Category Archives: Series

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.

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>.

Digital is the new ERP

We seem to have forgotten that the development of Enterprise Resource Planning (ERP) was more a response to regulatory pressure than a child of technical innovation. This is why many executives and board members are unsure why their firm needs an ERP (and the massive investment implied), as ERP’s primary purpose was to improve governance (and, consequently, reduced operational risk and cost) rather than to provide the firm with some new value-creating capability.

Just prior to ERP, a confluence of technical and non-technical factors had created a situation where a firm’s executives and board had little idea of the goings on beneath them. Important details were buried in spreadsheets, squirrelled away on desktop PCs, with only summary reports passed to the general ledger and data warehouses.

Without the compliance guide rails provided by Finance and IT it’s easy for lines of business to go astray. Not long after spreadsheet use became widespread, it was clear that the information in the general ledger which the executive and board were relying on to direct the company could not be trusted. While the firm appeared to be making money, how this profit was being generated was less certain. Nor was it clear what operational risks a firm might be implicitly accepting, unable to manage them.

At which point the regulator stepped in demanding improvements in governance and operations. Industry’s response was ERP: an integrated set of business processes that synchronise (in real time) departmental solutions with the general ledger, supported (and enforced) by information technology.

We seem to be approaching a similar situation with digital. Firms are finding that important details are buried in SaaS and online solutions outside the purview of the Finance or IT departments and which are only loosely integrated to core systems, and their systems of records are, well, no longer ‘systems of record’.

This state of affairs could be accidental. The business wants to do the right thing but finds it difficult to know what the right thing to do is. They’re operating in a complex and rapidly changing business environment with demanding customers, many (previously core) functions are outsourced to specialist partners and suppliers, and they don’t have complete visibility into everything that is done on their behalf. It’s also an environment where regulators are constantly tweaking the rules to try and shape firm behavior, making a firm’s ability to absorb constant regulatory change a skill in and of itself.

Less ethical groups see this disconnect between the general ledger and lines of business as an opportunity to shape the story reaching head office. Cosmetic accounting techniques might be used to temporarily remove liabilities from a balance sheet, or to inflate revenue or market capitalisation by, for example, abusing special-purpose entities via techniques such as round-tripping (where an unused asset is sold with the understanding that same or similar assets will be bought back at the same or a similar price), all hidden under the veil of a summary report periodically passed between the department and the general ledger. These are the types of behaviours that brought Enron and Lehman Brothers down.

The information silos of departmental computing, the paradigm before today’s ERP-enabled enterprise computing, drove business efficiency by enabling firms to manage larger volumes of data. LEO (the Lyon’s Electronic Office),1)Land, F n.d., The story of LEO – the World’s First Business Computer, <https://warwick.ac.uk/services/library/mrc/explorefurther/digital/leo/story/>. an example of an early (and possibly the first) general-purpose business computer in the world, elaborated orders phoned into head office by Lyon’s tea shops every afternoon, calculating production requirements, assembly instructions, delivery schedules, invoices, costings, and management reports. These departmental applications, however, didn’t enable managers to find or exploit opportunities between departmental silos.

Spreadsheets and desktop PCs changed this. A desktop PC on a line manager’s desk enabled the manager to download data from multiple departmental applications and smash the data together in a spreadsheet. The resulting insights enabled production to be streamlined, or identified opportunities for new products and services, reducing costs and creating new value for the firm. Success begets success, and more data was downloaded and spreadsheets created. Soon these spreadsheets became integral parts of business processes and morphed into operational tools, outside the purview of the departmental applications that drove the firm’s compliance and reporting processes. Often the only connection between these new business processes and the general ledger was a summary report uploaded periodically.

The solution, then, was to integrate these cross-department spreadsheets, and the new business processes they enabled, into the firm’s departmental applications. The result is what we know today as ERP.

Something similar is happening with ‘digital’.

Cloud and SaaS solutions’ low barriers to adoption, and a customer empowered to demand what they want at the price they want from a global pool of suppliers, is driving line of business managers to go outside the enterprise to meet their needs. It’s not that the required business processes don’t exist; it just takes too long to modify the business processes to support new products, supply chains, suppliers and partners. Managers find it easier to put a credit card into a SaaS solution than wait for the IT department to respond with a plan, cost and timeline.

Departments are building entire value chains outside the purview of Finance and IT, as they believe that this is the only way that they can effectively respond to market opportunities and threats. Often the only connection to the general ledger is a summary spreadsheet, capturing details from cloud solutions, uploaded every few weeks or so. While the firm might be making money, it’s not clear to the executive or board just how this money is being made. Nor the risks this creates. We’ve been here before.

If the regulators don’t see this as a problem today, they soon will, as there is clearly a risk that good actors will unintentionally do the wrong thing, and for bad actors to intentionally do the wrong thing. There’s also the emerging problem of third parties hiding in the shadows using your legitimate business to wash funds (just as Amazon and Airbnb have become a target for money launderers).2)Shah, S 2017, ‘Airbnb is reportedly being used to launder money’, Engadget, <https://www.engadget.com/2017/11/27/airbnb-russian-money-laundering-scam/>. Operational risk is escalating as firms transform themselves from asset managers into integrators of services and information. The networked environment firms these firms inhabit creates unique challenges, has all the asymmetrical risks of an online environment, and the lack of visibility is compounding associated risks.

The problem digital is creating is clearly similar in effect to that the one created by the introduction of spreadsheets and the desktop PC. The cause, however, is different. Rather than creating new business processes that span existing (departmental) ones, digital is resulting in duplicated business processes that run in parallel and which support particular products or initiatives within the firm. They are also combining internal and external services, reducing the control a firm has on the end-to-end process.

These processes are intended to be short lived, thrown together quickly and torn down just as quickly. A process might be required, for example, to support a new supply chain for a burger of the month, thrown up at the start of the month to bring in new suppliers and partners, and torn down at the end. The duplicated processes are to support short-lived business exceptions, not to span business silos.

It’s assumed that more precise and tightly defined processes, backed by teams focused on maintaining and updating these processes to make them ‘agile’, will bring the firm back into compliance. This is not working though.

So while the problem digital is creating is similar to that due to spreadsheets, the cause if different and consequently our solution must also be different. Indeed, one might see business processes as part of the problem rather than as part of the solution.

References   [ + ]

1. Land, F n.d., The story of LEO – the World’s First Business Computer, <https://warwick.ac.uk/services/library/mrc/explorefurther/digital/leo/story/>.
2. Shah, S 2017, ‘Airbnb is reportedly being used to launder money’, Engadget, <https://www.engadget.com/2017/11/27/airbnb-russian-money-laundering-scam/>.

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?

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.

To code or not to code, is that the question?

Over 2016-2017 Deloitte Centre for the Edge collaborated with Geelong Grammar School to run a national series of roundtables where we unpacked the common catchphrase “everyone should learn how to code” as we have noticed that there was no consensus on what ‘coding’ was, and it seemed to represent an aspiration more than a skill. We felt that the community had jumped from observation (digital technology is becoming increasingly important) to prescription (everyone should learn how to code) without considering what problem we actually wanted to solve.

What we found from the roundtables was interesting. First, yes, everyone should learn how to code a little, mainly to demystify it. Coding and computers are seen as something of a black art, and that shouldn’t be the case. A short compulsory coding course would also expose students to a skill and career that they might not have otherwise considered. However, the bigger problem lurking behind the catchphrase was the inability for many workers to productively engage with the technology. Many of us suffer from learned helplessness, where we’ve learnt that we need to use digital tools in particular ways to solve particular problems, and if we deviate from this then all manner of things go wrong. This needs to change.

The result of the roundtables were written up and published but Deloitte and Geelong Grammar School.

Cognitive collaboration

I have a new report out on DU PressCognitive Collaboration: Why humans and computers think better together – where a couple of coauthors and I wade into the “will AI destroy the future or create utopia” debate.

Our big point is that AI doesn’t replicate human intelligence, it replicates specific human behaviours, and the mechanisms behind these behaviours are different to those behind their human equivalents. It’s in these differences that opportunity lies, as there’s evidence that machine and human intelligence are complimentary, rather than in competition. As we say in the report “humans and machines are [both] better together”. The poster child for this is freestyle chess.

Eight years later [after Deep Blue defeated Kasparov in 1997], it became clear that the story is considerably more interesting than “machine vanquishes man.” A competition called “freestyle chess” was held, allowing any combination of human and computer chess players to compete. The competition resulted in an upset victory that Kasparov later reflected upon:

The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process. . . . Human strategic guidance combined with the tactical acuity of a computer was overwhelming.1)Garry Kasparov, “The chess master and the computer,” New York Review of Books, February 11, 2010, www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/. View in article

So rather than thinking of AI as our enemy, we should think of it as supporting us in our failings.

We’re pretty happy with the report – so happy that we’re already working on a follow on – so wander over to DU Press and check it out.

References   [ + ]

1. Garry Kasparov, “The chess master and the computer,” New York Review of Books, February 11, 2010, www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/. View in article

To code or not to code, is that the question?

Centre for the Edge is dipping our toe into the education waters again after last years report, , Redefining EducationWe’re collaborating with Geelong Grammar‘s School of Creative Education to look into “Does everyone need to learn how to code?”

Computers are at the heart of the economy, and coding is at the heart of computers. Australia’s prosperity depends on equipping the next generation with the skills they need to thrive in this environment, but does this mean that we need to teach everyone how to code? Coding has a proud role in digital technology’s past, but is it an essential skill in the future? Our relationship with technology is evolving and coding, while still important, is just one of the many new skills that will be required.

Prime Minister Malcolm Turnbull has called for the country’s schools to introduce IT skills to students much earlier than they do now, suggesting that children as young as five or six should be introduced to coding. President Obama affirmed the need for coding education in his final state of the nation address. Some educators, however, are already pointing out that that teaching coding on its own might not be enough.

We will be holding a series of round table discussions across Geelong, Melbourne,  Sydney, Adelaide and Perth in May 2016 to explore the following questions:

  • What is the intention behind “we need to teach everyone to code”?
  • What educational and social outcomes we should be striving for?
  • Are there key skills from “learning to code” not covered in the current curriculum?
  • Is there a better definition for digital literacy?
  • How does digital literacy relate to coding and the rest of computer science?
  • How do we demystify digital technology and bring the community along?

Please contact me if you are interested in participating.

To code or not to code, is that the question?

Image: Ruiwen Chua.