As innovation continues to expand, we can now foresee a time when the pace of change will outpace every worker’s ability to memorize the tasks they’re assigned to perform. When that happens, no amount of training will enable workers to perform their assigned standard operating procedures (SOPs).
Innovation is Accelerating
Unless we’re at the beginning of a new technological era, it can be challenging to grasp the amount and pace of innovation we experience. To illustrate, the dawn of the Industrial, Atomic, and Information Ages brought humanity both the promise of better lives and the fear of the planet’s extinction. We’re currently experiencing the same promise and fear as the artificial intelligence (AI) technological era advances and entrenches.
To get a sense of the dizzying pace of innovation that we’ve already adapted to, consider that it took humans hundreds of thousands of years to learn how to fly, but just 68 years to go from the first flight at Kitty Hawk to the first Mars landing in 1971. Since 1971, vaccines have been produced in months instead of decades; a single phone has become capable of running 120 moon landings simultaneously, and joyrides into space have become possible.
Innovation has become a self-perpetuating vortex of ideas, products, continuous optimization, and automation driven by ever-heightened expectations of change, sophisticated feedback loops, hungry investors, and vast amounts of money. For evidence of this, you need look no further than the US Patent and Trademark Office (USPTO). The USPTO Report (USPTO) on annual patent applications and grants shows that the number of utility patents granted totaled 52,423 in 1979. That number grew to 191,927 patents in 2009. It hit 391,103 patents in 2019. By conservatively projecting patent growth from 2019, we can estimate that 2.2 million patents will be granted in 2059 alone and 31 million will be granted over the next 40 years. That is important because every one of the 31 million patents represents a group of workers developing an entirely new body of knowledge that will require a raft of novel skills and processes for manual workers to perform if the patent is to become monetized. If you just shuddered at the thought of 31, 62, or 124 million training programs, you now understand the training problem.
The pace and challenge of innovation may be so disruptive that some people and businesses will be tempted to wait out this innovation. However, innovation is now an essential part of the US and global economies, and without ever-accelerating innovation, our economies would falter. While embracing innovation can be difficult and costly, the alternative would be disastrous.
The Impact of Accelerating Innovation
Despite the recent emergence and growth of digital labor and AI, the demand for manual labor will continue to grow. This will happen because manual workers are already responsible for producing most of the world’s products and for delivering all the world’s services, and that will not change significantly. Add to that the need for manual workers to build hundreds of millions of pre-patent product prototypes, minimum viable products, and pre-automation products that will continue to be made manually, and the future of manual work remains very bright through the foreseeable future.
Unfortunately, the continuing and growing necessity of manual workers doesn’t guarantee they’ll have easy work lives. In fact, they will face waves of new and constantly changing products, services, jobs, and so many novel SOPs that no amount of training will help enable their performance. Additionally, the quantity, speed, and often hazardous production quotas set upon those workers will be especially challenging because they often can’t stop performing a task to use traditional job aids or other performance support. Manual workers also perform tasks that prevent them from safely, efficiently, or sanitarily accessing aids on computers, phones, tablets, or even printing as they work. All combined, this means manual workers will be expected to perfectly perform novel SOPs with little to no training or access to traditional performance aids.
While manual work will never end, those jobs will constantly and rapidly change. The affected laborers will not be limited to traditionally low-paid workers, such as housekeepers, assemblers, installers, and bartenders; they will also include doctors, nurses, microbiologists, and the engineers and inventors who create innovations. Due to the continued importance of manual labor, businesses will ignore the needs and financial value of manual workers at their own peril.
The tens of millions of patents projected by USPTO data over the next 40 years indicate that a significant portion of future work will focus on developing and optimizing novel products and services. One characteristic of novel products and services is that they begin from scratch, so there’s no training until after the first prototype and SOP have been developed. Even after the first prototype and SOP have been developed, training is of little use to manual workers because SOPs constantly change until fully optimized, and only stable SOPs can be memorized.
Pfizer’s COVID vaccine invention provided a glimpse into how tens of millions of products and services will be developed. In 2020, Pfizer rapidly added manual workers to invent, scale up, and package their COVID-19 vaccine. The first manual workers Pfizer hired for the project were high-level scientists. The scientists optimized their vaccine recipe and were then either reassigned or fired. Because their vaccine required extremely low temperatures, a novel packaging process was needed. So, they hired a horde of manual workers to perform and optimize packaging SOPs. Once the packaging SOPs were optimized and automated, the manual packagers were either reassigned or let go. While Pfizer’s vaccine was highly successful and the company thrived, we have no idea what happened to the low-level manual workers who made it all possible.
Responding to Accelerating Innovation and Change
Businesses and governments have an ethical obligation to: a) help reduce unemployment periods for manual workers; b) ensure that workers leave jobs with new skills that increase their employability; c) enable workers to increase their wages and personal wealth; and d) provide life, healthcare, and other insurance during periods of both employment and unemployment if we are to continue using short-term manual labor to develop and optimize novel products and services. Additionally, manual workers must be provided with the ways and means to perform unlearnable tasks with little to no traditional training, as there will be no time to develop and deliver training for every innovation or every SOP through optimization.
Training and Learning Role
Like everything else, the role of training and learning (TL) must adapt to the times. To move forward in the era of AI and digital labor, the TL discipline must face the eventuality that innovation and change will outpace everyone’s ability to memorize all the tasks they’re hired to perform. To be clear, the value of training and memorization as we know them has waned since humans invented spoken language. New ways to enable, assure, and continuously improve performance must be developed.
For TL organizations to thrive in the future, they can’t set their own goals and objectives; they must align with those of their parent business. They must also be honest and realistic about what TL organizations are, how they work, and what can be expected of them. To illustrate this problem, ask anyone how training contributes to a business’s goal of rapidly growing a sustainable profit. The popular answer is that it helps workers perform more efficiently and effectively while on the job, even though we’ve known that to be untrue since Ebbinghaus’s 1895 research. Ebbinghaus proved that up to 60 percent of training content is forgotten within an hour of being trained, and up to 90 percent is lost within a week without spaced practice and coaching. Learning professionals know that practice and coaching are handed off to operations managers, which means operations managers are responsible and accountable for learning. Training organizations are only responsible and accountable for training.
It’s a fact that training and learning only lead to better performance when a worker has perfectly memorized the content, is able to perfectly recall the content when needed, the content hasn’t changed since it was memorized, and then it is perfectly applied to produce something of value. That is important to understand because it means a) learning requires memorization, b) memorization requires practice and coaching over time, and c) learning happens on the job, not in classrooms.
Training and Learning Challenges
The overarching goal and challenge facing every training and learning organization is not how to train more workers faster or how to foster more and better learning; it is how to grow the business’s sustainable profit rapidly. That goal is accomplished by enabling, assuring, and continuously improving work and worker system performance. To be blunt, both training and learning are poor performance enablers; neither can assure performance, and both only nominally support continuous improvement. For these reasons, if training organizations are to continue existing, they must develop new ways to enable and ensure optimal performance, rather than relying solely on perfect training or learning.
Rampant innovation poses three main challenges to—let’s not call them “training programs”; let’s call them “performance enablement solutions”:
The first challenge is to recognize the existence and astonishing growth in the number of “unlearnable” SOPs that manual workers must perform. Unlearnable SOPs are those that can’t be memorized/learned because a) there are more steps in the SOP than can be memorized, b) steps in the SOPs change faster than can be memorized, c) there are more SOPs to be learned at once than can be memorized, and d) the SOPs aren’t performed frequently enough to memorize. Under those conditions, no amount of training or learning will enable workers to perfectly perform an SOP that is inherently unlearnable.
The second challenge has two parts: the first part is a lack of data intelligence, and the second part is the lack of valid, unbiased, reliable performance ratio data (VURPRD) available for needs analysis. The adage “garbage in, garbage out” applies here.
The third challenge is the dwindling amount of time available for analyzing an ever-growing mountain of needs. There is not enough time for managers to huddle in meetings analyzing substandard data related to every innovation and business need. In fact, there hasn’t been enough time for training and learning professionals to conduct a proper needs analysis for decades, but the problem has been overlooked or papered over with alternatives, such as mini-needs analyses and client directives.
While it was once necessary to a) dispatch managers, industrial engineers, and others to observe and collect data across a derisory quantity of on-the-job performance, and b) convene a roomful of managers to share their opinions about that insufficient bit of unreliable data, those efforts can now be replaced by advanced intelligence augmentation (AIA) systems that can automatically capture large amounts of VURPRD without the bias, greed, and self-service that humans naturally add to their observations and data analysis. It is those AIA systems, combined with AI algorithms, that offer the best opportunity for businesses and governments to rapidly grow sustainable profits while protecting the environment and operating more ethically than ever before.
Final Note
If the training/learning/development/etc. discipline is to survive in a rapidly changing world, then it must, a) get honest with itself about training, learning, and performance enablement, b) adopt the single business goal and three objectives of every business worldwide, c) use innovative technologies to develop new ways to enable, assure, and continuously improve worker and work system performance, and d) develop work systems that support manual workers in the AI Era. This strongly suggests that TL organizations evolve into “performance organizations.”
