Introduction

In the era of AI-driven transformation, Learning and Development (L&D) departments face a strategic crossroads. Should they strive to become the organisation’s leading authority on learning technology, which could potentially overlap with IT’s role? Should they rely on SaaS solutions provided and managed by IT whilst focusing instead on developing content and human-centred strategies? Or should they broaden their scope to champion holistic human capital development, with AI as one tool among many? This question of ‘Who do we want to be?’ is crucial in defining L&D’s unique selling point (USP) as AI becomes an omnipresent factor in corporate innovation.

The AI and L&D Convergence

As L&D increasingly deploys AI for personalised learning paths, analytics and chatbots, the department’s operational needs begin to overlap with IT’s domain, including managing software licences, handling data security and integrating systems. Many IT teams also evaluate AI-based SaaS platforms for cost, compliance and enterprise architecture alignment (Gartner, 2021). Moreover, if L&D attempts to build or manage complex AI infrastructure alone, they may lack the deep technical skills or vendor relationships that IT possesses, whilst simultaneously sparking turf wars and political rivalry.

Three Possible Pathways

1. Technology Champion: L&D invests in developing its in-house technology expertise, hires data scientists, software developers, or AI/ML specialists, and leads the selection, configuration and even partial development of AI solutions. Whilst this might be a tempting path to take, L&D does not historically have significant data or technology skills and has long struggled to demonstrate value through data. Although gathering technical skills might be tempting, L&D departments should consider the reason for doing this, as competing with IT, technology and data teams at operational and board level is unlikely to be a successful strategy for L&D.
2. SaaS User: L&D takes a more traditional route, adopting off-the-shelf AI learning platforms provided through IT(or sanctioned through IT policy), focusing on: content reformatting, creation, and curation; learner engagement and analytics interpretation rather than building the technology backbone for learning. This is the current strategy adopted by many L&D departments and represents an iterative change rather than a disruptive or transformational change. Although this seems like a logical way forward, L&D should consider the real value of content curation; the need for significant headcount (when Generative AI has significant and democratisable content creation capability); platform or supplier investment (if L&D are capable of content generation with minimal headcount, then why appoint content generation suppliers?); and competition from marketing and communication functions.
3. Human Capital Architect: L&D positions itself as the guardian of people development at a strategic level, embedding AI and other tools, but primarily shaping the organisation’s approach to skill-building, culture and leadership. This approach plays into the people aspects of L&D and focuses on coaching, mentoring and facilitated tuition, which have long been strengths for L&D departments that lack technical or data skills. However, this route also lacks transformation, which many business leaders will expect in the age of generative AI.

Data Leadership

Underpinning all three avenues above are data needs. To configure agentic workflows and automations, virtual assistants and agents, and even SaaS tools for learner-facing use cases requires L&D to have a good grasp of organisational data to be used for agent training, content generation, and information retrieval.

A common talking point about AI in Learning at the moment is the use of AI for personalisation. Of course, we could argue that using a foundational model application (e.g. ChatGPT, Copilot, Gemini, Claude) has the possibility to personalise based on your previous prompts and responses, but this is a limited use case, until we consider real-time multimodal content generation. Another way L&D is approaching personalisation is through role-specific or geography specific artefacts (e.g. an avatar video in Japanese for the Japan market), which is also a limited use case. With a historic hesitation to collect personal data beyond what comes out of an HR Information System or an LMS, it is almost impossible to personalise content in a content at rest paradigm. Data Leadership in the AI revolution, across the three potential avenues for L&D evolution, requires a careful and deliberate curation of data, primary data from which to retrieve and generate user-facing information, and artefacts of automation (guiding prompt structures and supporting documentation for agentic workflows).

Factors to Consider

• Organisational Culture: Is the company open to L&D playing a core technical role? In technology-centric organisations, L&D might naturally evolve into a technology champion; in others, that might cause friction or duplicate IT’s domain (CIPD, 2022).
• Available Skill Sets: Does the current L&D team include or have access to staff well-versed in software development and coding, data science, AI ethics, and cloud architecture? If not, is the budget there to hire or train them?
• Strategic vs Tactical Focus: Some boards and C-suite leaders view L&D’s greatest value in shaping human capital strategy rather than in writing code or managing licences. Conversely, if the organisation sees AI as critical to gaining a competitive edge in learning, they may want L&D to be the innovation driver.
• Risk Management: Owning advanced AI systems brings complexities around data governance, scaling and updates. Partnering with IT or adopting SaaS solutions may lower these risks.
• Cost profile: licencing software from the market has an inherent cost smoothing effect, as development and maintenance cost are split across the client base that uses the product, rather than with an in-house development where the costs need to be borne by a single organisation

Implications for L&D’s USP

1. Technology Champion

• Advantages: L&D can tailor AI systems precisely to organisational needs, leading to unique solutions that yield deeper insights.
• Risks: High costs in building and retaining specialised technical talent, potential overlap with IT that leads to turf conflicts or inefficiencies.

2. SaaS User

• Advantages: Allows L&D to focus on content, learner engagement and interpretation of AI-driven insights without heavy tech overhead. Quicker deployment and updates from vendors.
• Risks: Less customisation and possibly less control over data. May rely heavily on vendor roadmaps and face subscription lock-in. Limited value in boosting competitive advantage if there is no barrier to entry for AI use.

3. Human Capital Architect

• Advantages: Positions L&D as a strategic player that integrates AI into broader workforce development. Strong alignment with organisational goals and culture, and a strong voice in the operating model of the future.
• Risks: May need to rely heavily on IT or external vendors for the technical side of AI. Risk of being disconnected from day-to-day platform, AI, or ethical decisions which could dilute user experience quality.

The Path Forward

Ultimately, L&D’s choice depends on its organisational context and aspiration. Departments could adopt a hybrid model by partnering closely with IT on the technical backbone whilst still developing some internal technical capability. The critical point is to articulate and communicate a clear differentiator:

• If focusing on technology leadership, invest in roles that can implement and manage AI systems end-to-end.
• If utilising SaaS solutions, excel in curation, design and stakeholder engagement to deliver the best possible learning experience.
• If taking a broad human capital perspective, build robust consultancy skills within L&D so it can advise business units on leveraging AI for talent development, succession planning and cultural transformation.

Without a clear route to a differentiator, L&D risks becoming spread too thinly across competing priorities, trying to dabble in multiple competing disciplines, and competing with multiple other disciplines and departments without the necessary skillset to do justice to the work needed. It is important to note that while upskilling is tempting, the gap in skills might be too great for existing L&D teams, and external support might be needed.

Conclusion

AI’s ubiquity in corporate learning forces L&D to examine its identity: is it a technology function, an integrator of vendor solutions or a strategic architect for human capital? Each path carries implications for budgets, skill requirements and collaboration with IT. By clarifying its differentiator in the AI era, L&D can maximise its impact, avoid confusion over responsibilities and solidify its role as a driver of workforce performance and culture.

References

• CIPD (2022). The Evolving Role of L&D: Strategy and Technology.
• Gartner (2021). Future of Work Trends: AI and Learning Platforms.