On bland economic models and the colonial mindset

Society and the economy

A team at Harvard has released a new version of the Atlas of Economic Complexity, an index of ‘economic complexity’. Journalists have pounced on the model to make that case—as they often do—that Australia is a second class country run by second rate politicians. The problem is that the model seems rather bland, only proving that Australia is a large country with a small population (and correspondingly small market) a long way from the major markets. We already knew this.

The atlas “interpret[s] trade data as a bipartite network in which countries are connected to the products they export, and show that it is possible to quantify the complexity of a country’s economy by characterizing the structure of this network”.1)Hidalgo, C.A. & Hausmann, R., 2009. The Building Blocks of Economic Complexity. Available at <http://www.tinyurl.com/y55fxc8s>. So complexity is a measure of integration into global and regional supply chains. This is assumed to correlate with the complexity of an economy.

Given that, any small populous country that is geographically situated near a large market (or cluster of markets) should do well. These countries are too small to export resources (due to lack of land and resources) while their domestic market is too small to soak up many finished goods. They are, however, well situated to be part of supply chains that feed the large market that they’re adjacent too, both importing and exporting intermediate goods. In a case of “no shit Sherlock”, countries like Singapore and Switzerland score quite well.

Large populous countries, such as the US, do ok as they can export products supported by their large domestic market as well as the large domestic market being a sink, importing products from other countries. Not as ‘complex’ as a less populous country importing and exporting intermediate goods, but there’s still a bit going on.

Small to mid-sized countries (in terms of population) that are far from major markets will do poorly. They’re too far from global or regional value chains to participate in them, and their domestic market is too small to support the development of finished goods for export. Here’s looking at you Australia.

Countries such as South Africa sort of fall into this bucket too, though being surrounded by a number of small markets does alleviate their problem somewhat. Australia, as we like to point out, is both a continent and an island. Being small and far from major markets is also why Australia doesn’t have a domestic car manufacturing industry: we’re not big enough to support a car assembly plant with domestic sales, while being too far away from major markets to export.

So the atlas does show a correlation, but it’s with population and geography more than anything else. Also, as the atlas is based on correlation, rather than a causal model, it don’t have anything to say about the future as they’re just extrapolating trends.

It’s a bit annoying that there’s not much to be learnt from the atlas. What is more annoying though is the colonial mindset in Australia that assumes that nothing good can come out of the colonies (Australia) as all good things come out of the colonial power (being Europe and the US).

References   [ + ]

1. ↑ Hidalgo, C.A. & Hausmann, R., 2009. The Building Blocks of Economic Complexity. Available at <http://www.tinyurl.com/y55fxc8s>.

Digital agency and the skills gap

Centre for the Edge, Event, To code or not to code, Work, worker, workplace

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

Centre for the Edge, Publications, Reconstructing work, Technology and its malcontents, Work, worker, workplace

I, along with Alan Marshall and Robert Hillard, have a new essay published by Deloitte Insights – The 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

Centre for the Edge, Publications, Technology and its malcontents

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

Centre for the Edge, Publications, Technology and its malcontents, The Firm

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/>.

Digital is the new ERP

Digital is the new ERP, The Firm

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

Centre for the Edge, Publications, Reconstructing work, Work, worker, workplace

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.