Responsive Orgs: Designing to Meet the Needs of the Modern Era

October 1st, 2019

Why we care:

Organizations, and the employees within them, are often compared to machines: leaders give employees a task, tell them to execute it, and approve any deviations. While this approach can work well for creating large efficiencies on clear tasks, it fails in times of massive and rapid change and complexity. We are now in one of those times.

We think it is time for an alternative approach – one that rethinks how we design organizations’ systems, processes, and measures to enable the entire organization to be more responsive.
This has been an ongoing conversation at RedThread for over a year and a half. Thankfully, the good folks at Glint agree with us, and have been kind enough to sponsor this research on understanding and creating a “Responsive Organization.”


Our hypothesis for this study is fairly simple:

The world has changed and requires a different kind of organization. Those that focus on being responsive – not just efficient – adjust better to external market pressures, which results in a competitive advantage.

What got us here won’t get us there

For at least the past 100 years, the corporate world has heavily focused on efficiency – and that has driven everything from the way we measure success to how we structure our organizations. And while a focus on efficiency has led to some excellent gains in productivity in the last century, signs point to the fact that it’s no longer enough. Specifically, as you can see in Figure 1, the last decade has experienced a much slower rate of productivity growth (which is essentially a measure of efficiency), than the two decades before it.

Figure 1 The Responsive Orgs: Designing Organizations to Meet the Needs of the Modern Era

Figure 1: Multi-factor productivity, 1987-2018 | Source: Department of Labor, Bureau of Labor Statistics

Why are we seeing this flattening? There are a significant number of potential reasons, but we believe that at least part of the problem is that we may be reaching the limit of efficiency. It is impossible to make a machine or system infinitely efficient; at some point, the effort required to make something more efficient is greater than the resulting benefits from the efficiency. At that point, a fundamentally new system is necessary to improve efficiency gains. Let’s use a second industrial revolution example: people can only weave textiles so quickly, no matter how hard they worked and how efficiently they moved. At some point, the only way to do this more quickly was to use a sewing machine.

Here in the fourth industrial revolution, we believe we are at this point where we have to fundamentally rethink how we conduct business to adapt to the fast-moving, technology-saturated world in which we all work. Our organizations need to become more responsive to all that change. However, to do it, we will have to change organizational structures, measures, and practices that were designed for efficiency, but not responsiveness.

Technology alone won’t solve our problems

We recently read a pretty interesting (and disturbing) study done by Korn Ferry that indicated that CEOs are so enamored with technology that they see it as a larger source of competitive advantage than their own people. Other disturbing statistics from that study are shown below:


Figure 2: CEOs think that technology, not people, will create most value in the future | Source: Korn Ferry CEO survey, 2016.

Of particular concern, is the last finding – that 44% of CEOs say the prevalence of robotics, automation, and AI will make people “largely irrelevant” in the future. While some of the technologies that are becoming embedded in our daily lives seem amazingly “smart,” they have limitations. For example, Jonathan Zittrain in The New Yorker shares the following point:

Consider image recognition. Ten years ago, computers couldn’t easily identify objects in photos. Today, image search engines, like so many of the systems we interact with on a day-to-day basis, are based on extraordinarily capable machine-learning models. Google’s image search relies on a neural network called Inception. In 2017, M.I.T.’s LabSix—a research group of undergraduate and graduate students—succeeded in altering the pixels of a photograph of a cat so that, although it looked like a cat to human eyes, Inception became 99.99-per-cent sure it had been given a photograph of guacamole… Inception, of course, can’t explain what features led it to conclude that a cat is a cat; as a result, there’s no easy way to predict how it might fail when presented with specially crafted or corrupted data. Such a system is likely to have unknown gaps in its accuracy that amount to vulnerabilities for a smart and determined attacker.

While we certainly believe in the power of technology, we cannot lose the human aspect of work. People are the ones who design the systems in which we all live – and are the ones who can do the deep thinking to identify when we need to build different and better ones.

A fundamental re-think on how we measure and manage

As organizations realize the benefits of moving away from a focus solely on efficiency, we think they will begin to rethink their organizational structures. This will lead to changes in the following (at a minimum):

  • Communication channels – Individuals at lower levels will have data and information they need to react to needs “on the ground.”
  • Power structures – Decision-making will be more decentralized.
  • Employee development – More autonomy and continuous development will ensure that employees have the skills and knowledge needed.
  • Metrics – Measurements of efficiency will begin to give way to other types of productivity metrics that focus more on innovation, agility, and responsiveness.

These changes will have profound implications for organizations’ people practices and require different types of leaders than in the past.

This Project:

With this study, we want to gain an understanding of the characteristics that make organizations better able to respond to their markets. In our initial discussions, several themes have emerged that will serve as the basis for the research:


Stacia Garr Redthread Research
Stacia Garr
Co-Founder & Principal Analyst