6 Ways to Leverage AI for Hyper-Personalized Corporate Learning
We celebrate the diversity of opinions and ideas in the workplace because we know that divergent thinking is necessary to create innovation. Yet, when it comes to developing our employees, we apply a one-size-fits-all approach to corporate learning and development (L&D) because it seems to be most cost-efficient. We move people through the same workshops, lectures, and virtual classroom seminars, which are nothing more than learning factories where everyone is expected to learn at the same pace and develop the exact same skills. Does this one-size-fits-all approach create innovation and new ideas? Of course not. Josh Bersin estimates that much of the $240B spent on corporate L&D goes to waste every year because most L&D programs are not tailored to meet the individual needs of employees.
Companies often select their best people across different departments and regions and put them all into a company-wide HiPo program; similarly, they take newly promoted managers who come from different backgrounds and put them all in the same company-wide ‘New Manager’ program. These standardized company-wide programs are ineffective because they aren’t tailored to meet the individual needs of your top employees.
A key reason cited for the lack of personalization is cost. It’s rational to assume that hyper-personalized solutions are simply inaccessible. But leveraging emerging research and technology allows us to offer individualized development at scale to employees without sacrificing quality.
1. Data-informed personalization
BetterUp has developed highly personalized L&D programs for top companies around the world for many years, and our data science and AI teams consistently find that individual needs vary. All HiPos are not the same. For example, women leaders are 25% more likely to focus on work-life balance than male leaders, newly promoted managers are 35% more likely to struggle with ‘time management’ compared to experienced managers, and leaders overseeing large teams are 70% more likely to focus on ‘strategic planning skills’ versus HiPo’s that have a smaller scope.
In order to personalize our approach to each individual, we built algorithms to analyze incoming data and behavioral patterns and identify the needs of an individual employee at a particular moment. For example, if a manager just went through a significant change like an M&A or restructuring, then our data suggests they are 30% more likely to focus on ‘Stress Management’. We need to tailor the learning experience to meet the needs of that individual.
2. Targeted recommendations
AI-driven personalization can be created at the micro-learning interaction level. We create personalized learning programs that combine multiple modalities - 1:1 coaching with an expert executive coach, specialized coaching in a targeted area like sales presentation or persuasive communications, and micro-learning content to hone specific skills (e.g., ‘delivering constructive feedback’). Since each individual is different in their unique way, they prefer different modalities. Some of us prefer podcasts, while others prefer written text or videos. Our studios team even creates interactive micro-learning content like games and interactive exercises that trigger accelerated learning. The BetterUp recommendation engine can suggest the right modality to the right user at the right moment.
3. Coach matching
Our algorithms match an individual to the coach who will work best for them based on their individual learning preferences and goals. We have thousands of coaches all over the world that bring varied experiences, backgrounds and expertise in different coaching methods. We take about 150 factors into consideration to make the right match, and we constantly monitor the performance of our AI algorithms to track their efficacy. As we collect more data over time, our algorithms get smarter. Two years ago our coach matching algorithm achieved over 80% accuracy, now it consistently performs at over 97% accuracy.
4. Organizational insights
People analytics teams in large companies sometimes attempt to do in-house data analysis to identify patterns within their own company. At BetterUp, we leverage data across our broader customer base to find deeper insights, and help companies benchmark themselves with others in their industry. We adhere to a strict privacy compliant system that combines data in aggregate across a larger population to preserve privacy and confidentiality. In partnership with independent industry organizations, we develop and implement strict ethics rules that span processes, 1:1 coaching interactions, technology infrastructure, and our partnerships. All individual-level data is encrypted and is never shared with any partner, and our systems are GDPR compliant.
5. Democratizing quality
Another benefit of large scale, algorithm-driven personalization is that it enables small companies to also offer high-quality, tailored programs to their employees. We have seen that SMB employees often have different needs compared to employees at larger corporations. For example, employees at SMBs are 50% less likely to work on ‘stress management’ but twice as likely to work on ‘goal setting and planning’ when compared to employees at larger corporations. In the past, these small and medium-size businesses were left out because they didn’t have the resources and scale of large enterprises to offer personalized L&D programs. With BetterUp, they can offer programs that are tailored exactly to the needs of their employees.
6. Global fluency
The extensive global scale dataset at BetterUp has allowed us to meet the needs of employees across different regions. Our data shows that US employees are 23% more likely to work on ‘Managing career transitions’, whereas their counterparts in other regions are 21% more likely to work on ‘Coaching and developing others’ and 10% more likely to work on ‘Influence and assertiveness’. At BetterUp, we offer personalized programs in major international markets around the world from France, Germany, and the UK in Europe, to Japan, China, India and Australia, in Asia and APAC. In total, our coaches speak 30 languages and coach in 60+ countries around the world.
In closing, I would say that after looking through these millions of data points from around the world, it’s clear to me that the one-size-fits-all approach to learning definitely does not work. We need to meet our employees where they are, and a highly scalable, AI-driven solution that combines expert human coaching with interactive micro-learning is the best way to help them develop and flourish in unprecedented ways!