Organisations have now experimented with AI in some form. Writing social media captions, generating image concepts and drafting newsletter copy are all useful applications that have delivered real efficiency for teams working with limited resources.
Most organisations have now experimented with AI in some form. Writing social media captions, generating image concepts and drafting newsletter copy are all useful applications that have delivered real efficiency for teams working with limited resources.
With the technology continuing to evolve, a new generation of AI solutions has emerged. The arrival of AI agents fundamentally changes how interactions with AI tools will work in the future, raising questions about how increased automation can be leveraged to support operations.
What Makes an AI Agent Different
For most, interacting with AI has been a simple, conversational exchange. While helpful in the moment, these interactions are generally limited as the help ends when you close the session. Conversely, AI agents operate on a task-based model. Once you define the objective, the agent independently determines the necessary steps. For instance, an agent might open a file, extract data from a connected platform, verify figures in a spreadsheet and then use those findings to draft a document. This entire process is completed without requiring human direction for each individual action.
Source: Claude.ai generated
This shift is already well underway. In early 2026, several leading AI companies launched dedicated agentic solutions. While their specific methodologies may vary, the overarching trajectory remains consistent. We are witnessing a shift from using AI as a tool for answering questions to employing it as an entity to which work is delegated.
In practical terms, agentic AI can:
Break a complex task into steps and execute them across multiple files and tools.
Pull data from connected systems, identify patterns and produce formatted outputs.
Adapt to feedback and adjust its approach mid-task.
Operate across documents, spreadsheets, emails and web sources within a single workflow.
Queue and manage multiple tasks in parallel.
In the context of delivering monthly performance reporting, for example, traditional generative AI tools would require teams to gather the data themselves before getting a written summary. With an AI agent, teams can instead point it to their analytics dashboards, their CRM and content calendar to directly gather the necessary data to synthesise into a formatted document ready for review. By shifting the burden of repetitive execution to AI, the use of agents enables teams to delegate tasks and only focus on providing expert refinement. This allows DMO teams to dedicate their time to more complex and high-priority strategic initiatives.
Source: Claude.ai generated
The implications of this shift extend far beyond time-saving. When a tool can autonomously manage a complex workflow from start to finish, it alters the relationship between teams, how work is structured and how human talent is allocated.
However, it is important to understand the limitations. Agents are capable of producing confident-sounding outputs that may contain factual errors, misinterpret context or miss nuance. As a result, they can make decisions that appear reasonable on the surface yet fail to align with an organisation's brand, values or strategy. For this reason, human oversight remains essential.
Why Organisational Readiness Matters
While these capabilities are impressive on paper, an AI agent is only as effective as the digital ecosystem in which it operates. The concept of an agent that can pull data from multiple platforms to compile and format a stakeholder report sounds transformative. However, when platforms are disconnected and data is inconsistent, the agent is rendered ineffective. Ultimately, the output will simply reflect the chaotic state of the information it was given.
This operational disconnect represents a gap that most organisations have yet to bridge. While the conversation around AI often focuses on the potential of the tools, the more critical question, especially as agents move from concept to product, is whether an organisation is actually ready to enable them.
Being “AI-ready” requires foundational work that makes everything else possible. Workflows must be documented clearly enough for a third party to follow and files need to be organised with consistent naming conventions. Equally, there must be clear protocols regarding what AI is and is not permitted to touch.
Admittedly, none of this is novel. These are the same operational fundamentals that organisations have always required to function efficiently. However, agentic AI makes these gaps significantly harder to ignore. As the tools become sufficiently capable, the primary bottleneck is no longer the technology itself, but rather the state of the information it is required to process.
As these tools assume greater responsibility, the question of trust becomes paramount. Drafting a social post is a low-stakes endeavour. If the tone is off, it can be quickly corrected. However, when an agent is aggregating data from multiple platforms to compile a stakeholder report, the margin for error narrows significantly.
When an organisation's reputation is attached to the documents it shares with its partners or the public, this is the precise point where governance becomes critical. If an organisation intends to delegate work to an AI agent, it must understand the principles guiding that tool’s decisions. This includes understanding the logic behind its actions.
Notably, Anthropic has taken a significant step forward by publishing its “Constitution”, while OpenAI has released its "Model Spec". These public documents explicitly articulate how the model is trained to behave, defining its refusal criteria and its approach to sensitive decisions. They represent some of the clearest examples to date of AI providers demystifying their internal rules. This transparency allows organisations to assess the underlying logic of the tool, ensuring it aligns with their standards, rather than judging it solely on its technical capabilities.
Consequently, these governance frameworks merit as much scrutiny as any technical feature list. The manner in which a provider approaches safety and accountability serves as a critical indicator of the confidence organisations can place in their outputs, particularly when those outputs are destined for external audiences.
How to Be AI-Ready
Becoming AI-ready requires having a clear intention and a willingness to conduct an honest audit of how an organisation currently operates. At a foundational level, this is more important than deep technical expertise or allocating increased budgets to upgrade an organisation's tech stack.
In fact, the most common starting point is often the most overlooked: process documentation. Many teams operate on workflows that function adequately on a day-to-day basis, yet exist solely within the unspoken habits of their employees. For instance, a monthly report might be successfully compiled only because a specific individual knows which spreadsheet to query and how the stakeholder prefers the formatting. While this institutional knowledge is valuable, it remains invisible to an AI agent if it is never written down. Consequently, documenting workflows, even informally, is the single most impactful step an organisation can take to prepare for agent-assisted work. It is the prerequisite for delegation, whether that work is being handed to a new team member or an AI tool.
This logic applies equally to data management. Agents function by accessing the files and systems they are directed towards; therefore, the quality of their output is inextricably linked to the quality of their operating environment. Two areas are paramount:
File organisation: If documents are dispersed across personal folders with inconsistent naming conventions and no shared structure, an agent will struggle to retrieve relevant information. Establishing clear storage protocols yields immediate benefits, regardless of whether AI is involved.
System connectivity: Agents maximise their value when they can access different platforms. If analytics dashboards, content calendars, CRMs and organisational cloud storage all operate in isolation, the agent is restricted to working within one silo at a time. Even simple integrations between these core tools can extend an agent’s operational scope.
Finally, review protocols must be permanently embedded into the process; not treated as a temporary measure that can be relaxed over time. This necessitates deciding upfront which workflows are suitable for agent involvement and clearly defining who holds the authority to approve the final output. This is simply the application of the same professional standards that apply to any work produced by an organisation.
What This Means for DMO Teams
DMO teams are stretched across data analysis, stakeholder coordination, multilingual content production, channel management and campaign reporting. Regardless of team size, the operational burden is significant, and the reality for most is that capacity has failed to keep pace with escalating demands.
Yet, the tasks that consume the greatest proportion of time in DMO operations are rarely the most complex. Rather, they are laborious because of the volume of small steps required across disjointed tools. For example, a multilingual content update requires a single asset to be translated, tonally adapted to be localised for different markets and reformatted for multiple channels. Even routine media monitoring involves scanning coverage across various publications, isolating relevant mentions and tailoring briefings for different internal audiences.
This is precisely the reason why AI agents are built to focus on strict adherence to process. Once directed to the relevant data sources and issued a brief, an agent can be left to produce a comprehensive first draft. The human role shifts from manual assembly to strategic review, validating the quality of the outputs. This approach saves significant time to boost organisational efficiency. Consider a content manager who currently dedicates three days every month to compiling reports. The true value lies in the agent's ability to free up time for higher-order tasks that demand human judgement, creativity and nuanced local knowledge.
There are three areas where DMOs could begin exploring these capabilities:
Routine reporting: Connecting agents to analytics platforms and content calendars allows for the generation of periodic updates that can be subsequently reviewed and refined. The steps are highly predictable and the time savings are immediate, making this the logical starting point.
Multilingual content production: Agents can translate, adapt and format campaign material across markets at pace. The human role transitions from translation to quality control and cultural judgement.
Stakeholder communications: Agents can be employed to prepare tailored briefings from shared data sources, ensuring teams focus on relationship management rather than administrative logistics.
None of these applications demands deep technical expertise. They do, however, require the organisational readiness discussed above to make agent-assisted workflows practical.
DTTT Take
Audit existing workflows: Conducting an honest audit of current workflows is essential to identifying a DMO team’s true capacity and uncovering hidden operational gaps. By documenting unspoken habits of team members, you can start preparing to avoid critical institutional knowledge from being lost when staff move roles. Ultimately, this process ensures that tasks can be seamlessly delegated.
Close the readiness gap before the capability gap: Ultimately, the gap between a productive team and an unreliable one is determined by how well an organisation prepares its processes, rather than the capabilities of the tools themselves. By documenting workflows, organising data and defining clear briefs now, you provide a solid foundation that allows both teams and AI agents to perform reliably.
Prioritise process transparency: As AI agents transition from simple tasks to detailed reporting, transparency becomes the essential foundation for building trust within the tourism sector. DMOs must scrutinise the underlying governance frameworks of these tools, ensuring they understand the logic guiding an agent's decisions. Ultimately, this transparency allows organisations to verify that an AI partner will act responsibly and maintain the DMO's reputation when producing critical, stakeholder-facing outputs.
Reviews will always remain fundamental: The expectation should not be that agents will eventually operate without review. The review layer is what ensures quality, maintains brand integrity and keeps DMOs accountable for their outputs. Teams that treat approval workflows as a permanent process will develop more resilient and trustworthy operations in the long-term.
Part 2: Comparing the AI Agents Ready to Change DMO Workflows
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Most organisations have now experimented with AI in some form. Writing social media captions, generating image concepts and drafting newsletter copy are all useful applications that have delivered real efficiency for teams working with limited resources.
With the technology continuing to evolve, a new generation of AI solutions has emerged. The arrival of AI agents fundamentally changes how interactions with AI tools will work in the future, raising questions about how increased automation can be leveraged to support operations.
What Makes an AI Agent Different
For most, interacting with AI has been a simple, conversational exchange. While helpful in the moment, these interactions are generally limited as the help ends when you close the session. Conversely, AI agents operate on a task-based model. Once you define the objective, the agent independently determines the necessary steps. For instance, an agent might open a file, extract data from a connected platform, verify figures in a spreadsheet and then use those findings to draft a document. This entire process is completed without requiring human direction for each individual action.
Source: Claude.ai generated
This shift is already well underway. In early 2026, several leading AI companies launched dedicated agentic solutions. While their specific methodologies may vary, the overarching trajectory remains consistent. We are witnessing a shift from using AI as a tool for answering questions to employing it as an entity to which work is delegated.
In practical terms, agentic AI can:
Break a complex task into steps and execute them across multiple files and tools.
Pull data from connected systems, identify patterns and produce formatted outputs.
Adapt to feedback and adjust its approach mid-task.
Operate across documents, spreadsheets, emails and web sources within a single workflow.
Queue and manage multiple tasks in parallel.
In the context of delivering monthly performance reporting, for example, traditional generative AI tools would require teams to gather the data themselves before getting a written summary. With an AI agent, teams can instead point it to their analytics dashboards, their CRM and content calendar to directly gather the necessary data to synthesise into a formatted document ready for review. By shifting the burden of repetitive execution to AI, the use of agents enables teams to delegate tasks and only focus on providing expert refinement. This allows DMO teams to dedicate their time to more complex and high-priority strategic initiatives.
Source: Claude.ai generated
The implications of this shift extend far beyond time-saving. When a tool can autonomously manage a complex workflow from start to finish, it alters the relationship between teams, how work is structured and how human talent is allocated.
However, it is important to understand the limitations. Agents are capable of producing confident-sounding outputs that may contain factual errors, misinterpret context or miss nuance. As a result, they can make decisions that appear reasonable on the surface yet fail to align with an organisation's brand, values or strategy. For this reason, human oversight remains essential.
Why Organisational Readiness Matters
While these capabilities are impressive on paper, an AI agent is only as effective as the digital ecosystem in which it operates. The concept of an agent that can pull data from multiple platforms to compile and format a stakeholder report sounds transformative. However, when platforms are disconnected and data is inconsistent, the agent is rendered ineffective. Ultimately, the output will simply reflect the chaotic state of the information it was given.
This operational disconnect represents a gap that most organisations have yet to bridge. While the conversation around AI often focuses on the potential of the tools, the more critical question, especially as agents move from concept to product, is whether an organisation is actually ready to enable them.
Being “AI-ready” requires foundational work that makes everything else possible. Workflows must be documented clearly enough for a third party to follow and files need to be organised with consistent naming conventions. Equally, there must be clear protocols regarding what AI is and is not permitted to touch.
Admittedly, none of this is novel. These are the same operational fundamentals that organisations have always required to function efficiently. However, agentic AI makes these gaps significantly harder to ignore. As the tools become sufficiently capable, the primary bottleneck is no longer the technology itself, but rather the state of the information it is required to process.
As these tools assume greater responsibility, the question of trust becomes paramount. Drafting a social post is a low-stakes endeavour. If the tone is off, it can be quickly corrected. However, when an agent is aggregating data from multiple platforms to compile a stakeholder report, the margin for error narrows significantly.
When an organisation's reputation is attached to the documents it shares with its partners or the public, this is the precise point where governance becomes critical. If an organisation intends to delegate work to an AI agent, it must understand the principles guiding that tool’s decisions. This includes understanding the logic behind its actions.
Notably, Anthropic has taken a significant step forward by publishing its “Constitution”, while OpenAI has released its "Model Spec". These public documents explicitly articulate how the model is trained to behave, defining its refusal criteria and its approach to sensitive decisions. They represent some of the clearest examples to date of AI providers demystifying their internal rules. This transparency allows organisations to assess the underlying logic of the tool, ensuring it aligns with their standards, rather than judging it solely on its technical capabilities.
Consequently, these governance frameworks merit as much scrutiny as any technical feature list. The manner in which a provider approaches safety and accountability serves as a critical indicator of the confidence organisations can place in their outputs, particularly when those outputs are destined for external audiences.
How to Be AI-Ready
Becoming AI-ready requires having a clear intention and a willingness to conduct an honest audit of how an organisation currently operates. At a foundational level, this is more important than deep technical expertise or allocating increased budgets to upgrade an organisation's tech stack.
In fact, the most common starting point is often the most overlooked: process documentation. Many teams operate on workflows that function adequately on a day-to-day basis, yet exist solely within the unspoken habits of their employees. For instance, a monthly report might be successfully compiled only because a specific individual knows which spreadsheet to query and how the stakeholder prefers the formatting. While this institutional knowledge is valuable, it remains invisible to an AI agent if it is never written down. Consequently, documenting workflows, even informally, is the single most impactful step an organisation can take to prepare for agent-assisted work. It is the prerequisite for delegation, whether that work is being handed to a new team member or an AI tool.
This logic applies equally to data management. Agents function by accessing the files and systems they are directed towards; therefore, the quality of their output is inextricably linked to the quality of their operating environment. Two areas are paramount:
File organisation: If documents are dispersed across personal folders with inconsistent naming conventions and no shared structure, an agent will struggle to retrieve relevant information. Establishing clear storage protocols yields immediate benefits, regardless of whether AI is involved.
System connectivity: Agents maximise their value when they can access different platforms. If analytics dashboards, content calendars, CRMs and organisational cloud storage all operate in isolation, the agent is restricted to working within one silo at a time. Even simple integrations between these core tools can extend an agent’s operational scope.
Finally, review protocols must be permanently embedded into the process; not treated as a temporary measure that can be relaxed over time. This necessitates deciding upfront which workflows are suitable for agent involvement and clearly defining who holds the authority to approve the final output. This is simply the application of the same professional standards that apply to any work produced by an organisation.
What This Means for DMO Teams
DMO teams are stretched across data analysis, stakeholder coordination, multilingual content production, channel management and campaign reporting. Regardless of team size, the operational burden is significant, and the reality for most is that capacity has failed to keep pace with escalating demands.
Yet, the tasks that consume the greatest proportion of time in DMO operations are rarely the most complex. Rather, they are laborious because of the volume of small steps required across disjointed tools. For example, a multilingual content update requires a single asset to be translated, tonally adapted to be localised for different markets and reformatted for multiple channels. Even routine media monitoring involves scanning coverage across various publications, isolating relevant mentions and tailoring briefings for different internal audiences.
This is precisely the reason why AI agents are built to focus on strict adherence to process. Once directed to the relevant data sources and issued a brief, an agent can be left to produce a comprehensive first draft. The human role shifts from manual assembly to strategic review, validating the quality of the outputs. This approach saves significant time to boost organisational efficiency. Consider a content manager who currently dedicates three days every month to compiling reports. The true value lies in the agent's ability to free up time for higher-order tasks that demand human judgement, creativity and nuanced local knowledge.
There are three areas where DMOs could begin exploring these capabilities:
Routine reporting: Connecting agents to analytics platforms and content calendars allows for the generation of periodic updates that can be subsequently reviewed and refined. The steps are highly predictable and the time savings are immediate, making this the logical starting point.
Multilingual content production: Agents can translate, adapt and format campaign material across markets at pace. The human role transitions from translation to quality control and cultural judgement.
Stakeholder communications: Agents can be employed to prepare tailored briefings from shared data sources, ensuring teams focus on relationship management rather than administrative logistics.
None of these applications demands deep technical expertise. They do, however, require the organisational readiness discussed above to make agent-assisted workflows practical.
DTTT Take
Audit existing workflows: Conducting an honest audit of current workflows is essential to identifying a DMO team’s true capacity and uncovering hidden operational gaps. By documenting unspoken habits of team members, you can start preparing to avoid critical institutional knowledge from being lost when staff move roles. Ultimately, this process ensures that tasks can be seamlessly delegated.
Close the readiness gap before the capability gap: Ultimately, the gap between a productive team and an unreliable one is determined by how well an organisation prepares its processes, rather than the capabilities of the tools themselves. By documenting workflows, organising data and defining clear briefs now, you provide a solid foundation that allows both teams and AI agents to perform reliably.
Prioritise process transparency: As AI agents transition from simple tasks to detailed reporting, transparency becomes the essential foundation for building trust within the tourism sector. DMOs must scrutinise the underlying governance frameworks of these tools, ensuring they understand the logic guiding an agent's decisions. Ultimately, this transparency allows organisations to verify that an AI partner will act responsibly and maintain the DMO's reputation when producing critical, stakeholder-facing outputs.
Reviews will always remain fundamental: The expectation should not be that agents will eventually operate without review. The review layer is what ensures quality, maintains brand integrity and keeps DMOs accountable for their outputs. Teams that treat approval workflows as a permanent process will develop more resilient and trustworthy operations in the long-term.
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