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How New Human-Machine Collaborations Could Make Government Organizations More Efficient – SPONSOR CONTENT FROM DELOITTE

Most of today’s jobs will not be here tomorrow. The World Economic Forum predicts that 65 percent of children entering primary school today will ultimately end up working in completely new job types that don’t exist today.

This represents an opportunity for government organizations and employees to intentionally redesign work and jobs to not only accommodate the role of technology and machines, but also to design for broader economic, workforce, and societal shifts.

For example, a government HR manager who now only hires full-time employees may need to start tapping into a pool of crowd workers or gig workers for certain types of work. A procurement department may now need talent with blockchain expertise to manage secure supply chains. Or given the increased use of algorithms in government systems, agencies now need to prevent algorithmic bias from creeping into public programs.

In a recent Deloitte survey of more than 11,000 business leaders, 61 percent of respondents said they were actively redesigning jobs around artificial intelligence (AI), robotics, and new business models.

The prevalence of automation, as well as machines working alongside humans, is increasing in government too. According to the US Office of Personnel Management, almost one-half of government agencies’ workloads could be automated and close to two-thirds of federal employees could see their workloads reduced by as much as 30 percent.

That makes it an imperative to focus not only on how humans and machines can best collaborate at work, but also how that collaboration can enable better work processes and create more value.

Although there are many ways in which humans and machines can work together, we typically identify the human as the supervisor or the primary worker. This view can be limiting. Machines work for us, with us, and sometimes they even help guide us.

To harness the real potential of the human-machine partnership in the workplace, we should consider the full spectrum of possibilities. Machines are already taking on a wide range of routine, manual tasks. When this lower-value, tedious work is automated, opportunities are created to reduce cost and redeploy staff to more valuable activities. For instance, the Food and Drug Administration’s Center for Drug Evaluation and Research (CDER) uses robotic process automation (RPA) in its drug application intake process. This slashed application processing time by 93 percent, eliminated 5,200 hours of manual labor, and saved $500,000 annually.

Automation also allows jobs to be split up between humans and machines. When a job is broken into steps or pieces, automating as many as possible, humans are left to do the rest and, when needed, supervise the automated work. U.S. Citizenship and Immigration Services uses chatbots to answer basic questions. This frees up time for employees to respond to more complicated inquiries.

There are many lenses to use when thinking about these partnerships:

Shepherd: A human manages a group of machines, amplifying their productivity. In this instance, a human might manage a fleet of autonomous buses. Or a nurse manager could oversee a group of hospital robots.

Extend: A machine augments human work, combines their strengths to achieve faster and better results, often doing what humans simply couldn’t do before. For example: A department of human services could use cognitive technology to help predict which child welfare cases are likely to lead to child fatalities. Once high-risk cases get flagged, they are carefully reviewed and the results are shared with frontline staff, who then choose remedies designed to lower risk and improve outcomes.

Guide: A machine prompts a human to help them adopt knowledge. Machines help humans learn new knowledge and skills; or adopt desirable attitudes and behaviors. For instance, a researcher can set up a custom digital assistant that not only knows what current research a person is doing but can also crawl the web for old and new research relevant to the topic that the researcher might not be aware of.

Redesigning the Work
Work redesign is fundamentally about making sure that government agencies—their work, workforces, and workplaces—keep pace with shifting opportunities and needs and prepared for the future. But architecting jobs can feel overwhelming without a clear idea of where to start. We recommend these three steps:

Step One: What Will the Future Look Like for Your Organization?
The first step is to think long-term. Imagine what the future could look like, determine how this could impact your organization, and plot out the course. There are a host of external forces—technological advancement, automation, changing customer demands and behaviors, or the rise of new business models—that could impact how your organization will deliver on its mission in the future.
Imagine you’re looking out to 2030. People are living longer and staying in the workforce longer. The composition of the workforce has changed too. Digital natives have joined the workforce, as well as more freelancers and contingent workers. Technology is omnipresent and AI, augmented reality, the Internet of Things (IoT), and robotics are integral parts of the workplace. Organizations may have new analytical capabilities, thanks to unprecedented volumes of data and increased computing power.

Think about what all these factors and the changes could mean for your organization. By zooming out, you will explore beyond what technology and other disrupters can allow you to improve on what you’re already doing. And envision how these changes could enable you to unlock entirely new outcomes.

Step Two: What Work Should Be Done Differently?
Given the vision you’ve imagined for your organization and the role disrupters can play, think about the current state of work across the organization. Ask: “What might we do differently across different jobs and work units to achieve greater impact and desired outcomes in the future?”

The process of deconstructing (and then reconstructing) work, and then defining the new roles that can support the new disciplines, can be broken down into four focused pieces with a simple framework: start, stop, change, and continue (S2C2). Some work will be catalyzed by technology and other disrupters and start anew. Meanwhile, some dull, dirty, or dangerous work may be automated or stopped entirely. Some work will be still critical to mission and business outcomes but will be changed by the application of technology. And, lastly, some work will continue relatively unchanged.

For example, a government agency might start hiring gig workers for certain skills such as data science. A transportation department might stop installing and maintaining traffic lights when autonomous vehicles become the norm. Given the dangerous nature of their work, firefighters might change how they extinguish fires and use drones and robots instead to assist them. Social workers will continue to visit their clients in their homes and build a personal connection.

The S2C2 lens can be applied to different levels of an organization—to a single role, across stages of a program, or at a high level within an organization’s department. Agency leaders can use this information to gauge the downstream impacts on jobs. Starting new activities might require leaders to create new roles while changing and stopping certain activities might require reskilling and redirecting talent.

In the future, the “matching” of evolving skills to evolving work within the context of a redesigned job should be very intentional. This can help ensure that you build a job that is a logical, holistic combination for a single person to have, rather than a somewhat haphazard mix as it often is today.

Think of it like this: What if by 2030, your current role no longer existed? How would the work get done? How would you rethink doing the work?

Step Three: Who Should Do the Work?
While reconstructing work, asking the question, “Who should do the work?” can help organizations explore new talent options such as crowd workers, gig workers, or digital labor. Depending on factors such as how specialized the task is, or whether the desired capability requires a security clearance, some talent options might be more suitable than others.

To engage outside perspectives, leaders may want to develop or use a new crowdsourcing platform. Working with digital labor or AI may require leaders to select the most appropriate form of human-machine collaboration to answer the previously asked question, “Should an AI technology augment the human worker or relieve them?” All these considerations feed into work redesign.

Looking to The Future
By reconstructing work, government organizations can not only capture efficiency gains through human-machine collaboration; more importantly, they can find new avenues to create value that may not have been possible before.

Future work scenarios don’t simply feature the human as supervisor of the machine. Instead, they consider the full spectrum of possibilities for human-machine pairings. And as humans and intelligent machines working together becomes the norm in the workplace, organizations have an opportunity to maximize the potential of both. This effort, when realized, can fundamentally help create better work processes for everyone and more value to taxpayers.

Read the full article here for a step-by-step guide to optimizing human-machine collaboration.

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