There are three major theories as to why the US, UK, and other developed economies have seen a stagnation in productivity growth in recent years. The first is that we’ve already squeezed all the major productivity gains we’re really going to get. The second is that we’re not measuring productivity correctly. The third is that productivity lags while major innovations are developed.

The first theory is interesting to economists but seems wrong on its face. If you’ve ever sat on the phone for hours trying to fix a screw up or had a tough business question to answer you know there are still things we’re not doing. I can stare across any office in the world and see that we haven’t quite finished optimizing the business.

At the same time, we probably can’t expect to see the kind of productivity gains in the economy that we saw from telecommunication, electrification, and digitization, at least not for a long while.

The second theory, that we’re not measuring productivity right seems to ring some truth. In the last few decades, business has transformed in two major ways. The first is globalization and the second is digitalization.

Major manufacturing has moved to developing economies, and largely the finance and data management resides in the developed economies. This trend is changing as developing economies mature, but this has largely been the state of the economy for the last few decades. This means that a national measure of productivity may not be as relevant as it once was.

With digitization, more work is being done by computers, but we’ve yet to complete digitization. Most decisions, even relatively minor decisions are made in the analog world. This requires people to sift through an ever increasing amount of data. While digitization realized major gains in the early 2000s, data exploded after that. Businesses are still figuring out how to use that for better productivity.

The third theory, that gains are on a long curve, also has a lot on it. Major investments in commercialized outer space applications, self-driving cars, and artificial intelligence are a drain on economic measures. A lot is being put into them, but only AI is beginning to pay off and is just on the verge of the kind of mass deployment it deserves.

What can we do about it?

If the first theory were true then I suppose we couldn’t do much. However, if bits of all of the theories are true, there is a lot we can do for an individual business.

First of all, we need to adapt our business processes to a data-driven economy. This means automating decision making, using workflow systems to drive actions but most of all being ready to change that as the situation on the ground changes.

Secondly, the most important thing is to equip our workers to deal with ever increasing amounts of data. This means deploying systems that let them find answers to what they need when they need it, if not a bit before. When you stare across the office, ask the question “who is waiting on something and why?” When you receive an email asking a question, ask “how could they have answered this question for themselves?”

Third, it is time to deploy AI and make use of data science throughout the enterprise. With more and more data, people can’t make the small decisions. It starts with an investment in and a strategy for AI. How is your company going to educate its workforce, adapt its business processes, deploy the technologies, operationalize them, and fully instrument the business? It is time to answer these questions.

So basically…

Measuring productivity is difficult and we’re probably doing it wrong. Meanwhile, innovation takes time and investment first looks like a drain on productivity. With increasing amounts of data and the centrality of data, we need to equip our workforce to work with that data. A key piece of working with data is deploying AI technologies and turning over the smaller, every-day decision to the machines.

Find out more

  • Enterprise Search Buyers Guide – If you are looking for an Enterprise Search Solution to increase your productivity, this buyers guide goes over the marketplace, terminology, features and selection criteria.
  • Fusion 4 Overview – Lucidworks Fusion has technologies that can help you answer questions and incorporates essential AI technologies
  • Head-n-Tail analysis webinar – Our previously recorded webinar on using AI to answer why users didn’t find what they need (and automatically correct it).
  • Contact us, we’d love to help you.