The Creative Problem Solver Will Thrive in a World of AI

Danny Bearzatto, Head of Workforce Design, Ventia

Danny Bearzatto, Head of Workforce Design, Ventia

To understand the future, we can learn from the past rather than be beholden to it.

In 2008, I worked with a UK telco to modernize how they optimised and allocated work to their workforce. We had been working with management and the “control center” to increase field technician utilization, improve service level agreement performance,and reduce manual work allocation. Process changes and system re-configuration had made a step change but not the leap we needed. Getting buy-in for the new way of working was a struggle.

One Wednesday morning, the fire alarm sounded. The building was evacuated and given the sensitive equipment in the building, this evacuation lasted hours. Un-beknown to everyone at the time, the fail over process did not activate. Calls were not diverted to other control centers and no one was alerted to backfill for our site.

What happened next was a complete surprise to everyone. Over the next few hours, the system allocated and optimized 100 percent of the work without any human intervention and the workforce did not notice. The impact on performance was profound. The field technician utilization jumped by 20 percent and SLA performance lifted by 10-30 percent depending on the metric. The system could outperform the humans, if we let it. In this moment, we had our case for change. From that day, the business adopted a new way of working and performance remained high.

While some of this was due to people keeping their hands off the system, this was only possible because we had trained the system. We had engaged with the creative minds - the “old heads,” who had been there and done that–and we understood the business logic that the “best” controller used to succeed. This was incorporated into the business logic, prioritization matrixes and the variables the system used.

The mundane, repetitive and simple decisions that previously consumed 60-80 percent of a controller’s day were now taken care of by the system. While this enabled the control center to become a centre of excellence and reduce costs, for those that were the creative problem-solvers, they thrived.

Advances In Technology Don’t Make The Field Worker Obsolete, They Remove The Noise From Their Day And Allow Them To Tap Into Their Creative Problem-Solver Brain To Tackle More Complex Problems More Often

They applied their knowledge and instincts to the hard problems - the problems that previously meantthey were going to miss profitability or disappoint a customer. This gave them a new sense of fulfilment in their work.

For most organizations, automated work optimization and allocation has resulted in manual effort reduction, work allocation centralization, workforce utilization improvement, travel time and carbon footprint reductions. While these are tangible benefits, this only solves the supply side of the demand-supply equation, i.e. optimising supply to complete the demand in the most efficient way possible.

What we are seeing now is the other side of the equation being tackled - the reduction of the need to make a field visit.The key enablers of this are advancements in the applicability and affordability of sensors, robotic process automation (RPA) and artificial intelligence (AI).

New use cases have emerged to tackle the demand side of the demand-supply equation:

Thermal sensors in a telephone exchange that see exactly which piece of hardware is heating up, remotely execute a stored procedure to divert traffic and restart the equipment

Drones flying along kilometres of high voltage powerlines using video and AI to determine which trees create a risk and triggering maintenance tasksto trim trees in exact locations

AI solutions that use sensors in garbage bins combined with social media and live traffic data to understand if unexpected events are occurring and alter rubbish collection cycles or set out temporary bins.

All of these reduce the annoying side of being a field worker. Likebeing sent to an overheated telephone exchange to open doors and vacuum some fans, drive endless kilometres in rough hazardous terrain to not even get the chainsaw out of the truck or being sent to pick up a pile of rubbish next to over flowing bins.

Consulting companies and economists have concluded that AI will change the employment market. I see a significant opportunity for the creative problem-solver.

The key to the success in my earlier story was the systemisation of the experience and knowledge of experts to enable the right decision to be made by the system. Sensors collect the data, RPA automates already established processes and AI makes decisions it is trained to make. Yes, AI needs people to train it, but what it relies on is data. While not all data is perfect or of the highest quality to be feed into AI– the insight from experts can help to bridge this gap and realise the promise of AI.

These advances in technology don’t make the field worker obsolete, they remove the noise from their day and allow them to tap into their creative problem-solver brain to tackle more complex problems more often. This means that as complexity of field tasks increase, field technicians complete less jobs per day, average task times increase and, in many cases, work from home. They will become the ‘person in the room’ working alongside AI to help it to solve problems that it can not on its own. On occasion they will still need to make a field visit themselves. Just less often.