AI was supposed to be nothing more than a tool to automate boring repetitive tasks. However, AI is much more than that. In fact, AI is already covering today’s headlines. Whether it’s executing geopolitics—with the US blacklisting some of China’s AI companies, finding homes—with AI searching for your perfect apartment, playing games—with AI playing your Angry Birds game, or showing your body—with the rise of full body deepfakes, AI has found its way to every element of our daily lives.
These recent examples show a wide range of AI’s possibilities, but for many people, AI’s potential for businesses is still rather uncharted territory. We believe that bringing AI into the future workforce is about human-machine collaboration. Cyrille Bataller, Managing Director Applied Intelligence, explains how that might look like.
AI’s current impact on the workforce of the future
Automation has always been viewed as a valuable addition to the current workforce, even though it had its limitations in the past. “Even robotic process automation is very basic in what it can do; as soon as there is natural language text or images that require categorization or that are used to make judgement calls, traditional RPA bots can get stuck, and therefore, even though they are high volume repetitive activities, they could not be automated before.”
Adding the human element is, therefore, key to realize AI’s full potential. “When you augment with AI, you can suddenly deal with much more complex repetitive tasks, so you can automate a lot more and generally what you automate is what is less interesting for people, so you are doing them a service as you are doing the tasks better faster and cheaper, freeing up people to focus on the more important side of their job,” Cyrille adds.
Clearly, your focus should be on the larger possibilities that AI and robotics bring when combined with human capabilities.
Three ways to enhance human-machine collaboration
Cyrille sees this intelligent automation coming to life in three ways, as a ‘virtual workforce’ to enhance businesses as well as worker experience and expertise.
These new levels of automation are liberating employees from mundane and repetitive tasks and allowing their jobs to be reimagined.
First, there’s the standard automation of high-volume, complex, repetitive tasks which most people think of. Yet, developments in new AI areas means the list of tasks that can be automated is growing at a fast pace. Natural language processing (NLP) allows organizations to analyze text documents faster than ever before, for instance.
Cyrille gives another example: “There’s an insurance company that receives 4 million customer emails per year in 3 different languages. They are keen to handle these emails appropriately and so have asked us for a target of 98 percent accuracy in our categorization. There are 400 categories in the types of emails received, and they had a team of 80 full-time people just routing these emails.”
“So, we’ve built a sophisticated categorization model with multiple layers in order to route these emails with high accuracy. To achieve 98 percent accuracy, we only automate about 25 percent of the routing, the rest are a bit below this confidence level, and is double checked by staff. If they relaxed the accuracy target to 95 percent, we could maybe handle around 70 percent of the emails. This means we are freeing up around 30 out of the 80 full-time employees, and these employees can now respond to emails instead of merely routing, to provide a better service to customers.”
“The second part is augmented decision-making. What I call collective experience: the 10 top experts in the company and their 100 best individual decisions are used to train an algorithm that will create a prediction. You then give this to the junior employees to help guide them towards what is likely the best decision to make. And by providing data insights: humans are not good at crunching high volumes of data, but machines are. Relying on a machine to show trends in a large data set helps us make the right decision faster.”
For example, a European land registry is using cloud-powered deep learning and computer vision to automatically compare property records and aerial images. This enables employees to investigate detected discrepancies and non-compliance with about 80 percent accuracy.
Cyrille continues with the Safe City test bed, a collaboration with the Singapore Government: “The third aspect is scaling new, innovative business services because we don’t have the capacity. For example, to watch every CCTV camera in a city, but when we have bots that are cheap, cloud-sourced and efficient, and can mimic what a human would do watching a camera—such as counting people and vehicles—then you can suddenly innovate. You can have 30,000 bots watch 30,000 cameras 24/7 that can alert you if any problems occur. Then there’s a need for more employees, not fewer, to handle the alerts – thus delivering a better smart city service to citizens.”
Beyond proof of concept
Be aware that one successful pilot or proof of concept is for sure not enough to launch a full-scale implementation of your virtual workforce across your organization. It is vital to make the pilot an operational one, where your business can securely test the impact on business operations and employees. Like the skills that are required to use it, the different work style it may need and the different tasks that people will need to do. In our view, the right path forward is executing a non-business-critical pilot before doing one that is customer-facing.
The right path forward is executing a non-business-critical pilot before doing one that is customer-facing.
Having practical evidence of how the pilot works and impacts everyone in the organization is critical in aligning key decision-makers within the organization and gaining their support. It will also help adjust the business processes, facilitate change management and enable scaling to production afterwards.
We’ve identified four must-do steps to bring down internal resistance to change:
Demystify. To counter popular misconceptions, providing in-depth information about how the virtual workforce will change jobs and your workplace is an essential step. It will help your colleagues to understand what is coming and why change is needed and discard any unfounded concerns they might have.
Demonstrate. Seeing is believing. Show in practice how the pilot works and what value it will bring employees and the organization.
Advertise. Communicating the benefits of intelligent automation is key to getting employees on board. The more people are informed, the wider the understanding will spread and the better the project will be accepted.
Demonstrate again. Prioritize low-hanging fruit across the different business units to show the value of the innovation and take away any resistance from sceptics. Involve them in the process to give them a sense of responsibility, recognition and pride.
The future of intelligent automation
An AI-based virtual workforce can generate powerful outcomes for the whole organization today. Transformational change should inspire you and your organization to accelerate and make bold moves towards the future.
You should disrupt before you can be disrupted.
“AI has a transformative power: when you suddenly have access to a human-like, cheap workforce that you can provision very fast, you have transformation potential for your organization and disruptive potential for your industry,” Cyrille emphasizes. “You should disrupt before you can be disrupted.”