Transformation Requires a Scientific Approach

AI, machine learning, and other new technology must replace subjective rules of thumb and one-size-fits-all programs for business transformation to...
David McCannSeptember 2, 2019

What it takes for businesses to succeed has changed dramatically in the last decade, and the 2020s portend even more change. Many organizations will need to undergo fundamental changes to keep up with evolving technology and competition.

But traditional transformation practices, generally grounded in subjective rules of thumb and one-size-fits-all programs, will not be sufficient. Our work with companies pursuing large-scale change suggests they will need a more scientific and evidence-based approach to change.

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Many companies today are undergoing transformation in order to apply AI and machine learning to product development, operations, marketing, and other operational aspects of the business.

But leading businesses will let go of established models and apply a scientific approach, including the application of AI and other new technology, when it comes to the design and execution of change programs. In so doing, they will gain an advantage in transforming their organizations for the next decade.

We see some emerging ways that companies can leverage the science of change to enhance their transformation:

Companies can use technology to identify the right talent to execute change. Traditionally, the only information available to identify workers with the skills to fill new positions has been subjective judgments of individuals’ track records in different roles. But advances in science and technology are unlocking new possibilities.

The study of neuroscience and advances in testing technology now allow for the rapid, scalable identification of cognitive and emotional traits. And AI can now find and refine complex relationships between these traits and job performance.

For instance, a project we were involved in used digital games to assess individuals’ cognitive and emotional traits, as well as their skills in a range of simulated business strategy scenarios. We found that different neuro-traits reliably predicted success in different situations.

Importantly, we saw that very few individuals were successful in all environments. That underscores the need to effectively align skills with roles—and the need to adopt new technologies to do so accurately and at scale.

Companies can leverage the intersection of behavioral science and data analytics to “nudge” employee behavior. As traditional, hierarchical management approaches give way to decentralized organizational structures, leaders will need strategies to shape their teams’ behavior indirectly while retaining the benefits of autonomy.

Empirical lessons from behavioral psychology can help leaders identify small interventions that may nudge employees into different, more productive behaviors. For example, in a randomized experiment, Virgin Atlantic found that giving reminders and goals for fuel use to pilots resulted in millions of dollars of cost savings.

Management can use data analytics to detect early warning signals. Understanding when and how change is needed is no mean feat. By the time traditional performance measures signal the need for change, it’s very often too late to recover.

But in an increasing number of areas, scientific study can reveal early warning signals — patterns that predict critical changes to complex systems. Just as in ecology, where certain configurations of vegetation in a dry region are indicators that the region will become completely barren, businesses may be able to identify early warning signs of impending shifts, such as clear, early data that their current growth engines will run out of steam.

For instance, an engineering firm, Thornton Tomasetti, has adopted new metrics to measure its vitality in order to recognize signs of deterioration before they show up in measures of financial performance.

Companies can deploy crowd-sourcing platforms to “gamify” change initiatives internally — and provide real-time feedback about what works and what doesn’t. As companies adopt wider arrays of change strategies, they will need new methods of reporting on and managing those programs. New technologies can help.

For instance, crowd-sourcing platforms may enable companies to “gamify” initiatives to get immediate feedback on how things are going. And dynamic program management platforms can give leaders the power to continuously adjust the initiatives rather than just follow rigid timelines.

In the big picture, lots of organizations are implementing major programs in order to use AI and machine learning effectively. But only a select few are using the capabilities of AI itself to enhance major, large-scale change programs. Instead, the majority still rely on subjective approaches.

In the next decade, successful companies will leverage increasingly powerful AI and other new technologies as a key part of their strategy and change-management programs.

Martin Reeves is the global director of the BCG Henderson Institute and a managing director and senior partner at Boston Consulting Group. Lars Fæste is global leader of BCG’s transformation practice and of BCG TURN.