Imagine you didn’t have to read all the emails sitting in your inbox; instead you could focus on the important ones, while the rest automatically sorted themselves out. Or say you were working for a retail company and all common customer requests that came through your social media and communications channels could be addressed automatically, giving you time to resolve the more complicated ones.
These don’t have to be imaginary scenarios. They can turn into reality with the adoption of intelligent automation.
With the development of artificial intelligence (AI) technologies, automation in the workplace has gone beyond simple robotic process automation (RPA). For example, AI-powered bots are already able to execute certain repetitive, complex business processes with more accuracy and speed than any human employee.
This type of applied intelligence is enabling what we call the ‘virtual workforce’: an intelligent software workforce that can emulate and complement the human workforce, elevating human jobs and transforming business. But saving time, improving productivity, and enhancing customer and employee satisfaction are only a few of the many benefits that intelligent automation offers.
Given this potential, it is not surprising that “Gartner, Inc. predicts that, by 2021, 70 percent of organizations will assist their employees’ productivity by integrating AI in the workplace.”*
Tapping into the value that the virtual workforce offers is a must for organizations looking to drive exponential growth. However, there is still some way to go before this becomes the norm. To help organizations on their journey to adopting the virtual workforce, this article will take a look at how far intelligent automation has come, and the three core ingredients to maximize its success: adopting a responsible approach, moving beyond proof of concept, and overcoming resistance to change.
The evolution of intelligent automation
The pace of AI developments over the last decade has enhanced the ability and accuracy with which virtual workers mimic human capabilities. Crucially, these new levels of automation are liberating employees from mundane and repetitive tasks and allowing their jobs to be reimagined. Payment and invoice reconciliation, response to customer service queries, and insurance claims processing are among the more familiar examples.
There are three key transformations that artificial intelligence enables:
Automation:There are more automated tasks today than there have ever been. Developments in new areas of AI means the list of tasks that can be automated is growing fast. Advances in natural language processing (NLP) allow organizations to analyze text documents faster than ever before.
We see, for example, a European insurance company using AI to route the four million customer emails, in multiple languages, it receives every year to the right service in their organization. This is enabling faster responses and increasing customer satisfaction.
Augmentation: Applied intelligence is transforming how people do their jobs. Take caseload processing. By equipping employees with AI assistants, case workers can handle incoming cases more effectively and efficiently, improving their overall productivity. Such AI assistants can help employees make the right decision faster, drawing on collective enterprise experience and data insights.
A European land registry, for instance, is using deep learning and computer vision, powered by the cloud, to automatically compare property records and aerial images. This enables employees to investigate detected discrepancies and non-compliance with about 80 percent accuracy.
Innovation: Intelligent automation opens up a new scale—and speed—of working. The flexibility and scalability of AI-powered automation equips organizations with large numbers of virtual workers to easily scale up or down according to business needs, allowing organizations to pivot to new opportunities as they arise.
Accenture’s Safe City test bed, a collaboration with the Singapore Government, is a good example. Using advanced video analytics applied to existing video surveillance cameras, the test bed successfully helped the government predict crowd behavior, coordinate resources, detect and respond to incidents and facilitate collaboration among the various agencies. As a result, it succeeded in delivering public service for the future.
Adopting a responsible approach
For all the benefits it brings, intelligent automation can raise questions about social and ethical responsibility, making it challenging to implement. Adopting a responsible approach to implementing the virtual workforce is key to doing it successfully.
So, what does responsible automation mean? In short, it means structuring projects, including pilots, with the organization’s strategic objectives in mind, while anticipating the unintended consequences that this might have on employees, customers and society as a whole.
A shift to responsible thinking also means articulating AI transformations with a people-centric business case, looking beyond the immediate cost cuts that intelligent automation might allow, to the longer-term impact it will have on all parties involved.
Take, for example, a contact center with a hundred employees. Replacing a quarter of employees with virtual workers to handle part of the customer interactions might help the center save costs but focusing solely on this would not be a responsible approach to intelligent automation.
Instead, the center should look at how it can apply the incremental investment capacity and the twenty-five freed up resources towards strategic initiatives, such as proactive outreach to customers. It should also communicate the benefits the transformation will have for employees and customers alike — for instance, AI bots will serve customers faster, improving customer satisfaction. In the meantime, the remaining seventy-five employees’ jobs would be elevated as they deal with more complex and interesting situations.
Moving beyond proof of concept
The vast majority of organizations are still either thinking about intelligent automation or doing a proof of concept. Or to be more accurate—they are doing many proofs of concept.
Few have been bold enough to go to production for specific use cases. Even fewer have developed and executed a strategy to leverage AI across the enterprise. This is hardly surprising given the complex organizational questions, business process adjustments, and change management issues that they need to handle. In fact, technology often proves to be the easier part.
Scaling to production from a proof of concept can be challenging – but doing so can unlock tremendous value for the organization.
However, one successful pilot or proof of concept is certainly not enough to launch a full-scale implementation of the virtual workforce across the enterprise. The pilot needs to be more than a technical demonstration in lab-like conditions. It needs to be operational so the organization can test the impact on business operations and employees: the skills that are required to use it, the different work style it may entail and the different tasks that people will need to do. In our view, the organizations that get it right are those that have first done 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.
Overcoming resistance to change
Concerns about the capabilities of AI and the impact of virtual workers on the human workforce means organizations are bound to meet resistance to change. Fear of job loss, concern about AI bots making decisions, and questions about the reliability of AI-powered technologies are some of the most common barriers organizations face when going down this route.
To help organizations counter these concerns, there are four must-do steps:
- Demystify. Prominent perceptions of the virtual workforce are too-often based on creative and far-fetched movie and pop culture representations of AI. To counter these, providing in-depth information about how the virtual workforce will change jobs and the workplace is a crucial step. It will help employees understand it better and discard any unfounded concerns they might have had.
- Demonstrate. After providing information, businesses must show in practice through an operational, non-business-critical pilot the impact the virtual workforce will have—as well as what value it will bring employees and organizations.
- Advertise. Communicating the benefits of intelligent automation is key to getting employees on board. The wider the acceptance extends, the better placed for success the project will be.
- Demonstrate again. After the success of the first intelligent automation project, businesses need to look at the possibility of creating cross-enterprise change with a second use case. What low-hanging fruit is there across the different business units? Prioritizing these cases will be key to seizing value from this innovation and gaining the support of any remaining sceptics.
The future of intelligent automation
An AI-based virtual workforce can generate powerful outcomes for the whole organization today. Businesses need to find those ambitious use cases where they can deliver radical outcomes – think 10x, not 10 percent. They should then trial them in a safe way, with a view to scaling quickly to reap the benefits early and disrupt their industries.
This is, however, just the beginning. Approached responsibly, the virtual workforce has the potential to reimagine how the wider society works, unlocking currently undreamed-of possibilities. This transformational change should inspire organizations to accelerate and make a bold move towards the future.
*Gartner Press Release, Gartner Predicts 70 Percent of Organizations Will Integrate AI to Assist Employees’ Productivity by 2021, January 24, 2019, Gartner Predicts 70 Percent of Organizations Will Integrate AI to Assist Employees’ Productivity by 2021