Nowadays, AI is everywhere. Or so they tell you, as almost every decision and announcement includes at least one mention of AI. Undeniably, AI is a powerful tool with vast potential that can increase your company’s efficiency by leaps and bounds.
However, AI is a vast field that encompasses a range of tools and solutions. When companies like TCS and Infosys talk about AI learning programs, it goes beyond simply using tools like ChatGPT.
Due to the novelty of AI combined with its rapidly increasing involvement in companies across the country, a new form of inequality has emerged between those who know AI and those who don’t. However, this barrier can be easily overcome with simple yet effective AI learning paths.
Understanding AI Literacy
The concept of AI literacy goes beyond simply using available AI tools. While using the correct prompt is certainly one part of AI utilisation in the office, there is much more that employees need to be aware of to truly use AI in a manner that is both efficient and appropriate.
The core component of AI literacy that every employee should be aware of includes:
- Awareness: Understand what AI can and cannot do. Employees should understand the capabilities and limitations of the tools at their disposal. At the same time, they need to know which AI tool is most appropriate for which kind of work.
- Application: Using AI is not just about using it to the fullest. It is also about using it responsibly. Overuse of AI can be counterproductive, decreasing the quality of work. AI, in all forms, needs human input to improve. As such, AI should be used as a helping agent rather than an output generator.
- Critical Thinking: No matter how good an AI tool is supposed to be, employees should always verify the outputs they receive. Check for unintended bias and the accuracy of the information/output provided by the AI tool. While AI may do the heavy lifting, a human’s critical thinking skills will always remain irreplaceable.
- Ethics & Governance: As with any tool, employees should be well aware of the ethics and governance policies governing the AI tools they use. What data can they give to the tool? What data are they allowed to extrapolate? Just because an AI tool can do something doesn’t mean employees should use it for the same. These moral dilemmas should be discussed in depth with all employees.
What employees should strive for is to increase their AI fluency. This means making them capable of using the AI tools at their disposal to the fullest.
Why One-Size-Fits-All AI Training Fails
Having the same AI training module for all employees, regardless of their roles or the tools they use, can decrease your team’s productivity rather than increase it. Every role demands a unique set of skills and a way of thinking.
As such, when companies decide to provide the same basic AI training to every employee, they can hardly expect unique outcomes. Instead of relying on the most common tools known around the world, companies need to do their research on what tools are actually suitable for their industry and various job roles.
While crafting AI training paths, multiple factors should be considered to personalise the learning experience, including:
Roles, Exposure, and Comfort
When creating training paths to educate your employees on AI tools, clearly distinguish their diverse roles, exposure levels, and comfort with technology.
Someone whose role may not require much AI usage may not require a long training session. Similarly, companies must first assess employees’ familiarity with AI tools and their comfort using them before providing training materials.
Generational and Educational Differences
AI is undoubtedly a technology that is everything that modern science stands for. As such, generational and educational differences play a major role in crafting training paths for different individuals.
For example, someone from a non-technical background and from an older generation may need lessons on basic technical jargon. On the other hand, a GenZ employee with a software degree might not need more than a basic refresher.
Balancing Information Overload
Create training paths that avoid overwhelming your employees with technical jargon and in-depth software discussions. Keep the initial teaching paths centred on what employees need to know to use the AI tools relevant to their initial job responsibilities.
Depending on individual employees’ interests and their aptitude for prompt engineering, the training can be tailored to provide them with more in-depth knowledge at a manageable pace.
Learning Paths v/s Standalone Workshops
Standalone AI workshops often include basic common-use knowledge of certain AI tools. On the other hand, proper learning paths focus on increasing employees’ AI fluency at an innate level.
With learning paths, companies allow employees to understand the link between their actual work and how AI can be used as a tool for their specific job. Standalone workshops provide an introduction to various tools that employees may or may not want to explore.
Building AI Learning Paths: A Step-by-Step Framework
A step-by-step approach to creating AI learning paths includes understanding not only the team’s capabilities but also setting realistic expectations for your employees and the business’s progress.
Step 1: Identify Learner Personas
Not every employee has the same educational and technical background, which can affect what they know and don’t know, even about the basic terms used in AI prompt engineering. Each job level and industry has its own needs and expectations that should be considered when crafting individual learning paths.
Some examples of learner personas include:
- Non-technical employees (HR, finance, operations, admin roles).
- Managers and People Leaders.
- Technical and data teams.
- Senior leadership and board members.
Step 2: Assess current AI awareness
Using online assessment tools and aptitude tests, companies should assess their employees’ existing AI awareness and their skill levels with different tools.
This can vary from person to person and is not necessarily dependent on age or educational background. For some, AI might be all too familiar due to personal interest, while for others, a lack of familiarity might be obvious despite their technical background.
Step 3: Define role-specific AI use cases
Individuals in different job roles do not use AI in the same manner. As such, the initial AI learning modules should focus on role-specific AI use cases. For example, a software developer can initially be taught how to use Cursor while a graphic designer can start with Nano Banana.
Step 4: Set learning objectives
Once an employee’s existing capabilities and basic use cases are evaluated, it’s time to establish the objectives of their learning modules. Set goals of what employees want to know, what they need to learn, what they should use, how they should evaluate AI outputs, and what ethics they should be aware of.
As the learning process progresses, these objectives can expand or shrink in scope, depending upon personal aptitude and interest. However, setting goals at the outset can go a long way toward increasing the collective workforce’s AI literacy.
Step 5: Choose learning formats
Every learner learns a bit differently. Some might prefer in-depth, long discussions, while others might prefer the microlearning format. Keep in mind that the ultimate goal of the learning process is to create an AI-literate workforce.
As such, allow employees to try different learning formats, such as microlearning, hands-on labs, and discussions, to see what resonates most with them. Based on the same, provide them with curated learning experiences that allow them thrive in their own way.
Step 6: Create progression levels
Provide different progression levels to employees for their AI literacy. Based on personal interests and aptitude, employees can explore AI learning paths beyond what is strictly required for their job role.
At the basic level, employees can progress to the intermediate and advanced levels by deepening their understanding of AI’s capabilities across different spheres. This not only enhances their individual skill sets but also strengthens the company’s overall AI capabilities.
What Goes Into an AI Learning Path
When creating AI training plans, make sure to cover the basics for each individual while leaving room for growth. The learning paths should include:
- Foundational Concepts: Employees should have a basic understanding of terms such as Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI), and automation. Knowing the similarities and differences between these terms can go a long way in one’s AI literacy.
- Practical, Role-Relevant Tools: In addition to introducing employees to common AI tools like ChatGPT, focus on tools that will help them in their specific domain of work.
- Prompting and Decision-Support Skills: The core use of AI includes creating effective prompts and guiding AI models through decision-making. Thorough training in this can drastically improve the overall output of humans and AI alike.
- Ethical AI, Data Privacy, and Bias Awareness: Each employee should be made aware of the ethical boundaries to be adhered to when using AI tools. Additionally, understanding the concept of data privacy and how AI models can also become biased is a fundamental limitation that each AI user should know.
- Human Skills: Beyond enhancing knowledge of AI’s workings, a good AI training path also focuses on improving human skills such as judgment, creativity, and accountability to expand the capabilities of all parties involved.
When crafting and presenting AI learning paths, HR has the responsibility of emphasising the importance of responsible AI learning. At the same time, managers need to be proactive in explaining to their employees how AI can be inculcated in day-to-day operations.
As with any training module, the learning and development team needs to ensure that the knowledge they provide is up to date with the latest developments and trends. At the same time, employees should be encouraged to experiment with AI while keeping ethical boundaries in mind to create something truly unique.
Crafting Correct Prompts
The most critical path in any AI training is educating employees on crafting prompts that yield the desired outcome. Though most AI tools accept human language input, that does not necessarily mean they can easily understand the user’s intent.
As such, a company’s AI training journey should include teaching employees what makes a prompt truly actionable. This includes:
- Clear Outcome: Instead of vague prompts, emphasise the importance of details when describing the desired outcome. After all, there is a big difference between “generate a picture of an apple” and “generate a landscape picture of a single apple in a green glass fruit bowl on a table. The table is in a modern kitchen with daylight filtering in.”
- Important Context: Explain why the context of a task is just as important as the actual task to create an output. For example, asking for a “summary of events in 2025” will generate a widely different outcome than “summary of events in 2025 to be published on a real estate platform.”
- Define a role for the AI: Let employees know they can prompt the AI to perform a specific role. As such, employees can ask AIs to “act as a financial strategist to create a tax plan” to have much better results than simply asking to “create a tax plan.”
- Constraints: Given the vast world of knowledge available to AI, constraints become heavily important. Employees should know that good results can only be achieved by providing actionable constraints, such as preferred image sizes, maximum word limits, or the use of information released after a particular date.
- Be Objective: Everyone wants their AI outcomes to be “good,” but that also means letting your AI tool know just what “good” means. For example, stating that “the generated strategy should yield at least 30% more profit” lets the AI know the exact target it’s pursuing while drafting a business revenue plan.
Measuring AI Literacy Progress
AI literacy cannot be measured simply by course completion metrics. Yes, an employee may know what prompts are and how they function, but that does not necessarily mean that they are truly adept at using this revolutionary piece of technology.
Instead, focus on metrics such as confidence, adoption, decision quality, and productivity gains. Ask:
- Has the employee started using AI more after completing their training?
- How have they started to integrate it into their work?
- How has AI usage affected the quality of their decisions and productivity levels?
- Are they moving beyond what they were shown when applying their knowledge?
All these questions can help companies understand the true scope of their workforce’s AI literacy. Focus on how performance and capability frameworks are affected to truly understand AI’s impact.
That said, AI’s increasing use and learners’ use do not mean everything is going perfectly. Throughout the process, make sure that you do not:
- Treat AI literacy as a tech-only initiative.
- Over-focus on tools instead of thinking skills.
- Ignore ethics and governance.
- Assume curiosity equals competence.
As AI becomes increasingly ingrained in everyday work across many companies, small missteps can lead to major operational failures. As such, tread with caution, but do not let the fear of failure control you.
In the End…
As AI continues to revolutionise how workforces operate today, employees and companies alike need to view AI literacy as an empowering skill rather than a disruption. By intentionally curating AI learning paths, companies can foster a culture that remains determined not to fall behind, all the while uplifting their employees and their capabilities.
When AI skills become a shared organisational capability rather than an individual burden, the entire workforce can improve performance by leaps and bounds.
So, inculcate AI literacy as part of your onboarding process and leadership development. From the grassroots to the C-suite, encourage AI literacy and all it can offer. After all, companies that invest in AI learning paths today will surely outlearn those that simply adopt tools.
