Learning and Development (L &D) is my favorite area of the enterprise because it's where transformation becomes reality. Almost every technology implementation or strategic change initiative demands success from the training function. Otherwise, things often just stay the same.
By “things," I mean operations. But those operations are managed and executed by people – by actual human beings who need to gain (and use) new knowledge to make a transformation effort meaningful. And since the core objective of L&D is to help people learn to “do" differently, the goal of L&D leaders is to get learners there as effectively and efficiently as possible.
So what's the best way to unlock learning? Plenty of enterprise leaders are asking – and wondering whether technologies like blockchain, artificial intelligence (AI), or augmented reality (AR) have the answer. But the question isn't a new one; it's something folks have grappled with throughout history.
Here at Conduent, we tell leaders searching for the “next big thing" in L&D to remember that transformative learning isn't about abandoning all the old approaches for disruptive technology (though disruptive technology is absolutely a part of it). We believe the best corporate training programs embrace evolution while also investing in the most effective learning elements employed throughout history.
Training Through Time
Career training exists in continual evolution. I see the space as progressing through four different models over the last few centuries.
- The “probable destiny" model, in which individuals are born into professions based on their lot in life (or the work of their forebears) and trained on-the-job over time. Learners contribute more and more as they acquire greater knowledge and capability
- The apprenticeship model, in which aspiring craftsmen are trained by experienced mentors until acquiring enough knowledge and skill to operate independently for commercial gain. Once they reach mastery, they take on apprentices of their own
- The facilitator-led model, in which learners are instructed directly or indirectly by educators adhering to structured curricula. Learners' knowledge or aptitudes are assessed at various milestones on the path set by the facilitator (entity or individual)
- The learner-centric model, in which people seek out and consume the knowledge they want to gain, when they want to gain it. Learners consume only the knowledge they need or desire (often in pursuit of an outcome) and may disregard non-essential information
Each L&D model above progressed from the one before it, but none has ever entirely replaced any other. Technology has simply adapted our preferences and behaviors, and we've evolved our learning models in turn.
- In yesteryear, the rise of cities enabled “guilds" of skilled craftsmen to recruit and train newcomers to mastery.
- Today, our attachment to small-screen devices (and our preference for instant gratification) drives our interest in acquiring useful knowledge on demand.
And yet, all four learning models remain relevant to this day. People are still “born" into family businesses, for example, and apprenticeship programs are seeing a comeback as a way to fill labor gaps in government services and the skilled trades.
And while learner-centric training is certainly on the rise (with many pointing to “micro content" and “AI-curated content” as the latest trends), the facilitator-led model will never go away. Many learners prefer the structure and schedule of a defined curriculum.
How Machine Learning Fits in the Landscape
The extent to which each learning model (still) matters depends on the business and the industry. But I'd argue that every robust corporate training program should include elements of all four:
- On-the-job experience should unlock opportunities for growth and development;
- Skills-oriented mentorship should be available to every employee; and
- Learners should have access to both formal curricula (via MOOCs or certification programs) and hyper consumable on-demand content (like peer-to-peer training or standalone webinars).
The emergence of new technologies won't make any of that less true. It will simply create new models and delivery methods.
AI and AR, for example, are driving us toward a fifth model of learning (in which machines will essentially be “trained" to train us). But human learning will always be the only outcome that matters in L&D, so adoption should align with effectiveness.
Companies are smart to be exploring new technology, but there's little to implement yet.
- Most AI has limited “intelligence" beyond mimicking the data set it's trained on.
- AR is rising, but its use cases for knowledge delivery – such as overlaying instructor-led virtual directions onto machine parts for assembly – are still just tech-powered mimics of the old models.
All of which is to say that new solutions will always emerge to help drive human knowledge forward, but none will abandon the best elements of the past.
Conduent has decades of experience creating and delivering solutions to help humans learn. We believe that new technologies and machine learning will play a large role in the future of L&D, and we're prepared to help our clients fully take advantage of them.
As an end-to-end digital interactions company, we don't believe in delivering L&D in a vacuum: Many offerings in our broader solutions suite connect or integrate with the learning function, which helps clients track and manage training effectiveness across their operations as they integrate new solutions.
Our methods also ensure clients can migrate to a learner-centric model (and into machine learning, too) while staying efficient and effective in their existing L&D programs. That helps leaders ease their teams into the transition, earning stronger outcomes from the transformation at large.
Ultimately, L&D succeeds when a company's most valuable resource – its people – learn to do things differently. Training humans has never been easy, but the history of L&D has laid a foundation for us to improve on. Just don't be surprised if the future of training feels awfully familiar when it gets here.