Today’s economic outlook and labor market are creating dynamic crosscurrents for business leaders. There is growing anticipation of a coming recession, forcing all of us to reassess our expenses and operations considering a renewed emphasis on profitability for public companies and likely challenges raising capital for private ones. Where high growth was the order of the day for the past 10+ years, the foreseeable future will be about operational efficiency and reducing the overhead and effort required to deliver goods and serve customers.
This fear of recession has led to recent layoffs at Meta, Intel, and Salesforce, and it is not only technology companies where layoffs are now occurring. This emptying out of talent from the technology behemoths might help offset the much-publicized labor shortage we all have been experiencing in the short-term, but it will likely not change the longer-term trends. There is a demographic shift underway, as legions of Baby Boomers retire from the workforce even as they continue to wield significant purchasing power and keep demand high. Despite an economic slowdown, generational shifts will continue to make finding skilled labor a challenge.
Companies will need to simultaneously build greater efficiencies in their businesses to survive a market reemphasizing profitability, while adjusting how they work to make up for a lack of skilled resources. All of this will drive a new wave of automation enabled by artificial intelligence (AI).
Why the labor shortage is destined to continue
Sources as diverse as Scott Galloway and John Mauldin believe the current labor shortage is not a cyclical blip but is instead a demographic trend. Significantly, due to declining birth rates, the number of potential workers for available jobs has been in a decline that began well before the pandemic.
There may be fewer workers per available job, but we are not making less work. In fact, the number of people working today in the U.S. has doubled over the past 50 years. In 1970 in the U.S., nearly 80 million people were employed full or part time. Today there are nearly 159 million employed. However, the birth rate—or number of live births per 1,000 residents—at 11 in 2021 is less than half the levels of the 1960s. As Mauldin notes, the underlying problem beyond immediate worker shortages is that we are making fewer humans.
The practical need for automation
One solution at the convergence of the looming economic and ongoing demographic shift is automation. The next generation of automation—one powered by a new generation of data-intensive applications, machine learning, and AI—can dramatically improve efficiency and work output while minimizing the need for more workers.
Before the pandemic, automation was already advancing, with increased use in a variety of fields from industrial robots to software-based process automation. This is only accelerating as companies are looking for more cost-effective and efficient ways to build products and serve more customers.
Automation is already very much part of daily life. Everything from how goods are made, stored, and delivered to the planes, trains, and even cars we ride in everyday include major elements of automation. And this is only accelerating. Businesses have been acquiring a growing number of robots to cope with the labor shortage, with machines ideally equipped to perform many manufacturing and fixed-function jobs. Even restaurants are increasingly deploying automation—Chipotle is testing a robot to make its tortilla chips, and even a pizza-making robot has emerged.
The next wave of automation for business
As sophisticated as these automation achievements are, these have been relatively easy compared with the next wave. Most of the automation we have put into place in the past 20 years has been using digital technology to automate repetitive tasks.
To unlock the next level of efficiency, the work we must automate now will need to focus on human tasks that are complex and require true decision-making. This next wave will focus on automating significantly more complex tasks, augmenting knowledge workers with cognitive automation, and implementing algorithmic decision-making into our applications.
Research by PwC states that the next automation wave will address repeatable tasks and the exchange of information (as in financial data analysis) that will be done by humans and automated systems working together. Doing this means we must increase our data management and processing capabilities, as AI-driven automation can process complex data sets much faster than people are capable of handling, leaving the available workforce with time for more important tasks. This is what Gartner calls “hyperautomation,” which the analyst firm believes has “shifted from an option to a condition of survival” in a post-COVID-19, digital-first world.
Early examples are already appearing. AI played a role in the development of Moderna’s COVID-19 vaccine and is increasingly used to speed up drug discovery. Alaska Air Group uses AI for flight planning and route optimization. And railroad Norfolk Southern uses machine vision and AI for predictive maintenance to reduce train and crew downtime. Even spaceships are becoming increasingly automated. The SpaceX Dragon docked with the International Space Station in 2020 through an entirely automated process.
Recessions tend to bring about big changes in the labor market when “firms let excess workers go and learn more about labor-saving technology to maintain their profits.” When the predicted recession arrives, automation will advance even faster across multiple domains. Industry leaders should get ahead of this curve, investing in automation and the underlying systems modernization required to make it possible before the situation gets dire.
A brave new world needs a bold new approach to data infrastructure
To rapidly respond to these dynamic changes, application development teams will need to manage significantly higher volumes of data, deployed in more diverse locations, and to be able to spend more time wiring software than managing the underlying infrastructure.
This will be problematic for the multitude of companies still using legacy, scale-up data infrastructure, or older data management platforms designed in the client-server and web 1.0 world. Despite recent examples of AI-enabled automation successes, the majority of business systems within the Global 2000 still use databases and data management tools that date back to the 1990s. These now antiquated systems are complex and require significant overhead to operate and scale, held together by the IT equivalent of brute force effort and costly workarounds. These systems have been augmented to serve our innovation to date using a variety of approaches, including stitching together a plethora of early ‘00’s open-source software into a “solution” to support data-intensive applications.
While this has been made to work, it is an inefficient and burdensome approach, and is hardly a platform for enduring digital transformation or a scalable approach to algorithm-augmented automation.
What is needed is a modern infrastructure stack that can more easily support the development of advanced automation to best augment a human workforce.
Despite any near-term economic hiccups and staffing changes, the long-term trends remain intact. To thrive in the coming years, we will need a push for greater automation to address an underlying and ongoing shortage of employees and a refocus on profitable growth. This will require similarly long-term focus and investment in the infrastructure needed to adapt to these trends.
To move forward, leaders need to move away from legacy technologies and instead invest in the emerging tools and technologies that are purpose-built for data-intensive, algorithmic-driven applications that will fuel the future. This is how existing businesses will reinvent themselves and how new businesses using these technologies will come into being.
The future of business will be built on modern technology, as it has been for the last 70+ years. It is time to uncuff your technology from the past and prepare for this next wave of AI automation.
Peter Guagenti is the chief marketing officer at Cockroach Labs.
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