since then that identifying someone's communication style is the first thing you consider.
What I'm excited about in regards to this article though is the addition of sentiment analysis and predictive analytics based on content-breakdown and scoring. It seems like a potential game-changer? I've also been thinking back to this old DARPA project called Wildfire, it was a desktop integrated AI that would basically watch activities and then time-part user behavior to predict a next activity, then raise an application or document as an intent. Not sure if it actually went any further than a prototype, it was probably way too early to actually be viable?
In regards to viability, what's different now, is that we now have all of the neccessary technologies to actually build a system like this that works. Today, the most valuable resource in the corporate environment is Attention and it's being squandered everyday based on the addition of all of these communication channels (Email, Messaging Clients, Workflow Products, Sneaker-net) being added and filled with messaging and tasks. Agile, Open-office, cube-to-desk exchange and all of these other productivity hacks have been implemented to increase communication, but we've kind of reached a sort of peak-communication situation that's beginning to cause diminishing returns in the case of productivity.
Communication is great, but too much communication can become toxic. What we need are smart messaging systems that can be deployed at the enterprise level and integrated into user workflow to learn how users respond to a message to identify questions like: what is the next action they take after reading that email? How quickly do they reply? How long was the message? What was the subject and topic? Who sent the message? How long was the message?...
As you can see, there are a huge number of questions. AI and Machine Learning solutions are probably the only thing that can actually figure out an answer or something close to it? Based on being able to answer these questions, communication and attention can be managed more efficiently, providing much greater value to the organization and less cognitive dissonance to the individual.