Learnlets

Recently, there’s been a lot of excitement about Generative Artificial Intelligence (Generative AI). Which is somewhat justified, in that this technology brings in two major new capabilities. Generative AI is built upon a large knowledge base, and then the ability to generate plausible versions of output. Output can in whatever media: text, visuals, or audio. However, there are two directions we can go. We can use this tool to produce more of the same more efficiently, or do what we’re doing more effectively. The question is what do we want as outcomes: quality or quantity?

There are a lot of pressures to be more efficient. When our competitors are producing X at cost Y, there’s pressure to do it for less cost, or produce more X’s per unit time. Doing more with less drives productivity increases, which shareholders generally think are good. There’re are always pushes for doing things with less cost or time. Which makes sense, under one constraint: that what we’re doing is good enough.

If we’re doing bad things faster, or cheaper, is that good? Should we be increasing our ability to produce planet-threatening outputs? Should we be decreasing the costs on things that are actually bad for us? In general, we tend to write policies to support things that we believe in, and reduce the likelihood of undesirable things occurring (see: tax policy). Thus, it would seem that if things are good, go for efficiency. If things aren’t good, go for quality, right?

So, what’s the state of L&D? I don’t know about you, but after literally decades talking about good design, I still see way too many bad practices: knowledge dump masquerading as learning, tarted up drill-and-kill instead of skill practice, high production values instead of meaningful design, etc. I argue that window-dressing on bad design is still bad design. You can use the latest shiny technology, compelling graphics, stunning video, and all, but still be wasting money because there’s no learning design underneath it.  To put it another way, get the learning design right first, then worry about how technology can advance what you’re doing.

Which isn’t what I’m seeing with Generative AI (as only the latest in the ‘shiny object’ syndrome. We’ve seen it before with AR/VR, mobile, virtual worlds, etc. I am hearing people saying “how can I use this to work faster”,  put out more content per unit time”, etc, instead of “how can we use this to make our learning more impactful”. Right now, we’re not designing to ensure meaningful changes, nor measuring enough of whether our interventions are having an impact. I’ll suggest, our practices aren’t yet worth accelerating, they still need improving! More bad learning faster isn’t my idea of where we should be.

The flaws in the technology provide plenty of fodder for worrying. They don’t know the truth, and will confidently spout nonsense. Generative AIs don’t ‘understand’ anything, let alone learning design. They are also knowledge engines, and can’t create impactful practice that truly embeds the core decisions in compelling and relevant settings. They can aid this, but only with knowledgeable use. There are ways to use such technology, but it comes from starting with the point of actually achieving an outcome besides having met schedule and budget.

I think we need to push much harder for effectiveness in our industry before we push for efficiency.  We can do both, but it takes a deeper understanding of what matters. My answer to the question of quality or quantity is that we have to do quality first, before we address quantity. When we do, we can improve our organizations and their bottom lines. Otherwise, we can be having a negative impact on both. Where do you sit?

A recent intersection of talks leads to an interesting issue for L&D. First, we recently talked to Guy Wallace about his recent book, The L&D Pivot Point. Then, we talked to Julie Dirksen about her new book, Talk to the Elephant. The interesting thing is that there’s some overlap between the two ideas that isn’t immediately obvious, but really important. The realization is that when we’re talking about barriers to success, thinking of one may not be enough.

So, Guy’s book is about taking a step above just thinking of course. He’s a proponent of performance improvement consulting, where you analyze the problem before you decree a course as a solution. The important recognition is that there can be multiple barriers to performance, including a lack of skills indicating a course. However, other reasons might be the wrong incentives, a lack of resources, etc. Sometimes a job aid can do better, some times neither that or a course will suffice.

Julie’s book, on the other hand, is a complement to her first book, Design for How People Learn. She recognized that even good design (what her first book did, eloquently) might not help learning stick, and looked at other barriers, such as managers extinguishing the learning. She was more focused on making the learning design succeed.

What she did, however, is provide a rich suite of potential barriers, along with solutions, and suggest that you may need to address more than one. That goes along with, and complements, Guy’s focus.

Just as you design programs that include messaging, training, support, rewards, and more, you should also ensure that you’ve analyzed all the barriers to performance. You might address learning, provide job aids, ensure incentives are aligned, prepare supervisors, and more. Addressing only a particular situation may not be sufficient. You may have several barriers, When it comes to solutions, one may not be enough. This argues (again) for rigorous analysis and a success focus, not just doing what you are comfortable with. In the long term, I reckon this is where we need to go as we move from learning to performance (and development). your thoughts?

We (the Learning Development Accelerator) just released Guy Wallace’s latest tome, The L&D Pivot Point. Then, we had an interview with him to explain what it’s about. Despite having a ring-side seat (I served as editor, caveat emptor), it was eye-opening to hear him talk about what it’s about! It really is about the pivotal point in L&D, when you move from just offering courses to looking at performance. It’s such an important point that it’s worth reiterating.

So, the official blurb for the book talks about his tried and tested processes. In the interview, he talks about how he’s synthesized the work of the leaders of the performance improvement movement, people like Joe Harless, Geary Rummler, Thomas Gilbert, Robert Mager, Thiagi, and more. While the models they used differed, Guy’s created a synthesis that makes sense, and more importantly, works. He talked about how he refined his work to balance effectiveness with efficiency. Moreover, his approach avoids any redundant work.

Interestingly, he also recounted how his approach achieved buy-in from the stakeholders to the extent that he had to fight to not keep them all on the team through all the stages! That’s a great outcome, and it comes from demonstrating value. He focuses on where performance needs are critical, and thus it has a natural interest, but too many of the approaches can stifle that interest. Instead, his intent focus on meaningful outcomes truly engages everyone from the performers to the executives.

Guy also is quite open about the problems facing our industry. Despite the necessity of starting as order takers (essentially, “you can’t say ‘no'”), he estimates that only 20% of the time is the problem a learning or skills problem. Which resonates with other data I’ve seen about the value of training interventions! Instead, there can be many drivers for problems in performance.  His approach includes detailed analyses that identify the root cause of the problem, and when to determine that it’s worth trying an intervention. He’s quite open about how that can lead to a shift in intervention focus. At other times, it might lead to a hiatus while problems get attention.

One other thing I found interesting in the interview was how he talked about potential barriers to success up front. While it might seem like a deterrent, he pointed out how it led to making sense later. That is, folks would soon see that, for instance, supervisor support was critical to success. He includes a rigorous analysis of potential barriers as part of the book.

Quite simply, L&D has a problem of going from go-to-whoa without considering whether a course is the right solution. Guy’s book is a way to avoid doing that, and systematically evaluating what the pivotal point should be for determining whether we can successfully intervene or not, and how. There’s much more: how to manage the process, deal with stakeholders, and test your assumptions. It’s in his own inimitable style (lessons learned on editing ;), but there’s deep wisdom there. That’s my take, at least, I welcome yours.