You can listen to the NotebookLM podcast version of this article. They still think I’m a he and not a she…
TL;DR
Canada faces a growing skills gap, with many workers either under- or over-skilled for their jobs as the demands of the workforce evolve. This gap is widening due to technological advancements and changing job requirements. However, solutions lie within learning and development (L&D) initiatives, where AI can play a transformative role.
AI can help address the skills gap through personalized upskilling and reskilling programs that adapt to individual needs, support flexible training formats, and provide more inclusive learning opportunities for underrepresented groups. AI-powered tools can also forecast future skill needs, enabling organizations to prepare their workforce for emerging challenges. With AI’s potential to improve accessibility, scalability, and engagement in learning, it presents an important opportunity to close the skills gap and enhance productivity.
To harness these opportunities, organizations should invest in AI-driven L&D programs, encourage partnerships between public and private sectors, and ensure ethical, secure AI implementation to support a more agile and skilled workforce.
According to some sources Canada is currently the second-least productive country in the G7. What that means is Canada produces less value per hour worked compared to most other G7 countries. For instance, in 2022, each hour worked in Canada produced a value of $53.30 USD, whereas the G7 average was $63.90 USD. It also means that we’re not using our workforce as efficiently as we could, which has an impact on economic growth and living standards. I see this, in part, as a learning and development failure that we need to fix!
If this is a new area for you, and if you haven’t already seen Andrew Chang’s April edition of About That, “Most educated, least productive: Why Canada’s falling behind“, check it out. He does an amazing job of explaining the larger issue in about 6 minutes.
Why are we so unproductive?
According to the Bank of Canada (and others), there are lots of reasons for our lack of productivity. Lack of private investment, regulatory barriers (which don’t help with investments), barriers to foreign competition resulting in monopolies that can be less productive or innovative, and a significant skill mismatch all have an impact on our collective productivity. This is not a straightforward, easily understood or solved problem. In fact, a lot of the contributors to our lack of productivity make Canada a great place to live and help us remain uniquely Canadian.
It’s the skill mismatch I want to focus on in his post. And in particular I want to look at a specific section of the skill gap problem. To my mind it’s the low hanging fruit. I’m an educator and not an economist so I’m going to try to stay within the scope of my profession with the understanding that these things are all connected and to understand one element we have to explore the rest.
The growing gap
From my understanding, there is a gap between the skills people have and what jobs now require, which can prevent workers from being employed effectively. This gap is growing as the world of work changes.
In “Bad Fits: The Causes, Extent and Costs of Job Skills Mismatch in Canada,” author Parisa Mahboubi notes about 13 percent of Canadian workers are either over- or under-skilled for their job in terms of literacy, numeracy and problem-solving…” She also says “the problem looks set to worsen in face of technological changes and aging demographics.
I’m not going to attempt to offer solutions to the issues around the number of highly skilled people we have in this country who are working in jobs where their skills and expertise are grossly underutilized. That is not a problem solved by L&D. So staying in my lane, while acknowledging there are other, pressing skill gap challenges.
So with that narrower scope, how do we move the needle on the skill gap from a learning and development perspective?
How to reduce the skill gap?
Some of the most effective ways to reduce the skill gap are within our reach and run down the very centre of the learning experience design and employee development lane. Here are a few areas where we can have an impact.
Upskilling and Reskilling Programs: By providing upskilling and reskilling opportunities we can help employees develop skills that will help them be more productive now and into the future. This may/could/should include expanding digital training to as wide a group as possible, and investing in micro-credentialing programs.
Flexible Training Formats: Offering flexible training options allows workers to balance learning with work and family responsibilities. This approach is essential for reaching more people, especially those facing financial or time constraints. That means differentiation at scale. At least I think it should mean that. Imagine having your choice to learn X in a variety of ways and at a variety of times. In the next section, we’ll go into why this is imaginable and doable now.
Inclusion of Underrepresented Groups: Improving upskilling and reskilling for underrepresented groups and those further from employment is crucial. This includes targeting skills training to groups disproportionately affected by economic and societal changes.
Let’s take a bit of a side road for a minute. Everyone on earth wants the same things, at their core. They want to feel safe, cared for, able to care for others, and to feel like their life has meaning – that they are productive members of their community. Too often it’s the ruling class (in Canada that would be the wealthier descendants of colonial settlers) that creates a barrier to this and supports othering. We need to stop that. We’re more alike than different and we all want pretty much the same things for ourselves and the people we love.
Lifelong Learning Initiatives: We need a comprehensive approach to lifelong learning so that people can adapt to the need for new skills. This requires public and private sector collaboration to identify skills needs and barriers. This is Club of Rome stuff – we need to anticipate what is needed and we need everyone to participate in finding the ways to fill those needs.
Investment in Digital Skills: As digital skills become increasingly important, investing in digital infrastructure and training is essential to prepare the workforce for future demands. We’ll touch on this again in a sec.
Public-Private Partnerships: Collaborations between businesses, governments, and post-secondary institutions can create comprehensive training programs that align with market needs. These partnerships help develop a skilled, agile, and adaptable workforce. We want that, right!?
These are some of the things we need to do and because of advances in technology, specifically AI, our ability to do these things is more achievable than it ever has been.
How can AI help?
AI can play a pivotal role in bridging the skills gap by enhancing training programs, predicting future skill needs, and fostering collaboration across sectors. But…
According to IBM “Canadian businesses saw an uptick in AI Adoption in 2023 vs. global peers”. However, 48% – close to half – of Canadian companies with over 1000 employees are still just in the exploration stage.
So let’s break this down, including some of the risks and challenges with each. Here’s how artificial (or augmented) intelligence can significantly contribute to reducing the skill gap and by extension increase productivity.
Upskilling and Reskilling Programs: AI has the potential to revolutionize upskilling and reskilling programs by providing highly personalized learning experiences that adapt to an individual’s needs. With AI-powered platforms, employees can engage in self-paced learning and receive recommendations based on their current skills, learning preferences, and industry trends. AI-driven assessments can identify gaps in knowledge and suggest targeted learning paths.
For example, AI systems like Coursera’s SkillGraph can assess and personalize an employee’s existing skills and recommend micro-courses that fit their goals. AI can also support micro-credentialing programs by making the assessment process more efficient and scalable. AI algorithms can automate the evaluation of tasks or projects tied to micro-credentials, ensuring a faster and fairer grading process. This can increase accessibility to certifications, allowing the demonstration of mastery in specific skills without waiting for human evaluators.
Challenges/Considerations: The risk here is over-reliance on automated systems. Without human oversight, assessments could miss nuance, and there could be biases in AI-driven recommendations based on past data. It’s essential to balance AI’s efficiency with human judgment to ensure fairness and accuracy.
Flexible Training Formats: AI offers the ability to support flexible, on-demand, and just-in-time learning by creating adaptive learning environments. For example, AI-driven platforms like edX or LinkedIn Learning can already adjust the pace and difficulty of training based on the learner’s progress, ensuring that learners don’t feel overwhelmed or under-challenged. These platforms can also provide content in various formats (videos, interactive quizzes, or simulations), catering to different learning preferences.
By using AI, companies can offer employees differentiation at scale, meaning they can give each worker the flexibility to choose when, where, and how they learn. AI can also track learning progress and provide nudges or reminders for when it’s time to revisit a skill, increasing engagement and accountability.
Challenges/Considerations: While AI offers flexibility, access to reliable digital infrastructure is still a barrier for some employees, particularly in underserved communities. Moreover, excessive reliance on AI recommendations could reduce a learner’s exposure to diverse perspectives that a more human-curated experience might provide.
Inclusion of Underrepresented Groups: AI can be a powerful tool in reaching underrepresented groups by identifying gaps in training and tailoring learning materials to specific needs. AI systems can analyze demographic data to determine which groups are not receiving equal access to upskilling opportunities. Once identified, AI can suggest targeted interventions to provide more inclusive training opportunities, ensuring that marginalized groups gain access to in-demand skills.
For example, platforms using natural language processing (NLP) can translate training materials into multiple languages, removing language barriers for immigrants and refugees. Additionally, AI-driven accessibility tools can create more inclusive content, such as using voice recognition or screen readers for individuals with barriers to typical learning formats.
Challenges/Considerations: There is a risk that AI could inadvertently reinforce existing biases if the data used to train AI models isn’t diverse enough. It’s crucial that AI systems are built and tested with inclusivity and accessibility in mind, and that organizations monitor their systems for any signs of discrimination.
Lifelong Learning Initiatives: AI can make lifelong learning more accessible by constantly updating learners on the latest skills and trends relevant to their careers. AI can serve as a personal coach, providing real-time feedback and tracking an individual’s learning journey across their lifetime. By analyzing market trends, AI can help us anticipate future skills that will be in demand, aiding in long-term career planning.
Platforms powered by AI, such as IBM’s Watson, are already helping businesses and individuals identify the skills they need to thrive in evolving industries. Public and private sectors can collaborate with AI systems to map out the broader skill needs of society and devise training programs that address those needs.
Challenges/Considerations: Lifelong learning through AI also brings up issues around data privacy. Tracking someone’s learning progress and skills over a lifetime could lead to concerns about who owns that data and how it’s being used. Clear regulations and ethical practices need to be in place to protect individual learning histories.
Investment in Digital Skills: As digital skills become increasingly important, AI can play a key role in identifying the specific digital competencies that are most relevant to a given job or industry. AI can analyze workforce data to pinpoint emerging skills and develop customized digital training programs. As an example, tools like Udacity’s AI-powered learning programs already focus on equipping individuals with skills in AI, machine learning, and other advanced digital fields.
AI can also be leveraged in digital infrastructure to support skill-building efforts. For instance, AI can improve virtual labs and simulations, enabling employees to practice coding, data analysis, or cybersecurity in a risk-free, hands-on environment.
Challenges/Considerations: A challenge here is ensuring equitable access to digital training tools. AI can widen the gap between those with access to advanced technology and those without, so careful consideration must be given to providing affordable and widespread access to these resources.
Public-Private Partnerships: AI can enhance collaborations between businesses, governments, and post-secondary institutions by facilitating data sharing and helping identify skill gaps in the workforce. By using AI-powered data analytics, these partnerships can forecast future skill requirements and design curriculum or training programs that meet those needs. AI can also help coordinate large-scale training efforts across multiple sectors, ensuring that efforts are aligned with real market demands.
For example, AI systems can analyze job market data to identify emerging trends and work with educational institutions to update their curriculum accordingly. AI can also help monitor the effectiveness of training programs in real time, allowing for swift adjustments to meet changing needs.
Challenges/Considerations: The challenge with AI in these partnerships is ensuring that data privacy and security are maintained, especially when sensitive employment and educational data are shared across sectors. Transparency in how AI is being used and how data is managed will be critical to maintaining trust.
AI’s role in enhancing productivity through learning and development is transformative, but careful, well informed implementation is necessary to address ethical considerations and ensure equal access to opportunities. With the right balance, AI can empower workers, close the skills gap, and drive lasting productivity improvements across Canada.
Let’s wrap this up by discussing how we might do this.
What do we need to do now?
It all begins with embracing AI, and the time to act is now. AI adoption is the linchpin for tackling the skill gap and boosting productivity, which are critical to our future. To stay competitive and to contribute meaningfully to the global challenges ahead, we must take decisive steps today. We need to skate to where the puck will be. AI is not just a tool—it’s the key to unlocking the potential of our workforce and ensuring that we are active participants in shaping the solutions of tomorrow.
Here are five ways we as learning designers and L&D specialists can encourage faster AI adoption while addressing ethics, security, and innovation concerns:
1. Start with Pilot Projects
Encourage leadership to begin with small, manageable AI pilot projects to test the technology’s feasibility and impact before scaling it across the organization. This reduces risk and builds confidence while also showing measurable success quickly. The ideal places to do this are within the realm of learning experience design, training, and professional development.
2. Upskill and Reskill Employees
Invest in reskilling and training employees to handle AI tools safely and ethically. This is the only way to reduce the skill gap that often slows adoption. This includes not just ICT workers, but also non-technical staff to ensure widespread readiness for AI integration.
3. Prioritize Ethical and Transparent AI
Address ethical concerns by ensuring we adopt AI solutions that are transparent, explainable, and free from bias. Implement AI governance measures to track the data and decisions generated by AI models. This builds trust both within the organization and with external partners and collaborators.
4. Strengthen AI Security and Privacy
Make security a priority by advocating for and supporting strong data privacy protections and governance frameworks to prevent data breaches and misuse. This reduces security risks and helps maintain regulatory compliance, which is, and should always be, a key concern for everyone.
5. Leverage Innovation through Partnerships
Encourage innovation by forming partnerships with AI startups, universities, or research institutions. These collaborations can provide organizations with access to cutting-edge AI technologies while sharing the costs and expertise needed to drive adoption.
I know, this has been a bit of a journey. If you’re still with me – thank you and good on you!
I truly believe this is the defining issue of our time, especially for those of us committed to helping individuals and organizations learn and grow. AI isn’t just about technology—it’s about transforming how we learn, adapt, and prepare for the future.
It’s clear that AI holds immense potential to transform the way we learn, work, and grow. But this potential can only be realized if we take action now. For those of us committed to human learning and development, this is our moment to lead the way.
Start by exploring how AI can be integrated into your workplace, your training programs, and your approach to your own professional growth. Push for AI adoption in a way that is ethical, inclusive, and secure. The future isn’t just something to prepare for—it’s something we have the power to shape, starting today.