Understanding the two waves of digital transformation is important to be prepared for what lies ahead in the age of automated organizations.
Two waves of digital transformation took hold of the the world in the last five decades. The first started with the use of computers in organizations in the 1970s and contributed to the transformation of organizational processes from analog to digital.
The second, the one in which we’re currently situated, is marked by the increasing presence of artificial intelligence in organizations. No matter the size of your company or the importance of your role, it’s likely that you are or soon will be surfing this wave. In order to prepare, it’s important to understand how we got here.
For centuries, the main drivers of economic growth have been technological innovations. The most disruptive of these have been attributed the name, general-purpose technologies or GPTs by economists. This category of technologies affects entire economies and strongly impacts societies. They catalyze waves of complementary innovations, whereby a single innovation becomes a platform for subsequent innovations.
Examples of GPTs include the domestication of plants and animals which occurred about 10 000 years ago, the introduction of electricity in homes and businesses and the reliance on the steam engine during the Industrial Revolution. Subsequently, semiconductors, the personal computer, the Internet and most recently artificial intelligence have been included in the GPT captegory.
The first wave of of Digital Transformation
From analog to digital
The first seeds of of the digital economy were planted in the 1970s in the form of semiconductors. These devices enabled the development of microprocessors and modern computers.
Over the next two decades, computers began to become increasingly necessary for supporting internal organizational processes and boosting the operational efficiency of organizations.
The World Wide Web
Another shift occurred in the 1990s when computers began connecting to each other through global digital networks. As the internet left labs and research facilities, they found an increasingly important role in organizations and people’s lives. Geographical location ceased to be a barrier for the free flow of knowledge and communication. International collaboration grew exponentially and underlined a trend that ultimately gave birth to a new wave of globalization. If in the 1970s and 1980s organizational processes became digital with the use of computer, in the 1990s and 2000s they became borderless with the advent of the internet.
With the internet reaching every corner of the world, the beginning of the 2010s brought another another major shift — the rise of connected devices and cloud computing. Suddenly, seemingly any object, not just computers, could be connected to a network and participate in the generation of data. From mobile phones to refrigerators, a deluge of data was being generated, generating a need for storage.
Cloud computing was the response that enabled all this information to be stored and accessed at a fraction of the cost of local infrastructure. Organizational processes could now not only be borderless, but also generate data at any stage of a process and through almost any medium.
The impact on organizations was a double-edged sword. If the rise of big data was associated with enormous opportunities for understanding processes, it also contributed to increasing complexity. Companies had to adapt to all this data and that anxiety that came with not being able to act on it.
The second wave of of Digital Transformation
The first driver of the second wave of digital transformation is what MIT’s Erik Brynjolfsson and Andrew McAfee call the Second Machine Age. While the “First Machine Age,” better known as the Industrial Revolution, was marked by the creation of mechanical machines that supported physical human labor, the “Second Machine Age” involves the use of software-driven machines to automate many cognitive tasks performed by humans.
At the heart of this revolution is artificial intelligence and more specifically machine learning. As coined by Arthur Samuel in 1959, “machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.”
What this means in practical terms is that a machine can keep improving itself without humans having to explain exactly how to accomplish all its tasks. These machines identify patterns in data and make decisions with minimal human intervention. From an organizational perspective, machine learning has the potential to have a similar impact on organizations today as the steam engine had in the Industrial Revolution.
The second driver of this shift is process automation. We’ll specifically be exploring two important definitions in our next articles: robotic process automation (RPA) and intelligent process automation (IPA).
- RPA is distinct from traditional automation. In robotic process automation, the software robots are configured to execute steps identically to a human user. They are configured (or “trained”) using demonstrative steps, rather than being programmed using code-based instructions. Broadly speaking, a software robot is a virtual worker who can be rapidly “trained” by a manager in a way that is similar to how an employee would train a colleague. Robots have an additional advantage that they learn much faster and retain knowledge better.
- IPA goes further, and adds intelligence to RPA. Like a RPA, an IPA mimics the tasks carried out by humans, and over time and with the use of machine learning, learns to perform them even better, autonomously.
As explained in Harvard Review’s cover article The Business of Artificial Intelligence, automation brought by artificial intelligence will drive organizational changes at a human and structural level. There will be a significant impact on work as repetitive tasks and occupations once performed by humans become automated by robots. Leaders and managers can also expect a deep impact on how organizations are structured as business processes and business models are redesigned to accommodate human-robot collaborations.
A recent study by McKinsey found that about “half the activities people are paid to do globally could theoretically be automated using currently demonstrated technologies. The same study also found that in about 60 percent of occupations, at least one-third of the activities could be automated. However, very few occupations—less than 5 percent—consist of activities that can be fully automated.”
Although a significant proportion of many activities could be automated, the study suggests that very few activities will be entirely automated. Automation is likely to happen at a task level, not at a job-level. Humans will still have an important role in organizations, and managing the human-robot transition and coexistence will be a key function of tomorrow’s business.
The Automated Organization
The organization of the next decades is very likely to be completely different from that of the 1970s, when computers were introduced. The automation of repetitive tasks is likely to free up humans to do more creative and productive work.
Automation will also make processes even more decentralized than when the internet was introduced. Improvement will happen faster and with limited human intervention. Processes will become more reliable as well. Artificial intelligence is also likely to make companies more data-driven. Decisions will be more precise and made at a faster pace.
Predicting the full impact of this second wave is very hard, if not impossible. The great fear about automation is that it will put millions of people out of work. Of course, job loss is likely to happen as IPA takes over certain tasks traditionally performed by humans. However, with more data, more reliable processes and faster decision-making, productivity is likely to soar and new jobs that don’t exist today, are likely to be created.
A survey conducted by Harvard Business School revealed that most managers are committed to a human augmentation strategy whereby human and machine work is integrated, rather than replacing humans entirely. Only 22% of executives indicated that they considered reducing headcount as a primary benefit of AI.
In the end, is up to us to decide how smooth this transition should be.
The Game Plan
In the next articles, we’ll cover what this new era of automation represents for organizations and society in more detail. With a better understanding of the drivers of this new transformation, we’ll start detailing an action plan every company can follow to be prepared for the new automated economy.
Want to learn more about how to transform your organization with artificial intelligence? Download our guide “Modern Digital Transformation”.