Category Archives: Centre for the Edge

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

​Fears 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>.

Digitalizing the construction industry: A case study in complex disruption

I, along with a Robert Hillard and Peter Williams, have a new essay published by Deloitte Insights, Digitalizing the construction industry: A case study in complex disruption1)Evans-Greenwood, P et al. 2019, ‘Digitalizing the construction industry: A case study in complex disruption’, Deloitte Insights,<https://www2.deloitte.com/insights/us/en/topics/digital-transformation/digitizing-the-construction-industry.html>.. The case study elaborates on one of the examples we used in Your next future.2)Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.

In that essay we made the distinction between simple disruption – disruption due to a particular disruptive technology, the thing the comes to mind first for most people when they think of disruption – and complex disruption – where the disruption is due to a confluence of (mainly social) factors. Think the telegraph (simple disruption) vs the global multi-modal container network (complex disruption). Many current disruptions – artificial intelligence, blockchain, etc – tend to be complex (rather than simple) disruption. We’re seeing an environmental shift, as individuals and firms realise that the current environment (with many things available cheap and on-demand) presents opportunities to find new ways to use old technologies to create new ‘disruptive’ operating models, rather than there being a massive wave of new technologies as many pundits claim.

One of examples we used to illustrate the shift was the building industry. There’s a lot of noise about technologies such as 3D printing or brick-laying robots disrupting the building industry, but this is unlikely as the industry’s product is the building process, not the buildings it produces. Builders will simply integrate these new technologies into their process if and when they become commercially viable. The invention of a new building process, however, where a builder uses old technologies in new ways to create a new, and superior, operating model has the potential to disrupt the industry.

Your next future mentioned a design for manufacture and assembly (DFMA) process – where a building is completely modelled in 3D before the model is split up and feed to numerically controlled machines in a factory, with the components shipped to the construction site for assembly – as potentially disruptive. Versions of the process current at the time of publication were roughly 30% faster than a conventional build (due to moving some work to the controlled environment of a factory where rain delays aren’t a problem, and enabling the optimisation of vertical transport on site). They were slightly cheaper, and had the potential to be much cheaper. And there’s the possibility to integrating new materials into the process, materials which couldn’t be used in a conventional process due to on-site restrictions.

Since that essay was published what was then a potential disruption looks like it might be about to tip into actual disruption. This is the subject of the case study.

In 2018 a project in the Melbourne CBD hit problems as the cranes and trucks required to move materials onto the site would block a lane that was the sole access to the homes of many local residents. The solution the builder (Hickory) came up with was to build at night: the machinery would arrive around 9 pm and lift DFMA components (via the Hickory Building System) onto the site, installing an entire floor in four-six hours. Once a floor is complete the floor below is weather-proof and there are no lives edges. The machines are gone before the residents wake. During the day the trades go through the completed floor and finish the interior. There was some skepticism as building is considered noisy, though a trial one night showed that the residents would hardly notice the nighttime construction.

And here’s where we might be seeing a potential complex disruption crystallise into actual disruption. The build proceeded, and the city council was so happy they are considering that all high-rise building to be done at night. This would, with the stroke of a pen, bar conventional builders from the market until they undertake the multi-year journey to develop their own operating model based a DFMA process.

The case study looks at the development of DFMA building processes, the challenges they faced and how they’ve been overcome, and the potential impact on the market. It also looks at how firms might also anticipate similar complex disruptions in their own market, pointing out that conventional market-scanning practices looking for disruptive technologies can actually be counter productive as they cannot predict complex disruption, and we’re in a market with there appears to be more complex disruption than simply disruption.

It’s an interesting story, and a local story which is nice, so head over the the Deloitte web site to read Digitalizing the construction industry: A case study in complex disruption.

References   [ + ]

1. Evans-Greenwood, P et al. 2019, ‘Digitalizing the construction industry: A case study in complex disruption’, Deloitte Insights,<https://www2.deloitte.com/insights/us/en/topics/digital-transformation/digitizing-the-construction-industry.html>.
2. Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.

Your next future: Capitalising on disruptive change

I and a coauthor have a new report out on DU Press: Your next future: Capitalising on disruptive change.1)Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>. Disruption is something we’d been puzzling for some time as it’s a fuzzy and poorly defined concept despite all the noise it generates. It’s also concerning that few, if any, of the theories have much predictive power.

Our contribution is fairly straight forward.

First we make that point that disruption, as the term is commonly used, covers a broad range of phenomena. This creates tension between our desire for a comprehensive definition, one encompassing this broad scope, and the need for a precise definition, so that we are all clear on what we’re talking about.  Many academic theories (such as Clayton Christensen’s) come unstuck when it’s pointed out that the theory might refer to some disruptive phenomenon, but they don’t account for many other phenomena that can also be considered disruptive.

Consequently we must acknowledge that disruption operates are at least three different levels of abstraction:

  • At the highest level are long-term whole-of-economy shifts that disrupt all of us. The shift from stocks to flows – which we try and measure in the Shift Index2)Evans-Greenwood, P & Williams, P 2014, Setting aside the burdens of the past: The possibilities of technology-driven change in Australia, Deloitte Australia, viewed 26 October 2017, <https://www2.deloitte.com/au/en/pages/technology/articles/shift-index-key-findings.html>. – is one of these.
  • At the mid-level are disruptions focused on a sector or industry. Our colleagues in the US have be cataloging these in the Patterns of Disruption series.3)Hagel, ,J, Seely Brown, J, Wooll, M, & de Maar, A 2015, Patterns of disruption: Anticipating disruptive strategies in a world of unicorns, black swans, and exponentials, Deloitte University Press, <http://dupress.com/articles/ anticipating-disruptive-strategy-of-market-entrants/>.
  • At the lowest level are the things that disrupt us, our firm.

It was the observation that value used to be objective and defined relative to the market, in terms of product feature-function, but now value is more commonly defined subjectively, relative to the firm and the firm-customer relationship, that prompted us to look at disruption with a wider lens and make this subjective disruption the subject of a our essay.

Next we wanted to create a model of disruption that was predictive, which could be fed into a strategy-formation process to enable a firm to identify concrete actions that would enable a firm to prepare for a (potential) disruption and either capitalise on it or defuse it (i.e. neuter the disruption). The resulting model relies on three observations.

  • Disruption is degenerate. A single outcome, a disruption, might be triggered by a large number of different processes. This means that will be impossible to understand disruption by identifying and analysing individual contributors without considering the complex relationships between them.
  • Disruption is constructive. While technology is important to a disruption, technology alone is not enough and we need to also consider social and commercial forces as well that come together to trigger a disruption.
  • Disruption is subjective. A new technology might disrupt our sector or industry, but it may not disrupt us. The reverse is also true. Our concern is disruption to our business, not markets (via patterns of disruption), the economy (via the Big Shift) or disruption in general.

The result a model that shows us why we we cannot predict disruption by identifying ‘disruptive technologies’, but which does enable us to do something about shaping how we approach disruption.

We’re pretty happy with the result, which you can find at DU Press.

References   [ + ]

1. Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.
2. Evans-Greenwood, P & Williams, P 2014, Setting aside the burdens of the past: The possibilities of technology-driven change in Australia, Deloitte Australia, viewed 26 October 2017, <https://www2.deloitte.com/au/en/pages/technology/articles/shift-index-key-findings.html>.
3. Hagel, ,J, Seely Brown, J, Wooll, M, & de Maar, A 2015, Patterns of disruption: Anticipating disruptive strategies in a world of unicorns, black swans, and exponentials, Deloitte University Press, <http://dupress.com/articles/ anticipating-disruptive-strategy-of-market-entrants/>.

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.

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.

To code or not to code: Mapping digital competence

We’re kicking off the next phase of our “Should everyone learn how to code?” project. This time around it’s a series of public workshops over late January and early February in Melbourne, Geelong, Sydney, Western Sydney, Hobart, Brisbane, and Adelaide. The purpose of the workshops is to try and create a mud-map describing what a digitally competent workforce might look like.

As the pitch goes…

Australia’s prosperity depends on equipping the next generation with the skills needed to thrive in a digital environment. But does this mean that everyone needs to learn how to code?

In the national series of round tables Deloitte Centre for the Edge and Geelong Grammar School hosted in 2016, the answer was “Yes, enough that they know what coding is.”

The greater concern, though, was ensuring that everyone is comfortable integrating digital tools into their work whatever that work might be, something that we termed ‘digital competence’. This concept was unpacked in an essay published earlier this year.

Now we’re turning our attention to the question: What does digital competence look like in practice, and how do we integrate it into the curriculum?

We are holding an invitation only workshop for industry and education to explore the following ideas:

  • What are the attributes of a digitally competent professional?
  • How might their digital competence change over their career?
  • What are the common attributes of digital competence in the workplace?
  • How might we teach these attributes?

If you’re interested in attending, or if you know someone who might be interested in attending, then contact me and we’ll add you to the list. Note that there’s only 24-32 places in each workshop and we want to ensure a diverse mix of people in each workshop, so we might not be able to fit everyone who’s interested, but we’ll do our best.