We have a new essay published in Deloitte Insights, A moral license for AI: Ethics as a dialogue between firms and communities. This collaboration with CSIRO’s Data61 looks into the challenge of creating ethical AI, picking apart the problems and proposing a way forward. There’s a launch event on the 2nd of September, 2020, which you can register for via Zoom.Continue reading A moral license for AI
In the past few weeks we’ve written about need for management to get out of the bunker mentality that uncertainty and a rapidly unfolding crisis had pushed us into, and how we have a perfect storm for innovation with demand collapsing for the old thing while new demand is popping up as we adapt, and the government is (effectively) subsidising innovation by providing unsecured loans and underwriting payroll (via JobKeeper). Put these together and you have the potential for (some) firms to emerge from the crisis stronger and more capable than they went in. New winners and losers will be created, and so on. That leaves one question unanswered: Where should a firm look for these new opportunities?
Now we’ve had a go at creating an innovation mud map to help find where a firm might innovate. Our hope is for this to be a conversation starter inside firms.
There is a few reasons for this.
First is that we seem to have a perfect storm for innovation. The pandemic means that doing the old thing is not an option for many firms, with many of us stuck at home and the government restricting how businesses operate. At the same time, new demand is appearing and we all learn how to live in the new normal.
Second, and as we pointed out in Getting out of the crisis, the business environment post shutdown is unlikely to be the same as pre shutdown. The best way to prepare is to experiment now, to learn how consumer behaviour is changing, and start building the assets and services that will you’ll use in the new normal.
Third, and finally, the government is providing unsecured loans and is even willing to underwrite payroll. The government is effectively paying you to innovate.
There’s already early evidence that firms who are experimenting and adapting will emerge from this shutdown more efficient and effective than they went in. Now would appear to be the perfect time to innovate.
The middle of a crisis might not seem to be the best time to think about the longer term. It can be important though, once immediate problems are dealt with, for management teams to consider how their firm will trade its way out of the crisis, rather than just reacting to events as they unfold.
The common assumption of a “V”-shaped economic contraction is unlikely to be true as when restrictions are lifted they’re likely to be lifted incrementally. The more a firm can do to innovation and keep the business running—rather than putting it into hibernation—the more likely the firm is to emerge from the other side. This means that:
An optimist would consider this a great time for experiment, to look for new opportunities, to find new ways to do old things, and to find new things to do.
I and a coauthor have a new report out on DU Press: Your next future: Capitalising on disruptive change.[ref]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>.[/ref] 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 Index[ref]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>.[/ref] – 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.[ref]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/>.[/ref]
- 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.
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),[ref]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/>.[/ref] 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).[ref]Shah, S 2017, ‘Airbnb is reportedly being used to launder money’, Engadget, <https://www.engadget.com/2017/11/27/airbnb-russian-money-laundering-scam/>.[/ref] 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.
Deloitte ran a series of breakfasts recently for the retail community, and they kindly asked C4tE to participate. My contribution, which you can find at Scribd or embedded below, sprang out of our recent report The Future of Exchanging Value: Cryptocurrencies and the trust economy(FoEV) when, during a chance conversation, Robbie (the left-brained person who leads the Spatial team) pointed out that that we were arguing for a new trust architecture in retail.
The nutshell explanation of the idea is:
- The current retail model is a constructed environment and shopping a learnt experience. This model is a response to the creation of mass market products and supply chains.
- The model is build on there pillars: customers identifying a need, searching for a solution to the need, and then transacting with a merchant that they may not know or trust. Money – cash – facilitates this, as it enables us to transact with someone we don’t know and may never meet again.
- However, a number of trends we saw in FoEV suggest that this model might be breaking down. The mid-market dies, consumers seized control of the customer-merchant relationship, peers replaced brands, value is now defined by the consumer rather than the producer, payments are moving away from the till, and shopping is becoming increasingly impulse driven.
- What will retail look like in a world where need is never fully formed, search is irrelevant, and transactions are seen as distasteful? What is the new trust architecture?
See what you think of the presentation and feel free ping us if you have any thoughts.
The two reports mentioned in the presentation are:
- The future of exchanging value: Uncovering new ways of spending
- The future of exchanging value: Cryptocurrencies and the trust economy
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.
I have a new post up on the Deloitte Strategy blog.It’s the result of a chat I was having the other day with an economist colleague who opined that “platforms are an essential part of the sharing economy”.
As I point out in the post:
These platforms might be sufficient to kick-start the sharing economy, but they’re not necessary for its long term survival. There are alternative approaches to creating sharing economy solutions that do not rely on a centralised platform.
Platforms solve what we might call the discovery problem. When we’re creating a market it needs a mechanism for buyers and sellers to discover each other.
Rendezvous – where buyers and sellers meet at a common location – is probably the most common solution to discover. It’s also the one that firms prefer as it’s the easiest to monetise.
As I point out later in the post:
The recent emergence of blockchain – a distributed ledger solution – from the shadow of Bitcoin might be a sign that something has changed in the environment, something that is tipping the advantage away from centralised solutions and toward distributed ones.
This could be a big deal, as it blows a rather large hole in the business models of the sharing economy firms.
Check out the post and see the whole story.
I have a new post up on the Deloitte Strategy blog, which I wrote with Richard Millar.
Platforms are all the rage. In the modern digital economy many organisations are looking to create platforms, rather than simply building a traditional value-chain driven company (otherwise known as a ‘pipe’).
In this context, a platform is a business model designed to facilitate exchanges between interdependent groups; as opposed to a pipe, which is centred on the sourcing, production and distribution process. The successful companies of the past focused on controlling distribution (something which is increasingly difficulty in our highly-interconnected digital world), while it’s thought that successful companies in the future will focus on controlling access to customers (which they can do by creating a platform that attracts the best customers).
Platforms are where the smart money is going (particularly if your platform is seen as scalable). There’s even a Platform Strategy Summit where you can learn the tricks that will make your platform successful.
This recent obsession with platforms raises some concerns though, as it seems to confuse cause and effect.
You can find the entire text over at the Strategy blog.