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How Automation Will Redefine The Organisation, Leadership and Workers

Posted by Mathew French

24 September 2019

Automation will certainly change human resources as we know it, but the impact will reach far beyond the HR department to encompass the entire employee lifecycle. Organisations that manage such transformation successfully will be those in which leaders and HR professionals recognise what tasks machines can do significantly better than humans and automate only those tasks. Focusing human effort on activities that humans do best will result in a happier, more engaged and more productive workforce overall. Designing such an organisation requires a much longer-term people strategy (one that goes beyond the usual succession planning), coupled with transparent dialogue with the existing workforce about the benefits of automation on what it means for them. This Blog in the HR automation series explores the broader implications that automation will have on leadership, employees and the entity of the organisation itself.

In the last Blog in the this series on whether to automate or not to automate, we outlined the four step framework for applying automation to work outlined in the book 'Reinventing Jobs.' Now we will dive deeper into the broader impacts this type of transformation will have on leadership, employees and the nature of the organisation.

The New Organisation

In Reinventing Jobs, the optimisation of human-automation collaboration presents both challenges and opportunities beyond just individual jobs, affecting the relationships among jobs, as well as organisation units and structures overall. Consequently, Jesuthasan and Boudreau assert that leaders must anticipate and design for organisation-level implications as they integrate automated and human work. They propose two strategies leaders can use that will help accomplish this:

  1. The outside-in approach: Here, leaders identify the big picture of how automation will impact their companies and industries. They develop automation visions to align their organisations’ decisions. Using those broad trends, they identify ways that their own organisations’ work can evolve to accommodate changes.
  2. The inside-out approach: Leaders start by reinventing jobs where reinvention best improves costs, risks, and productivity. As they make specific changes in jobs, they observe patterns that reveal new organisational structures, relationships, and networks that enhance and support the job-level reinvention. Organisational changes evolve from the work-level reinvention.

Using either approach, leaders must anticipate how automation will impact their organisations in the following contexts:

  • Strategies: What their companies will and will not aim to achieve, and how. For example, integrating automation into an oncology centre may allow its strategy to evolve from simply providing the best surgical cancer treatment to making doctors and patients partners in making the best possible treatment decisions.
  • Structure: Internal power, information, authority, and accountability. With automation enhancing cancer surgery (per the above example), the surgeons at an oncology centre would share power with AI-powered machines. Now, the surgeons share authority and power that they previously held exclusively, with AI designers and robotic technicians.
  • Processes: The way decisions are executed. The reinvented oncology centre might now use analysis generated by cognitive automation to inform doctors, nurses, and patients of the best treatment options. No longer is this process the sole responsibility of a single doctor or nurse, and now the patient may see the same information provided to the caregivers.
  • Metrics: The benchmarks used to measure success. Previously, the oncology centre evaluated the cost and effectiveness of surgery, but the newly automated centre would also measure the patient experience.
  • People practices: How the work experience of the human workers is designed and managed. In the newly automated oncology centre, the sourcing, selection, training, rewards, engagement, culture, and careers of the human workforce will change to support virtual teams, self-managing caregivers, and greater treatment experimentation. All human workers must develop their capacity for continuous learning and adaptive flexibility.

Ultimately, automation reinvents not just jobs, but organisations, including leaders who can understand and embrace optimal automation to create organisations that are more agile, work-centric, and have fewer boundaries than ever before.

The New Leadership

Law firm Taylor Vinters believe that even leadership and how leaders lead will not be immune from automation. They believe the most effective future leaders will be those who recognise the potential value of automation, in the specific context of their own organisations and sectors.

Such leaders will spend some time thinking about how technology can augment their workforce over the next 5 to 10 years. What tasks or processes might be done more efficiently or reliably by machines rather than humans? How can they better make use of data they already collect and process to improve financial performance, enhance existing products or services or offer new ones?

These leaders will engage with tech developers who may be able to create bespoke solutions to meet their specific needs (and may even employ them in-house). They will also use data scientists to exploit and analyse the huge volumes of information they already have.

Alongside this, they will also consider the long-term impact of automation on future resourcing requirements and communicate openly with existing employees about the future. Effective consultation with the workforce will help to maintain good staff morale and productivity by addressing any underlying concerns about job security and being clear about how employees will be given the skills to work alongside any new technology.

Engaging in the process of reinventing jobs will thus transform work and organisations as well as leadership. Future leaders must become more adept at:

  • Constantly deconstructing and reinventing jobs as technology continues to evolve.
  • Coordinating work delivered by combinations of humans and automation.
  • Nurturing human-robot collaboration.
  • Navigating work that’s perpetually changing.
  • Engaging employees and customers as collaborators.
  • Finding and nurturing employees’ competencies.
  • Serving as a hub for a boundary-less talent ecosystem that includes regular employees, contractors, gig workers, volunteers, AI, and robotics.

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The five transformative changes that will redefine leadership are:

  1. Mindset: Leaders must think in terms of perpetual change, constantly recombined tasks and skills, and humans as collaborators with automation.
  2. Ability: In the future, the best workers won’t be highly qualified for specific jobs, but rather superb learners who excel at analysing information and finding connections in disparate data.
  3. Reward: Salaries and permanent jobs will be replaced by rewards for deconstructed tasks and short-term positions. Compensation will be regularly renegotiated as jobs are reinvented.
  4. Deployment: Leaders will become responsible for matching employees to work that’s constantly reconfigured through tasks, skills, and capabilities, rather than jobs.
  5. Development: Leaders and workers will work together to navigate a connected array of development options, focused on the tasks and skills that evolve with the combinations of human and automated work.


Deconstruct and Reconfigure Your Work

To anticipate how their own roles will evolve in the future, Reinventing Jobs recommends that individual workers should:

  • Deconstruct their current jobs into tasks.
  • Identify which tasks will likely be substituted by automation, which will be augmented by automation, and which will be transformed by automation.
  • Determine how to reinvent themselves to better fit the tasks that will require and enhance the value of human workers.
  • Imagine their future work descriptions as they could appear in two, five, and ten years.
  • Consider how they’ll fit in a future organisation—one that’s a digital, agile, and boundary-less.

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Ten Considerations To Solve For

McKinsey argues that in the search for appropriate measures and policies to address these challenges, it is not wise to try and roll back or slow the spread of technology. They advocate that companies and governments should harness automation and AI to benefit from the enhanced performance and productivity contributions as well as the societal benefits. McKinsey is confident that such technologies will create the economic surpluses that will help societies manage workforce transitions. Rather, McKinsey believes that the focus should be on ways to ensure that the workforce transitions are as smooth as possible and that this is likely to require actionable and scalable solutions in several key areas:

  • Ensuring robust economic and productivity growth: Strong growth is not the magic answer for all the challenges posed by automation, but it is a prerequisite for job growth and increasing prosperity. Productivity growth is a key contributor to economic growth. Therefore, unlocking investment and demand, as well as embracing automation for its productivity contributions, is critical.
  • Fostering business dynamism: Entrepreneurship and more rapid new business formation will not only boost productivity, but also drive job creation. A vibrant environment for small businesses as well as a competitive environment for large business fosters business dynamism and, with it, job growth. Accelerating the rate of new business formation and the growth and competitiveness of businesses, large and small, will require simpler and evolved regulations, tax and other incentives.
  • Evolving education systems and learning for a changed workplace: Policy makers working with education providers (traditional and nontraditional) and employers themselves could do more to improve basic STEM skills through the school systems and improved on-the-job training. A new emphasis is needed on creativity, critical and systems thinking, and adaptive and life-long learning. There will need to be solutions at scale.
  • Investing in human capital: Reversing the trend of low, and in some countries, declining public investment in worker training is critical. Through tax benefits and other incentives, policy makers can encourage companies to invest in human capital, including job creation, learning and capability building, and wage growth, similar to incentives for private sector to invest in other types of capital including R&D.
  • Improving labor-market dynamism: Information signals that enable matching of workers to work, credentialing, could all work better in most economies. Digital platforms can also help match people with jobs and restore vibrancy to the labor market. When more people change jobs, even within a company, evidence suggests that wages rise. As more varieties of work and income-earning opportunities emerge including the gig economy, we will need to solve for issues such as portability of benefits, worker classification, and wage variability.
  • Redesigning work: Workflow design and workspace design will need to adapt to a new era in which people work more closely with machines. This is both an opportunity and a challenge, in terms of creating a safe and productive environment. Organisations are changing too, as work becomes more collaborative and companies seek to become increasingly agile and nonhierarchical.
  • Rethinking incomes: If automation (full or partial) does result in a significant reduction in employment and/or greater pressure on wages, some ideas such as conditional transfers, support for mobility, universal basic income, and adapted social safety nets should be considered and tested. The key will be to find solutions that are economically viable and incorporate the multiple roles that work plays for workers, including providing not only income, but also meaning, purpose, and dignity.
  • Rethinking transition support and safety nets for workers affected: As work evolves at higher rates of change between sectors, locations, activities, and skill requirements, many workers will need assistance adjusting. Many best practice approaches to transition safety nets are available, and should be adopted and adapted, while new approaches should be considered and tested.
  • Investing in drivers of demand for work: Governments will need to consider stepping up investments that are beneficial in their own right and will also contribute to demand for work (for example, infrastructure, climate-change adaptation). These types of jobs, from construction to rewiring buildings and installing solar panels, are often middle-wage jobs, those most affected by automation.
  • Embracing AI and automation safely: Even as we capture the productivity benefits of these rapidly evolving technologies, we need to actively guard against the risks and mitigate any dangers. The use of data must always take into account concerns including data security, privacy, malicious use, and potential issues of bias, issues that policy makers, tech and other firms, and individuals will need to find effective ways to address.

Stay tuned for the next Blog in this series as we explore how to automate intelligently during your digital transformation.

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Image credit: EY - How to automate intelligently

Image Source: Ernst& Young: How do you ensure that you are automating intelligently

Topics: performance management, HR automation, Performance Management Software, Employee Onboarding Software, The future of work, Automation, McKinsey, Redesigning work, Employee onboarding

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