Category Archives: Publications

Reconstructing jobs

Some coauthors and 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.

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, 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, View in article

Bitcoin, Blockchain, and Distributed Ledgers: What questions should we be asking?

Bitcoin, Blockchain & distributed ledgers: Caught between promise and reality

The latest report from the Centre for the Edge is out, Bitcoin, Blockchain & distributed ledgers: Caught between promise and reality. This report follows on from the one published in February, The Future of Exchanging Value: Cryptocurrencies and the trust economy (FoEV).

In the FoEV we looked at cryptocurrencies and Bitcoin, however we had to set aside a discussion on the technologies that underpin cryptocurrencies and their broader affect on society as the report was already quite long.

With Bitcoin, Blockchain & distributed ledger we pick up where we left off, and take close look at the opportunities and problems created by these technologies, and their regulatory implications.

We didn’t want this report to be yet-another explainer, since there’s already a lot of those out there, with the ensuing arguments over technology that invariably seem to follow them.

So rather than focus on the technology – the solution space – we focus on the potential applications – the problem space, to try and understand not just what is possible, but what is practical. This is resulted in a fail compact and pragmatic report focused on a few key areas, as we point out in the report’s introduction.

In From Bitcoin to Distributed Ledgers, we compare the Bitcoin’s ledger with the more familiar physical ledgers that preceded it, and develop the concept of a distributed ledger7 de ned in terms of the problems solved rather than the technologies used.

In A map of the distributed ledger landscape, we identify questions that should be asked when considering a new distributed ledger, creating a map of the solution landscape.

In Regulation, we explore the potential regulatory implications of these solutions, though we only focus on what is different with distributed ledgers. How does one regulate something no single person or organisation is accountable for?

In Applications, we review the strengths and weaknesses identi ed in the previous two sections to develop an understanding of what a distributed ledger can be and what it can’t be.

Finally, in Conclusions, we look at the technology’s potential and what the future might hold.

You can find the report on the Deloitte Australia web site.

The Future of Exchanging Value: Cryptocurrencies and the trust economy

FoEV2_coverOur latest piece at Centre for the Edge is out: The Future of Exchanging Value.

This report started life as a followup to a report we published in 2012. As we say in the current report:

Our findings in that report centred on the realisation that we were reaching the end of the initial build-out
of a digital payments infrastructure. The task of provisioning the infrastructure merchants require to accept real-time digital payments, or for two individuals to settle a debt, was largely complete. Consequently, our focus had shifted to streamlining the buying journey – from the pieces and parts to the whole.

Our key point then was that the future of exchanging value would be shaped by social forces – how payments fit into the end-to-end consumer experience – rather than the technological challenge of deploying yet-another generation of payments solutions.

This new report, which was intended to be a short update, when in an entirely different and much more interesting direction.

Our key insight this time is that we’re all thinking about money the wrong way.

It’s common to assume that we use money (cash, currency…) to build trust relationships. This assumes that our adoption of money stems from the coincidence of wants. I need shoes. You have shoes. You want a fish. I have a chicken. We use money to bridge the gap.

The problem is that this assumption is incorrect. As David Graeber points out in Debt: The First 5,000 Years, debt came before barter and the coincidence of wants. Most folk in antiquity didn’t need money. They knew everyone they interacted with, and could rely on the community to enforce the collection of a debt if need be. Money’s first use was as a measure of value, typically to help calculate damages in a criminal or civil manner. Communities had carefully drawn up lists to capture exactly what you owed, in a convenient currency, someone if you destroyed their house, stole their food. In Somalia, for example, they use camels (commodity money). The other uses of money – as a medium of exchange and store of value – came later.

This is a fascinating fact, is it points out that we have the consumer-merchant relationship backward. We’re focusing on the transaction when we should be focusing on the relationship. The future of payments is not micropayments and tap-and-go. Indeed, the future of payments might be to use a loyalty scheme (a complimentary currency) to anchor the relationship and then move the transactions from the centre of the relationship to the edge. This ties is cultural preferences that we have, and which equate money and transactions as “dirty”. The future of payments might be not to have payments at all.

Bitcoin and the whole cryptocurrency thing is influenced by this too. There’s a huge amount of noise in this area at the moment, and everyone one is waiting for the killer app that will drive Bitcoin (or another cryptocurrency) into mass adoption. If, however, you view Bitcoin adoption as a cultural problem, rather than the search for a killer app, then you end up at the conclusion that no cryptocurrency will become much more than a large niche. The best equivalent in the current environment that we’re all familiar with would be a large frequent flyer scheme. It’s hard to scale trust, even with technology support, and these frequent flyer schemes seem to up near a nature limit.

There is one use case for currencies growing larger, though: when a sovereign nation mandates that you pay taxes in a specific currency. This trick is behind all the major currencies, and was used by the colonial powers to pull conquered land into their monetary system. Acquire currency to pay tax, or we send the bruisers around.

We conclude in the report that the best analogy for cryptocurrencies is rum and cigarettes. Rum was used in Australia’s early days when there wasn’t enough government issued currency to go around. Cigarettes were used by prisoners or war as they had few other options.

We can expect cryptocurrencies to see some adoption in countries where the population doesn’t trust – or can’t access – the national currency. Argentina springs to mind. Cryptocurrencies are mush less useful in other countries with mature and stable economies.

A similar argument can be made against cryptocurrencies as internal reserve currencies. (And that argument is in the report.)

There’s a lot more in the report, and I’ve been told that it’s a bit of a ripping yard. Go grab a copy and read it.

Redefining education

Our latest piece at the Centre for the Edge is out: Redefining education.1)Peter Evans-Greenwood, Peter Williams, Kitty O’Leary (2015) The paradigm shift: Redefining education, Deloitte Australia.

When we did an Australian version of the Shift Index2)The Shift Index in Slides @ PEG we saw that while Australia has a pretty good digital foundation and society seems to be adapting to the shift fairly well, we’re not realising as much value as it could be. Or put another way, while we’re using digital technology to create new knowledge flows, we’re not as proficient at realising their value.

With the Shift Index complete we turned our attention to education, as it seemed logical that education would be the most effective fulcrum to use to improve our performance.

We took the major trends from the Shift Index – the move from stocks to flows, and from push to pull – and, as a bit of a thought experiment, applied them to the education sector to see what we came up with. This resulted in a slide deck The Future of the Education Sector3)The Future of the Education Sector @ PEG and now this report.

The major finding in the report is that our relationship with knowledge is changing, and consequently our relationship with education is changing. The snappy version of this is “Why remember what you can google?”. The longer story has interesting implications for the education sector as by changing what it means to be educated has all sorts of potential knock-on effects for education and educators.

The report is our attempt move the current debate beyond pedagogy and edu-tech, funding and Australia’s ranking on international league tables to consider if our changing relationship to knowledge (the shift from knowledge stocks to knowledge flows, highlighted in the report) is changing the role and purpose of education and (by extension) the education sector.

The report is on Deloitte’s web site, and I’d love to year your throughs.

References   [ + ]

1. Peter Evans-Greenwood, Peter Williams, Kitty O’Leary (2015) The paradigm shift: Redefining education, Deloitte Australia.
2. The Shift Index in Slides @ PEG
3. The Future of the Education Sector @ PEG

Setting aside the burdens of the past

The first report from the Australian Centre for the Edge on the Australian Shift Index, Setting aside the burdens of the past: The possibilities of technology-driven change in Australia, has just been published. (Press release here.)

We’ve worked hard on this over the last six months or so and I’m very happy with this report as an introduction to what we’ve done. If you’re interested in how technology is driving change both in business and in society in general, then I highly recommend that you head over and grab yourself a copy. (And if we’re in something like the same neighbourhood I’d love to catch up for a coffee to discuss. Or feel free to leave a comment below.)

The Shift Index was created as a tool to help us understand if the rapid pace and increasing uncertainty we feel in the business and social spheres is real, or if it is just an illusion created by the always-on environment we live. (This is a bit like how nationalised news brings us stories of shootings in other regions leading us to think that crime has increased, when in actual fact crime has been decreasing.)

As we say in the report:

The world is changing faster than ever. However, we can only respond to and manage a change if we can measure and understand it. If we want to respond as a community, then we need to find a way to quantify the change. We need to ask ourselves whether the perceived change is real, and if it is, how we can capitalise on it.

The short answer is that the world is definitely changing and that Australia, Australians and Australian businesses are successfully adapting to the changes. We can’t, however, rest on our laurels as the drivers of change are still present and it doesn’t look like they will dissipate for some time.

The concept behind the Shift Index is that developments in digital infrastructure (computing, storage and networks) is driving increases in information flows, and that these information flows are reconfiguring society by tipping the balance of power from the merchant to the consumer.

The framework we used as our starting point was developed by the US Center for the Edge, founded by John Hagel and John Seely Brown. The US Shift Index was developed in 2009 and has been updated each year since then.

Our goal with the Australian Shift Index was to take the US framework and build a comparable index for Australia, allowing us to take the lessons learned from the US index and translate them to our local context. At the same time, we tailored the index – tweaking or changing some of the metrics used – to create a version that is uniquely Australian and which can provide us with insight into the particular challenges we face here.

The methodology defines three groups of metrics:

  • The Foundation Index measures the price-performance of computing, storage and network technologies, the penetration of these technologies into society, and change in regulation to support the adoption of these technologies. This is the lead indicator in the Shift Index.
  • The Flow Index measures the resulting increase in information flows in terms of virtual flows (mobile phone and internet usage), physical follows (attendance at conferences, business travel, and money transfers) and flow amplifiers (social media and the like).
  • The Impact Index measures the impact of these changes across the Australian market (competitive intensity, labour productivity and stock price volatility), firms (asset profitability and the like) and people (consumer power, brand disloyally, returns to talent, and increased in executive turnover). This is the lag indicator for the Shift Index.

The result is three high-level metrics that quantify the the drivers for the change, the change itself, and it’s impact.


Image source: Centre for the Edge

There’s ten major findings in the report:

  • Fast adopters: Australians have a good track record for adopting new technology. Our challenge is to continue adapting, and to find opportunities to leverage these technologies within our institutions.
  • Tech-driven change: The permeation of cheap, powerful computing, communications and storage technologies is driving change and will continue to do so into the foreseeable future.
  • Knowledge flows: New technology has resulted in new flows of information at unprecedented volumes.
  • Higher competition: The Australian market has become more competitive as a result of new technology and knowledge flows.
  • Capital over labour: Australia’s focus has shifted away from labour and towards investment in new technologies for more efficient workflows.
  • Knowledge economy: Australia has shifted from an industrial and agricultural economy to a creative, service-based economy.
  • Unrealised potential: There is a big gap between our technological capabilities and the way we currently use technology to solve problems.
  • Economic strength: Australia’s economy is strong and demonstrates better asset profitability than the US.
  • Recession-proof: The global downturn in 2008 was only a pause in our progress and has not halted Australia’s transformation.
  • Future success: Our continued prosperity depends on how well our knowledge workers can find new ways of using technology to solve problems.

These ten findings are only the tip of the iceberg though. While the report answers some interesting questions, or raises even more questions, questions that we intend to delve into further.

Image source: macinate.

Technological Considerations of AML/CTF Programs

I had the chance in the last couple of months to review the (very old) chapter Technological Considerations of AML/CTF Programs chapter the I wrote with a couple of colleagues for LexisNexis’s Anti-Money Laundering and Financial Crime publication. The world has changed quite a bit since then so it was more like a recreation than a simple revision.

LexisNexis have kindly made an extract available, which you can find below via a Scribd embed. If you’re interested then head over to LexisNexis (or I suppose we can catch up for a coffee or something).

Outsourcing in an increasingly complex world

Outsourcing in an increasingly complex worldSometimes posts become a tad to long and unwieldily to drop onto the blog. One such post was a thing I put together around some work I’ve been doing over the last few years on outsourcing. A friend suggested that, rather than letting it languish, it could be interesting to clean it up and publish the result as a (short) ebook; which is what I’ve done.

Find the blurb below, and to can grab the complete text from the iBookstore or Lulu (epub) (Amazon is in the pipeline).

Outsourcing in an increasingly complex world

by Peter Evans-Greenwood

Support independent publishing: Buy this e-book on Lulu.

Pressure on margins is driving organizations to increasingly rationalize and externalize supporting functions as they search for more efficient and flexible delivery approaches.

Most common approaches to outsourcing center on establishing target service levels and a unit cost, treating the negotiation of an outsourcing engagement in a similar fashion to the procurement of other materials that the business needs.

Outsourcing, however, is becoming more complicated as we move functions closer to the heart of the business into the hands of partners and suppliers. This represents a shift from an approach based on paying invoices for the raw materials we need to run the business, to one based on delegating core, business-critical functions to suppliers, and then requiring them to deliver the outcomes that we need.

Crafting a successful outsourcing engagement in this environment requires us to align the supplier’s incentives, and therefore their objectives, with the client’s business drivers. It’s not enough to take a piecemeal approach, imposing additional requirements and constraints in the hope that these will shape supplier behaviour.

It’s a truism that what gets measured is what gets done; outsourcing is no different. Existing approaches to crafting outsourcing agreements attempt to shape supplier behavior by imposing large and inconsistent sets of requirements, with the result that both parties search for loopholes in an attempt to optimize their position.

A successful contract will be based on the customer’s business drivers, aligning supplier incentives with them to ensure that the agreement drives the right behaviors