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11th June 2025

AI is Already in the Room

Why it’s Time to Reframe the Conversation in Corporate Learning

In the late 1990s and early 2000s, hesitation around the internet was widespread.

Concerns about privacy, reliability, and information overload were prevalent in corporate circles. Fast forward twenty-five years, and digital infrastructure has become not only accepted, but essential to how organizations operate, communicate, and learn. We’re now seeing a similar pattern with artificial intelligence.

Many HR and L&D leaders are understandably cautious. Applying artificial intelligence to something as human as learning raises important questions – around privacy, quality, and the role of instructors in the digital age. But history tells us that major technology shifts often begin with doubt, before becoming integrated into daily life.

Rather than asking if they should use AI, many leaders are now exploring a more strategic line of inquiry: how to use AI responsibly and effectively – and what to look for when choosing tools.

AI in learning: Evolution, not replacement

The idea that AI might dehumanize learning by replacing teachers is a common concern voiced by learning professionals and HR leaders. In reality, the most effective applications of AI in corporate language learning are designed to empower, rather than replace, teachers.

When it comes to language learning and communication, human connections remain central. Where AI can bring the most value is in supporting parts of the learning experience that were previously difficult to scale or personalize, such as one-on-one speaking practice or targeted feedback. This creates more opportunities for learners to build fluency and confidence, especially in between sessions with a human instructor, and in the real world.

The best learning models offer a hybrid approach, with a balance of human and AI-powered support – leading to more effective use of teacher time and better learning outcomes.

Not just another chatbot: What makes learning AI meaningful

The rise of tools like ChatGPT has brought AI into the spotlight. But not all AI is designed for learning.

General-purpose models have a broad knowledge, but they often lack the structure, subject alignment, and pedagogical design needed for effective skill-building. In language learning, for example, consistency, feedback, and curriculum alignment are key.

This is where task-specific models, trained on high-quality proprietary learning data and with focused guardrails, stand apart. AI that draws from real learner behavior, teacher feedback, and structured content is fine-tuned to support actual progress.

The rise of tools like ChatGPT has brought AI into the spotlight. But not all AI is designed for learning.

Learning from learner data, responsibly

Another important consideration is data privacy. With AI systems often reliant on large volumes of data, concerns around transparency and control are valid. Likewise, at a time when almost any app can market itself as “AI-powered,” choosing AI partners who have experience in handling learner data safely remains key.

In learning environments, the most responsible AI tools are designed to work with anonymized behavioral data – such as lesson completion rates, speech patterns, or engagement trends – within a closed-loop proprietary system. The goal isn’t to monitor individuals, but to understand where learners are struggling and what’s driving improvement, to deliver more personalized support at scale.

What HR leaders should be asking

The trajectory of most digital technologies follows a familiar arc: early skepticism, cautious experimentation, then gradual integration. What changes the curve is leadership and strategy.

Rather than asking, “Is AI safe?” or “Is it going to replace teachers?”, the more strategic questions for HR and L&D leaders might be:

- Where can AI help us personalize at scale, without significantly increasing costs? 

- How can data insights improve support for learners? 

- What’s the role of human facilitation when digital tools get smarter? 

These questions shift the narrative from risk mitigation to capability building, and position HR leaders as stewards of these innovations.

Integrating AI in learning is not about abandoning traditional methods. It’s about understanding where new tools can enhance them. Human-led instruction will always be central to meaningful learning. But with the right AI support, it becomes more targeted, more inclusive, and more impactful.