The Evolving Role Of The Chief Learning Officer In The Age of AI

Forbes

3 days ago

Beena Ammanath - Global Deloitte AI Institute Leader, Founder of Humans For AI and Author of "Trustworthy AI" and "Zero Latency Leadership."
Most enterprises are somewhere on a transformative journey with AI. Some are still mastering the basics and experimenting with ideas; others are already running complex applications at scale. The common denominator for every organization on the road to AI-fueled operations is the need for skilled talent. This has created a nearly unquenchable demand for AI-ready employees.
The imperative is to look internally and determine how to help everyone—from the boardroom to the C-suite to the lines of business—gain the skills and knowledge they need to use and drive business value with AI.
This is a known challenge, but what is less clear is who in the organization is responsible for workforce upskilling. In many organizations, the chief learning officer (CLO) is the epicenter of skills and knowledge development, and the CLO's role is beginning to include the challenge of equipping every employee with the AI skills they need.
The task is complicated by the fact that innovation is moving quickly, and what is new and differentiating today may soon be table-stakes capabilities or left behind entirely. A 2024 Deloitte survey of workplace skills found that 70% of responding workers said "they worked at a company that pushed employees to learn a new technology-based skillset, only for that technology to fall out of use." If organizations are perpetually chasing skills development for the latest groundbreaking innovation, they may miss out on a more transformational, more strategic opportunity to prepare their workforces for the future, whatever technologies it holds.
A People-Focused Perspective
The CLO is positioned to answer today's call for AI workforce development in part because CLOs do not look at the workforce purely through a technology lens. This is a human challenge. For CLOs, now is a moment to lead, and tactically, there are several avenues to explore.
The basics of learning apply to AI, and for most employees, the imperative is to build AI literacy and a working familiarity with the applications used in the organization. Workshops, virtual demonstrations, speakers, self-directed learning and third-party training may be good places to start. A recent Deloitte Generative AI survey found 67% of responding organizations have invested in internal tools to help employees build GenAI familiarity, and 59% have published training and learning resources for talent development. The approaches and types of materials for learning other skills are likely to be effective with AI.
State-of-the-art LLMs can be wonderful teachers. The core of their value is their capacity to consume large volumes of information and present it in consumable, natural language outputs. This is ideal for helping employees master the basics of the AI lexicon, the history of innovation, and AI functionality and model types. Using an LLM, workers can revisit a topic multiple times and ask for explanations and examples until they feel confident in their new knowledge. Of course, LLMs are susceptible to inaccurate or entirely false outputs, but when it comes to basic AI literacy, most LLMs are likely good enough to establish foundational knowledge and familiarity.
While some AI risks are evident (e.g., inaccurate outputs), many are not, nor are the ethical implications of how AI is developed, deployed and managed. Workers need support in learning how to use and manage AI responsibly, and there is reason to think focused learning and training are effective. A Deloitte report investigated technology ethics training, which revealed that 70% of respondents who received ethics training changed their behavior when working with technology. Given the opportunity, most employees are likely to put technology ethics knowledge to good use, which is important not just for ethical application but also for managing AI in light of regulations, enterprise culture and industry standards.
GenAI-enabled applications and, more recently, AI agents, are expected to increasingly liberate workers from mundane tasks and even entire workflows. With more time and capacity, these employees can be dedicated to higher-level, more strategic work. In this, they will need the human skills of teamwork, communication, problem-solving and critical thinking. However, human skills development often does not receive as much attention as technical skills. Providing opportunities for the workforce to improve its people skills is important, regardless of how powerful or capable AI becomes.
Final Thought
The CLO may not have total insight into the workforce skills needed across business units. The AI knowledge needed for accounting and payroll may be different than that required for customer engagement, enterprise strategy and other functional domains. The leaders of these business units likely have an incisive view into the kinds of skills their workers need to develop. Coordination and collaboration among executives and other leaders can help reveal where the organization's AI initiatives can be enhanced with a focus not on technology but on people.
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