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While the idea of machines “taking over the world” makes for compelling science fiction, businesses are discovering that the most powerful use cases for intelligent automation (IA) involve people and machines working together as a team. Of course, managing a mixed workforce of human workers and “digital workers” presents its own unique challenges. Here’s a look at the critical challenges—and how to address them.
Intelligent automation has turned the corner from science fiction to business reality and is quickly becoming an essential capability for companies across industries. This rapid shift often fuels concerns that machines could take everyone’s jobs.
To help quell the anxiety, business leaders should address it openly and directly. Companies often pursue intelligent automation in silos without explaining the vision to their constituents, which only feeds into this anxiety and creates an environment of mistrust, where whispered rumors take the place of facts and clear messaging.
From the workforce’s perspective, the key benefits offered by IA include improved job performance and greater job satisfaction. As such, IA should be positioned as a tool to augment human workers—not replace them—and liberate them from repetitive, tedious tasks so they can focus instead on activities that offer more value, and oftentimes are more rewarding. (See also: Superteams and Automation Services)
Although cost reduction is an important benefit offered by intelligent automation, it shouldn’t be highlighted as the primary objective. Instead, IA should be framed in a way that helps people operate more efficiently and effectively, with cost reduction simply being a natural outcome of that process.
One of the best ways to get workers on board with IA is to involve them in the design and implementation effort. Direct and early involvement helps reduce the fear of the unknown and create a sense of personal ownership. Automation then becomes something people are actively involved in doing, rather than something that is being done to them. This helps overcome resistance to change and lays the foundation for a culture of collaboration between people and machines.
Similarly, business units should be closely involved in a company’s intelligent automation initiatives since they are closer to both the business processes and the workforce. In particular, they should retain significant autonomy and control over IA decisions, rather than having the technology forced on them from above. Leadership can provide useful direction, impetus, and structure, but ultimately decisions and actions related to IA should be led by the business units.
As with most things in life, talk is cheap when it comes to intelligent automation. To give weight and meaning to its positive statements about IA, management needs to back those statements up with tangible action.
A critical step to the effective management of a mixed workforce of humans and machines is to formally redefine people’s job roles. Clearly defining the activities and skills that are uniquely human, while at the same time identifying the benefits (and responsibilities of) their digital counterparts helps lay the groundwork. These redefined job roles should place much greater emphasis on activities and skills for which humans are uniquely suited to add value, such as creative analysis, human intuition and judgment, situational awareness, flexibility, emotional sensitivity, personal communication, and the ability to cope with unexpected situations and exceptions.
Within their narrow domain of expertise, many bots are already capable of outperforming their human counterparts. However, even the most sophisticated IA systems lack the general knowledge and flexibility to match the creativity, sensibility, and adaptability of an average human. That’s why humans continue to play a primary role in critical activities such as research and innovation, complex problem-solving, and high-impact customer interactions.
Human workers should be responsible for ensuring their digital counterparts remain functional throughout the day and quickly resolve any problems that arise. They should watch for process exceptions and system defects and then determine how to address them. They should also constantly look for ways to optimize and improve the processes and IA systems for which they are responsible.
One promising approach is to put human workers in management roles where they have overall responsibility for a business process—and for developing and improving the capabilities of their digital workers to execute that process as effectively as possible. In this model, the human process owner’s performance and productivity is directly tied to the IA’s performance and productivity, giving the human a built-in incentive to embrace and improve the IA systems. For example, digital workers could be assigned as direct reports to the human process owner, with the human’s work performance rated against metrics that depend in part on the productivity of the digital workers that report to them.
To be able to get the most value from a mixed workforce of people and machines, human workers will need new and improved skills. Most of the required skills can be obtained by retraining the existing workforce. However, there will likely be a few skill gaps that can only be filled through hiring and recruiting, which may also help accelerate the acquisition of advanced technology skills and drive IA-based process re-engineering.
In order to collaborate effectively with digital workers, humans will need to shift from a ‘do the work’ mentality to a ‘drive the work’ mentality where they are primarily responsible for controlling the work process, managing and maintaining their digital counterparts, and identifying opportunities for continuous improvement. Also, once freed from personally doing all the work, human employees can strategize to find new ways to contribute by engaging in higher value-add activities they may not have had time for previously.
Performance evaluations for humans working with machines must be tailored to the new roles, skill requirements, and expectations of a mixed workforce environment. Performance criteria and incentives should not only focus on how well humans do their own assigned job tasks but also their ability and willingness to collaborate with machines as part of an augmented workforce.
In the beginning, special reward programs might be needed to encourage workers to adopt and embrace IA. However, collaboration with machines should eventually become a general expectation and standard part of the job.
One way to help people embrace IA as a normal part of their jobs is to evaluate and reward them based on their actual work output, not the number of hours they spend working. With an output-based model, workers have a built-in incentive to accept any help they can get—including help from non-human workers. Similarly, people will have an incentive to become early adopters and embrace training that helps them get the most value from their new automated teammates.
To help reinforce the desired behaviors, human workers should be reviewed against the new IA collaboration requirements at least annually (and more frequently in the early stages when the new requirements are still taking root). People should be judged not only on the breadth and depth of their traditional skills but also on the number of digital workers they collaborate with and how effectively they use their digital counterparts to achieve the desired output levels. Human workers should be rewarded as they expand their skills and demonstrate a willingness to manage and collaborate with machines.
Managing digital workers is conceptually similar to managing human workers. The main differences are in the methods and details.
Just like human workers, digital workers should be “hired” based on specific job criteria. Also, they need to be brought on board and trained, and then “introduced” to their human teammates in a way that helps them be accepted and embraced. In addition, digital workers need clear goals and performance guidelines; clear roles and responsibilities; ongoing training and skills development; and regular performance evaluations. In addition, just like any other worker, digital workers should be subject to corrective action and/or termination if they don’t perform as expected.
Companies need methods and approaches to identify digital workers that are delivering inaccurate outcomes or performing tasks of little or no value. Also, intelligent automation should be designed with formal controls to help ensure robust performance and compliance with current and future audit requirements.
A digital worker’s first day on the job is its hardest because it has the least amount of data about how to do things properly. However, as the IA learns (or is improved through logic updates) it will be able to steadily earn the trust of its human teammates.
Business units should be responsible and accountable for the effectiveness of their digital workers. IA performance should be reviewed at least annually, with a focus on volume, reliability, and quality. Key metrics to consider include: downtime due to maintenance or problems, actual vs expected processing volume, and mid- and post-deployment survey ratings by humans who worked with the IA. After the review, the business unit can decide whether to keep the digital worker, seek additional training to improve its performance or terminate its use.
An integrated performance monitoring tool could help by enabling management to see the individual and combined output and effectiveness of human workers and their associated digital workers across entire business processes.
Decades ago, when the auto industry first introduced robots to the shop floor, a number of workers responded with anger and fear by sabotaging the machines (Salpukas, 1972). Although the mentality toward automation has since evolved, organizations should continue to actively reinforce a work environment and culture that fosters collaboration—not conflict—between people and machines.
For most companies, widespread adoption of intelligent automation is only a matter of time. And in many industries, the time is now. A company can help overcome fear and resistance to change by clearly explaining its IA strategy and assuring workers that the goal is not to replace them with machines but to augment their capabilities—enabling them to do their jobs better and faster while focusing on tasks that are more valuable and rewarding.
Gina Schaefer is a managing director who leads the Robotic and Intelligent automation practice at Deloitte Consulting.
Ryan Sanders is a senior consultant within Deloitte’s Analytics & Cognitive Consulting practice.
Douglas Williams is a managing director within Deloitte’s Analytics & Cognitive Consulting practice.
Martin Hoeedholt is a senior manager within Deloitte’s Strategy & Analytics Consulting.
Saurabh Vijayvergia is a senior manager within Deloitte’s Analytics & Cognitive Consulting practice.
Salpukas, A. (1972) Young Workers Disrupt Key G.M. Plant. Ohio: The New York Times, [accessed 19 June 2020].