Coaching Networks - how to upskill the frontline workforce for an age of automation?

Mila Budeva
Mila Budeva
Nov 20, 2017

The rapid development of technology has faced the frontline workforce with a two-fold challenge.

On the one hand, the anticipated automation of processes in retail, manufacturing, warehousing and logistics poses the question of whether frontline workers will be able to adapt and acquire the skillsets required by new tasks performed in collaboration with machines.

On the other hand, an ageing workforce in sectors such as field service and construction is exacerbating the skills gap. It’s been estimated that by 2050, the UK will have a deficit of 36,800 engineers and 66,800 construction workers, meaning that human knowledge and expertise in the field is rapidly decreasing while technological complexity is growing [1].


In this context, it is important to raise the question:


Will technology evolve in such a way that will upskill frontline workers for these challenges?


Let's look at some of the latest trends in the technological upskilling of desk bound workers to understand the opportunity for deskless workers. As we hope to communicate in the following series of articles, the opportunity is huge and incredibly exciting for organisations looking to improve the productivity and compliance of operations, whilst also empoweing their frontline workforce for an age of automation.


Coaching networks and guided workflows

As Gordon Ritter from Emergence Capital notes, ‘AI-based Coaching Networks can be workers’ best defence in their race against machines' [2]. These networks use the abundance of data on workers’ activities and behaviour to identify best practices, using machine learning. Then, with the help of new intuitive user interfaces, predictive and prescriptive analytics, these best practices are communicated with workers as next best actions, in real-time and in context, guiding them through their daily activities.


Coaching networks are already being used to drive the productivity, skills and performance of desk-based workers. For example, an AI solution can listen in on a sales rep’s call and, based on its learnings, provide real-time recommendations to improve their chance of closing a sale. Similarly, AI can help customer support agents to improve service and reduce average handling time.


So how can coaching networks and guided workflows be implemented in the monitoring and execution of frontline operations?


Coaching networks for the frontline workforce

AI, Machine Learning (ML), connected devices, Big Data and IoT can equally be used in field and on-site operations, directing frontline workers to the highest priority tasks and providing them with task specific knowledge and best-practice actions and step-by-step guidance on how to implement these.


In the instance of retail operations, next best actions can be determined from a variety of systems, some of which are prevalent in most of modern trade environments already, such as electronic point of sale systems (EPOS) and e-commerce click and collect systems. Others are newer technologies in the process of adoption, such as shelf edge sensors, robotic cameras and image recognition technologies. Algorithms analyse the collected data in-store and:


zaptic-form-tick.png predict issues such as out of stocks or longer queueing times;

zaptic-form-tick.png prescribe pre-emptive or corrective actions based on the best practices found in the data.


This is all communicated with workers via alerts, notifications and guided workflows, using the most intuitive user interfaces for the device types which are readily available to workers. These are commonly mobile but also wearables and AR, supporting devices such as Google Glass further upstream in the supply chain.


For example, a store associate can receive an alert on their smart watch to re-stock milk cartons, based on data from cameras in the fridge identifying lower on-shelf availability. In another instance, a store associate performing an inventory cycle count can follow step-by-step guidance on their tablet, walking them through each step of the process and enabling them to simultaneously record and share data with the head office.


What is more, providing frontline workers with guidance at the point of action, ensures that everyone performs tasks to the same standard and facilitates the onboarding of new employees and seasonal staff. This way, retailers can ensure consistency across outlets and among store associates of varying levels of experience.


The opportunities beyond retail and customer facing roles in other sectors such as field service are enormous. To provide just one example, thyssenkrupp Elevator uses Microsoft’s Azure IoT Suite combined with the HoloLense to direct workers to the highest priority tasks and provide them with real-time guidance and access to remote assistance, improving the safety and efficiency of thousands of service engineers [4].


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