Welcome! I have been a little bit swamped at work and off for a short holiday, so apologies for the ‘silence’ in the last few weeks 😉 

This week, the focus is on learning. We talk a lot about new technologies, about news and information - but if you’re not applying and learning, it’s not going to get you or the company any benefits.

We’re going to cover:

• Short view on State of AI 2024: Are we using it as much as we say?

• State of Learning & Development in companies in 2024: Key stats

• Learning Velocity and potential ways to measure and accelerate

Let’s jump in!

State of AI in 2024: We’re not walking the talk.

One of my ‘pinned’ thoughts about AI, and every technology, is that history repeats itself on the Hype Cycle:

This means two things: we overestimate the results, and underestimate the time that it takes for productivity or implementation. It’s a human flaw, well documented in Khaneman’s Thinking, Fast and Slow (which if anyone reading this hasn’t grabbed, you should).

Recently, also MIT robotics pioneer Rodney Brooks spoke out saying that people are vastly overestimating generative AI. Here’s why:

• Overestimated capabilities: People see generative AI perform a task and assume it can do everything a human can in that area, which isn't true. Generative AI is good at specific things, but not generally intelligent. Clearly, the promise of AGI would be a counter-argument to this, but it’s yet to be seen.

• Not human-like: Generative AI shouldn't be treated like a human with a human-like understanding. It's a tool, and using it for tasks where it doesn't make sense (like giving warehouse robots instructions in natural language) can slow things down.

• Focus on solvable problems: We should focus on using generative AI for problems that are well-defined and where robots can be easily integrated. This avoids the need for complex AI and keeps things efficient. Many times we try to use it for cases that don’t require it, and also we need to bake in that it’s like the roof on a house; you can’t skip the foundations (data quality and quantity, product/market fit etc).

• Exponential growth myth: Brooks argues that advancements in AI won't necessarily follow an exponential curve. He uses the iPod as an example; storage size didn't keep doubling as some might have predicted. There are practical limitations (could be compute power, energy etc).

Another recent survey from Retool showcases the varied levels of implementation / maturity at companies in 2024, and it’s clear how generally speaking we’re far from the top. Moreover, the progress indicated by respondents is largely not transformative (as reporting on the outliers might have you believe)—or at least not yet, with the impact of those use cases rated an average of 6.7 out of 10. As we get more clear on the obstacles and challenges around adoption, reality checks in.

Last one I picked is the response on which types of roles want more investment in AI. There’s an interesting (but perhaps not surprising) difference between Entry-level employees and Director/VPs. I find this interesting because even if there may be different goals (learning vs cutting costs), individual contributors and managers could have a similar desire to implement this technology. Reality comes into play when this clashes with fear of job redundancies on one end, and lack of feasibility on the other. It’s comprehensible to want AI to be a magic-wand, but when you get to the real use cases, it’s not always the case.

The Bottom Line: We’re slowly but surely coming off the peak of inflated expectations. ‘AI Washing’ is a real thing (check out a good write-up from Prof G on this here). The irony is that we’ve wanted tools to see where AI is being used, but we’re probably going to need others that show which companies are NOT using AI really, and just slapping the word in earnings calls and marketing landing pages. On the organizational side, we need real programs that tackle L&D, Job design, skill, and use case mappings. Which brings us to the rest of today’s story.

Learning & Development: Essential, but few are actually doing it.

90%of organizations are concernedabout employeeretention andproviding learning opportunitiesis the No.1 retention strategy. Here’s why:

However, the reality is quite grim; most initiatives are at ‘planning’ stages, and only a handful of companies have implemented reskilling programs effectively, and even their efforts have been subscale and of limited impact.

Linkedin’s report highlights the metrics as a key starting point for L&D programs, but lists performance reviews and employee productivity as the top two ways to track business impact.

My personal experience and hunch is that few have actually a good way to measure these elements, first because it’s difficult (yes, most human-related measurements are complex..) and another big problem is that everyone is doing it differently. There’s no one-size-fits-all, but if we don’t move towards a basic level of shared practices that are applicable across-industries and roles, it’s going to be hard to set the right foundations for credentials, mobility and so on.

As we saw from the chart above, many companies and individual employees are focused on legacy ‘large’ learning initiatives. Other than the long waiting times, there’s another big problem: things get old fast in 2024 and beyond.

Which leads up to the final chapter for today.

Learning Velocity and Skill Agility: the keys to success in our fast-paced world.

In today’s world, where the pace of change is so high, and ‘black swan’ events are more frequent, you are no longer required to master one specific subject of matter/expertise. On a technical side, you are required to master adaptability and therefore capability to learn new things quickly. On the emotional side (separate article will come on this) it’s about resilience. It’s true, that in order to be adaptable you need the so-called ‘growth-mindset’:

• Intelligence and abilities are not fixed: People with a growth mindset believe that their intelligence and skills can be improved through effort, practice, and learning.

• Challenges are opportunities: They see challenges and setbacks as opportunities to learn and grow, rather than failures.

• Effort leads to success: They believe that hard work and dedication are key to achieving their goals.

• Embrace feedback: They view feedback as a way to identify areas for improvement and are open to learning from others.

• Lifelong learning: They embrace lifelong learning and are constantly seeking new knowledge and skills.

But my addition is that you need Learning Velocity and Skill Agility.

Let’s start with the first:

Learning Velocity – the rate at which an individual or company can acquire and apply new skills. 

Imagine you're on a road trip to acquire knowledge. Your learning velocity would be like your miles per hour. A high learning velocity means you're making significant progress and acquiring new knowledge quickly. A low learning velocity means you're learning slowly or not retaining information as well.

It can be influenced by several factors:

• Teaching methods: How information is presented can significantly impact how quickly someone learns it.

• Prior knowledge: Existing knowledge can act as a foundation for building new skills and understanding new concepts.

• Motivation and engagement: Someone who is interested and motivated will generally learn faster.

• Practice and application: The ability to apply what you learn is crucial for solidifying knowledge and skills.

So that’s the theory. But how do we put it into action?

First of all, following Peter Drucker’s quote: “What gets measured gets managed” we must think about measurement.

Below is a simple example of a chart showing a Proficiency Score which would evaluate whether learning is ‘Beginning’, ‘Developing’ and ‘Accomplished’ through time.

A key question here is how to define each of these stages, especially the final one. If you think of this applied to Gen-AI for example, how can you say that someone has ‘Accomplished’ that learning if in a week there will be new developments?

Accomplished needs to be defined as a ‘dynamic state’, and as the beginning of a journey, not a destination, especially in fields like Gen-AI.The focus should be on building a strong foundation, applying knowledge, and continuously learning to adapt to advancements. Also, critical thinking and a growth mindset are crucial for staying in the "Accomplished" in a rapidly changing environment. The only alternative is to recalculate and evaluate the proficiency score regularly according to the level of change/disruption of the technology.

Once this has been established, we need to break down this matter in a ‘first-principles’ kind of way, defining the core-building blocks:

• Mastery of Core Concepts: Having a solid understanding of the fundamental principles of Gen-AI, including its capabilities, limitations, and potential applications.

• Ability to Apply Knowledge: Being able to use Gen-AI tools and techniques to solve specific problems effectively. This could involve tasks like generating different creative text formats, translating languages, or writing different kinds of content.

• Critical Thinking and Adaptability: Being able to critically evaluate the outputs of Gen-AI models, identify potential biases, and adapt to new advancements in the field. This is crucial because Gen-AI is constantly evolving, so staying updated is essential.

• Continuous Learning Mindset: Possessing a growth mindset and actively seeking out new information and developments in Gen-AI. This ensures you stay "accomplished" in a dynamic field.

Once we’ve set the basis with measurement, we need to move on to the strategy and practice. Here’s the four key pillars:

• Consume – ingest as much quality information as possible

• Analyze – think critically about what you’ve learned

• Create – build or do something with this new knowledge

• Teach – the highest form of learning is teaching others (which is part of why I do this thing 😉)

The first pillar is particularly difficult in today’s world because their is so much abundance of information, that often I feel both FOMO (fear of missing out) or the ‘Drinking from a firehose’ sensation. My strategy to solve for this is the following:

1. Audit your subscriptions: Regularly review newsletters, websites, and social media accounts you follow. Unsubscribe from those that no longer provide value or that you don't have time to engage with.

2. Seek depth over breadth: Prioritize in-depth analysis and thought leadership over superficial news and entertainment.

3. Utilize information management tools: Tools like Feedly can aggregate content from various sources, while Readwise helps you save articles for later highlighting and summarizing key points. Knowledge management tools like Obsidian can help you organize your learnings and build connections between ideas.

In the ‘Consume’ phase, I also look out for communities (Reddit, Discord) which have active discussions on the topic I’m learning.

The Analyze and Create phases can be helped by Chatbots - as I’ve mentioned in my last article (AI can help you to think) we learn through writing, teaching, and generally speaking through ‘dialogue’ with humans or machines in this case. You can get inspiration on what product or project to create, if you don’t already have so from your company or job.

The teaching phase, can take place through writing / producing content for others, or through mentoring inside of a company. The real-life problem of this phase is time (and goals). We should think to have a % of time allocated to this for everyone, and to make peer-mentoring a core goal inside of the L&D area (which as we’ve seen, directly contributes to retention, and therefore innovation and company productivity and performance).

Learning Velocity enables Skill Agility, which means how fast and well an individual or company can embrace new skills. It’s like the former is the engine and the latter the fuel, but neither can work alone 🙂 

TL; DR

The ever-changing landscape of work demands a new approach to learning. By mastering learning velocity and skill agility, you can equip yourself to thrive in a dynamic environment. Remember, learning velocity is the engine that propels you forward, and skill agility is the fuel that keeps you moving. Neither can work alone.

However, continuous learning isn't just about acquiring new skills; it's also about cultivating the right mindset. In the next article, we'll delve into the emotional side of the equation, exploring the importance of resilience and growth mindset in fostering a lifelong love of learning. By combining these aspects, you can ensure that your learning journey is not just efficient but also fulfilling.

Speaking of consuming quality information, here’s a few that I’d recommend checking-out: