Considering its impact on our lives, few of us are familiar with Moore’s Law. It comes from the co-founder of Intel who, in 1965, noticed that the number of transistors that could fit onto a computing chip was doubling every year (another way of saying that computing power was doubling every year). He projected this trend to continue far into the future and published an influential paper proposing a theory that explains the exponential growth in the rate of technological change.

In other words, if it seems like everything is changing more quickly than it used to, it’s because it is — exponentially so.

Moore’s Law has major implications for every aspect of organizational strategy and operations, and training is no exception. Consider long-term orientations for training initiatives, which are still relevant for abstract concepts like leadership development but hardly so for technology. It is only wise to engage people in long-term learning if the training material remains relevant long enough to deliver an acceptable return on investment (ROI). Thanks to Moore’s Law, it is increasingly difficult to do so in technical training (increasingly indistinguishable from operational training), simply because the technology becomes outdated so quickly. While the challenge is sufficiently complex to render the idea of a simple set of new rules absurd, there are, nonetheless, important principles we can glean from seeing how the world of technology is adapting to its own ever-increasing rate of change.

The Rise of Agile

In software development, a new methodology, aptly named “agile,” has gained mass adoption. There are many components to the agile framework, including how to support collaboration, how to focus on client (rather than organizational) needs when building products, and how to shape operating plans and execution strategies. One of its primary tenets is to work in small steps. In agile, there is no building the product and then introducing it to customers to see what happens.  There is only building the smallest first piece (or most fundamental component) of the product, introducing it to customers, taking their feedback, making changes, testing those changes and then moving on to the next-smallest piece.

Agile guarantees that you avoid this nightmare scenario: investing years (and millions) in a product, only to discover at the very end of the process that people hate what you built. It also supports organic and feedback-driven iteration, which are both helpful when you’re building something new.

There are many success stories to emerge from agile methodology, and virtually all of these successes provide artifacts of at least two underlying principles: Every step is small, and everything is subject to change.

Stop Training for Knowledge

What do these principles mean for training? For starters, we need to stop training for knowledge and start training for adaptability. We don’t need people to know how to work the machines, because that process is going to change. Instead, we need people to know where to go when they need guidance on how to work the machines, because that information can stay the same, even as the underlying technology changes. Whether it’s a person, an intranet or an app, if people know where to go to answer their questions, the biggest problem is already solved.

As a result, the function will become less about driving learning and more about providing precisely the guidance people need at precisely the time they need it. Enter contextual content or, more specifically, contextual guidance: In the time it takes to enter a Google or YouTube search, people can access expert advice and how-to instructional manuals in sophisticated detail. (There are many graduate students who never would have made their way through their statistics programs without YouTube.)

This approach works, because in giving us just what we need, just when we need it, it saves time, effort and cognitive load. It fits neatly with what we know about the human condition, which is generally in a constant search of ways to reduce cognitive load. But the experience is hardly optimized — consider all the information you have to wade through, not to mention the lack of certainty in the accuracy of the underlying content.

The Opportunity for Learning and Development

Here is where innovators in the training industry have an amazing opportunity. While it will be crucial to pivot from long-term knowledge transfer to short-term problem-solving, the expertise of training professionals remains inexorably relevant: how to help people with information. The trick will be in identifying salience: Which guidance is most helpful in which situations?

Assuming learning and development (L&D) leaders can answer these questions and build a content library accordingly, the next consideration is distribution. If the organization is housing the content in a centralized location, the design of that location makes all the difference. The ease or difficulty with which employees can find the content they need will be the first make-it-or-break-it moment. Yet despite its huge importance, this challenge may not be a daunting one for much longer.

One of the more exciting possibilities of machine learning (ML) and artificial intelligence (AI) will be a growing capacity to proactively sense a user’s needs and then deliver the most appropriate content. Whether a learner is receiving a system error in a software program or has to replace a particular part of a machine, ML and AI will not only sense what is happening (and try to fix it first) but also guide the user through the exact steps to address it.

Even the best futurists are wrong far more often than they are right. No one can guess the future of technology, which makes things difficult for training professionals, and which is why we need a paradigm shift — away from the long-term transfer of knowledge and toward the provision of contextual content that can have the greatest impact on people performance. As we transition to this new paradigm, training professionals would do well to take a cue from software development: Keep your training programs small, and be ready to change at a moment’s notice. That way, you’ll always be ready for the next big thing.