When it comes to ongoing support to improve employee performance, the approach to training is often one of reaction; we assess performance, identify opportunities for growth and then provide the right learning solution. While this tactic is proven to work, especially with proper data and analysis, the issue is time. In the corporate landscape, it is not uncommon to experience a delay between the identification of a performance gap and the implementation of a solution to bridge that gap.
Artificial intelligence (AI) holds the power to transform this approach, making it more predictive and targeted. This would enable learning and development (L&D) to anticipate individual learning needs, customize the solution specifically for each learner and determine the optimal timing for its delivery.
Such a tailored approach bolsters the likelihood of success for the learner while also strengthening L&D as a strategic driver to the business. By proactively identifying learners’ specific needs, we can provide appropriate and personalized learning solutions before the need arises.
This shift would be massive and could completely change the way L&D operates. At the same time, the scope of this change can feel overwhelming. So, where do you start?
There are three key factors to consider when preparing to use AI in this way:
- AI is not the vision or strategy: A vision should be technology agnostic. The reason behind this is that technology evolves rapidly and it’s nearly impossible to predict its future course. AI can help achieve the vision, but it should not be the vision. Instead, focus on developing a robust learning strategy that meets the needs of your audience and aligns with the objectives of the business. In essence, view AI as an enabler for your vision and strategy, rather than the focal point of them.
- AI is only as good as the data you have: Just as a car runs well on clean fuel, AI’s performance relies on accurate data. Many professionals in corporate L&D have experienced the bloat of dated, inconsistent and inaccurate data in a learning management system (LMS). We must invest time and effort to ensure our data is accurate and clean, so that AI tools work efficiently. This includes, but is not limited to: cleaning existing data, adhering to consistent processes for collecting data, and ensuring that our learning content is well-structured and easily accessible for AI analysis.
- Get ahead of it: Generative AI systems that enable content creation, like ChatGPT, Bard and Perplexity AI, offer immense potential and are rapidly evolving. Tools such as these will be critical in creating highly personalized content. Get your teams familiar with this technology now so that they can evolve alongside it, maximizing its benefits and staying ahead of the curve.
One cannot underestimate the significance of quality data in ensuring AI’s success, whether it’s applied to L&D or any other area in which AI might be deployed. If we expect AI to help us make better decisions regarding our learning solutions, that data must be reliable.
There’s a lot of excitement about AI’s potential to transform the way people learn and grow. Now is the time to: Establish a solid vision and strategy as a map for your destination, produce clean data so the car has clean fuel, and don’t wait to make sure your people know how to drive the car.
