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Forecasting The Next IT-Driven Productivity Take-Off

by Andrew White  |  February 25, 2018  |  Comments Off on Forecasting The Next IT-Driven Productivity Take-Off

Here are a few facts:

These might seem disconnected or even conflicted positions. They are not. But to understand why we need to take a broad look at what are the drivers/enables and impediments/disables to innovation and growth. Technology alone cannot drive growth. We need to look at demand, and supply, of people, process, technology and data, to understand what is happening in our economy today.

IT has played a key role in driving increased productivity in the past. History shows us that capital deepening (that is, increasing investment in information and technology to help improve efficiency of labor) in one period led to increased productivity growth in later periods; sometimes those “later periods” were much, much later.  Thus there is a varying lag between such innovation, investment/diffusion, and recognized macro-level growth, even if one firm that did the pioneering work still achieved great things before everyone else.  This is the challenge with firm-level analysis and industry level analysis.

However, spend itself is not the simple answer here.  At a macro level it seems that increasing spending on IT will lead to better results.  Clearly this is an over simplification.  What it is a firm needs to spend on is a major challenge that has not been adequately addressed in research.  There are of course many attempts at calling out what kinds of innovation and technology are more disruptive than others.  One important example is McKinsey’s “Disruptive technologies: Advances that will transform life, business, and the global economy”.from 2013.  This a really nice piece of work but for the punchline.  The list of technologies is a good list, make no mistake, but it is not a list of apples that can be compared.  For example the following is included in the list:

  • Mobile internet
  • Cloud technology
  • Automation of knowledge work
  • Internet of things

These are all good and interesting technologies – but the list and report miss a key point: What is common among most of the top 10 technologies?  What is the underpinning capability?  And that is the point of this blog.  And spending on technology is only part of the puzzle.

Even if the right kind of technology is available, what is need to help assure productivity improvement?  There was also a need for the workforce to be able to leverage the new technology; and separately management capability needed to evolve to organize firms and resources differently (as in business model changes).  This then leads to a summary of prerequisites for effective, productivity-inducing innovation.

Figure 1: The Four Criteria for Effective Long-lasting and High Productivity-Inducing Growth









Source: Gartner, Inc., © 2018

Even this list of four dimensions understate the complexity involved, not least being how few policy or decision makers can explicitly bring about the right combination.  More recently the idea that certain kinds of technology innovation also need to be aligned for all these things to work together to drive increased growth.  For example, capital deepening seems straight forward enough, but if the spending is on the wrong kind of technology innovation, or innovations that lack high-productivity-inducing complementaries, then no amount of management capability or workforce skills can counter the weakness.

This explains many reports whereby we see large sums of money being sunk into technology innovation but we see no real pathway to increased productivity.

The nature of the complementary innovation is the more interesting aspect that is becoming clearer.  But it is a new understanding that has started to emerge or be described, even though itself it was well known.  In truth, we are recombining ideas and connecting dots to come up with a more rounded view of what takes place all around us.

Most research looks at innovation and the rates at which it creates new technologies. Other work looks at how costs, especially related to the IT/producing sector changed, (think when China entered the WTO and kept labor costs down) and as result IT costs as inputs to IT-using indicators changed. Some other research has even looked at the different value proposition of tangible assets (think CPU, servers, plant and equipment) compared to intangible assets since this changes the accounting methods used to derive growth and productivity. See the most recent view in Capitalism without Capital.

A few works take an expansive view and try to look at economics and technology. Brynjolfsson and McAfee’s excellent ‘The Second Machine Age’ concludes a set of polices to help manage and cope with the new digital revolution. But no analysis I can find looks to compare the aggregate demand and supply sides of the equation, to determine the net likelihood that a boom in IT-based productivity will indeed result in improved economic growth at a reduced cost. That is where I am focused: To find a pattern that would help predict when enough of the right kind of four-factors (see figure 1) are aligned.

On February 8th in The Economist there was a most interesting article: Free Exchange: Great Good to a one- Central Banks must occasionally gamble that faster productivity growth is possible. This marvelous article seeks to connect monetary policy with IT innovation, and the complex dynamics that can lead to growth. The highlight of the article is the example of Alan Greenspan and his decision to keep interest rates low at a time when unemployment was at levels normally triggering interest rate hikes. The result was a prolonged period of growth. A by product, it turns out, and not the focus of this blog, is the financial crisis of 2009.

The article calls out the connection between economic policy as defined by central bank policy and the set of factors leading to a pent-up ability to convert IT and labor into increasingly productive work. That is the same situation we face today at a macro level. And I believe we are at the same position as Alan Greenspan was at in 1999.

If you go back to McKinsey’s top 10 technologies, and look at the common capabilities that underpin all, you will determine that information and technology supports all of them in different ways.  Information, as in intangible assets, including IP, process design and so on – all has to be instantiated in software in order to be if any use.  The technology is what provides the compute, processing power and networking.  So how has information and technology changed over the years?  Hardware spend used to dominate IT (or what economists call ICT) spending early on.  Somewhere in the late 1980s, software spend started to outstrip hardware and by the 1990s it became the larger of the two.

But unlike hardware, software has many more form-factors.  A CPU is a CPU, and innovation drove the price/capacity aspect of processing power.  Software however is quite different; it described what a company does; it is used to yield insight on a decision; it is used to store records and documents.  And it gets even more complicated: CRM in the 1990s is very different to CRM in the 2000s and 2010s: the maturity of the four dimensions in figure 1 have changed dramatically over time.

This is the missing link, the missing dialog, needed to help explain WHY productivity changes over time.   It is this factor that warrants more analysis and research in terms of its impact on how firms operate.  To make my point, take a look at AI, ML and deep learning.   Yes, the technology is making significant inroads with new and recent innovation; yet the ability of a firm to adopt and leverage the technology, for that capability to diffuse across an economy, lots of things have to change and fall into line.  Either these four (see figure 1) dimensions fall into place luckily, the invisible hand and desire of profit leads the effort, or public policy does so.  It has to be one of the three.  See 2018 Won’t See a Massive Productivity Boost From AI – 2019 Might Show It.

Category: innovation  productivity  

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

Andrew White is a research vice president and agenda manager for MDM and Analytics at Gartner. His main research focus is master data management (MDM) and the drill-down topic of creating the "single view of the product" using MDM of product data. He was co-chair… Read Full Bio

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