People are better connected today than ever, yet at the same time, they are also extremely disconnected. We’re connected with technology and people on a virtual level, but disconnected from people on a human level.
The integration of AI in various industries is transforming how we approach skills development. Employee training with AI is at the forefront of this revolution for better or worse. It sometimes offers innovative solutions and often promises to enhance efficiency and engagement.
As with any technological advancement, it’s essential to proceed with caution and not rush to rely solely on artificial intelligence for every training need. It’s far from a silver bullet that can be shoehorned into every training need.
While AI can analyze vast amounts of data to tailor personalized learning experiences, it often misses the nuanced understanding of human behavior. Even things that use AI but have a human hand behind them lack humanity. AI can’t predict the unpredictability of real-world scenarios that are critical in effective employee training.
AI benefits employee training in some ways while in others it detracts.
Consider a scenario where an AI-driven training module recommends communication strategies solely based on data. Such an approach may overlook the subtleties of human interaction, emotional intelligence, and cultural context.
As employers and training professionals, there’s a pressing need to strike a balance and use AI only when it adds value to employees, rather than simply adding value to those working in Learning and Development (L&D). It’s about leveraging AI’s capabilities while recognizing its limitations.
This post will help you understand the opportunities AI presents in transforming employee training, as well as the pitfalls it creates in the name of saving time for L&D rather than improving employee learning outcomes.
The Rise of AI in Employee Training
The rapid adoption of machine learning algorithms and natural language processing has ushered in a new era for creating employee training with AI for better or for worse (mostly worse). Organizations are exploring help through chat with AI, real-time responses delivered through AI chatbots, and even virtual coaching assistants.
The shift is often driven by the speed of delivery that L&D can achieve by using AI in various types of training. Unfortunately, this usually means creating more content with little to no value, but creating it faster. It’s not necessarily driven by employee demand or even organizational demand. That’s not true when done meaningfully, with purpose, and to add value, but that’s often not the case.
However, there is a demand for scalable, data-driven learning solutions that can evolve alongside business needs; that’s where it will rise and add value.
Employee training is affected in almost every way by AI, but not always for the better.
While AI excels at processing patterns across large datasets and predicting certain outcomes, it may struggle to account for human factors. It has no idea what motivates people, how they prefer to learn, or the organizational culture as a whole.
As the technology matures, L&D must decide which elements of training truly benefit from AI and which require the empathy and adaptability only humans can deliver. In this sense, the rise of AI in employee training represents both an exciting opportunity and a formidable challenge for L&D professionals seeking to innovate responsibly.
AI for Personalized Learning
One of the most compelling promises of employee training with AI is the ability to personalize at scale. Traditional “one-size-fits-all” training often leaves high performers under-challenged and struggling employees overwhelmed.
An AI-driven learning experience platform (LXP) can analyze individual progress and engagement metrics, and with the right information, identify knowledge gaps to curate targeted content and suggest next steps. This individualized approach can accelerate skill mastery by adapting to pacing and difficulty in real-time. However, it still relies on good content, which AI cannot create.
Despite these advantages, personalization algorithms must be carefully designed to avoid creating learning silos. If an AI engine continually presents only familiar content, employees may miss important information for their roles. A good example of this is an AI-powered recommendation system that only suggests training materials related to an employee’s current job responsibilities, limiting exposure to new skills or areas of expertise.
AI can be used to deliver personalized content in real-time but it must be done with purpose.
Personalization should not replace mentorship and social learning opportunities. When it comes to content itself, AI can be used in relevant scenarios for personalized learning. Yes, you can have AI avatars respond with unique information based on an answer an employee provided. But, it’s always important to ask if the AI avatar is actually adding value or simply creeping people out, which is very likely.
Combining AI-driven adaptive opportunities with human-created learning, such as e-learning not written or narrated by AI, and social learning opportunities often yields the richest outcomes. By keeping humans in the loop in a significant way, rather than creating more boring content without their involvement, organizations ensure that personalized training remains dynamic, contextually relevant, and aligned with broader business objectives.
Enhancing Training Efficiency with AI
Efficiency gains are frequently touted as the primary benefit of employee training with AI. The primary focus for instructional designers is on writing content, utilizing synthesized narration, and incorporating AI-generated talking heads. None of these are focused on employees and adding value for them; it’s focused on saving instructional designers’ time, which is not a good reason to use AI or add the right type of efficiency to training.
AI for training is more valuable and helps deliver better results for analytics, ensuring that training has an impact. While it is being used for those things, it’s going too far into simply speeding up development. The goal should be for AI to efficiently handle more of the analytics, freeing up valuable time for L&D teams to focus on strategy and content creation.
AI-driven analytics can sift through engagement data to identify which modules deliver the biggest impacts, enabling rapid iteration and optimization of training.
Analytics is one of the best ways to implement AI and help make a positive impact on training effectiveness.
It’s worth questioning whether speed alone translates to meaningful learning. The risk of prioritizing throughput over depth can manifest in superficial assessments and gamified experiences that reward completion rather than comprehension.
Overdependence on AI-generated insights may blind organizations to qualitative feedback gleaned from focus groups or informal coaching sessions. To truly enhance efficiency, companies should pair AI’s data-crunching power with deliberate human oversight. This ensures that streamlined processes also uphold quality, relevance, and the human connection that motivates employees.
Currently, efficiency is being used primarily to aid those developing training, rather than to support employees. That will eventually hurt training, and content will start to look the same, becoming neither engaging nor effective in any way.
The Limitations of AI in Training
As much as AI can accelerate employee training in certain areas, it also introduces inherent limitations. Algorithms operate on historical data, meaning they may perpetuate outdated practices or fail to anticipate emerging skill needs in rapidly changing industries.
AI also struggles with context switching and cannot easily adapt training scenarios to unforeseen real-world variables, such as sudden market shifts or interpersonal dynamics. It also tends to focus on the positive side of things a bit too much, without balancing the good with the bad.
The most significant and critical constraint lies in content creation. While AI can generate quizzes, summaries, and even simulated dialogues, they often lack uniqueness or humanity and lack important aspects that people bring to training.
The content it creates lacks the creativity and domain expertise that seasoned instructional designers and subject matter experts provide. Consequently, organizations should treat AI as an augmentation tool that helps in the beginning phases of content production, but can’t create content very well at all. It doesn’t fully replace the nuanced insights of human designers who understand how to tie everything back to a performance objective.
The Lack Of Humanity In AI
One of the starkest criticisms of employee training with AI is its inability to replicate genuine human empathy. While chatbots might answer straightforward queries, they can’t offer nuanced encouragement or read emotional cues when someone is struggling. It relies solely on prompts, which aren’t always the best.
The absence of this human element can leave employees feeling isolated, reducing engagement and undermining the trust crucial to effective learning.
AI also lacks the moral judgment and ethical sensitivity that humans bring to courses, as well as trainers. In scenarios involving sensitive topics such as diversity, equity, or mental health, automated modules may inadvertently perpetuate stereotypes or offer tone-deaf guidance.
When people are involved, while not perfect, they can navigate difficult conversations, offer personalized support, and adjust teaching approaches on the fly to respect individual backgrounds and emotional states.
AI Narration and AI Talking Heads Suck
For instructional designers, it’s often seen as easier to use AI narration because it’s always available and always consistent. Same with talking heads. The problem is that they suck, really bad. Both of them. Yes, even newer versions, which I know are always improving, but never to the level of a human.
On the surface, these can scale content delivery and maintain consistency in messaging. However, employees often report that robotic voices and synthetic faces feel impersonal, monotonous, and disengaging. The research behind this, although dated, ultimately supports the conclusion that it was a bad idea in the end.
Using AI to create content that’s delivered directly to employees should be done deliberately and for impact, not because it’s faster.
The lack of authentic inflection, eye contact, and spontaneity detracts from the learning experience rather than enhancing it. In practice, AI-generated talking heads should be used sparingly if at all. Instead of relying entirely on synthetic narrators and on-screen personality, consider employing AI to draft scripts or suggest visual elements, then converting those into something human.
For talking heads, consider foregoing them completely. They’re not necessary, whether AI or human. They’re not adding value and are simply a financial suck.
Instructional videos are more effective when audiences sense the genuine passion and expertise of a real person.
Balancing Human Touch with AI Assistance
Striking the right balance between AI and human-centered training is essential for maximizing learning outcomes. AI should function as an enabler when it comes to training employees. It should handle repetitive tasks, mine performance data, and recommend the right content, while humans focus on creating real content that has humanity in it, solves problems, and facilitates answers or discussions that spark critical thinking or ‘aha’ moments.
To achieve this balance, organizations can adopt a “human-in-the-loop” model. Here, AI systems surface actionable insights, such as which topics employees find most challenging, while people interpret that data to guide providing the right content. This synergy ensures that technology enhances efficiency and customization without sacrificing the interpersonal dynamics and nuanced expertise that underpin deep learning.
Ensuring Data Privacy and Security
The collection and analysis of data forms the backbone of effective employee training with AI, but also introduces significant privacy and security concerns. Personal information can paint an intimate portrait of an individual’s strengths, weaknesses, and work habits. If mishandled, this data may expose employees to profiling, bias, or even unauthorized surveillance.
To mitigate risks, organizations must implement robust data governance policies and procedures. This includes clear consent protocols, encryption of sensitive records, and strict access controls that limit who can view or manipulate analytics.
Regular audits, third-party security assessments, and compliance with data protection regulations (such as GDPR or CCPA) are non‐negotiable. By demonstrating transparency and prioritizing ethical data management, companies can build trust and safeguard the integrity of AI‐driven training programs.
Overcoming Bias and Diversity Challenges in AI-driven Training
Despite its data-centric prowess, AI is only as unbiased as the datasets and algorithms that made it. Historical training materials may reflect cultural or gender biases, which can be amplified when used to inform automated recommendations.
For instance, an AI system might inadvertently steer females toward roles traditionally associated with women or underrepresent certain groups in leadership simulations.
There’s a lot of bias built into AI, which means it should be used carefully and with purpose.
Combatting these pitfalls requires a proactive approach. Training data should be audited for representation gaps and stereotypes, and algorithms tested for discriminatory outcomes. Engaging diverse stakeholders in program design ensures multiple perspectives shape content.
Organizations can also regularly retrain AI models using new, balanced datasets that reflect evolving workforce demographics and inclusive best practices. These measures help ensure that algorithms account for equity rather than entrench bias.
Measuring the Effectiveness of AI Training Programs
While AI can be used too heavily to reduce development time at the expense of effectiveness, measuring how AI is impacting training can help. It could either reduce the use of AI when it’s not helpful or, in certain cases, prove to be valuable.
Traditional metrics, such as completion rates and post-test scores, provide a baseline but don’t capture deeper behavioral changes or business outcomes, as is often the case with most training. Advanced analytics can track real-time performance improvements and also whether help desk tickets are being opened more frequently in one group rather than another.
Organizations should define clear KPIs before launching AI initiatives, aligning them with strategic objectives such as reducing time to competency, lowering error rates, or increasing employee engagement scores. By incorporating both quantitative and qualitative measures, businesses can determine whether AI interventions genuinely drive results and iteratively refine programs for maximum impact.
Wrap Up
AI holds tremendous potential to revolutionize how we train employees as long as it’s done with purpose and implemented in meaningful ways. Using it to speed up the delivery of bad content simply won’t do any good, and it also won’t help to speed up development in a meaningful and effective way.
Examining AI from an employee-improving perspective is crucial to ensuring it adds value to learning rather than detracts from it. Treat AI as an augmentation tool, not a wholesale replacement for human expertise.
By combining AI-driven insights with the emotional intelligence and creativity of people, organizations can deliver learning experiences that are both scalable and deeply impactful. In navigating this new landscape, a vigilant and skeptical mindset will ensure that AI enhances, rather than erodes, the richness of employee development.
Content creation is not the right place to implement AI in its current form. Analytics and higher-level needs are where it excels and can significantly impact how we use data to tell the story of how training is affecting employees. For content creation, working with individuals who incorporate humanity into their corporate training design is essential. That means no synthetic narration, no AI-generated pages of content, and definitely no AI talking heads.
Schedule a consultation to discover how working with someone who prioritizes people and job impact over creating content faster can benefit you. We’ll ensure that people are well-trained on the content in a way that allows them to apply it to their jobs, rather than getting lost in an AI-generated mess.