With artificial intelligence (AI) and digital transformation tools garnering increased attention, we are seeing a rapid rise in technology’s adoption for innovations across industries. But one glaring issue that continues to plague technology adopters is that the urgency to embrace technology-led shifts far outstrips the supply of talent needed to make it happen — both in terms of the quantity and quality.

The World Economic Forum predicts that technological adoption will remain a key driver of business transformation. However, it also estimates (that approximately 85 million jobs will be replaced with AI-powered automation of rote tasks. But is the current workforce ready for the reskilling needed for this scale of overhaul?

Clearly, there’s a need to generate a skilled talent pool to cater to the new world order. This is where a robust learning and development (L&D) program comes in — complete with thoughtful approaches around delivering high employee engagement.

Comprehensive training initiatives are not only needed to create an adept workforce, but also to assure employees’ job security, progress and growth — especially in this turbulent time of layoffs, largely due to enterprises concentrating on AI-driven efficiencies and automation. These initiatives also double up as a sign of trust to boost organization morale not just for employees who feel valued with training efforts, but also to potential investors who are looking for trustworthy leadership with a future-proof mindset.

This article outlines how we can leverage popular emerging tools and technologies such as AI that are disrupting the workforce to deliver impactful upskilling and reskilling, examining how AI will streamline the employee L&D process, add efficiencies and deliver a high return on investment (ROI).

AI’s Relevance in Employee Training

Today, by leveraging AI’s capabilities, organizations have an unprecedented opportunity to create relevant, impactful learning experiences at scale. Here’s how:

  • Curation of relevant training content: AI-driven insights and analytics can be leveraged to curate relevant training content. This will inevitably lead to higher efficacy of training needs and L&D initiatives.

Trending generative AI tools like ChatGPT, Google’s Bard and Perplexity AI the highly talked about content generators, Dall-E 2, the AI image generator, can also be employed to supplement learning initiatives. ChatGPT can be sought out by employees to find tailored information from across the internet, along with contextualized responses to even follow up questions. The customized and realistic content can motivate learners to understand novel and difficult concepts in an engaging manner.

  • Employing AI for improved engagement: While curated content solves a big hurdle in training, delivering the content in an engaging and effective manner is an entirely different barrier. Recent iterations of generative AI products can provide creative measures to help training teams circumvent this issue. For example, text-to-video AI tools help create captivating learning videos complete with human-like avatars based on the input of words.

Incorporating augmented and virtual reality (AR/VR) tools can also make for immersive training experiences, particularly in curating digital worlds where real life contexts can be explored by learners. How mistakes and consequences are only limited in virtual environments is a striking feature with great potential in the realm of employee onboarding and development. Walmart, for example, has used VR to replicate real-life scenarios while training new associates, advancing training impact in the areas of new technology, soft skills and compliance.

If training efforts are concentrated in STEM disciplines, there are pertinent tools geared towards highly relevant applications based on AI tools. According to research published in the STEM education journal, “learning prediction” comprised 29% and intelligent tutoring systems comprised 25% of application categories of AI techniques in STEM education. These are simple but effective examples of leveraging AI for improved engagement and interaction.

  • AI-driven analysis and assessment: By tapping AI’s analytical abilities, organizations can also help make informed decisions that extend beyond improving trainee engagement. AI assistants can free up trainers’ time by taking up routine tasks like grading assignments, checking attendance and more, which grant instructors added bandwidth to connect with each student better and nurture their learning. Learners’ progress can also be assessed with greater efficiency as AI itself can automate assessment creation, offer instant feedback and more. For instance, AI-based learning applications such as Duolingo or Babbel employ speech technology to analyze the learners’ pronunciation and provide feedback.

Having AI-driven tools at hand can help teams deliver insights on the in-demand training programs and patterns of employee engagement, particularly with tools such as sentiment analysis which in this case can help with understanding employees’ perceptions on their training needs. With insights from relevant stakeholders and on employee’s dispositions towards training and engagement, management teams can focus on efforts that might have the best returns in their specific contexts, saving time and resources that can be better used elsewhere.

Maximizing Benefits of AI in L&D by Curbing Bias and Ethics Challenges

While there are plenty of benefits in leveraging AI, there should also be cognizance amongst stakeholders regarding its pitfalls in these contexts. There may be some risks that come with AI that could adversely affect L&D goals, but taking steps to curb those concerns can also significantly augment returns and improve overall organization efficiency.

  • Curbing AI-led bias and discrimination: The amplification of biases and discrimination, erroneous information presented convincingly, and lack of transparency can be some of the outright concerns associated with AI in training. The Stanford Social Innovation Review analysis of 133 biased systems across industries from 1988 to present showed that 44.2% demonstrated gender bias and 25.7% demonstrated both gender and racial bias. This can result in unfair allocation of resources and the knowledge acquired through it. Systems may not acknowledge the barriers affecting marginalized groups with varying levels of skills resulting from socioeconomic impediments.
  • Preventing AI systems from hallucinating and presenting false information: The concerns do not stop here, however. With reported instances of ChatGPT hallucinating information, there are also concerns regarding its consequences around teaching learners new concepts. Expecting someone to have the critical mindset about what is being taught is important. Equally critical are the implications of these error-filled teachings. Addressing this caveat is, hence, important.
  • Employing Data Governance and AI Cognizance: Responsible AI L&D strategy must also necessitate that organizations make informed decisions towards data, governance and human resources (HR) frameworks to implement a seamless AI-driven learning environment. In a MIT Sloan Management Review survey of 1,093 participants representing organizations from 96 countries, 84% believed that responsible AI should be top management priority. Execution in this regard can involve mapping out smaller steps which can act as substantial guides. For example, planning for talent development in industries that are more prone to potential automation-led overhaul, familiarization with industry updates and best practices is imperative. This also fulfills the objectives of bolstering employee trust, as a TalentLMS survey states that 49% of U.S. employees state they need training on using AI tools. AI cognizance and ethics training could, therefore, fulfill an essential step in the process.


Ultimately, technology can be an enabler in the process of self-improvement and catapult teams towards desired success. Employees as well as organizations should look for ways to leverage AI and technology to stay competitive and relevant. To make this possible, particularly while the field is still nascent, all stakeholders should strategize and act in a timely manner. The onus lies on us to make concerted efforts towards responsible AI integration in an organization’s training initiatives.

As companies leverage generative AI, every learning leader must shoulder the responsibility of ensuring that their processes are ethical and inclusive, including for talent development and engagement. They should formulate comprehensive AI strategies by consulting diverse stakeholders as formulating a guided and comprehensive L&D strategy requires input from researchers, employees, policymakers and innovators. This assuages the problems AI tools have been feared to bring. Hence, strategic collaborations must be prioritized as companies double down on building a skilled employee base and engaging them using the very same AI tools that are primed to disrupt the workforce.