Editorial
Put “AI upskilling” into a search engine and you’ll get no less than 21 million different results ranging from how to do it right, why a strategic approach is needed, why companies are falling behind and why some people are completely disengaging from AI training out of fear and disillusionment.
There is an undercurrent of fear, of missing out, of messing up or of AI eliminating jobs and changing the workforce en masse.
AI-Enabled Talent Remains a Sourcing and Training Challenge
Despite AI’s pervasiveness and the billions spent on upskilling annually, businesses still struggle with finding, training and placing the right talent. A staggering 62% of organizations lack the workforce expertise required to implement AI responsibly.
This shortage has tangible consequences — IDC predicts that by the end of 2025, 90% of businesses will experience delays in product releases, reduced customer satisfaction and lost revenue due to technical skills gaps.
Provide Environments for Practice and Failure
The talent issue persists because most organizations are forgetting a key part of how we learn: through practice. Indeed, 74% of employees who are concerned about their AI skills are disappointed with their employer’s AI training programs.
AI is fundamentally a hands-on technology — you learn it by using it. Since AI will be embedded in almost every workplace process, learning how to use it through theory alone is akin to someone trying to scuba dive after watching a few videos.
Incorporating practice environments into your learning ecosystem will give people the opportunity to apply their theoretical knowledge in scenarios and to AI tools they’ll use in their roles. It’s vital that they aren’t just let loose within a sandbox or live environment using real data, as this introduces unnecessary risk into your training. Instead, they should practice in non-production environments that mirror real-world tools without putting operations or data privacy at risk.
That also gives a safe space for someone to fail, which is a key part of someone truly mastering a skill. Research suggests that a 16% failure rate leads to optimal skill development.
Related Article: Building the Skills to Succeed as an AI-Augmented Worker
Define What AI Means for Your Organization (and Yours Alone)
Taking a step back from current trends and thinking carefully about what AI means for your organization is a critical step in designing effective AI upskilling, training and enablement.
For many leaders, AI skills — and building and buying those skills — are a top priority, and this pressure often filters down to the people designing and doing the training. But, “we need to learn AI” could mean a lot of different things, even within the same organization.
When a leader or stakeholder comes to you stating this problem, figure out the hidden message behind it (which may have nothing to do with AI at all). It might stem from fear, a new product launch, competitor investments or market trends. Knowing your starting place will give you a focus for your initial training and enablement.
Understand How AI Creates ‘Evolved Skills’
AI changes skills and how we think about skills. It’s important to get every stakeholder on board with how skills are changing in your organization and how assessments are shifting because of this. Notably, in creating a new skill category: evolved skills. Evolved skills are those where success is measured not by following a set process, but by achieving effective outcomes using a mix of AI and human reasoning.
Since generative and agentic AI can now complete lower-order tasks, such as basic information recall and data cleansing, human workers will need to elevate their skills and tasks to higher-order activities including critical reasoning, evaluating AI’s outputs and providing feedback. Indeed, even individual contributors will, to a degree, become managers, since they will be expected to "manage" the AI agents working alongside them.
We are moving from a time of traditional skills (AKA, where you learn a process, practice it and then demonstrate proficiency through completing that process) to evolved skills, where competency depends on producing a desired outcome (through many different possible processes and iteration using AI agents). That’s an important change, because it means practice and experiencing an AI tool becomes vital. Completion is becoming much more subjective than objective. The only effective way to evaluate someone’s use of AI and their work within an AI-augmented workplace is to subjectively compare the produced outcome with a range of pre-defined acceptable outcomes and determine if the result therefore demonstrates someone’s skill.
For example, you may ask a developer to debug code using a co-pilot. It won’t be the process that’ll be assessed in this scenario but whether the developer has successfully used AI along with their human reasoning and critical appraisal skills to detect and resolve bugs.
Assign the Right Training to the Right Roles and Responsibilities
Most of the struggle organizations face is simply identifying what they really need when they decide they need to upskill on AI, and if that upskilling is as profound as they thought. Take the time to solve this. The vast majority of employees (task workers and knowledge workers) won’t need specialist AI skills as they won’t develop AI models or roll out new AI at scale. Instead, they’ll need the ability to use AI technology (such as a co-pilot) within the responsibilities and constraints of their role.
In some circumstances, they may create AI-powered software and use it to write code (if they are a software developer or engineer). These individuals will need to understand AI APIs and other AI-related commercial services.
There will be a select few who’ll need very specific knowledge of AI to a high level, such as data scientists and those tasked with data governance and the AI strategy. Their skills will involve using and understanding commercial AI platforms, building and training models and managing the company’s AI technology stack.
Related Article: AI Skills Training: Strategies for Technical Teams vs. End-Users
Focus on One Skill, One Cohort at a Time
There's a saying: "How do you eat an elephant? One bite at a time." That's the approach that leaders can take when embarking on their AI upskilling strategies.
Focus on a handful of AI and AI-enabling skills that will support the highest priority projects and implementations. Identify those projects based on business goals, then break it down further into the tasks each project requires to succeed. From that, you can split tasks into the skills needed to fulfill each activity. Department heads and project leads can provide insights into each level of this to ensure your subsequent training is aligned with skill needs.
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Succeeding in the AI-Augmented Future
The road to AI mastery isn’t about training everyone on everything, it’s about aligning people, processes and technology in a deliberate, contextual way. Leaders who embrace this shift and invest in practical, role-specific upskilling will position their organizations to thrive in the AI-augmented future.
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