During the average day, we get distracted and are forced to switch between apps, email, and messaging channels more than 400 times. This means that about every 40 seconds, our attention at work is being disrupted.
This constant fragmentation of our time and concentration has become the new normal, and while many workers have adapted willingly to the situation, the bigger picture is that it is eroding our ability to focus on a task and be productive. Some research has gone so far as to suggest it has a detrimental impact on our IQ and brain cells.
This continuous partial attention, a term coined by ex-Apple and Microsoft consultant Linda Stone, means the average employee is in a constant state of alertness, unable to give complete focus to anything. This is unsustainable, and as employee experience rises to be a c-level priority, businesses need to seek a solution.
Increasingly it seems the answer may lie in artificial intelligence (AI) and machine learning (ML). Curious to discover more, we’re working hard to build immersive demonstrations and scenarios to evaluate how technology might enable workers to regain their focus and concentration in the future.
Technology Needs to Be Task-Focused
A McKinsey Global Institute study suggests workers spend nearly 20 percent of their time looking for information internally or tracking down colleagues. This is often because various apps and systems don’t talk to each other either, creating much unnecessary, manual work. Workflows are very often driven by the needs of the application or the constraints of the system rather than by what works best for the business or the employee.
As a result, today’s IT experience is application-centric. But technology arguably needs to focus more on the task or outcome that the user is trying to achieve. As AI and ML capabilities mature, microapps and other intelligent features will become better integrated at a task-based level, enabling the worker to focus on the job at hand. These intelligent systems will be able to organise a vast array of micro-functionality in a way that gives the user exactly what they need, at the time they need it.
Creating a Personalised Recommendation Experience
In our personal lives, we are used to recommendation systems in online consumer retail and digital entertainment services. What if we could apply those same concepts to an enterprise work environment? It could significantly help with focus.
We recently showcased a high street bank concept to show how AI-driven task and workload management could help employees in many different types of roles and industries, who all have an ever-present set of background activities, but need to be able to put them aside when clear focus on a foreground task is required.
In this bank scenario, the branch manager introduced an automated workflow, incorporating workspace intelligence and microapps, so that when a high-value customer entered the bank, that individual could be prioritised with all relevant information, forms, ID checks and financial specialists made immediately available through a single user interface, in just one click.
Creating Access to Several Types of Tools
People like to work in different ways, and the rise of intuitive, no-code workflow and automation tools will enable business owners and individual employees to build their own experiences that match the way they want to work. AI technology, or virtual assistants, will learn from what users do manually and recommend, or even automatically create, workflows, removing unnecessary distractions from their day.
In the era of context-driven AI, offering a choice of tools and APIs is important. I like to compare this to a Lego set, whereby an individual can start with a pre-designed model that works in the common case, but add parts, remove parts, and change it as much as they like to adapt it to their desired state. The bigger the range of building bricks, doors, wheels, axles, and propellers that all fit together in a variety of ways, the more imaginative and bespoke the final creation can be.
Creating Focus
It is common for workers to confuse importance with urgency, which can lead to overload and a lack of focus. In the bank example, providing a response to a customer request for a sales quote may be important, but if a high-value customer enters the branch, they immediately become the most urgent priority.
Systems of engagement will increasingly need to perform intelligent filtering and prioritisation, showing a user only what they need at the time they need it, deferring other notifications and tasks as necessary. Once we reach the point when systems can intelligently decide what, and what not, to put in front of a user, continuous partial attention could potentially become a phenomenon of the past. With time, knowledge workers will be enabled to retrain their brain to focus for longer periods of time, on creative projects or cognitive work, without distraction. It goes without saying this will have a tremendous impact on business productivity in the future.
