At the precise moment when knowledge-specific work is becoming increasingly automated, workers are progressively losing their ability to undertake deep cognitive tasks. Their concentration is shattered into a thousand shards by networking and communications tools.
This is the “deep work hypothesis” expounded by Cal Newport, a professor at Georgetown University, who says knowledge workers are ever more engaged in shallow work: “Non-cognitively demanding, logistical-style tasks, (are) often performed while distracted. These efforts tend not to create much new value in the world and are easy to replicate.”
For example, they spend an inordinate amount of time sending and receiving emails as opposed to thinking deeply about matters such as business strategy. Or using Facebook (an easily replicated task) as opposed to building the distribution systems which run Facebook (a hard to replicate task). Deep work is an essential skill for anyone looking to get ahead in the global economy.
In parallel, the very nature of work is changing. MIT economists Erik Brynjolfsson and Andrew McAfee state we are in the “throes of a great restructuring” and explain: “Our technologies are racing ahead but many of our skills and organisations are lagging behind.”
As a result, machines are becoming smarter, but we aren’t upskilling human beings at the same rate. They go on to say that this great restructuring is segregating jobs, where those whose skills can easily be automated will lose out, whereas others will thrive and become more valued. They identify this later group as individuals with the ability to work with intelligent machines: in other words, extracting results from intricate machine environments.
The emergence of Machine Learning and Artificial Intelligence (AI) make it possible for tedious tasks humans have performed to be automated by machines, fuelling speculation that we are on the cusp of mass unemployment and economists to propose there will be a hollowing out of middle level, white-collar jobs.
A study by the McKinsey Global Institute suggested that the nature of work is more likely to change than be automated out of existence. Imagine not having to do tedious tasks which every knowledge worker is manacled to — managing inboxes, booking time sheets, re-keying data into multiple systems. Instead, employees can break the fetters and do more challenging interesting work, arguably increasing productivity.
In addition, we should consider what skills will be in demand in the future — human resource and strategy officers identify the top three future job skills as being: complex problem solving, critical thinking, and creativity. What if Machine Learning and AI can be harnessed to sharpen the top three skills needed in the future by human workers?
This would partially address the issues raised by the deep work hypothesis, by equipping knowledge workers with the tools to undertake meaningful work which was cognitively demanding.
Consider the following scenario. An agent working in an airline contact centre that also organises holidays has just taken a complex booking from a customer for a large reunion in which 10 families are meeting up in Dubai for a wedding. The agent provides the AI Virtual Assistant with a set of travel requirements that consist of: multiple flight origination points with connecting flights; three families require car rental options while the others need minibus rentals; two couples prefer a high-end beach hotel, whereas the others would be happy with a mid-market accommodation next to the metro.
Once the agent feeds the requirements into the AI Virtual Assistant, it churns out a number of scenarios plus details of adjacent service providers based on the criteria provided. The agent then guides the software, adjusting the itinerary based on the conversation with the customer who made the booking on behalf of the group.
This is an iterative process in which the agent’s ability to cognitively mange this complex set of requirements is amplified by AI. It frees up the agent to apply the unique human touches, such as empathy, required to close such a high value transaction.
Augmenting human abilities
Elsewhere, understanding what obstacles an organisation is unwittingly placing before its customers can be really hard to determine. One European bank needed to improve the customer experience for its branches and websites for two lines of business — mortgages and personal banking. They wanted to simplify mortgage and the line-of-credit application and approval processes.
To do so, they implemented journey analytics, to provide a 360-degree view of the customer application process, including visibility into marketing campaigns. They used the tool to predict whether a customer will achieve a specific milestone on their journey.
The milestone might be asking for a quote or buying an insurance product. The real-time visualisation provides context about what is happening on the customer journey. If there are any barriers making it hard for customers to deal with the organisation, these can be identified and remedied.
AI-enabled tools such as these mean that customers are routed to human agents, where required, based upon their real-time behaviours.
The bank saw an increase in their customer satisfaction ratings to 4.43 out of 5 and 11 per cent of chats resulted in a positive outcome, such as booking a branch appointment or a scheduled call back.
Using Machine learning and AI tools to augment humans workers ability to think critically and creatively, has the potential to address the relevancy of the knowledge worker in a fast automating economy. As well as providing humans with fulfilling work, as opposed to mundane routine tasks, which will sooner or later be automated out of existence.
Rehan Khan is a managing consultant for BT based in Dubai.