Work overload, lack of time, changing schedules, high pressure and stress – we’ve all had to deal with it occasionally and some of us face it almost every day. The stress causes us to do less than optimal things at less than optimal times. Having a personal coach to keep you on track with your tasks would be fantastic but is hardly ever realistic.

The PlannerScape team has set out to create a system consisting of intelligent tools for managing time and tasks in an increasingly decentralized workplace. We help you do the right things at the right time and to give you back your time. Our approach is based on years of literature study and observations. Below I will briefly explain what we are working on at the moment and what is ahead.

Tasks are more similar than they are different

One of our most important findings that PlannerScape is based on, is that everyone prefers to manage tasks and time in their own way. Some people prefer simple to do lists, while others prefer an Urgent-Important Matrix or Kanban board. Some prioritize tasks by urgency, while others by economic benefit. Across the board, tasks are essentially the same regardless of the chosen method. Only the ways to display or prioritize are different.  PlannerScape offers different ways of managing tasks and time, so you can work in teams and each team member uses his or her preferred way of displaying what to do.

Creating an unburdening user experience

A second notable finding is that people don’t want to spend significant time configuring their tasks. (They want to save time, not spend time!) The need to enter tasks and task properties turns out to be an important detractor for the usage of these tools. While different ways of prioritizing tasks require different inputs, one of PlannerScape’s goals is to reduce data entry to a minimum. One way we do this is by using machine learning to deduce properties of tasks. With thorough training, an AI program can make reasonably good predictions. Users only need to make corrections.

See below for some results of a demo we did on the 29th of August at a meet-up in Hong Kong. In a live demo of our AI software, we asked the audience for example tasks to enter in to the program. After pressing the ‘Plan’ button, all fields were automatically filled in based on trained ‘experience’ stored in the AI model. Below, a green field means that the AI is confident of this answer. Orange and red fields mean that the AI is not certain.

Overcoming technical challenges

It’s important to note that the examples were not pre-programmed in the system. Things like context, importance and to whom the task could be delegated are deduced on the fly from ‘experience’ stored in the model. Initial results are very promising, but we’re focusing on further improving the accuracy. There are different ways to do this. One way is with more training. Another is more relevant training. Not every user is the same, so we need to extend the details of a task in our machine learned model to include the context of our user as well. In future blog posts, I’ll go in to more detail on how we attempt to do this.

Asking the right questions

One other way PlannerScape tries to help you achieve your goals is by offering the option to delegate or outsource tasks. When you enter a task, for example “Buy flowers” you know exactly who it is for, when the flowers need to be there and what the occasion is. The person you outsource the task to needs to know this as well. To make a task suitable for delegation or outsourcing you need to provide much more detail about what you expect. Instead of you having to think about the instructions, PlannerScape will need to ask you the relevant questions and give suggestions for answers. This requires a different kind of artificial intelligence as it uses different data structures than ‘just’ deducing properties.

The Tasks Ahead

Apart from other technical challenges (like how PlannerScape actually helps you, like a GPS, to navigate time and do the right things at the right time), we have other tasks ahead. To introduce our PLAN token, which is designed to facilitate low overhead transactions for outsourced tasks (and to activate certain features in PlannerScape), we are preparing a Token Generation Event (“TGE”). In future blog posts we’ll explain about the legal aspects and token economics (“tokenomics”).

The scope of PlannerScape is broad and exciting and we cannot do it alone. We are working on partnerships in different areas, ranging from technical partnerships to launching partners for outsourcing tasks, as well as partners for the TGE. We’ll inform you about those as well through this blog. If you think you can help, let us know.

Task and time management aren’t always easy, but we’re here to help. Please stay tuned to learn more about task and time management in an increasingly demanding world, as well as about the solution for the future of work: PlannerScape.

The easiest way to engage with our project is to join our Telegram discussion group.