Category Archives: Work, worker, workplace

Digital agency and the skills gap

The concluding report from Deloitte Centre for the Edge and Geelong Grammar Schools’ collaboration looking into digital skills in the workplace, Digital agency and the skills gap,1)Evans-Greenwood, P & Patston, T 2019, Digital agency and the skills gap, Deloitte, Australia, <https://www2.deloitte.com/au/en/pages/public-sector/articles/to-code-or-not-to-code-coding-competence.html>. has been published by Deloitte, Australia. This report pulls together the results from across the project to provide an overview of the journey and the findings.

There’s a huge amount of angst in the community that our education system cannot keep up with rapid technological change, however this project shows that this is likely not the case. What we’re seeing is, in many cases, not a lack of skills, but an inability to navigate an increasingly complex digital environment. While digital skills are important, knowing when and why to use these skills is more important, particularly in a world where new knowledge is no further away than a search engine accessed via a smart phone.

Workers are unable to make the connection between the skills they have and the problem infant of them, making this a problem of unknown knowns. It’s not that workers lack skills, what they lack is discernment, the ability to read the digital environment around them and make sharp judgements about when and why particular digital tools and skills should be used. A lack of discernment limits a worker’s digital agency, their ability to act freely in a digital environment.

Solving this problem is not simply a question of teaching students and workers more, and more relevant digital skills. We need to focus on fostering in them the discernment required for them to develop the work habits that will enable them to make the most of digital technology.

The project was a long and fascinating journey so this concluding report itself is quite long, around 12,000 words. A much shorter business-friendly summary, The digital ready worker,2)Evans-Greenwood, P, Patston, T, & Flouch, A 2019, ‘The digital-ready worker: Digital agency and the pursuit of productivity’, Deloitte Insights, <https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/learned-helplessness-workforce.html>. was published last week by Deloitte Insights. A lot of valuable insights were dropped on the cutting room floor to create that summary, hence this report.

This report provides a summary of project’s journey, from the initial provocation through the roundtables, the more recent workshops, to the development of the project’s conclusions, as well as providing a detailed exploration of the findings. If you’re an educator (K12 or post-secondary) you might find this longer report more valuable as it digs into the details of the models presented, and does a better job of exploring the implications of the findings. If you were involved in any of the projects, the this report will join a number of dots and provide that ah-ha moment.

Revisiting the concept of learned helplessness, the report shows how the solution to learned helplessness is not to teach students more digital skills, but to foster their digital agency, their capacity to act independently and make their own free choices in the digital workplace. The concept of digital natives is explored in light of what the project discovered, resulting in a new model of digital competence in the workplace the identifies four archetypes: the digital naïf, digital pragmatist, digital explorer, and digital evangelist.

Finally, the report develops a progression capturing how one’s digital agency changes over time, and explores how digital agency might be fostered in both students and workers, and the changes this implies.

References   [ + ]

1. Evans-Greenwood, P & Patston, T 2019, Digital agency and the skills gap, Deloitte, Australia, <https://www2.deloitte.com/au/en/pages/public-sector/articles/to-code-or-not-to-code-coding-competence.html>.
2. Evans-Greenwood, P, Patston, T, & Flouch, A 2019, ‘The digital-ready worker: Digital agency and the pursuit of productivity’, Deloitte Insights, <https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/learned-helplessness-workforce.html>.

The digital-ready worker: Digital agency and the pursuit of productivity

We have a new essay published on Deloitte Insights, The digital-ready worker: Digital agency and the pursuit of productivity, which is the result of a collaboration between Centre for the Edge and Geelong Grammar School.1)Evans-Greenwood, P, Patston, T, & Flouch, A 2019, ‘The digital-ready worker: Digital agency and the pursuit of productivity’, Deloitte Insights, <https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/learned-helplessness-workforce.html>. As the blurb says, this essay looks into how:

To be effective in an increasingly technological workplace, workers must know, not just how to use digital tools, but when and why to use them. Critical to this ability is digital agency: the judgment and confidence required to navigate and be effective in unfamiliar digital environments.

There’s a lot of concern at the moment of a growing skills gap, the gap between the the skills held by graduates and those demanded by employers. Studies have been done to measure this growing gap, and significant resources have been devoted to updating curricula in an attempt to close the gap, all to no avail.

If we peal the lid of these studies we see they rely on aggregate skills data, typically from O*NET, which means that they’re limited to seeing a single negative view of how technology affects jobs, one where technology automates skills making workers redundant. The problem is that this isn’t the only pathway for technology to affect work. There’s also a positive pathway, where technology automates skills making the workers’ remaining skills more valuable, as well as a “no net change” pathway (or collection of pathways) which we have empirical evidence for but are yet to pull apart and understand.2)Spenner, KI 1983, ‘Deciphering Prometheus: Temporal Change in the Skill Level of Work’, American Sociological Review, vol. 48, no. 6, p. 824, <http://www.jstor.org/stable/2095328?origin=crossref>.

One of the key insights, if not the key insight, from Centre for the Edge and Geelong Grammar Schools’ To code or not to code collaboration, was that many of the problems we’re seeing in the workplace are likely due to learned helplessness,3)The term “learned helplessness” is borrowed from the psychology literature, drawing upon the work of Martin Seligman and many others. See, for instance, Martin E. P. Seligman, “Learned helplessness,” Annual Review of Medicine 23, no. 1 (1972): pp. 407–12. where a person suffers from a sense of powerlessness arising from a persistent failure to succeed. We’re teaching students how to use particular digital tools in particular ways, but we’re also teaching them that these tools are fragile and using them the wrong often results in problems and might even ‘brick’ the device. Rather than framing the problems we’re seeing in the workplace as the result of a growing skills gap due to the destruction of skills, it might be more appropriate to frame them as a problem of unknown knowns. It’s not that the worker doesn’t have the skills required, their problem is making the connection between the skill and the current problem they’re working on.

The solution to this problem isn’t to provide students with more, and more relevant, digital skills. Indeed, that approach is unlikely to help as the students are not lacking in skills. While it’s important to know how to use particular digital tools, it’s more important to know when and why these digital tools should be used. What students lack is discernment, the knowledge and experience required to make observations and sharp judgements about which digital tools might be useful and how they will affect the work. We need to foster in students the attitudes and behaviours—something we’ve taken to calling a predilection—that help them navigate the digital workplace and develop the habits that enable them to integrate digital tools into their work. Ultimately the solution is to foster digital agency in students, to help them develop the literacies, knowledge, skills and predilections the need to act independently and make their own free choices in the digital workplace.

The essay explores, in some detail, the concept of learned helplessness in the digital workplace, and how we might might foster digital agency in both students and workers. There’s also a few of useful models for thinking about this problem, helping us move beyond misleading dichotomies like Digitial Native vs Digital Immigrant which have proven to be wrong.

You can find the entire text over at Deloitte Insights. Feel free to leave a comment here with your thoughts.

References   [ + ]

1. Evans-Greenwood, P, Patston, T, & Flouch, A 2019, ‘The digital-ready worker: Digital agency and the pursuit of productivity’, Deloitte Insights, <https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/learned-helplessness-workforce.html>.
2. Spenner, KI 1983, ‘Deciphering Prometheus: Temporal Change in the Skill Level of Work’, American Sociological Review, vol. 48, no. 6, p. 824, <http://www.jstor.org/stable/2095328?origin=crossref>.
3. The term “learned helplessness” is borrowed from the psychology literature, drawing upon the work of Martin Seligman and many others. See, for instance, Martin E. P. Seligman, “Learned helplessness,” Annual Review of Medicine 23, no. 1 (1972): pp. 407–12.

Digital agency and the skills gap

In 2016, Deloitte Centre for the Edge and Geelong Grammar School hosted a series of roundtables looking into digital skills and the challenges of the digital workplace. The project that emerged from those roundtables took three years to peel back layer after layer of assumptions to discover that it’s likely that our graduates are suffering from a lack of discernment, rather than a lack of digital skills.

On the 30th of October Deloitte is hosting an event in the Melbourne office around close of business to launch the reports that lay out the project’s findings, and to explore where to next. Fill out the contact form at the bottom of this post if you’re interested in attending.

The future will be digital and mastery of digital technology is seen as an essential skill. The growing skills gap—the gap between skills held by graduates and those employers seek—is a cause of great concern. Prescriptions range from new curricula, new technology-driven pedagogy, through to blowing it all up and starting again.

Our research shows that while digital skills, knowing how to use digital tools, is important, knowing when and why to use them is more important. The challenges graduates experience in the workplace a more likely due to a lack of digital agency. They suffer from learned helplessness, struggling to navigate a workplace saturated in, even defined by, digital tools.

The event will consist of a plenary discussing the findings and a panel to dig into the details. Can we fix the education system? Or do we need to disrupt it?

You can find the previous reports from the project on the Deloitte web site.

Reconstructing jobs: Creating good jobs in the age of artificial intelligence

Fear of AI-based automation forcing humans out of work or accelerating the creation of unstable jobs may be unfounded. AI thoughtfully deployed could instead help create meaningful work.

This is a 25 minute presentation providing an overview of the report Reconstructing jobs (published in 2018) from the Edge Session just after the report was published.

This third essay in the series, Reconstructing jobs, takes a step back and looks these jobs of the future might look like. The narrative is built around a series of concrete examples – from contact centres through wealth management to bus drivers – to show how we might create this next generation of jobs. These are jobs founded on an new division of labour: humans creating new knowledge, making sense of the world to identify and delineate problems; AI plans solutions to these problems; and good-old automation to delivers. To do this we must create good jobs, as it is good jobs that make the most of our human abilities as creative problem identifiers. These jobs are also good for firms as, when combined suitably with AI, they will provide superior productivity. They’re also good job for the community, as increased productivity can be used to provide more equitable services and to support learning by doing within the community, a rising tide that lives all boats.

Teaching creativity in the 21st century

In 2017 Deloitte Centre for the Edge hosted a public lecture by James C. Kaufman, PhD; a professor of educational psychology at the University of Connecticut as well as a creativity & education expert, where he discussed the challenges of teaching and assessing creativity. This is a 20 minute bite-sized version of the 90-minute lecture.

We noticed the similarity between creativity and our recent work on digital competency, which we published in “From coding to competence”. Both are depend more on attitudes and behaviours than knowledge and skills. Both are also tightly tied to context, and don’t transfer easily between domains.

The lecture is derived from Dr Kaufman’s cutting-edge psychological research and debunks common misconceptions about creativity, describe how learning environments can support creativity, while providing insights into teaching and assessing creativity within the established curriculum.

The lecture covers:

  • What is creativity?
  • Seeing creativity as a development trajectory and advancing along this trajectory.
  • Creativity across domains (not just ‘art’), and the ‘cost’ of creativity.
  • Measuring creativity
  • How can people become more creative?

The new division of labor: On our evolving relationship with technology

I, along with Alan Marshall and Robert Hillard, have a new essay published by Deloitte InsightsThe new division of labor: On our evolving relationship with technology1)Evans-Greenwood, P, Hillard, R, & Marshall, A 2019, ‘The new division of labor: On our evolving relationship with technology’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/the-new-division-of-labor.html>.. This is the latest in an informal series that looks into how artificial intelligence (AI) is changing work. The other essays (should you be interested) are Cognitive collaboration,2)Guszcza, J, Lewis, H, & Evans-Greenwood, P 2017, ‘Cognitive collaboration: Why humans and computers think better together’, Deloitte Review, no. 20, viewed 14 October 2017, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-20/augmented-intelligence-human-computer-collaboration.html>. Reconstructing work3)Evans-Greenwood, P, Lewis, H, & Guszcza, J 2017, ‘Reconstructing work: Automation, artificial intelligence, and the essential role of humans’, Deloitte Review, no. 21, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-21/artificial-intelligence-and-the-future-of-work.html>. and Reconstructing jobs.4)Evans-Greenwood, P, Marshall, A, & Ambrose, M 2018, ‘Reconstructing jobs: Creating good jobs in the age of artificial intelligence’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/creating-good-jobs-age-of-artificial-intelligence.html>.

Over the last few essays we’ve argued that humans and AI might both think but they think differently, though in complimentary ways, and if we’re to make the most of these differences we need to approach work differently. This was founded on the realisation that there is no skill – when construed within a task – that is unique to humans. Reconstructing work proposed that rather than thinking about work in terms of products, processes and tasks, it might be more productive to approach human work as a process of discovering what problems need to be solved, with automation doing the problem solving. Reconstructing jobs took this a step further and explored how jobs might change if we’re to make the most of both human and AI-powered machine using this approach, rather than simply using the machine to replace humans.

This new essay, The new division of labour, looks at what is holding us back. It’s common to focus on what’s known as the “skills gap”, the gap between the knowledge and skills the worker has and those required by the new technology. What’s often forgotten is that there’s also an emotional angle. The introduction of the word processor, for example, streamlined the production of business correspondence, but only after managers became comfortable taking on the responsibility of preparing their own correspondence. (And there’s still a few senior managers around who have their emails printed out so that they can draft a reply on the back for their assistant to type.) Social norms and attitudes often need to change before a technology’s full potential can be realised.

We can see something similar with AI. This time, though, the transition is complicated as the new tools and systems are not passive tools anymore. We’re baking decisions into software then connecting these automated decisions to the levers that control our businesses: granting loans, allocating work and so on. These digital systems are no longer passive tools, they have some autonomy and, consequently, some agency. They’re not human, but they’re not “tools” in the traditional sense.

This has the interesting consequence that we relate to them as sort-of humans as their autonomy and agency affects our own. They’re consequently taking on roles in the organogram as we find ourselves working for, with and on machines. This also works the other way around, and machines find themselves working for, with and on humans. Consider how a ride-sharing driver has their work assigned to them, and their competence is measured, by an algorithm that is effectively their manager. A district nurse negotiates their schedule with a booking and work scheduling system. Or it might be more of a peer relationship, such as when a judge consult a software tool when determining a sentence. We might even find humans and machines teaching each other new tricks.

As with the word processors, we can only make the most of this new technology if we address the social issues. With the word processor it was managers seeing typing as being below their station. The challenge with AI is much more difficult though, as making the most of this new generation of technology requires us to value humans to do something other than complete tasks.

The essay uses the example of superannuation. Nobody wants retirement financial products, they want a happy retirement, the problem is that ‘happy retirement’ is no more than a vague idea for most of us. We need to go on a journey through sorting out if what we think will make us happy will actually make us happy, setting reasonable expectations, and adjusting our attitudes and behaviours to balance our life today with the retirement we want to work toward. This is something like a Socratic dialogue, a conversation with others where we create the knowledge of what ‘happy retirement’ means for us. Only then can we engage the robots-advisor to crunch the numbers and create an investment plan.

The problem is the disconnect between how the client and firm derive value from this journey. The client values discovering what happy retirement means, and adjusting their attitudes and behaviours to suit. The firm values investments made. This disconnect means that firms focus their staff on clients later in life, once the kids have left home and the house is paid off. The client, on the other hand, would realise the most value by engaging early to establish the attitudes and behaviours that will enable the magic of compound interest to work.

As we say in the conclusion to the report:

However, successfully adopting the next generation of digital tools, autonomous tools to which we delegate decisions and that have a limited form of agency, requires us to acknowledge this new relationship. At the individual level, forming a productive relationship with these new digital tools requires us to adopt new habits, attitudes, and behaviors that enable us to make the most of these tools. At the enterprise level, the firm must also acknowledge this shift, and adopt new definitions of value that allow it to reward workers for contributing to the uniquely human ability to create new knowledge. Only if firms recognize this shift in how value is created, if they are willing to value employees for their ability to make sense of the world, will AI adoption deliver the value they promise.

You can find the entire essay over at Deloitte Insights.

References   [ + ]

1. Evans-Greenwood, P, Hillard, R, & Marshall, A 2019, ‘The new division of labor: On our evolving relationship with technology’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/the-new-division-of-labor.html>.
2. Guszcza, J, Lewis, H, & Evans-Greenwood, P 2017, ‘Cognitive collaboration: Why humans and computers think better together’, Deloitte Review, no. 20, viewed 14 October 2017, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-20/augmented-intelligence-human-computer-collaboration.html>.
3. Evans-Greenwood, P, Lewis, H, & Guszcza, J 2017, ‘Reconstructing work: Automation, artificial intelligence, and the essential role of humans’, Deloitte Review, no. 21, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-21/artificial-intelligence-and-the-future-of-work.html>.
4. Evans-Greenwood, P, Marshall, A, & Ambrose, M 2018, ‘Reconstructing jobs: Creating good jobs in the age of artificial intelligence’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/creating-good-jobs-age-of-artificial-intelligence.html>.

Reconstructing jobs

Some coauthors and I have a new report out: Reconstructing jobs: Creating good jobs in the age of artificial intelligence.  This essay builds on the previous two from our “future or work” series,  Cognitive collaboration and Reconstructing work, published on DU Press (now Deloitte Insights) as part of Deloitte Review #20 (DR20) and #21 (DR21) respectively.

Cognitive collaboration‘s main point was that there are synergies between humans and computers, and that solution crafted by a human and computer in collaboration is superior to, and different from, a solution made either human or computer in isolation. Reconstructing work built on this, pointing out the difference between human and machine was not in particular knowledge or skills exclusive to either; indeed, if we frame work in terms of prosecuting tasks than we must accept that there are no knowledge or skills required that are uniquely human. What separates us from the robots is our ability to work together to make sense of the world and create new knowledge, knowledge that can then be baked in machines to make it more precise and efficient. This insight provided the title of the second essay – Reconstructing work – as it argued that we need to think differently about how we construct work if we want the make the most of the opportunities provided by AI.

This third essay in the series, Reconstructing jobs, takes a step back and looks these jobs of the future might look like. The narrative is built around a series of concrete examples – from contact centres through wealth management to bus drivers – to show how we might create this next generation of jobs. These are jobs founded on an new division of labour: humans creating new knowledge, making sense of the world to identify and delineate problems; AI plans solutions to these problems; and good-old automation to delivers. To do this we must create good jobs, as it is good jobs that make the most of our human abilities as creative problem identifiers. These jobs are also good for firms as, when combined suitably with AI, they will provide superior productivity. They’re also good job for the community, as increased productivity can be used to provide more equitable services and to support *learning by doing* within the community, a rising tide that lives all boats.

The essay concludes by pointing out that there is no inevitability about the nature of work in the future. As we say in the essay:Clearly, the work will be different than it is today, though how it is different is an open question. Predictions of a jobless future, or a nirvana where we live a life of leisure, are most likely wrong. It’s true that the development of new technology has a significant effect on the shape society takes, though this is not a one-way street, as society’s preferences shape which technologies are pursued and which of their potential uses are socially acceptable.

The question is then, what do we want these jobs of the future to look like?

Redefining education @ TAFE NSW >Engage 2017

C4tE AU was invited to TAFE NSW’s annual >Engage event to present a 15 minute overview of our Redefining education report, which had caught the attention of the event’s organisers.

The report ask a simple question:

In a world where our relationship with knowledge has changed – why remember what we can google? – should our relationship with education change as well?

and then chases this idea down the rabbit hole to realise that what we mean by “education” and “to be educated” need to change in response.

The presentation is a 15 minute TED format thing. You can find it on Vimeo.

The report is on Deloitte’s web site.

“Tiger, one day you will come to a fork in the road,” he said. “And you’re going to have to make a decision about which direction you want to go.” He raised his hand and pointed. “If you go that way you can be somebody. You will have to make compromises and you will have to turn your back on your friends. But you will be a member of the club and you will get promoted and you will get good assignments.”

Then Boyd raised his other hand and pointed another direction. “Or you can go that way and you can do something – something for your country and for your Air Force and for yourself. If you decide you want to do something, you may not get promoted and you may not get the good assignments and you certainly will not be a favorite of your superiors. But you won’t have to compromise yourself. You will be true to your friends and to yourself. And your work might make a difference.”

He paused and stared into the officer’s eyes and heart. “To be somebody or to do something.” In life there is often a roll call. That’s when you will have to make a decision. To be or to do. Which way will you go?”

—John Boyd from “Boyd: The fighter pilot who changed the art of war”

Image: Wikicommons

Reconstructing work

Some coauthors and I have a new(wish) report out – Reconstructing work: Automation, artificial intelligence, and the essential role of humans – on DU Press as part of Deloitte Review #21 (DR21). (I should note that I’ve been a bit lax in posting on this blog, so this is quite late.)

The topic of DR21 was ‘the future of work’. Our essay builds on the “Cognitive collaboration” piece published in the previous Deloitte Review (DR20).

The main point in Cognitive collaboration was that there are synergies between humans and computers. A solution crafted by a human and computer in collaboration is superior to, and different from, a solution made either human or computer in isolation. The poster child for this is freestyle chess where chess is a team sport with teams containing both humans and computers. Recently, during the development of our report on ‘should everyone learn how to code’ (To code to not to code, is that the question? out the other week, but more on that later), we found emerging evidence that this is a unique and teachable skill that crosses multiple domains.

With this new essay we started by thinking about how one might apply this freestyle chess model to more pedestrian work environments. We found that coming up with a clean division of labour between – breaking the problem into seperate tasks for human and machine – was clumsy at best. However if you think of AI as realising *behaviours* to solve *problems*, rather than prosecuting *tasks* to create *products*, then integrating human and machine is much easier. This aligns better with the nature of artificial intelligence (AI) technologies.

As we say is a forthcoming report:

AI or ‘cognitive computing’ […] are better thought of as automating behaviours rather than tasks. Recognising a kitten in a photo from the internet, or avoiding a pedestrian that has stumbled onto the road, might be construed as a task, though it is more natural to think of it as a behaviour. Task implies a piece of work to be done or undertaken, an action (a technique) we choose to do. Behaviour, on the other hand, implies responding to the changing world around us, a reflex. We don’t choose to recognise a kitten or avoid the pedestrian, though we might choose (or not) to hammer in a nail when one is presented. A behaviour is something we reflexively do in response to appropriate stimulus (an image of a kitten, or even a kitten itself poised in-front of us, or the errant pedestrian).

The radical conclusion from this is that there is no knowledge or skill unique to a human. That’s because knowledge and skill – in this context – are defined relative to a task. We’re at a point that if we can define a task then we can automate it (given cost-benefit) so consequently there are no knowledge or skills unique to humans.

What separates us from the robots is our ability to work together to make sense of the world and create new knowledge, knowledge that can then be baked in machines to make it more precise and efficient. If we want to move forward, and deliver on the promise of AI and cognitive computing, then we need to shift the foundation of work. Hence the title: we need to “reconstruct work”.

The full essay is on the DP site, so head over and check it out.