Executive summary. Artificial intelligence is transforming organisations foremost through how people learn, judge, and collaborate with machines. Drawing on insights from the 2025 EFMD CLO Roundtable and the cases of UBS and Siemens, the paper argues that AI elevates—not replaces—human judgment and creativity. Predictive AI sharpens decisions, while generative AI expands creative capacity, increasing the importance of context, ethics, and critical thinking. Corporate Learning therefore moves to the organisational core, evolving from content provision to strategic orchestration of human–machine collaboration and continuous learning and adaptation.

Key takeaways

• Learning becomes strategic infrastructure: Corporate learning shifts from a support function to a central engine, shaping AI-enabled transformation, innovation, and growth.
• Human judgment gets amplified: As AI improves prediction and generation, critical thinking, contextual understanding, and ethical awareness become decisive capabilities.
• L&D must be reinvented: Learning functions evolve into ecosystem orchestrators and translators between technological, organisational, and human domains.

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From Hype to Responsible Use

Few technologies have captured the imagination as quickly as Artificial Intelligence. In less than three years, AI has evolved from a niche tool to a strategic imperative — cutting across industries, functions, and geographies. Yet as the pressure to adopt intensifies, the gap between technological potential and organisational readiness continues to be wide.

Against this backdrop, senior learning and development executives from over 20 global companies from four continents gathered in late September at London Business School for the 2025 EFMD CLO Roundtable. Under the title “Embracing AI to Unleash Growth and Innovation: An Opportunity for Corporate Learning?” they explored a pivotal question:

How can corporate learning not merely respond to AI-driven change — but become a driving force behind it?

At the heart of the discussion was a shared conviction: innovation through AI is less a technological challenge than an organisational one. It demands structures, skills, and mindsets that enable people to use AI responsibly and productively.

The event opened with a masterclass by Professor Keyvan Vakili (London Business School) on Identifying and Exploiting AI Opportunities. As EFMD’s Director of Corporate Services, I had the pleasure of facilitating a session to explore the role of corporate learning in accelerating AI transformation, showcasing two corporate use cases — UBS and Siemens, presented by Katarina Rosen (UBS) and Jenny Lin (Siemens), respectively.

This article synthesises the key insights from this Roundtable. It combines academic perspective and corporate practice into a single narrative: AI does not end the need for learning – it redefines it.

Identifying and Exploiting AI Opportunities

Professor Vakili argued that productive engagement with AI requires a deep understanding of its internal logic. His central thesis: AI is not one technology, but a spectrum of cognitive systems built on two fundamental capabilities: prediction and generation.

Predictive AI enhances decision-making by improving the prediction part of it, e.g., detecting patterns, estimating probabilities, and recommending solutions in routine contexts, from credit risk models to medical diagnostics or dynamic pricing. Frequent, low-visibility decisions are prime targets for Predictive AI, and these are the hidden levers of efficiency. As prediction becomes more commoditised by using AI, judgment becomes more important.

Generative AI, by contrast, produces new content — text, code, imagery, and ideas — reshaping the boundaries of human creativity. Creative and communication-intensive tasks offer fertile ground for Generative AI as a main source of productivity and innovation gains.

Yet both forms of AI create a new tension — the precision–persuasion trade-off. Predictive AI provides precision but lacks persuasion; Generative AI excels at persuasion but might lack accuracy. The implication is profound: the use of AI makes human judgment more valuable than ever.

AI allows the automation of tasks that require less judgment; humans can augment themselves with AI for tasks that require more judgment. As research shows, lower-skilled employees gain most from working with GenAI and therefore they are more likely to delegate tasks to it. However, they are also the most likely to overlook errors due to their lower level of expertise.

This proposes a conundrum for strategic workforce planning and talent pipelining: the tasks that junior talent usually get assigned are the ones that increasingly get automated, but given their limited context knowledge, juniors lack the judgment to properly augment AI. How and where can they then develop expertise? We can already observe a drop in job openings for career starters, whether this is only a temporary phenomenon remains to be seen. However, the development of critical thinking and judgment that normally comes with experience must become a main goal of education and traineeships. Incidentally, in his Critique of Pure Reason, Kant defines stupidity not as ignorance but as the absence of judgment.

For corporate learning, this broadens the mission. Learning functions must not only equip employees with the knowledge of AI tool potential, but also to critically reflect and interpret their outputs responsibly.

The Role of Corporate Learning in AI Transformations

The deployment of AI will lead to a reconfiguration of human work. Learning, therefore, becomes the critical mechanism through which this reconfiguration takes place. Corporate learning must become an architect of human–machine collaboration and enable organisations to reinvent how they learn, decide, and create value. I introduced a framework describing four complementary arenas where corporate learning can and should accelerate AI transformation:

• Arena 1 — Learning and Development itself: AI allows significant productivity gains along the entire learning value chain, from automated needs anticipation, rapid design of learning programmes, personalised learning journeys and tutoring at scale, through to assessing impact by monitoring vast sets of data. AI will allow a better integration of learning and work. The coming human-machine collaboration may require a fresh look at leadership and competency models. The skill sets in L&D must also evolve.
• Arena 2 — The Workforce: AI and robots will automate tasks, with some jobs being more affected than others. Strategic workforce planning must identify skills (and jobs) that will erode, endure, and emerge, for L&D to offer a rich array of systematic upskilling, reskilling, and redeployment opportunities — not as one-off campaigns but as a permanent operating model. Innovations like talent marketplaces will become instrumental, as will a focus on essential human skills. This will allow staff to remain employable, provided they are willing to learn and adapt, and to regard AI as a friend, not an enemy.
• Arena 3 — The Business: Businesses and enabling functions must learn to leverage data and algorithms in their own domain, e.g., pricing, logistics, R&D, or customer engagement. In addition to providing staff with basic AI literacy, L&D must orchestrate a segmented approach to capability building up to expert level by aligning learning agendas with business priorities and performance metrics. Cultural readiness may vary across businesses, and a few might require change management support throughout AI deployment.
• Arena 4 — The Enterprise Ecosystem and Society: AI impacts also the corporation’s supply chain, customers, and entire business ecosystem. Corporate learning functions may be well advised to take a broader view on enablement, beyond the enterprise. In addition, it should contribute to the discourse on ethical AI and mitigating AI risks, within the organisation and beyond, and promote the responsible use of AI.

UBS and Siemens both presented a use case on AI adoption. The two companies are global leaders in their markets and have approached AI transformation in an exemplary manner, playing fully across all four arenas. However, the following use cases zoom in on UBS’ engagement in Arena 3 and Siemens’ coverage of Arena 1.

Case Study: UBS – AI People Enablement for an AI-powered Company

UBS has approached AI not as a technology programme but as an enterprise-wide capability-building effort. The firm’s AI People Enablement initiative, jointly led by HR and Technology, illustrates how corporate learning can effectively anchor large-scale transformation.

The initiative supports the UBS ambition to become an AI-enabled institution. It focuses on both the technical and human dimensions of change—building understanding, skills, and mindset for responsible AI use across all divisions and functions.

At its core are seven workstreams, designed to reach across the entire workforce:

• AI People Enablement – 7 workstreams addressing the entire workforce
• AI awareness and foundational knowledge – establishing a baseline understanding for all employees of AI concepts, risks, and opportunities.
• Leadership development – helping leaders and line managers champion AI adoption.
• Organisational transformation – helping UBS’ senior leaders design the right structures, processes, client engagement and talent strategies to enable the successful adoption of AI.
• GenAI tool proficiency – training employees to use tools effectively, including M365 Copilot and the UBS in-house AI assistant, Red.
• Expert learning – deep technical development for data and AI specialists.
• Reskilling – bridging emerging skill gaps.
• Embedding AI – into HR employee lifecycle processes such as hiring, employee listening and performance management.

A central pillar of leadership engagement is the Oxford Saïd AI Senior Leadership Journey, a multi-cohort programme for over 250 of the firm’s senior leaders. Delivered through immersive on-campus sessions, case studies, and simulations, it enables leaders to create an AI Blueprint, a strategic roadmap identifying where AI can deliver measurable business impact while maintaining ethical and regulatory integrity. In addition, UBS has built an AI Expert Network with industry leaders from select AI companies and academic institutions, providing strategic advisory support to the firm’s senior leaders.

Complementing this top-down engagement, UBS has an AI Champions programme, mobilising more than 1,000 employees across 20 countries so far. These champions function as internal ambassadors, sharing use cases, facilitating peer learning, and creating momentum for responsible adoption. And colleagues are benefiting from comprehensive learning offerings catered to all levels of expertise and experience, with over 170,000 trainings completed to date.

The impact of the enablement programme gets monitored at three levels: (1) the quantity and quality of AI-related learning, (2) the capabilities built regarding AI, and (3) the number of AI users and prompts, as well as surveyed AI sentiment. The internal enablement programme is accompanied by a global employer branding campaign, positioning UBS as an AI-enabled employer of choice, aiming at attracting AI-savvy talent to the bank.

The key insight from this case is clear: UBS is on an ambitious and comprehensive AI transformation journey, with corporate learning being the central mechanism to accelerate responsible and productive AI adoption.

Case Study: Siemens – Harnessing AI for Learning and Growth

For more than 178 years, the guiding purpose of Siemens—We create technology to transform the everyday, for everyone—has shaped the company’s evolution. Today, this transformation extends to how its people learn, collaborate, and grow within a platform-based learning ecosystem.

At the heart of Siemens are its people. Supporting their ongoing development, My Learning World (MLW) serves as a global learning experience platform for over 300,000 active users, offering access to over 180,000 learning resources. MLW integrates learning paths, channels, and knowledge boards, curated by Siemens’ learning experts, into a coherent architecture that is infused with AI.

AI is not a peripheral feature; it is embedded throughout the learner and curator experience. AI Assistant Zoiee provides conversational search, guidance, and knowledge retrieval for 300,000 learners, and authoring tools, including video creation via the SynthAIze Studio, for the hundreds of content curators at Siemens.

These tools create an environment where learning becomes both adaptive and agentic — able to interact with users in natural language, anticipate needs, and suggest next learning steps in the flow of work.

In addition, Siemens is currently introducing its Growth Talks Bot, an AI companion that supports people and leaders in preparing and conducting performance and development conversations.

Siemens’ long-term vision, described as the Smart P&O Companion, goes further: an agentic AI framework that connects the company’s people and organisational (P&O) ecosystem, linking HR systems, learning platforms, and vendor tools into one intelligent infrastructure.

The lesson from Siemens is about reframing learning as a platform for organisational intelligence. AI enables Siemens to scale personalisation and autonomy while maintaining alignment with corporate purpose. By empowering its people to invest ample time in learning, Siemens ensures they gain not just the skills for today, but the skills for life. In doing so, the company turns its learning and growth ecosystem into a strategic asset, one capable of continuously sensing, adapting, and evolving, alongside the business.

Synthesis: What We Can Learn from the UBS and Siemens cases

Despite operating in very different industries, the UBS and Siemens cases converge on three critical principles.

First, learning is strategic in nature. At UBS, L&D operates at the intersection of HR, technology, and business, shaping how the firm becomes AI-powered. At Siemens, learning platforms are embedded into the company’s digital backbone. In both cases, learning is not reactive but constitutive.

Second, leadership and culture are the multipliers of adoption. UBS invests heavily in leadership journeys that translate AI strategy into behavioural change. Siemens fosters community-based learning to sustain experimentation.

Third, new roles are emerging for Learning and Development. L&D professionals must evolve from content creators to content curators, and to ecosystem orchestrators, integrating data, platforms, and partnerships. They also function as translators between technological and human domains, ensuring that ethical, legal, and cultural dimensions remain visible.

Conclusion: From Technology to Transformation

Artificial Intelligence’s most profound impact will be in how people think, decide, and create differently. AI does not diminish the human role, it redefines it. Predictive systems enhance decisions but elevate the value of judgment. Generative systems accelerate creativity but heighten the need for context knowledge.

Corporate Learning moves to the very centre of the organisation. It becomes an engine for adaptation. As seen at UBS and Siemens, when learning functions operate as strategic orchestrators, they not only enable technology adoption; they also shape the very trajectory of innovation and growth.

Eventually, this means that Corporate Learning must reinvent itself. It is no longer about providing learning but about enabling learning. The age of artificial intelligence is, at heart, an age of amplified learning. Organisations that cultivate curiosity, critical inquiry, and ethical awareness will lead it.