When is the future?
Whenever I think about “the future”, I inevitably remember all those crazy movies and videos made in the 80’s and 90’s, showing people in weird-looking silver suits and helmets, with all sorts of pointless and hilarious gadgets. What’s really funny to me is how incredibly precise some predictions were – we’re now talking into our watches and can play music or a movie by talking to a tiny little device, but also how completely outlandish others were – smellevision, flying houses, and snail-mail sent via rockets.
In reality, “the future” is actually far more subtle than writers and moviemakers had anticipated: a lot of the advancement we are witnessing now comes from humans gaining the ability to collect, store and interpret data in an automated way. William Gibson, a fiction writer widely credited with the creation of the cyberpunk subgenre, said the following: “the future is already here – it’s just not evenly distributed”, referring to the fact that some people already live in a time ahead of most, whereas others are still a significant number of years behind.
In spite of its repeated use in a socio-economic context, which was arguably not its intended purpose, the phrase applies exceptionally well when we look at the level of technology and the broadness of its application in various work domains. We’re currently witnessing a fundamental retooling of the enterprise: automation drives people towards creative thinking roles, making data-driven decisions. Product managers optimize platforms by analyzing user interactions, marketers optimize conversion through customer behavior insights, recruiters optimize the hiring process with the help of candidate funnels and the list goes on. Every function of the enterprise is now growing faster with the help of automated data collection and real-time data insights.
So what about Learning & Development, what’s happening here? There are a plethora of buzzwords that you may have heard in recent years: digital transformation, future of work, re/up/microskilling, future-proofing the business, digital intelligence, adaptability quotient, gig economy, and so on. And there are many experts who talk about what these big ideas mean, their impact on our work lives, what we should strive toward, and how bright the future ahead of us looks like.
Although I agree in principle – we should indeed be knowledgeable in what these concepts mean – I would argue that there is a huge gap between how these abstract and complex terms are presented to wider audiences and the very basic routine of daily L&D management, where they should ultimately be applied. It’s wonderful to think about the importance and impact of being tech-savvy, but how does it specifically apply to basic L&D work, like creating a training plan, extracting weekly reports, scheduling classes, centralizing feedback, calculating budgets, analyzing skill gaps, and understanding learning needs?
(Why) Is automation relevant in L&D management?
Initially, when I started outlining the content for this article, I had built in my mind a very straightforward sequence of what automation is and how and where it could be applied in Learning & Development management, based both on past personal experience, but also as a result of our customer research efforts at Nifty Learning. Speaking to L&D professionals, we discovered that as much as 30% of their time is currently wasted in tasks that a machine is very much capable of doing and, in some cases, the number goes much higher. For other business functions, this type of work has already been successfully taken on by software – there’s sales automation, marketing automation, recruitment automation, you name it. So how come, when you search for “learning & development automation”, the results vary greatly and the term isn’t yet established? Based on the variety and complexity of information that an L&D professional needs to process, the countless daily decisions that need to be made, the need for automation always seemed like a default to us.
Therefore, I’m using this opportunity to take a step back and start from the beginning: before we dive into the “what” and the “how”, let’s have a look together at the “why”. Of course, depending on the organization you are currently a part of, you may find that the reasoning applies to you to a wider or narrower extent. As the saying goes, there is no one size that fits all.
I started out with some research on the topic of learning & development automation, in an effort to not say what has already been said. What I discovered felt a bit odd: people aren’t really searching for “learning & development automation”. Sure, L&D is a very wide domain, with many, many specific subdomains and, yes, automation is all the hype right now, more recently with the spectacular rise of RPA companies, which has forever put the term on the map. But the combination of the two isn’t something that L&D professionals look for on a weekly or even monthly basis. And the resources I could find, while performing this search myself, mostly focus on the learner-facing aspect of learning & development automation: how to identify the best kind of content in the fastest way possible, how to generate and publish that content, how to optimize engagement with the content, advertising the learning offer and so on. These are, of course, very valid and important aspects of Learning & Development in any organization.
My favorite search results were the ones where the L&D function is itself being prompted and expected to help employees understand automation, which I found somewhat ironic. But the results felt lacking in another area of L&D: management, administration, overview, strategy, data gathering, decision making. Let’s call this the behind-the-curtains perspective, that of the L&D professional who has a population of employees to support in their professional development and relies on their tech infrastructure, team, and wider organization to perform optimally.
Why aren’t more people searching online for the automation of L&D operational work?
Could this be a case of the shoemaker’s child always walking barefoot? There are lots of search results on how employees themselves should be learning about digital intelligence, to align with the times and maintain competitiveness, both for themselves, as individual contenders on the work market, but also as part of their company, to support the growth of the business. The implicit expectation is that L&D, as a critical support function, is responsible for driving this momentous adaptation to technology forward for the entire company. So how come L&D people aren’t searching for ways to make their own work-life easier and to take advantage of current technological advancements?
Based on numerous conversations with L&D professionals, I’ve found that there is a set of possible causes for this, that unfortunately influence each other in a negative way. Historically, L&D has always been considered a sub-domain of HR, assuming a more passive role within the organization. Only in recent years and maybe with the extra nudge of 2020 have we started seeing L&D take center stage, pushing the agenda of business agility and digital transformation forward, as a matter of priority.
Within this hierarchical setting, L&D hasn’t really had a strong enough voice to demand its place at the table and explicitly ask for technological advancement: apart from a couple of very dedicated pioneers, L&D tech is a follower, not a leader in the enterprise tool kit. That could also be happening because, for a very long time, L&D hasn’t been able to prove its business value, being perceived as more of a “money spender”, rather than “value creator” in the company – this, happily enough, is changing as we speak, with countless companies and individuals working on defining and measuring L&D’s positive business impact.
And with L&D sitting under HR, it’s often the case that L&D professionals aren’t necessarily tech aware or savvy, sometimes relying on “the way we’ve always done it” to keep doing their job, other times overwhelmed with the massive amount of (administrative) workload to even consider any sort of technology adoption.
When ultimately and inevitably the time comes for L&D Operations to become more “cost-effective”, what ends up happening, especially in larger companies, is that the current HR solution’s L&D module is added to the tech suite, giving the L&D teams a pre-built, often rigid technical framework, that doesn’t necessarily fit with the culture and dynamics of how learning management actually happens within that organization. This leaves L&D professionals with a never-quite-right kind of technological support and a lot of loose ends to manually tie, in order to stay afloat.
Even worse, I’ve personally seen a case of a company attempting to cut costs by attempting to reduce its learning catalog to a quarter of the normal size, because “fewer courses means fewer costs”, disregarding the very basic fact that no two courses are the same and they address different needs. The administrative chaos this decision has generated is absolutely indescribable.
This being the situation and the wider context, I’m hoping that the information below can help shed some light on the potential benefits of having an increased interest in automation and the many ways it can make a difference in both L&D job effectiveness and, why not, satisfaction.
Ok, but can I actually do it for real?
Let’s start with a look at “what” you would need to automate and “how” you could get started.
Have a look at your current work and identify tasks that are repetitive, fairly manual and take up a lot of your time. Try to capture – in a spreadsheet or a text document – the specific actions that you take, but also write down the triggers (what prompts me to perform this action), inputs (what information do I receive that must be processed), and, of course, outputs (what is the expected result after I finish performing this action). This, in short, is called business process analysis.
Such an internal audit of your day-to-day work, maybe over the course of one month, will start showing you some patterns. You will very likely discover two things: you have quite a high number of workflows that are, in essence, the same, but that only vary in inputs and outputs, and you will also identify process exceptions and what causes them. This pattern-and-exception identification is the perfect starting point in your learning and development automation efforts.
Before we move forward, though, I’d like to address the possible perception that automation couldn’t really work or add value to the specific work performed in L&D, because the type of activities require a lot of human intervention and don’t lend themselves well to being automated. After all, L&D is about understanding employees’ needs and the company’s strategic direction, looking for the best kind of learning opportunities, performing learning needs analyses, catering to the very diverse and very specific needs of each learning group or individual.
Here I would propose a quick mental exercise: if you are currently in an L&D role, do you use spreadsheets on a daily basis for any part of your work? If so, do you use multiple sheets for multiple purposes? Do you find yourself copy/pasting one type of data in at least two different places, more than once a day? Do you have a big ol’ spreadsheet that centralizes your work, together with your L&D colleagues’ work, for that “big picture” overview needed to make sense of things or that “give me a number” demand from management? Do you have processes that you must track over the course of weeks or months that are comfortably nestled in a table with at least 15 columns? I believe you know where I’m going with these questions. As a rule of thumb, if you’re regularly using spreadsheets for one or more of your daily tasks, chances are you are a good candidate for learning & development automation. As you’ll notice, most of the questions above are around data and reporting, but the same line of thinking can be applied to other types of work: scheduling, cost tracking, survey interpretation, compliance monitoring, etc. And this applies even in spite of having more advanced technology that you already rely on, like an LMS or LXP, performance management or employee survey tools, maybe some budgeting or resource management technology. The bottom-line question is: how much of the work happens in between or outside the software solutions that you have at hand, as opposed to being independently executed by them?
If you’re that fortunate L&D professional who’s got everything under control, has all the necessary data at hand, and always knows what stage any learning initiative is in, congratulations! This is no easy feat and you are clearly on top of things. My only hope is that you end up reading these lines and want to share your good practice story with others as well.
But one common theme that I’ve encountered in now hundreds of conversations with L&D professionals is that, in spite of finding it quite easy to specifically define what their job consists of and how to execute their tasks, they find it hard or sometimes impossible to explain why the workload is overwhelming or why tasks don’t end up getting done on time or at the level of quality they would love them to be – of course, barring the situation when the team is understaffed. And when I ask what the most common time traps tend to be, the answer focuses more often than not around menial, repetitive tasks, not being able to get to the right kind of data in time to make the right decisions, and just low-value busywork that is hard to put into words or measure precisely.
An awesome revelation came to us when we interviewed the Global L&D Manager of the Services Division in a large bank. Her scope of work extended to a couple of thousand employees and accounted for about 10% of the bank’s total workforce and she had a team of 3 people supporting her. When we met, she was just about to outsource the kind of work that I am referring to above to either their HR admin provider or to an intern/junior role. She had specifically built a business case, categorizing and time tracking the type of work she did every day by splitting it into value adds – tasks strictly related to getting employees closer to good quality, useful L&D content – and time traps – copy/pasting data, extracting the same type of report repeatedly, sending the same reminder to managers every couple of days, formatting tables, sending calendar invites, booking resources, etc. It was an absolute goldmine! She was sketching out her process on a piece of paper lying between us on the table, in the conference room, explaining her method and, in the end, told us her very saddening, but not at all surprising conclusion. After painstakingly tracking her effort over a longer period of time, she realized that she spent at least 3 months out of her entire year in time traps. Then, as she raised her head from drawing on the paper, she let out a very frustrated sigh and said “but I swear it feels more like 6 months, I just can’t figure out how else to measure it and prove my point further”.
One very likely answer to that dilemma is the sneaky and very wasteful context switching time cost that has become the attention of psychologists in more recent years: you’re completely blind to it, but it’s still time that passes without you actually making progress in your work. Going back to learning & development automation and its relevance in this particular example, it becomes quite obvious that there are very important, measurable benefits to leaving the repetitive work in the “hands” of a supporting tool – whether it’s just being able to stay on top of things and to stop falling behind on your current work, or saving precious time that’s currently being wasted in low-value tasks, in order to focus on more creative, enriching L&D activities. Or rather, to frame it a bit differently, it’s not that certain tasks are less important than others. After all, all of the work needs to be done, in order to consider the scope of your L&D work completely addressed. But some of these activities can take as little as 30 seconds to complete, could very well be done by a machine, but generate that context switching time cost which can sometimes be as high as 20-25 minutes for every single switch.