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What Will Drive Innovation in through the Reset

by Andrew White  |  April 22, 2020  |  Comments Off on What Will Drive Innovation in through the Reset

The human tragedy and losses are still yet unknown and growing. Indeed, we are not exactly out of the woods with Covid-19. China, just 10 days ago, was worried about a resurgence of Covid-19 cases; Singapore last week took extensive shut-down steps (or circuit breaker, as they call it) to prevent the growth of an enemy.  We will be in this hugely uncertain period (or reset) for some time to come. But Churchill once said, near Britain’s Darkest Hour, “Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning.”

Gartner is using the word reset to describe this giant dislocation that is taking place right now; the unhinging of the global economy and society, and the aftermath. The immediate wave that stated in China and has now wound its way around the world, is the first stage of the reset, and there are three phases or periods. We call this initial period: Response.

This period is characterized by immediate reactions to crisis. As healthcare systems are overwhelmed, economic systems are shut down or dislocated or dismembered. Things stop working. Emergency measures are undertaken with little planning. This phase is all about survival. In business speak this is characterized by cash preservation. Non-cash generating work was shorn; employees let go as revenue dries up. The final stage here is described with public sector reactions defined by economic, financial and social packages to keep things afloat.

The second phase is just being experienced by China and, like the first, will wind itself around the world over time just as a tie around a spindle. In fact, in the US and some other advanced economics some firms are already working on this stage or have finished it already and now looking to the third and final. But this second stage, Recover, is all about taking decisions that preserve some ability to move beyond survival.  The Response period was quite short; the Recover period is going to be longer, and we really don’t yet know its direction since the shift to the third phase will be dependent on a vaccine.

In this second phase executive leaders will need to make critical business decisions with even less data and with more uncertainty. They will need to learn how to make such decisions in this landscape: from survival toward healing; the slow and gradual re-starting of economic and social activities; planning is now possible; new platforms will emerge to a) monitor for relapses toward crisis, and b) preservation of resilience capabilities. 

AI and machine learning will help, along with other data and analytics capabilities. But a re-engineering of how decisions are taken, by whom or what, is required.  I say “what” since increasingly more machines will be given the ability to decide.  Actions can be taken faster with such remote and automated decision tools.  In many cases this can be as effective as human decision making.  And there is no time to consult with a specialist. This is not an option. This period time is critical; your actions are required now, and you have no time to develop cute hypothesis.

Cost optimization and short-term value generating activity will be explored. New processes focused on data visibility and business resiliency will emerge. Digital transformation, once though or as an option, will be accelerated to preserve some semblance of a future.

The final phase will be where new investment opportunities will be exploited by those firm and organizations that built up resiliency patterns into their organizational DNA in the second phase. But as we look out from April 2020, which organizations will be ready for this third phase, and which will not be, is not clear.

Those organizations that built out effective cost-optimization and resilience planning will be well positioned to invest surgically in key, strategic opportunities that arise: new markets, perhaps formed during the great reset, will become established for the long term; older markets that were laid low, may come back or die out completely.  Other markets will be changed forever, and wholly new opportunities may emerge. But not all firms will get past phase 2.

Many highly leveraged firms will be at risk; debt will be at record levels in public and private so anyone who has cash will be predatory. Good firms, in any other economic cycle, will (perhaps unfairly) go under. The entire efficient allocation hypothesis will be upside down as market and public sector forces seek an uneasy equilibrium.

But opportunity there will be. And firms made of sterner stuff will be able to flex musicals that others, lean to the bone, won’t be able too. But what will be at the heart of this new phase of the great reset? What capability will be the main trigger of growth, innovation and dominate?I think that the following will be critical, in this order:

  • Software
  • Data, analytics and AI

Even before the Covid-19 crisis the drivers of economic growth were in question. For years aggregated economic data from OECD and US BEA suggested that productivity had been on a go-slow for some time. From the World Bank January 2020:

A broad-based slowdown in labor productivity growth has been underway since the global financial crisis. The pace of improvements in key drovers of labor productivity- including education, urbanization, and institutions – has slowed or stagnated since the financial crisis and is expected to remain subdued. To rekindle productivity growth, a comprehensive approach is necessary: facilitating investment in physical, intangible, and human capital; encouraging reallocation of resources toward more productive sectors; fostering firm capabilities to reinvigorate technology adoption and innovation; and promoting a growth-friendly macroeconomic and institutional environment.” World Bank Group: World Economic Prospects, chapter 3, Fading Promise- How to Rekindle Productivity Growth, p193, January 2020.

Pundits and economists have been studying this issue for many years. This enthusiasm for the topic hearkens back to Erik Brynjolfsson’s popular opine from a paper in 2003 (Computing Productivity: Firm Level Evidence): we see productivity growth all around us; just not in the data.  This idea, or productivity paradox, suggests that while we do find productivity improvements at firm level, in the aggregate these improvements do not show up.  The paradox has been argued over ever since; some studies argue the data was wrong in the first place and the drop off in productivity is not valid; some have re-calculated the base; some argue we don’t measure the right things anyway since we live in a services economy more than a production economy; and yet others argue that we have run out of big ideas that made such impressions on younger economies.  Either way, the argument persists in much of the data.  See Once More with Feeling: What’s Wrong with Productivity?

Some research has centered on the nature of what drives innovation and growth, and how some innovations beget or help subsequent innovations develop. The concepts of General-Purpose Technology (GPT) as an innovation platform has been introduced; on which other innovations may emerge sometime late.  I have called these other dependent or combinatorial innovations Special Purpose Technologies(SPT) as they only exist due to the generality of the previous platform.  See Where You Spend Your Firms’ Capital Matters for a summary explanation for how GPTs and SPTs interplay and grow over time.

There are some other interesting ideas that may further the exploration. More interesting is the fact that tangible and intangible assets seem to have different impacts on growth and productivity. In fact, much historical growth has been tied to the telecoms and communications industry to help people communicate, increasingly more so in more complex organizational structures, and faster too.   This is what the BEA used to call ICT – information and communication technology; what others might call IT.

But more recent studies have also explored the nature of how spending on tangibles and intangibles has change. The bulk of early industrialization and the computer age was driven by  hardware (communications) and pure compute (processing) power.  These were the main drivers of productivity growth.  Yet sometime in the late 1970s or perhaps early 1980s things changed.  Communication technology, hardware, had reached a zenith: real-time was possible with any number of groups.  Processing an order, or an email, was as fast as it could be for humans to work or use.  To bring this closer to home, PC gaming technology finally outpaced the rate and scale and quality that the human at home with keyboard and mouse and screen could cope with.  As result of this hardware peak, innovation shifted toward software.  From around this time, spending on things like software, IP, and brand increased at a faster rate than hardware and compute.  This in terms trigged new hardware opportunities.  Why did software become more useful to productivity than hardware and compute? 

The reality is that hardware and compute is as close to commodity as anything can be.  This is what Nicholas Carr meant with his, “IT Doesn’t Matter”.  The problem is IT and ICT are not just about hardware and compute.  The “information” in IT was the stepchild of IT for many years, until this change.  Software and how ideas are encapsulated in decision making methods and business behavior is hugely differential. As such, software and IP are where the real value lays in a modern economy, not with hardware and compute. It’s not that hardware and commute don’t matter – they are a given, required.  We all have access to what is needed, in a general sense.  It is that software and IP provide a bigger contribution to differentiation and innovation.  Organizations know this since this is where they are spending their money.  See Intangibles, Investment, and Efficiency.

A headline in the US print edition of the FT on April 16th caught the wave: Software stocks look like downturn winners. The article explored how software firms, those focused on collaboration and distributed workforce at home, look likely to do well. There is some hardware support here but for the most part, this is a rallying cry again for software.  This is somewhat self-evident in a shut-down, remote working scenario.  But the point is clear: It is software that provides the flexibility, the adaptability, to change and adjust to any condition.  Hardware is like our bones, our veins, our infrastructure.  Software is like what we can do when we think and form ideas.  Both are required; but we are past the physical nature of survival.  See 2018 Won’t See a Massive Productivity Boost From AI – 2019 Might Show It.  I was wrong in the blog, of course.  We won’t see the impact of AI in “the data” until 2021 at the earliest.

The great reset will diminish many parts of the economy and much of IT spending. Gartner’s Forecast Analysis: Global Recession Scenario is bleak reading. But hidden in the data are gems that suggest software will fall less than the other parts of IT spending. IT services, another intangible asset, shows sign of immediate decline and early recovery in some regions.

So, in this next phase of the reset we will be hard pressed to do what the World Bank suggests. We don’t have time to develop a coordinated plan. Executive leaders need to know now where to cut, where to protect spend, and where (and when) to invest. The decision-making capabilities of old won’t cut it. A wholly new approach, based on data, analytics and AI is needed to streamline the effort.  It won’t just be people either that make decisions.  To scale and excel in the opportunities emerging, we need new ideas, and new methods to take and automate responses and decisions:

  • New data; old data
  • Big data; small data
  • Real data; synthetic data

Decision making needs to be re-engineered.  Now.

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Category: data-and-analytics  innovation  

Andrew White
Research VP
8 years at Gartner
22 years IT industry

Andrew White is a Distinguished Analyst and VP. His roles include Chief of Research and Content Lead for Data and Analytics. His main research focus is data and analytics strategy, platforms, and governance. Read Full Bio




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