By Amanda Julian, PhD
The rise of Artificial Intelligence (AI) and Machine Learning (ML) signifies more than just a technological transformation; it's reshaping the landscape of various industries on a global scale. The realm of leadership development happens to be a prime candidate for this wave of innovation. While many sectors have rapidly embraced the latest technological advancements, leadership training programs seem to lag behind, with many providers in this space still depending on obsolete methods devoid of real-time adaptability.
However, the silver lining here is the expansive potential that AI and ML bring to the table for the design and delivery of leadership training and development. Their capabilities open doors to real-time feedback and personalized training, among other benefits. The purpose of this white paper is to delve deeper into the opportunities presented by the integration of AI and ML in leadership training, not just to enhance but to revolutionize the way leaders are nurtured and cultivated for the challenges of tomorrow.
Real-Time Feedback
Diving into the specifics, one of the most immediate impacts of AI on leadership development is its capability to facilitate real-time feedback. Feedback plays an indispensable role in the learning process by pinpointing an individual’s strengths and areas requiring improvement, subsequently laying out actionable steps for development(1). Within a training context, feedback equips learners with the clarity to discern any gaps between their actual and intended performance, facilitating corrective action if needed(2). It becomes paramount to ensuring the effective transfer of skills to real-world applications.
A prevalent challenge in traditional leadership training is creating opportunities for timely and relevant feedback. Traditional training interventions will often employ role-playing scenarios to allow participants the opportunity to practice and garner feedback on new skills or behaviours, although this necessitates the involvement of either a coach or a fellow participant. Moreover, once training is completed and leaders return to their roles to put what they’ve learned into action, getting feedback on these new practices can become a delicate subject. Direct reports might hesitate to provide candid feedback without an assurance of anonymity. Also, research suggests that the potency of feedback diminishes when it's not immediate(3), making it evident that infrequent assessments, such as annual 360-degree leadership assessments often used in organizations to get leaders feedback, don't fully harness its potential.
This is one area where AI integration offers transformative possibilities; that is, offering real-time feedback for organizational leaders. There's an immense opportunity to engage in risk-free practice sessions throughout a training intervention, through AI-powered role-play simulations(4). This can allow individuals to gather feedback and refine their approaches before transitioning these behaviours to actual professional settings. Further, AI has the potential to be leveraged to process text-based communications to direct reports, or even analyze transcripts from video interactions from team meetings or individual check-ins, subsequently delivering instantaneous feedback in accordance with best practices.
The educational sector offers a testament to this potential. Notably, research spearheaded by Stanford researcher Dr. Dorottya Demszky, underscores the success of an AI-driven feedback tool that leverages natural language processing(5). In this case, the tool evaluates classroom transcripts, furnishing educators with feedback anchored in pedagogical best practices. The results? Observable shifts in teaching behaviours and enhanced student learning and satisfaction. For HR/L&D professionals, this signals the dawn of an era where AI not only complements but elevates the feedback process, driving more effective leadership training outcomes.
Personalized Learning Experiences
People are not all the same, and so it follows that their learning experiences should not be either. The widely accepted notion that learning experiences must be tailored specifically to match individuals’ distinct "learning styles," categorized as visual, auditory, or kinesthetic (among others), has been largely debunked(6). However, this doesn't negate the fact that there is diversity among learners(7). For instance, each person’s background knowledge, inherent level of interest, and perceived relevance of the learning material are pivotal in shaping effective learning experiences(8).
Additionally, in the context of leadership training, the pertinent knowledge, skills, or behaviours to be trained may vary based on factors like an individual's goals, as well as their role, organization, or industry. Training providers typically navigate this by performing a training needs analysis, assessing the specific training requirements of a group, and subsequently tailoring a program to align as best as possible with these identified needs(9).
Excitingly, AI and ML present immense potential in this domain. These technologies have the ability to not only adapt to each learner’s unique needs, interests, preferences, and proficiency levels, but can also analyze large amounts of organizational data to pinpoint the specific skills or competencies that need to be enhanced or developed in a given training initiative.
It therefore becomes possible to create a dynamic, responsive training ecosystem, by leveraging data to personalize content, offer recommendations, and to adapt to a learner’s proficiency and progress. This data-driven approach ensures that training programs are precisely aligned with individual development needs and organizational goals, ushering in a new era of hyper-personalized learning experiences.
Nudging and Reminders
The Kirkpatrick model, a renowned framework for evaluating the effectiveness of training programs, identifies four key criteria that should be applied to assess the efficacy of any training initiative: participant reactions, learning, behaviour change, and tangible results (e.g., return on investment or ROI)(10). A prevailing assumption exists that learning acquired in a controlled environment seamlessly translates to practical application in the workplace, leading to behaviour change. However, reality often tells a different story, with some estimates suggesting a mere 10% transfer rate of training to actionable skills and behaviours(11).
There are, however, evidence-based methods that can be leveraged to better facilitate the translation of learning into practice. Dr. Katy Milkman, a distinguished professor at the Wharton School of the University of Pennsylvania, underscores major barriers to behaviour change in her book “How to Change."(12) She emphasizes forgetfulness and inertia as two significant barriers that impede change.
According to the Ebbinghaus forgetting curve, new information is rapidly forgotten; nearly half of new knowledge can be lost within 20 minutes of acquisition(13). Knowing this, its very feasible that busy leaders, juggling myriad responsibilities, can easily overlook the integration of new learnings into their day-to-day practices. Research affirms that reminders can be quite effective in combating forgetfulness, but their timing is critical for maximum impact(14). That is, they need to be provided immediately before they are meant to be acted upon, in order to be effective.
Inertia, or “laziness” as Milkman refers to it, presents an additional challenge. Nudge Theory(15), anchored in behavioural economics, suggests that subtle modifications to the environment or context of decision-making can “nudge” individuals towards desired behaviours. One effective strategy involves the implementation of proactive “defaults,” such as transforming opt-in programs into opt-out ones. Employing nudges can be pivotal in propelling behavioural change and facilitating the integration of new skills and competencies in the workplace, such as automatically scheduling one-on-ones with direct reports to offer feedback and support(16).
AI and ML are adept at analyzing extensive datasets to pinpoint optimal timings, mediums, and contexts for deploying reminders and nudges. These technologies ensure interventions are meticulously tailored to the individual learner's needs and preferences. By evaluating real-time data, AI and ML continuously refine nudges, enhancing their personalization and contextual relevance, and bolstering their influence on leadership development and behavioural adaptation.
AI-based nudging to impact memory and decision-making in organizations isn’t a novel concept. Companies like Humu are testament to this, employing AI to extract insights from employee surveys and nudge employees towards behaviours that instigate positive change(17). Extending these AI-enhanced reminders and nudges to leadership training and development augments the potential impact, promising heightened skill acquisition and behavioural transformation.
AI-Driven Coaching
Coaching has steadily emerged as a prominent approach to leadership development, increasingly endorsed and adopted particularly among senior leaders in organizations. In response to the demand for more personalized and accessible leadership development offerings, several companies, including those such as BetterUp and Torch, have created applications that adhere to an on-demand coaching model(18).
HR experts are now speculating that the coaching model may be amplified by the incorporation of intelligent chatbots(19). These AI-driven tools are designed to provide real-time, evidence-based suggestions, answers, and tips tailored to the individual’s unique needs and challenges. Unlike human coaches, who are constrained by the limits of their cognitive capacity, AI-powered chatbots have the advantage of accessing and processing a vast expanse of information instantaneously. While human coaches offer valuable insights drawn from personal experiences and knowledge, they may need to search extensively for information, are not immune to cognitive biases, and are not very scalable (hence the concentration of use at the uppermost levels of an organization). In higher education, early adoption of AI-chatbots for administrative and student services is yielding success, showcasing their potential to enhance efficiency and the student experience (20).
Looking Toward an AI-Enhanced Future
AI and ML are poised to significantly transform the field of leadership development, introducing a new era characterized by enhanced feedback mechanisms, personalized learning, and chatbot coaching. These technological advancements promise innovation but also usher in complex ethical considerations, the depth of which we will not fully explore here. As organizations and service providers embrace AI and ML, a conscientious approach to mitigating risks is paramount.
The safeguarding of employee data is a fundamental concern; the collection and analysis of extensive data necessitates stringent measures to ensure privacy and security. Transparency in the deployment and operation of AI and ML is equally crucial, fostering an environment of trust and clarity for all stakeholders involved.
Another vital aspect to consider is the potential for bias within AI and ML algorithms. The risk of perpetuating or even amplifying existing biases is real, leading to skewed outcomes that could particularly impact leadership development. In a realm where diversity and inclusion are pivotal, avoiding algorithmic biases that favor a homogenous leader model is essential.
As we anticipate the broad integration of AI and ML into leadership development, these ethical and operational considerations are not just ancillary concerns but core to the responsible adoption of technology. Balancing innovation with ethical responsibility will be instrumental in unleashing the full potential of AI and ML, steering leadership development towards an era marked by inclusivity, innovation, and integrity.
References
1. Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119, 254–284. http://dx.doi.org/10.1037/0033-2909.119.2.254
2. Lacerenza, C. N., Reyes, D. L., Marlow, S. L., Joseph, D. L., & Salas, E. (2017). Leadership training design, delivery, and implementation: A meta-analysis. Journal of Applied Psychology, 102(12), 1686-1718.
3. Rao, M. S. (2014). It really does pay to give employees a pat on the back: The dos and don’ts of effective feedback. Human Resource Management International Digest, 22(4), 40–43.
4. Field, E., Hancock, B., Imose, R., & Yee. (2023, July 19). Middle managers hold the key to unlock generative AI. McKinsey & Company. https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-organization-blog/middle-managers-hold-the-key-to-unlock-generative-ai
5. Spector, C. (2023, May 8). Feedback from AI-drirven tool improves teaching, Standford-led research finds. Stanford News. https://news.stanford.edu/2023/05/08/ai-feedback-tool-improves-teaching-practices/
6. Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105-119.
7. American Psychological Association. (2019, May 30). Belief in learning styles myth may be detrimental [Press release]. https://www.apa.org/news/press/releases/2019/05/learning-styles-myth
8. Riener, C., & Willingham, D. (2010). The myth of learning styles. Change: The Magazine of Higher Learning, 42(5), 32-35.
9. Lacerenza, C.N., Reyes, D.L., Marlow, S.L., Joseph, D.L., & Salas, E. (2017). Leadership training design, delivery, and implementation: A meta-analysis. Journal of Applied Psychology, 102(12), 1686-1718.
10. Kirkpatrick, J. D., & Kirkpatrick, W. K. (2016). Kirkpatrick's four levels of training evaluation. Association for Talent Development.
11. Brown, K. G., & Sitzmann, T. (2011). Training and employee development for improved performance. In S. Zedeck (Ed.), APA handbook of industrial and organizational psychology, Vol. 2. Selecting and developing members for the organization (pp. 469–503). American Psychological Association. https://doi.org/10.1037/12170-16
12. Milkman, K. (2021). How to Change: The Science of Getting from where You are to where You Want to be. Penguin.
13. Clearwater, L. (n.d.). Understanding the science behind learning retention. Indegene. https://www.indegene.com/what-we-think/reports/understanding-science-behind-learning-retention
14. Milkman, “How to Change.”
15. Thaler, Richard H., and Cass R. Sunstein. Nudge: The final edition. Yale University Press, 2021.
16. Milkman, “How to Change.”
17. Wakabayashi, D. (2018, Dec 31). Firm led by Google veterans uses A.I. to "nudge" workers toward happiness. The New York Times. https://www.nytimes.com/2018/12/31/technology/human-resources-artificial-intelligence-humu.html
18. Bersin, J. (2023, March 22), The role of generative AI and large language models in HR. Josh Bersin. https://joshbersin.com/2023/03/the-role-of-generative-ai-and-large-language-models-in-hr/
19. Bersin, “The role of generative AI…”
20. Rouhiainen, L. (2019, October 14). How AI and data could personalize higher education. Harvard Business Review. https://hbr.org/2019/10/how-ai-and-data-could-personalize-higher-education