Artificial Intelligence in Destination Organisations: A Leadership Question Rather Than a Technological One

There has been a lot of discussion recently about artificial intelligence in destination organisations, and much of it tends to focus on the tools themselves. What is often overlooked is that the real challenge is not access to technology, but leadership alignment. Most organisations can now use AI in some form, yet the extent to which it creates meaningful change varies widely. That difference is less about technical capability and more about how leadership frames, prioritises, and governs its use.

At the moment, many organisations are still in an early stage of adoption. AI is primarily used at the individual level to support tasks such as drafting, summarising, or basic analysis. These applications are useful, but they do not fundamentally change how the organisation performs. As a result, the gap between those using AI and those not using it remains relatively small. The more significant shift happens when AI becomes embedded in organisational processes and starts to influence how work is structured, rather than simply how individual tasks are completed.

A helpful way to understand this is by distinguishing between front stage and backstage activities. Destination organisations spend a considerable amount of time on internal processes that are essential but largely invisible to external stakeholders. These include reporting, data management, coordination, and preparatory work. While necessary, they consume a significant share of organisational capacity and often limit the time available for higher-value activities such as stakeholder collaboration, strategic development, and community engagement.

This is where AI has the potential to make a real difference. Its immediate value is not necessarily in producing more visible outputs, but in reducing the operational burden of these backstage functions. When that burden is reduced, time and attention can be redirected toward work that requires human judgement, contextual understanding, and relationship-building. In this sense, the value of AI extends beyond efficiency. It allows for a redistribution of organisational capacity toward areas that genuinely matter.

At the same time, it is important to be clear about the limitations. AI can support information processing and accelerate workflows, but it does not replace human interpretation, accountability, or trust. In destination management, where credibility and stakeholder relationships are central, over-reliance on automated outputs can quickly become a problem. Communication that lacks authenticity or sensitivity to context is usually recognised as such, and it can erode trust rather than strengthen it. For that reason, AI should be understood as something that augments human capability, not something that replaces it.

There is also a timing dimension that organisations need to take seriously. AI development does not follow a linear path. In the early stages, the benefits can seem incremental, which makes it easy to treat adoption as optional or non-urgent. However, as capabilities evolve, the gap between organisations that have developed internal competence and those that have not is likely to widen. Delaying adoption therefore has strategic consequences, affecting not only current efficiency but also future adaptability.

Importantly, integrating AI is not a technical exercise. It is an organisational process. In practice, effective adoption tends to come down to a small number of clear actions. Organisations need to set a direction, define expectations for use, invest in targeted capability building, and create mechanisms for measuring and sharing what is being learned. Together, these elements help create an environment where experimentation is intentional rather than ad hoc.

Leadership plays a central role throughout this process. The introduction of AI inevitably raises questions about roles, responsibilities, and the future of work. If these questions are not addressed openly, uncertainty can turn into resistance. When leaders are clear about the purpose of AI and the boundaries of its use, adoption becomes more manageable. This includes being explicit about which aspects of work are expected to change and which remain fundamentally human.

For destination organisations, these issues are particularly relevant. The sector operates with limited resources, increasing accountability, and complex stakeholder environments. AI offers a way to ease some of the operational pressures that come with this. At the same time, it increases the need for strong leadership, particularly in terms of providing strategic clarity and maintaining organisational coherence.

In that sense, artificial intelligence is not a strategy in itself. It is a capability whose value depends on how it is integrated into the organisation. When leadership provides direction, aligns its use with purpose, and maintains a clear distinction between technological capability and human responsibility, AI can strengthen organisational effectiveness. Without that alignment, it risks adding complexity without delivering real value.

Ultimately, the adoption of AI in destination organisations is best understood as a leadership challenge. Technology is necessary, but it is not what determines the outcome. What matters is how organisations choose to integrate, govern, and make sense of these tools within their broader mission.